Multi-station wind-solar collaborative energy storage optimization control system

Abstract

The invention relates to the technical field of cooperative control, in particular to a multi-station wind and light cooperative energy storage optimization control system, which comprises a wind and light output change identification module, an energy storage window state screening module, a linkage section intersection calibration module, a multi-station energy storage priority allocation module and a cooperative regulation and control instruction arrangement module. According to the method, the wind power climbing extreme value and the photovoltaic descending section are extracted, the matching relation between the power generation complementary characteristic and the energy storage response time is established, the time sequence scheduling precision is enhanced, the SOC and the operation state are fused to perform multi-dimensional screening on the energy storage units, the non-adjustable resources are eliminated, the execution reliability is improved, and the output fluctuation and energy storage window intersection is constructed; standardized scheduling time slices are generated, unified positioning of tasks is achieved, energy storage resource grouping and conflict elimination are completed based on available duration, priority ranking is clear, scheduling coordination is guaranteed, redundant conflict items are removed, instruction stream integrity and control coverage are improved, and execution efficiency of multi-station cooperative scheduling and system stability are enhanced.

Description

Technical Field

[0001] The present invention relates to the field of collaborative control technology, and in particular to a multi-station wind-solar collaborative energy storage optimization control system. Background Art

[0002] The field of collaborative control technology includes the unified coordination and orderly management of multiple energy forms such as wind power, photovoltaics, and energy storage in multi-energy systems. The core content is the control design of the dispatching strategy of distributed energy resources to ensure the coordination and consistency of various energy forms in time and space dimensions. It mainly involves the control logic of the power electronic interface, the regulation mechanism of the energy flow path, and the linkage management of energy conversion equipment. It is committed to achieving load sharing and dynamic balance of multiple energy forms at the operational level, and has an important technical foundation for improving energy utilization efficiency and optimizing resource allocation.

[0003] Among them, the multi-station wind-solar collaborative energy storage optimization control system refers to an energy management method that uses a centralized master station dispatching system to uniformly coordinate and control the energy storage devices configured at each station between wind power stations and photovoltaic power stations distributed at multiple geographical locations. It mainly targets the problems of unstable and time-varying output of wind power and photovoltaic power generation, and covers wind power power forecasting, photovoltaic output monitoring, energy storage status assessment, and load-side response capability assessment. Specifically, it is based on the collection of power information, voltage and frequency status, weather forecast data, etc. from each station, and uses dispatching logic to dynamically adjust the charging and discharging timing of the energy storage system, and redistribute energy during wind and solar output periods, so as to achieve optimal configuration of energy flow paths and implementation of the overall system control strategy.

[0004] The existing coordinated regulation of wind, solar and energy storage relies heavily on static scheduling windows or large-grained prediction models. These make it difficult to capture effective regulation opportunities in a timely manner in scenarios with rapidly changing output, resulting in delayed or untriggered energy storage responses and reduced resource utilization efficiency. The availability of energy storage equipment is judged in a relatively simplistic manner, based solely on SOC threshold settings without integrating the equipment's operating status with grid-connected regulation restrictions. This can lead to equipment that does not meet regulation requirements being mistakenly identified as dispatchable. Scheduling task periods are not standardized with coding, increasing the risk of the same energy storage unit being concurrently called by multiple tasks, which can easily lead to command conflicts in areas with intensive concurrent scheduling. Resource scheduling has not yet formed a refined priority allocation mechanism, making it impossible to rationally allocate task loads based on the remaining energy storage capacity and actual distribution in multi-station parallel tasks. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and propose a multi-station wind-solar collaborative energy storage optimization control system.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: a multi-station wind-solar collaborative energy storage optimization control system includes: The wind and solar power output change identification module obtains the output sequences of wind power and photovoltaic power generation units at multiple stations, extracts the extreme points of wind power output ramp rate and the duration of photovoltaic output power decline, determines the power generation complementary period, and generates a wind and solar power fluctuation timestamp data set for each station; The energy storage window status screening module identifies energy storage units that can enter the dispatch state based on the SOC status information and operating status identifiers of multiple stations, screens out energy storage devices in the maintenance lock state or the task mutual exclusion state, extracts the corresponding available sections as the control reference time period, and generates a set of available time windows for energy storage at each station; The linkage section intersection calibration module performs time window overlap analysis on the complementary segments and available intervals on a site-by-site basis based on the wind and solar fluctuation timestamp datasets of each station and the energy storage available time window set of each station, marks the overlapping time range, and performs time period coding to generate a multi-station scheduling time slice set. The multi-station energy storage priority allocation module groups resources according to the scheduling time slice set, performs cross-validation of available time, screens energy storage units with no resource conflict records, records station ownership and scheduling task counters, and generates a multi-station energy storage scheduling priority list.

[0007] As a further solution of the present invention, the wind-solar fluctuation timestamp data set includes a ramp rate extreme value mark, a power drop period mark, a power generation complementary period mark, and a site output trend summary; the energy storage available time window set includes SOC filtering results, operating status screening results, an adjustable time period mark, and an energy storage unit control qualification mark; the scheduling time slice set includes a linkage overlapping time range mark, a period unique identifier, a cross-site linkage section mapping table, and adjustable section time alignment information; the energy storage scheduling priority list includes a resource grouping tag, an energy storage unit availability verification result, a resource conflict screening record, and a scheduling task count mark.

[0008] As a further solution of the present invention, the wind and solar power output change identification module includes: The output sequence extraction submodule obtains the output sequences of wind power and photovoltaic power generation units at multiple sites, extracts the unit time output power data and time series data of each wind power and photovoltaic power generation unit, synchronizes them by site, aligns the sequences according to the time step, and generates a unified time scale output sequence matrix; The change trend identification submodule detects the maximum value of the power change rate in the wind power output based on the unified time scale output sequence matrix, selects the time points in the wind turbine output change rate that exceed the output change rate threshold, marks them as extreme value points, and obtains the wind power output change rate extreme value point set; The complementary period marking submodule analyzes the time overlap between the extreme points of the wind power output change rate and the photovoltaic output decline interval based on the set of extreme point sets of the wind power output change rate and the time interval of the continuous decline in photovoltaic output power, calculates the complementary index value of each overlapping section, selects the time period with a complementary index value greater than the complementary threshold, marks it, and obtains the wind-solar complementary marking period set.

[0009] As a further solution of the present invention, the energy storage window status screening module includes: The SOC state extraction submodule obtains the SOC state information of energy storage at multiple stations, collects the current SOC value, nominal capacity, and available capacity of each energy storage unit, calculates the SOC change trend of each unit within the observation interval, and the difference between the minimum SOC threshold and the current state, determines whether it is within the discharge capacity range allowed for scheduling, and generates an energy storage SOC screening state set; The operation state elimination submodule filters the state set based on the energy storage SOC, reads the current operation state identification information of each energy storage unit, parses the state code field, identifies the maintenance lock flag and the task mutual exclusion flag in the state code, and eliminates the corresponding unit to obtain the operation state available unit set; The available window generation submodule establishes an available segment index based on the available unit set in the operating state according to the unit index and time field, counts the continuous available duration and the minimum continuous available segment of each unit in a given scheduling cycle, calculates the window effective index value of the energy storage unit in the scheduling cycle, outputs the time period with an index value greater than the set window scheduling threshold as the available time period, and generates a set of energy storage available time windows.

[0010] As a further solution of the present invention, the linkage section intersection calibration module includes: The time overlap determination submodule, based on the wind-solar fluctuation timestamp dataset of each station and the energy storage available time window set, calls the start and end time of the wind-solar complementary segment of each station and the start and end time of the corresponding energy storage unit adjustable window, determines the relative order of the start and end points of the time period, determines and marks the overlapping segments, and obtains the wind-solar-energy storage time overlap segment set; The linkage overlap calculation submodule calculates the start and end overlap length, intersection center time and joint output expected value for each overlapping segment according to the wind, solar and energy storage time overlap segment set, obtains the scheduling potential interval of each overlapping segment, and calculates the first The overlapping strength index of the overlapping segments is calculated, and the overlapping segments with index values ​​lower than the set strength threshold are retained to establish an overlapping strength screening interval set; The time slice identifier generation submodule filters the interval set according to the overlap intensity, reads the station number, overlap start and end time and overlap intensity of each section, generates a unique identifier string for each schedulable time period, allocates and embeds each field in the time slice data structure, and establishes a multi-station scheduling time slice set.

[0011] As a further solution of the present invention, the multi-station energy storage priority allocation module includes: The time slice grouping submodule obtains the multi-station scheduling time slice set, extracts the station number, start and end time, scheduling type and energy storage unit number corresponding to each time slice in turn, divides all time slices according to the scheduling area, classifies and establishes three logical scheduling groups: frequency regulation, peak regulation and standby, and generates the scheduling time slice grouping results; The duration verification submodule extracts the energy storage unit numbers in each group based on the scheduling time slice grouping results. Combined with the available time length data of the energy storage units within the scheduling cycle, it determines whether each energy storage unit can completely cover the start and end periods corresponding to the time slice, detects scheduling conflicts, and eliminates them to obtain a conflict-free energy storage unit set. The priority generation submodule counts the number of times each energy storage unit is active in the scheduling time slice based on the set of conflict-free energy storage units, records the station number, the number of participating time slices, and the number of assigned tasks, accumulates the number of scheduling tasks, and prioritizes all energy storage units according to the call frequency and the scheduling saturation of the station to which they belong to generate an energy storage scheduling priority list.

[0012] As a further embodiment of the present invention, the system further comprises: The collaborative control instruction arrangement module establishes a unified instruction sequence based on the energy storage dispatch priority list, verifies the time period coverage of all dispatch instructions, partitions the output by station dimension, and generates multi-station wind, solar and energy storage joint dispatch instructions; The wind, solar and storage joint dispatching instructions include the AGC instruction set, the AVC instruction set, the time period deduplication results, and the station control instruction output set.

[0013] As a further solution of the present invention, the collaborative control instruction arrangement module includes The standard instruction generation submodule extracts the scheduling time slice data, station number, and scheduling type of each unit based on the energy storage scheduling priority list, generates a standard control instruction format for each scheduling segment, maps different types of tasks to various control target parameters, and uniformly encapsulates them into a scheduling instruction structure according to specifications to establish a standard control instruction set; The conflict period elimination submodule performs time period cross-checks on all control instructions based on the standard control instruction set according to the site dimension, extracts control instructions for the same energy storage unit and wind power photovoltaic unit in adjacent time periods, eliminates instruction conflict entries, and obtains a conflict-free control instruction set; The station instruction collection submodule classifies each instruction according to the station number based on the conflict-free control instruction set, merges and outputs the instructions within the same station, arranges them in order of instruction numbers to form a unified cross-station scheduling instruction integration area, and generates multi-station wind, solar and storage joint scheduling instructions.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, by extracting the extreme values ​​of wind power climbing and the photovoltaic decline sections, a matching relationship between the complementary characteristics of power generation and the energy storage response time is established, the timing scheduling accuracy is enhanced, the SOC and operating status are integrated to screen the energy storage units in multiple dimensions, and non-adjustable resources are eliminated to improve the execution reliability. The intersection of output fluctuation and energy storage window is constructed to generate standardized scheduling time slices, realize unified task positioning, complete the grouping and conflict elimination of energy storage resources based on the available time, clarify the priority sorting, ensure the scheduling coordination, remove redundant conflicting items, improve the integrity of the instruction stream and the control coverage, and enhance the execution efficiency and system stability of the coordinated scheduling of multiple stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a system flow chart of the present invention; Figure 2 This is a flow chart of the wind and solar power output change identification module of the present invention; Figure 3 This is a flow chart of the energy storage window status screening module of the present invention; Figure 4 This is a flow chart of the linkage section intersection calibration module of the present invention; Figure 5 This is a flow chart of the multi-station energy storage priority allocation module of the present invention; Figure 6 This is a flow chart of the collaborative control instruction arrangement module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0018] See also Figure 1 The multi-station wind-solar collaborative energy storage optimization control system includes: The wind and solar power output change identification module obtains the output sequences of wind power and photovoltaic power generation units at multiple stations. Based on the output change trends within each station, it extracts the extreme points of wind power output ramp rate (monitoring points with the maximum power change rate of wind turbines per unit time) and the duration of photovoltaic output power decline (the time interval during which the output power of the photovoltaic power generation system continues to decrease). It then determines the complementary power generation period (the time period during which wind power and photovoltaic power generation exhibit complementary power characteristics). It then aggregates and marks the internal extraction results of each station to generate a timestamp dataset of wind and solar power fluctuations at each station. The energy storage window status screening module identifies energy storage units that can enter the dispatch state based on the SOC status information (State of Charge data of the battery energy storage system) and operating status identification (equipment operating status coding in accordance with the IEC61850 standard) of multiple stations. It screens out energy storage units in the maintenance lock state (equipment in a protective state for planned maintenance) and the task mutual exclusion state (the energy storage system is unavailable due to executing other scheduling tasks). It then extracts the corresponding available segments as the reference time period for regulation and generates a set of available time windows for energy storage at each station. The linkage segment intersection calibration module uses the wind and solar fluctuation timestamp dataset and the energy storage available time window set at each station to perform time window overlap analysis (based on the Allen interval algebra time relationship analysis method) on the complementary segments and available intervals on a site-by-site basis. It then marks the overlapping time ranges where linkage relationships exist, encodes the time intervals using UUIDs (using the unique time interval identifier generated by the RFC4122 standard), and generates a multi-station scheduling time slice set. The multi-station energy storage priority dispatch module groups resources for all linked time slices based on the scheduling time slice set (using a resource classification method based on grid scheduling regulations). It then cross-validates the available time of the energy storage units associated with each time slice, screens for energy storage units with no resource conflict records, and records the station ownership and scheduling task counters to generate a multi-station energy storage dispatch priority list. The coordinated control instruction arrangement module establishes a unified AGC / AVC instruction sequence (standardized instruction format for automatic generation control / automatic voltage control) based on the energy storage dispatch priority list, and performs time period coverage verification on all dispatch instructions, removes time period overlap items and instruction conflict detection, and partitions the output according to the site dimension to generate multi-site wind, solar and storage joint dispatch instructions.

[0019] The wind-solar fluctuation timestamp dataset includes ramp rate extreme value marks, power drop period marks, power generation complementary period marks, and station output trend summaries. The energy storage available time window set includes SOC filtering results, operating status screening results, adjustable time period marks, and energy storage unit control qualification marks. The scheduling time slice set includes linkage overlapping time range marks, period unique identifiers, cross-site linkage section mapping tables, and adjustable section time alignment information. The energy storage scheduling priority list includes resource grouping tags, energy storage unit availability verification results, resource conflict screening records, and scheduling task count marks. The wind-solar-storage joint scheduling instructions include the AGC instruction set, the AVC instruction set, period deduplication results, and the station control instruction output set.

[0020] See also Figure 2 , wind and solar output change recognition module includes The output sequence extraction submodule obtains the output sequences of wind power and photovoltaic power generation units at multiple sites, extracts the unit time output power data and time series data of each wind power and photovoltaic power generation unit, synchronizes them by site, aligns the sequences according to the time step, and generates a unified time scale output sequence matrix; To obtain the output sequence of wind power and photovoltaic power generation units at multiple stations, it is necessary to first collect the original power output data of multiple wind power and photovoltaic stations in the same observation period. For each station, extract the output power record per unit time. In this example, the sampling interval is set to 1 minute. Taking the observation period from 10:00 to 12:00 as an example, the output power values ​​of wind farm station W1 and photovoltaic station S1 are collected in each minute. The output power of wind farm W1 from 10:00 to 10:05 is 320, 335, 355, 360, and 370 respectively. , 378kW, and the power of photovoltaic field S1 in the same period are 210, 205, 198, 190, 180, and 170kW respectively. The above data are used to establish an initial data matrix at the minute level. With time as the index, the data of each station are synchronized and aligned, and a horizontally arranged data structure is formed according to the unified time step. Then, the synchronized data is structured and the wind power output matrix and photovoltaic output matrix are generated according to the station category. In the matrix, each row represents a time point, and each column represents a specific station. The unified data structure is shown as follows: Table 1 Schematic diagram of wind power and photovoltaic output sequence

[0021] As shown in Table 1, the output sequence data matrix formed after synchronization processing can be used for subsequent fluctuation analysis and processing, and finally the unified time scale output sequence matrix is ​​obtained.

[0022] The change trend identification submodule detects the maximum value of the power change rate in the wind power output based on the unified time scale output sequence matrix, selects the time points in the wind turbine output change rate that exceed the output change rate threshold, marks them as extreme value points, and obtains the extreme value point set of the wind power output change rate; Based on the unified time scale output sequence matrix, it is necessary to perform continuous power difference on each wind turbine at unit time step, and then calculate the power change rate using the formula ,in, Representing wind power in The power change rate per unit time in a period of time, Representing wind power in The power value in a period of time, Representatives represent wind power in the The power value in a period of time, Representative Period of time, Representative The time of each period is calculated step by step from 10:00 to 10:05 for W1 in Table 1, and the results are: 15kW / min for 10:01-10:01, 20kW / min for 10:01-10:02, 5kW / min for 10:02-10:03, 10kW / min for 10:03-10:04, and 8kW / min for 10:04-10:05. Based on the power change rate analysis, the period with a change rate greater than the set threshold is determined as an extreme point. In this embodiment, the threshold is set to 12kW / min. The setting basis is that the power change rate per unit time of the wind turbine in the normal operation mode is usually maintained in the range of 6~11kW / min. When the power change rate is greater than the range, the power change rate per unit time is greater than the set threshold. When the fluctuation is limited to about 10%, it can be regarded as entering the non-steady-state climbing zone. Therefore, the threshold is set by adding 1.2 times the standard deviation of the wind power output change rate to the sample mean. Combined with the actual fluctuation amplitude, it is set to 12kW / min to ensure the balance between sensitivity and misjudgment rate. This value will fluctuate with the gradient of group capacity and wind speed change, but will not move up linearly with the expansion of instantaneous maximum output change. The two time periods of 10:00-10:01 and 10:01-10:02 meet the conditions, with corresponding change rates of 15 and 20kW / min, and the marked time points are 10:01 and 10:02. They are used as the extreme points of wind power output change rate for subsequent matching with the photovoltaic downward trend, and finally the set of extreme points of wind power output change rate is obtained.

[0023] The complementary period marking submodule analyzes the time overlap between the extreme value points of wind power output change rate and the photovoltaic output decline interval based on the set of extreme value points of wind power output change rate and the time interval of photovoltaic output continuous decline, and uses the formula: ; Calculate the complementary index value of each overlapping segment , filter the time period with the complementary index value greater than the complementary threshold, mark it, and obtain the wind-solar complementary marked time period set, where, Indicates that wind power The power change rate per unit time in a period of time, Indicates the The time span corresponding to the complementary segments, Indicates that photovoltaic The total power reduction of the complementary segments is Indicates the duration of the photovoltaic downslope. represents the mean of the time spans of all overlapping segments, Indicates the total number of complementary segments; According to the extreme value point set of wind power output change rate and the time interval of continuous decline of photovoltaic output power, it is necessary to analyze the coincidence relationship between wind power increase and photovoltaic decrease, and to make statistics on the total amount of decrease and duration of photovoltaic output decrease. For example, the output power of S1 in Table 1 continued to decrease from 210kW to 170kW from 10:00 to 10:05, with a total decrease of 40kW and a duration of 5 minutes. By calculating the intensity of photovoltaic decrease trend At the same time, the wind power output in this section changes from 320 to 378kW, with a difference of 58kW and a unit time change rate of 11.6kW / min. Combined with the average time span minutes, substitute into the complementary index formula: ; In this embodiment, the complementary judgment threshold is set to 20. The setting basis is the complementary index value distribution characteristic analysis results obtained after the index calculation of multiple groups of wind and solar output time periods. The data is shown in When the value is greater than 20, the time overlap and output complementarity of the wind power ramp-up phase and the photovoltaic power ramp-down phase are both above 90%, and the exponential distribution shows an obvious fault phenomenon. There is a slope change inflection point between 18 and 20, so the critical value is set as 20 as the judgment threshold. The fluctuation amplitude increases and tends to rise, but the sensitivity to the change in the downward trend itself is small. Therefore, as an overall judgment indicator, it has good stability and adaptability. If the value meets the conditions, it is judged that this time period constitutes a wind-solar complementary relationship. Finally, the time period from 10:00 to 10:05 is added to the complementary time period set. Further expansion of multiple sample segments for analysis is as follows: Table 2 Wind-solar complementary analysis data

[0024] As shown in Table 2, by substituting sample data from different time periods into the formula, the complementary indexes obtained are 55.5, 41.735, and 23.0, respectively, all exceeding the threshold of 20. Therefore, the corresponding time periods of 10:00-10:05, 11:20-11:26, and 12:15-12:19 are marked as wind-solar complementary periods, effectively screening out periods with significant complementarity, and finally obtaining the set of marked wind-solar complementary periods.

[0025] The complementary index value is a quantitative indicator that measures whether the upward trend of wind power output and the downward trend of photovoltaic output form an effective complementary relationship within the same time period. The larger the value, the more the increase in wind power can cover the reduction in photovoltaic power, and the higher the temporal overlap between the two. This index comprehensively considers the increase formed by the wind power output change rate and the time span, compares the difference with the decline intensity of photovoltaic power in the same period, and normalizes it based on the time offset factor, thereby reflecting the synchronization and amplitude compensation relationship of the output fluctuations of the two. By setting the complementary judgment threshold, if the complementary index for a certain period of time exceeds this value, it can be determined that the two types of energy, wind and solar power, have formed a relatively coordinated complementary operating state during this period. It is the core quantitative basis for identifying complementary behaviors in wind and solar power output regulation and joint scheduling.

[0026] This formula is designed to evaluate the degree of complementary matching between wind power ramp-up and photovoltaic power ramp-down in a specific period of time. It represents the total output increment of wind power in the complementary period, which is obtained by multiplying the unit time change rate by the time span to obtain the total power increase; Indicates the intensity of the downward trend in photovoltaic power output. By multiplying the total amount of photovoltaic power decline by the duration and then taking the square root, this value reflects both the magnitude and duration of the decline, increases sensitivity to extreme declines, and enhances the significance of the trend. Subtracting the two reflects whether the increase in wind power is sufficient to compensate for the intensity of the photovoltaic decline. The absolute value is then taken to eliminate directional bias and focus on the degree of compensation matching itself. A time deviation penalty term is used to weight the deviation of the actual complementary segment span from the mean. When a time span deviates from the overall trend, a moderate penalty is imposed to prevent isolated segments from interfering with the overall complementarity assessment. The formula utilizes a weighted difference normalization structure to comprehensively evaluate multiple time periods at the summation level, ensuring that the complementarity measurement is both sensitive to temporal continuity and discriminative of trend strength.

[0027] See also Figure 3 , the energy storage window status screening module includes: The SOC state extraction submodule obtains the SOC state information of energy storage at multiple stations, collects the current SOC value, nominal capacity, and available capacity of each energy storage unit, calculates the SOC change trend of each unit within the observation interval, and the difference between the minimum SOC threshold and the current state, determines whether it is within the discharge capacity range allowed for scheduling, and generates an energy storage SOC screening state set; Acquire the SOC status information of energy storage at multiple sites, perform minute-level sampling on the connected battery energy storage system data source, and collect the current SOC value, nominal capacity, and available capacity of each energy storage unit. In this example, three energy storage units E1, E2, and E3 are taken as examples. Their current SOC values ​​are 63%, 58%, and 39%, respectively. Their corresponding nominal capacities are 100kWh, 120kWh, and 90kWh, and their available capacities are 52kWh, 66kWh, and 29kWh. The above SOC values ​​are normalized and the deviation between them and the set dispatch participation threshold is calculated. The dispatch participation threshold is set to 45%, which is based on the fact that the lithium battery system operating at normal ambient temperature has a SOC value of 45%. When OC is lower than 40%, there is a risk of over-discharge loss. However, when it is around 50%, it has the ability to be dispatched and released while retaining a certain redundancy. Therefore, the median of this interval is taken as the judgment benchmark, and the lower limit of available capacity is set at 30% of the nominal capacity. That is, if the available capacity of a unit is lower than this ratio, it cannot be included in the available range for dispatch. Combined with the data analysis in the above table, the SOC values ​​of E1 and E2 are higher than 45% respectively, and the available capacities are 52% and 55% respectively, both of which meet the conditions. E3 is eliminated because its SOC is 39% and the available capacity is 32.2%. Its SOC value does not meet the dispatch conditions. Therefore, E1 and E2 are determined to be dispatch response units, forming a preliminary screening state set. The specific data is summarized as follows: Table 3 Energy storage unit SOC status data table

[0028] As shown in Table 3, E1 and E2 meet the conditions of 45% SOC threshold and 30% available capacity ratio, so they are included in the scheduling candidate set to obtain the energy storage SOC screening state set.

[0029] The operation status elimination submodule filters the state set based on the energy storage SOC, reads the current operation status identification information of each energy storage unit, parses the state code field, identifies the maintenance lock flag and task mutual exclusion flag in the status code, and eliminates the corresponding unit to obtain the set of available units in the operation state; Based on the energy storage SOC screening state set, the operating status identification field information of each energy storage unit is called to determine its status. The status code that complies with the IEC61850 standard is extracted from the field and its meaning is parsed. The maintenance lock state in the status code is usually marked as 105, indicating that the current energy storage unit is in the planned maintenance stage. The task mutual exclusion state is marked as 109, indicating that the energy storage unit is participating in other scheduling tasks and cannot respond to new task requests. The status codes of E1 and E2 are parsed. E1's current status code is 101 (normal operation) and E2's current status code is 109 (task occupied). Therefore, in this step, E2 should be eliminated from the candidate set, and only E1 should be retained. The elimination type is recorded as task mutual exclusion, and the current judgment window is marked with a timestamp of 09:30-10:00. A matching table of time fields and unit indexes is established. Finally, the operating state energy storage units that can be scheduled in the current cycle are screened, including only E1, and the final set of available operating state units is obtained.

[0030] The available window generation submodule establishes an available segment index based on the available unit set in the running state according to the unit index and time field, counts the continuous available duration and the minimum continuous available segment of each unit in a given scheduling cycle, filters out fragmented segments less than 10 minutes, and continuously marks the remaining segments and numbers the start and end time using the formula: ; Calculate the The window effective index value of each energy storage unit in the scheduling period , the time period with an index value greater than the set window scheduling threshold is output as the available period, and the energy storage available time window set is generated, where, Indicates the Unit No. The maximum available energy for the available time period, Indicates the minimum energy threshold required in the corresponding segment, Indicates the the length of the available time slot, Indicates the The average length of all available periods of an energy storage unit, Indicates the The discharge capacity value corresponding to the available time period, Indicates the The number of all available time slots identified for each energy storage unit during the dispatch period; According to the available unit set in the operating state, analyze all available time periods of the confirmed adjustable E1 unit in the scheduling cycle. The time period in the historical operating interval that continuously meets the SOC threshold and operating state is from 09:15 to 09:35, which lasts for 20 minutes, and obtain the maximum available energy in this period. , the minimum energy threshold , discharge capacity , combining all available segments (only 1 in this example), calculate the average duration Minutes, enter the formula to calculate, because , so the denominator is 1, and the final calculation is: ; The dispatch window threshold is set to 25. This value is set based on the lower limit requirement of the energy storage response capability of the dispatch task in the system. This value has been verified by the dispatch power load calculation and is used as the identification threshold of the dispatch control unit. Therefore, when A time window greater than this value is considered available. For example, the current E1 value is 56.71, which is much higher than this standard. Therefore, 09:15 to 09:35 is confirmed as an available time window, and the energy storage available time window set is generated. The relevant parameters are summarized as follows: Table 4 Energy storage window effectiveness parameters

[0031] As shown in Table 4, the E1 unit has strong discharge capability and sustainability in the corresponding section. The index value exceeds the threshold and is eventually included in the scheduling available window to obtain the energy storage available time window set.

[0032] The window effectiveness index value is a numerical indicator that measures the comprehensive dispatching capability of an energy storage unit during its available time period within a specific dispatch cycle. This indicator not only reflects the net energy that can be released during this time period, but also integrates the continuous stability of the time period and the intensity performance of the discharge capacity. The higher the value, the stronger the dispatching response capability and time adaptability of the energy storage unit during this time period, making it more suitable for inclusion in load regulation or energy balance tasks. The construction of this value means that the dispatching system no longer relies solely on SOC or duration when screening available windows. Instead, it integrates multi-dimensional performance factors to generate a numerical reference standard with significant discrimination and screening directionality, providing a clear basis for optimal sorting of dispatching strategies.

[0033] The operational logic of the formula is to comprehensively evaluate the performance of the energy storage unit in the three dimensions of energy, time stability and capacity intensity in each available time period within the scheduling cycle. Specifically, the numerator It represents the net adjustable energy value of the energy storage unit in the available period. It is the difference between the maximum releasable energy of the energy storage unit and the minimum safety threshold value of the period. It can reflect the scheduling release potential of the period. The larger the value, the stronger the scheduling flexibility. The absolute value calculation is introduced to measure the difference between the current period length and the average adjustable period of the unit. If the period length is close to the average value, the closer the denominator is to 1, the smaller the impact on the net energy index. If the deviation is large, it will play the role of penalty weight, so that the period with large volatility will be downgraded, thus preferring stable and continuous periods. The right-hand side item This term represents the discharge capacity during that period. A square root operation is used to reduce the impact of extreme capacity data, maintaining a moderate value with an increasing trend. This represents the enhanced contribution of energy storage intensity to window scheduling capabilities. Ultimately, the modified net energy index and capacity impact are weighted and superimposed to comprehensively assess the window's availability value. The overall formula design unifies the three dimensions of time stability, energy adjustability, and intensity capability through a combination of linear and nonlinear methods, improving the rationality and universal adaptability of the selected results.

[0034] See also Figure 4 , the linkage section intersection calibration module includes: The time overlap determination submodule is based on the wind-solar fluctuation timestamp dataset and the energy storage available time window set of each station. It calls the start and end times of the wind-solar complementary segment of each station and the start and end times of the corresponding energy storage unit adjustable window, determines the relative order of the start and end points of the time period, determines and marks the overlapping segments, and obtains the wind-solar-storage time overlap segment set; Based on the wind-solar fluctuation timestamp dataset of each station and the energy storage available time window set of each station, it is necessary to match the wind-solar complementary section of each station with the energy storage scheduling window section one by one, extract the start and end timestamps of each section, and uniformly use the RFC1123 time format standard to represent it. Assume that the wind-solar complementary section of station A is from 10:20 to 10:45 on May 15, 2024, and the energy storage window section is from 10:30 to 11:00. The start times are marked as "Thu, 15May202410:20:00GMT" and "Thu, 15May202410:30:00GMT", and the end times are "Thu, 15May202410:45:00GMT" and "Thu, 15May202411:00:00GMT". 00GMT". The relative position relationship of the two segments on the time axis is determined by comparing the time fields. The Allen interval algebraic model is used to analyze the temporal relationship between the two segments. Pairwise matching determines that the relationship type is "overlap", indicating that the two time periods partially overlap. According to the model definition, relationships such as "during", "starts", "finishes", and "equals" can also be identified. Only types that meet the intersection are retained. If the wind-solar complementary period of a site is 11:10-11:20 and the energy storage available period is 10:50-11:05, and the segment relationship is "before", it does not meet the screening criteria and is eliminated. The time periods that meet the overlapping relationship are numbered and recorded, and summarized to form the first overlapping segment set. To enhance implementation clarity, the following is the overlap determination data for three groups of example sites: Table 5 Time overlap relationship judgment table

[0035] As shown in Table 5, it is finally determined that the sections of stations A1 and A3 have a time overlap relationship, and a set of wind-solar-energy storage time overlap sections is generated.

[0036] The linkage overlap calculation submodule calculates the start and end overlap lengths, the intersection center time, and the expected value of the combined output for each overlapping segment based on the set of wind, solar, and energy storage time overlaps, and obtains the scheduling potential interval of each overlapping segment using the formula: ; Calculate the Overlap strength index of overlapping segments , retain the overlapping segments whose index values ​​are lower than the set intensity threshold, and establish an overlapping intensity screening interval set, where, Indicates the duration of the overlapping segments, Indicates the total adjustable power capacity of wind, solar and storage combined within the corresponding section. Indicates the Section The combined output value at a given time point, represents the average combined output of the corresponding section, Indicates the load forecast error weight for the hour corresponding to the center time of the corresponding section; According to the set of overlapping time segments of wind, solar and energy storage, the overlapping capacity of all overlapping segments is evaluated in turn. It is necessary to extract the duration of each overlapping segment, the load forecast error of the hourly segment at the central time point, and the average power value and deviation of the joint output sequence. Let the overlapping segment numbered K1 correspond to the A1 station, with a start and end time of 10:30 to 10:45 and a length of 15 minutes. The recorded power value sequence is 120, 118, 115, 110, 108, 107, 106, 106, 107, 108, 110, 112, 115, 118, 120kW. The calculated joint average output is about 112.5kW. Assuming the forecast error is 6%, the value is assigned. , and then extract the maximum capacity of 120kW and the total deviation of 25kW, and further process the numbers K2 and K3. The comprehensive parameters are shown in the following table: Table 6 Key parameters of overlapping sections

[0037] Substitute them into the formula to calculate the overlap strength: K1: ; K2: ; K3: ; Based on the intensity threshold set to 30 (this value is set based on the comparison between the degree of joint response fluctuation in the system and the boundary of the scheduling safety range, and those with an intensity index lower than 30 have advantages in output stability and scheduling matching), it is finally determined that K1 and K2 meet the retention criteria, and the overlapping intensity screening interval set is obtained.

[0038] The overlap intensity index is a numerical evaluation result used to measure the coordinated dispatching capabilities of the wind-solar complementary period and the energy storage available section within the same time window. Its core significance lies in quantifying the comprehensive coordination between the continuity of multi-source output, capacity support and output volatility within this time period. The smaller the index, the closer the duration of the period is to the actual output capacity of the adjustable capacity, and the more stable the combined output is, and the less affected by load forecast errors. Therefore, it is more suitable for use as a priority time slice for system scheduling tasks. On the contrary, if the index value is large, it means that the adjustable capacity within this period does not match the continuous demand or the output fluctuates greatly, which is not conducive to forming a stable energy allocation response section and its scheduling priority needs to be lowered. Therefore, the overlap intensity index is essentially a composite scheduling adaptability evaluation indicator that integrates the three characteristics of "duration adaptability", "capacity saturation" and "fluctuation stability".

[0039] The operational logic of the formula is to conduct a composite quantitative evaluation of the structural characteristics and output behavior during the overlapping period. The item reflects the matching degree between scheduling continuity and capacity balance, where represents the duration of the overlapping period, It represents the nonlinear reduction index of the combined power capacity. The absolute value of the difference between the two expresses the degree of deviation between the scheduling demand duration and the capacity supply. If the two values ​​are closer, the smaller the value is, indicating that the scheduling stability of this section is higher. Then, Characterizes the degree of fluctuation of wind, solar and storage combined output during this period, indicating the discreteness or non-steady-state degree of power output. The larger the value, the more severe the fluctuation, which is not conducive to scheduling. Therefore, it needs to be introduced as a risk indicator; this fluctuation value is included in the denominator The correction, It represents the load forecast error weight corresponding to the hour at the center of the overlapping segment. It is used to express that when the forecast uncertainty is high, the fluctuation needs to be aggravated to ensure the robustness of the dispatch evaluation. The sum of the above two items constitutes the total dispatch intensity index. The smaller the value, the more suitable it is as a scheduling priority section. Therefore, the formula as a whole superimposes the two attributes of "time-capacity matching" and "output fluctuation correction" in parallel to form a comprehensive judgment standard for the quality of the scheduling segment.

[0040] The time slice identifier generation submodule filters the interval set according to the overlap intensity, reads the station number, overlap start and end time, and overlap intensity of each section, generates a unique identifier string for each schedulable time period, allocates and embeds it into each field in the time slice data structure, and establishes a multi-station scheduling time slice set; The interval set is filtered according to the overlap intensity, and the number, station ID, start and end time fields and overlap intensity results of each segment are called. A unique identification code UUIDv4 format identifier is generated for numbers K1 and K2 respectively, and the UUID module in the Python standard library is used to randomly generate string values.

[0041] Assign K1 the value "0d6f8894-5a61-4c7f-b390-1cf0bcd1b2f0"; Assign the value of K2 to "a3897f12-28e0-4bce-94d3-cd6f4e44f781"; The UUID and timestamp fields are packaged and combined into a structure. The fields include the overlap number, station number, start time, end time, intensity value, and UUID code. The corresponding structure is as follows: Table 7 Scheduling time slice structure diagram

[0042] As shown in Table 7, both overlapping segments are uniquely identified and have integrated time fields, forming a segment-level control task package that can be called by the system scheduling, and finally establishing a multi-station scheduling time slice set.

[0043] See also Figure 5 The multi-station energy storage priority allocation module includes: The time slice grouping submodule obtains a set of multi-station scheduling time slices, extracts the station number, start and end time, scheduling type, and energy storage unit number corresponding to each time slice, divides all time slices into scheduling areas, and classifies them into three logical scheduling groups: frequency regulation, peak regulation, and standby, to generate scheduling time slice grouping results; Obtain a collection of multi-station dispatch time slices. For each time slice record, extract the start and end times, task type, and associated energy storage unit number. The regions are divided according to the regional dispatch principles in the State Grid dispatch regulations. For example, S01 and S02 belong to the Northwest Dispatching Area and the Central China Dispatching Area, respectively. Based on this, the time slices are further divided into frequency regulation, peak regulation, and standby according to the dispatch task type field, and numbered T1, T2, T3, etc. Time slices belonging to the same station and with the same task attributes are collectively identified to form physical dispatch subgroups and task type logical subgroups. These are structured and managed by establishing a multi-level dispatch index. The energy storage unit numbers involved in each group are recorded, and a group index table is established for information maintenance, forming the following grouping structure: Table 8 Time slice scheduling group structure table

[0044] As shown in Table 8, multiple time slices have been effectively grouped according to the scheduling objectives and the stations to which they belong, and the scheduling time slice grouping results are obtained.

[0045] The duration verification submodule extracts the energy storage unit numbers in each group based on the scheduling time slice grouping results. Combined with the available time length data of the energy storage units within the scheduling cycle, it determines whether each energy storage unit can fully cover the start and end periods corresponding to the time slice. It then detects scheduling conflicts and eliminates them to obtain a conflict-free energy storage unit set. Based on the scheduling time slice grouping results, all energy storage unit numbers in the table are extracted item by item. Combined with the previously recorded energy storage available time window information, cross-validation is performed to determine whether its adjustable time period covers the allocation time slice requirements. Records with continuous scheduling but a total available time of less than 15 minutes are marked as conflict. For example, E2 is assigned to T2 and T5, with a time interval of only 3 minutes, but T2 lasts for 8 minutes and T5 lasts for 5 minutes. Although the total time is 13 minutes, it is less than the set threshold. Therefore, E2 is marked as a scheduling conflict state and removed. At the same time, E1 and E3 are retained as non-conflict units. The following conflict verification data table is established: Table 9 Energy storage dispatch duration verification table

[0046] As shown in Table 9, it is finally confirmed that E1 and E3 meet the continuous scheduling requirements, and E2, which has scheduling overlap but does not meet the time benchmark, is eliminated to obtain a conflict-free energy storage unit set.

[0047] The priority generation submodule counts the number of times each energy storage unit is active in the scheduling time slice based on the set of conflict-free energy storage units. It records the station number, the number of participating time slices, and the number of assigned tasks. It then accumulates the number of scheduled tasks and prioritizes all energy storage units based on their call frequency and the scheduling saturation of the station to which they belong, generating a storage scheduling priority list. Based on the conflict-free energy storage unit set, the frequency of each unit appearing in the task table is recorded, i.e., the number of adjustable time slices. The scheduling activity is calculated by combining the station number to which it belongs and its proportion in the total number of time slices. Assuming that the total scheduling task segments are 5, E1 appears in 4 segments with an activity of 0.8, E2 appears twice with an activity of 0.4 but has been eliminated, and E3 appears 3 times with an activity of 0.6. Further statistics are collected to determine whether there are scheduling conflicts. The following energy storage scheduling indicator set is constructed: Table 10 Energy storage unit scheduling attributes

[0048] As shown in Table 10, E1 and E3 are assigned priorities based on the dispatch frequency, dispatch consistency, and saturation level of the region to which they belong. A dispatch order list is generated from high to low priority, and finally a list of energy storage dispatch priorities is obtained.

[0049] See also Figure 6 , the collaborative control instruction arrangement module includes The standard instruction generation submodule extracts the dispatch time slice data, station number, and dispatch type of each unit based on the energy storage dispatch priority list. It generates a standard control instruction format for each dispatch segment, maps different types of tasks to various control target parameters, and uniformly encapsulates them into a dispatch instruction structure according to specifications to establish a standard control instruction set. After obtaining the energy storage scheduling priority list, it is necessary to extract the task type of each energy storage unit in its corresponding time slice. Combined with the two control requirements of AGC (Automatic Generation Control) and AVC (Automatic Voltage Control), standardized instruction structures are constructed. AGC instructions calculate the target power based on the average output value and adjustable amplitude of the energy storage unit. Assume that the scheduling target of the E1 energy storage unit during the time period of 09:00 to 09:15 is 112.5kW. At the same time, the AVC instructions are set within ±0.02kV of the access point reference voltage. Assume that the adjustment target of the CMD002 instruction is 0.98kV, with a start and end time of 09:20 to 09:40. In addition, the AGC control instruction numbered CMD003 in the S02 station has a target power of 108.0kW and a scheduling period of 10:00 to 10:30. All field structures must include: instruction number, station number, control type, start time, end time, and control target value, forming the following initial control instruction structure: Table 11 Control command structure of each station

[0050] As shown in Table 11, all instruction format fields have been aligned with the scheduling template requirements to establish a standard control instruction set.

[0051] The conflict period elimination submodule, based on a standard control instruction set, cross-checks all control instructions at the site level. It extracts control instructions for the same energy storage unit and wind power photovoltaic unit in adjacent time periods. If there is an intersection in the start and end times of the instructions, the intersection duration is calculated and a determination is made as to whether it exceeds the scheduling tolerance threshold. If the intersection exceeds the threshold, it is considered an instruction conflict, and subsequent instruction entries are eliminated to obtain a conflict-free control instruction set. According to the standard control instruction set, a cross-time coverage analysis is performed on the energy storage units and wind and solar power generation units involved in the instruction set to determine whether there is time overlap or inconsistent control types. First, the start and end time periods of all instructions are extracted, and multiple instructions under the same station are compared pairwise. Assume that CMD001 and CMD002 control the S01 station respectively, and the interval between the two instructions is 5 minutes, which does not constitute a conflict. If the start and end time of instruction CMD004 is 09:10 to 09:25, and there is a 5-minute intersection with CMD001, a time conflict elimination judgment is performed. The maximum allowable intersection of the scheduling system is set to 3 minutes. The current intersection exceeds the threshold, and CMD004 with the later time needs to be eliminated. The same treatment is applied to inconsistent types. For example, if CMD005 issues AGC and AVC instructions in the same time period on the same energy storage device, the AGC instruction is retained according to the time period priority identifier. Finally, the conflicting instructions are removed and the instruction status is summarized to obtain a conflict-free control instruction set.

[0052] The station instruction collection submodule classifies each instruction according to the station number based on the conflict-free control instruction set, merges and outputs instructions within the same station, arranges them in order of instruction numbers to form a unified cross-station scheduling instruction integration area, and generates multi-station wind, solar and storage joint scheduling instructions; According to the conflict-free control instruction set, each dispatch instruction needs to be classified and output according to the station number, and the AGC and AVC instructions of each station are stored in the station control task queue respectively. The dispatch center reads the task queue data and issues control instructions through the dispatch execution engine, groups all instructions according to the station number, and arranges the AGC and AVC tasks within the same station in ascending order according to the instruction number. These tasks are written into the dispatch cache structure and form the following collection view, ultimately building a complete cross-station instruction integration system: Table 12 Multi-station control task collection table

[0053] As shown in Table 12, the control instructions of multiple stations have been integrated and classified into different zones, and the joint dispatching instructions of wind, solar and storage at multiple stations have been successfully established.

[0054] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. Multi-station wind-solar collaborative energy storage optimization control system, characterized by: The system comprises: The wind and solar power output change identification module obtains the output sequences of wind power and photovoltaic power generation units at multiple stations, extracts the extreme points of wind power output ramp rate and the duration of photovoltaic output power decline, determines the power generation complementary period, and generates a wind and solar power fluctuation timestamp data set for each station; The energy storage window status screening module identifies energy storage units that can enter the dispatch state based on the SOC status information and operating status identifiers of multiple stations, screens out energy storage devices in the maintenance lock state or the task mutual exclusion state, extracts the corresponding available sections as the control reference time period, and generates a set of available time windows for energy storage at each station; The linkage section intersection calibration module performs time window overlap analysis on the complementary segments and available intervals on a site-by-site basis based on the wind and solar fluctuation timestamp datasets of each station and the energy storage available time window set of each station, marks the overlapping time range, and performs time period coding to generate a multi-station scheduling time slice set. The multi-station energy storage priority allocation module groups resources according to the scheduling time slice set, performs cross-validation of available time, screens energy storage units with no resource conflict records, records station ownership and scheduling task counters, and generates a multi-station energy storage scheduling priority list.

2. The multi-station wind-solar coordinated energy storage optimization control system according to claim 1 is characterized in that: The wind-solar fluctuation timestamp dataset includes a ramp rate extreme value mark, a power drop period mark, a power generation complementary period mark, and a site output trend summary. The energy storage available time window set includes SOC filtering results, operating status screening results, an adjustable time period mark, and an energy storage unit regulation qualification mark. The scheduling time slice set includes a linkage overlapping time range mark, a period unique identifier, a cross-site linkage section mapping table, and adjustable section time alignment information. The energy storage scheduling priority list includes a resource grouping tag, an energy storage unit availability verification result, a resource conflict screening record, and a scheduling task count mark.

3. The multi-station wind-solar coordinated energy storage optimization control system according to claim 1 is characterized in that: The wind and solar power output change recognition module includes: The output sequence extraction submodule obtains the output sequences of wind power and photovoltaic power generation units at multiple sites, extracts the unit time output power data and time series data of each wind power and photovoltaic power generation unit, synchronizes them by site, aligns the sequences according to the time step, and generates a unified time scale output sequence matrix; The change trend identification submodule detects the maximum value of the power change rate in the wind power output based on the unified time scale output sequence matrix, selects the time points in the wind turbine output change rate that exceed the output change rate threshold, marks them as extreme value points, and obtains the wind power output change rate extreme value point set; The complementary period marking submodule analyzes the time overlap between the extreme points of the wind power output change rate and the photovoltaic output decline interval based on the set of extreme point sets of the wind power output change rate and the time interval of the continuous decline in photovoltaic output power, calculates the complementary index value of each overlapping section, selects the time period with a complementary index value greater than the complementary threshold, marks it, and obtains the wind-solar complementary marking period set.

4. The multi-station wind-solar coordinated energy storage optimization control system according to claim 1 is characterized in that: The energy storage window status screening module includes: The SOC state extraction submodule obtains the SOC state information of energy storage at multiple stations, collects the current SOC value, nominal capacity, and available capacity of each energy storage unit, calculates the SOC change trend of each unit within the observation interval, and the difference between the minimum SOC threshold and the current state, determines whether it is within the discharge capacity range allowed for scheduling, and generates an energy storage SOC screening state set; The operation state elimination submodule filters the state set based on the energy storage SOC, reads the current operation state identification information of each energy storage unit, parses the state code field, identifies the maintenance lock flag and the task mutual exclusion flag in the state code, and eliminates the corresponding unit to obtain the operation state available unit set; The available window generation submodule establishes an available segment index based on the available unit set in the operating state according to the unit index and time field, counts the continuous available duration and the minimum continuous available segment of each unit in a given scheduling cycle, calculates the window effective index value of the energy storage unit in the scheduling cycle, outputs the time period with an index value greater than the set window scheduling threshold as the available time period, and generates a set of energy storage available time windows.

5. The multi-station wind-solar coordinated energy storage optimization control system according to claim 1 is characterized in that: The linkage section intersection calibration module includes: The time overlap determination submodule, based on the wind-solar fluctuation timestamp dataset of each station and the energy storage available time window set, calls the start and end time of the wind-solar complementary segment of each station and the start and end time of the corresponding energy storage unit adjustable window, determines the relative order of the start and end points of the time period, determines and marks the overlapping segments, and obtains the wind-solar-energy storage time overlap segment set; The linkage overlap calculation submodule calculates the start and end overlap length, intersection center time and joint output expected value for each overlapping segment according to the wind, solar and energy storage time overlap segment set, obtains the scheduling potential interval of each overlapping segment, and calculates the first The overlapping strength index of the overlapping segments is calculated, and the overlapping segments with index values ​​lower than the set strength threshold are retained to establish an overlapping strength screening interval set; The time slice identifier generation submodule filters the interval set according to the overlap intensity, reads the station number, overlap start and end time and overlap intensity of each section, generates a unique identifier string for each schedulable time period, allocates and embeds each field in the time slice data structure, and establishes a multi-station scheduling time slice set.

6. The multi-station wind-solar coordinated energy storage optimization control system according to claim 1 is characterized in that: The multi-station energy storage priority allocation module includes: The time slice grouping submodule obtains the multi-station scheduling time slice set, extracts the station number, start and end time, scheduling type and energy storage unit number corresponding to each time slice in turn, divides all time slices according to the scheduling area, classifies and establishes three logical scheduling groups: frequency regulation, peak regulation and standby, and generates the scheduling time slice grouping results; The duration verification submodule extracts the energy storage unit numbers in each group based on the scheduling time slice grouping results. Combined with the available time length data of the energy storage units within the scheduling cycle, it determines whether each energy storage unit can completely cover the start and end periods corresponding to the time slice, detects scheduling conflicts, and eliminates them to obtain a conflict-free energy storage unit set. The priority generation submodule counts the number of times each energy storage unit is active in the scheduling time slice based on the set of conflict-free energy storage units, records the station number, the number of participating time slices, and the number of assigned tasks, accumulates the number of scheduling tasks, and prioritizes all energy storage units according to the call frequency and the scheduling saturation of the station to which they belong to generate an energy storage scheduling priority list.

7. The multi-station wind-solar coordinated energy storage optimization control system according to claim 1 is characterized in that: The system further comprises: The collaborative control instruction arrangement module establishes a unified instruction sequence based on the energy storage dispatch priority list, verifies the time period coverage of all dispatch instructions, partitions the output by station dimension, and generates multi-station wind, solar and energy storage joint dispatch instructions; The wind, solar and storage joint dispatching instructions include the AGC instruction set, the AVC instruction set, the time period deduplication results, and the station control instruction output set.

8. The multi-station wind-solar coordinated energy storage optimization control system according to claim 7 is characterized in that: The collaborative control instruction arrangement module includes The standard instruction generation submodule extracts the scheduling time slice data, station number, and scheduling type of each unit based on the energy storage scheduling priority list, generates a standard control instruction format for each scheduling segment, maps different types of tasks to various control target parameters, and uniformly encapsulates them into a scheduling instruction structure according to specifications to establish a standard control instruction set; The conflict period elimination submodule performs time period cross-checks on all control instructions based on the standard control instruction set according to the site dimension, extracts control instructions for the same energy storage unit and wind power photovoltaic unit in adjacent time periods, eliminates instruction conflict entries, and obtains a conflict-free control instruction set; The station instruction collection submodule classifies each instruction according to the station number based on the conflict-free control instruction set, merges and outputs the instructions within the same station, arranges them in order of instruction numbers to form a unified cross-station scheduling instruction integration area, and generates multi-station wind, solar and storage joint scheduling instructions.

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