Multi-station light and landscape cooperative energy storage optimization control system

Abstract

The present application relates to the technical field of cooperative control, in particular to a multi-station wind-solar cooperative energy storage optimization control system, which comprises a wind-solar 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 control instruction arrangement module. In the present application, the wind power climbing extreme value and the photovoltaic descending section are extracted to establish a matching relationship between the power generation complementary characteristics and the energy storage response time, thereby enhancing the timing scheduling accuracy, fusing the SOC and the operating state for multi-dimensional screening of the energy storage unit, excluding the non-adjustable resources, improving the execution reliability, constructing the output fluctuation and the energy storage window intersection, generating the standardized scheduling time slice, realizing the task unified positioning, completing the energy storage resource grouping and conflict elimination based on the available time length, making clear the priority order, guaranteeing the scheduling coordination, removing the redundant conflict items, improving the instruction flow integrity and the control coverage, and enhancing the execution efficiency and the system stability of the multi-station cooperative scheduling.

Description

Technical Field

[0001] This invention relates to the field of collaborative control technology, and in particular to a multi-site wind-solar collaborative energy storage optimization control system. Background Technology

[0002] The field of collaborative control technology encompasses the unified coordination and orderly management of various energy forms such as wind power, photovoltaics, and energy storage in multi-energy systems. Its core content is the control design of dispatch strategies for distributed energy resources to ensure the coordination and consistency of various energy forms in temporal and spatial dimensions. It mainly involves the control logic of power electronic interfaces, the regulation mechanism of energy flow paths, and the linkage management of energy conversion equipment. It is committed to realizing 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-site wind-solar-coordinated energy storage optimization control system refers to an energy management method that coordinates and controls the energy storage devices configured at multiple geographically distributed wind and solar power stations through a centralized master station dispatch system. It mainly addresses the problem of unstable and time-varying wind and solar power output, and covers wind power prediction, solar power output monitoring, energy storage status assessment, and load-side response capability assessment. Specifically, it uses the collection of power information, voltage and frequency status, and weather forecast data from each station as a basis, and uses dispatch logic to dynamically adjust the charging and discharging sequence of the energy storage system, and redistributes energy during wind and solar power output periods to achieve optimized configuration of energy flow paths and execution of the overall system control strategy.

[0004] In current wind-solar-storage coordinated regulation processes, reliance on static scheduling windows or large-granularity prediction models is prevalent. This makes it difficult to capture effective regulation opportunities in scenarios with rapidly changing output, resulting in delayed or failed energy storage responses and reduced resource utilization efficiency. Availability assessments for energy storage devices are often simplistic, based solely on SOC thresholds without considering device operating status and grid connection constraints. This can lead to situations where devices failing to meet regulation requirements are mistakenly identified as available for scheduling. The lack of standardized coding for scheduling task periods increases the risk of multiple concurrent calls to the same energy storage unit, potentially causing command conflicts in areas with high concurrent scheduling activity. Furthermore, the absence of a refined priority allocation mechanism for resource scheduling makes it impossible to rationally allocate task loads based on remaining energy storage capacity and actual distribution in multi-site parallel tasks. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a multi-site wind-solar collaborative energy storage optimization control system.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a multi-site wind-solar coordinated energy storage optimization control system includes:

[0007] The wind and light output change recognition module obtains output sequences of wind power and photovoltaic power generation units of multiple stations, extracts wind power output ramp rate extreme points and photovoltaic output power drop duration, determines power generation complementary periods, and generates wind and light fluctuation timestamp data sets of each station;

[0008] The energy storage window state screening module identifies energy storage units that can enter a dispatch state according to energy storage SOC state information and operating state identifiers of multiple stations, filters out energy storage devices in a maintenance lock state and a task exclusion state, extracts corresponding available sections as a regulation reference period, and generates available time window sets of energy storage units of each station;

[0009] The linkage section intersection calibration module performs time window overlap analysis on the complementary sections and available intervals of each station based on the wind and light fluctuation timestamp data sets of each station and the available time window sets of energy storage units of each station, marks overlapping time ranges, and performs period coding to generate a set of dispatch time slices of multiple stations;

[0010] The multiple-station energy storage priority allocation module groups resources according to the set of dispatch time slices, cross- verifies available durations, filters out energy storage units without resource conflicts, records station attribution and dispatch task counters, and generates a multiple-station energy storage dispatch priority list.

[0011] As a further scheme of the application, the wind and light fluctuation timestamp data set includes ramp rate extreme point markers, power drop period markers, power generation complementary period identifiers, and station output trend summaries. The available time window set of energy storage units includes SOC filtering results, operating state screening results, regulatable time period identifiers, and energy storage unit regulation qualification markers. The set of dispatch time slices includes linkage overlap time range markers, period unique identifiers, cross-station linkage section mapping tables, and regulatable section time alignment information. The energy storage dispatch priority list includes resource grouping tags, energy storage unit availability verification results, resource conflict filtering records, and dispatch task count identifiers.

[0012] As a further scheme of the application, the wind and light output change recognition module includes:

[0013] The output sequence extraction submodule obtains output sequences of wind power and photovoltaic power generation units of multiple stations, extracts unit time output power data and time sequence data of each power generation unit of wind power and photovoltaic power, synchronously organizes the data by station, aligns the sequences according to time steps, and generates a uniform time scale output sequence matrix.

[0014] The change trend recognition submodule detects the maximum value of the power change rate in wind power output based on the uniform time scale output sequence matrix, filters out time points in the wind turbine output change rate that exceed the output change rate threshold, and marks them as extreme points to obtain a wind power output change rate extreme point set.

[0015] The complementary period labeling sub-module analyzes the time coincidence relationship between the wind power output change rate extreme point and the photovoltaic output decline interval according to the wind power output change rate extreme point set and the time interval of the sustained decline of the photovoltaic output power, calculates the complementary index value of each coincidence section, screens the time period with the complementary index value greater than the complementary threshold value, and labels the time period to obtain a wind-photovoltaic complementary labeled period set.

[0016] As a further scheme of the present application, the energy storage window state screening module comprises:

[0017] The SOC state extraction sub-module obtains the SOC state information of 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 in the observation interval, the difference between the minimum SOC threshold value and the current state, judges whether it is in the discharge capacity interval allowed to be dispatched, and generates an energy storage SOC screening state set;

[0018] The running state elimination sub-module reads the current running state identification information of each energy storage unit based on the energy storage SOC screening state set, analyzes the state code field, identifies the maintenance lock identification and task mutual exclusion identification in the state code, and eliminates the corresponding unit to obtain a running state available unit set;

[0019] The available window generation sub-module establishes an available section index according to the unit index and the time field based on the running state available unit set, counts the continuous available duration and the minimum continuous available section of each unit in a given dispatching period, calculates the window effective index value of the energy storage unit in the dispatching period, outputs the time period with the index value greater than the set window dispatching threshold value as the available time period, and generates an energy storage available time window set.

[0020] As a further scheme of the present application, the linkage section intersection calibration module comprises:

[0021] The time overlap determination sub-module calls the start and end time of the wind-photovoltaic complementary section of each station and the adjustable window start and end time of the corresponding energy storage unit based on the wind-photovoltaic fluctuation timestamp data set of each station and the energy storage available time window set, judges the relative sequence of the start and end points of the time period, judges and labels the overlapping section, and obtains a wind-photovoltaic energy storage time overlapping section set;

[0022] The linkage overlap calculation sub-module calculates the start and end overlap length, intersection center time and joint output expected value of each overlapping section according to the wind-photovoltaic energy storage time overlapping section set, obtains the dispatching potential interval of each overlapping section, calculates the overlap intensity index of the first

[0023] ​The time slice identification generating submodule screens 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 identification string for each dispatchable time slice, assigns and embeds each field in the time slice data structure, and establishes a multi-station dispatch time slice set.

[0024] As a further scheme of the present application, the multi-station energy storage priority allocation module comprises:

[0025] The time slice grouping submodule obtains the multi-station dispatch time slice set, sequentially extracts the station number, start and end time, dispatch type and energy storage unit number corresponding to each time slice, divides all time slices according to the dispatch area, classifies to establish three logical dispatch groups of frequency modulation, peak regulation and backup, and generates a dispatch time slice grouping result;

[0026] The time length verification submodule extracts the energy storage unit number in each group based on the dispatch time slice grouping result, combines the available time length data of the energy storage unit in the dispatch cycle, judges whether each energy storage unit can completely cover the start and end time period corresponding to the time slice, detects and eliminates the dispatch conflict state, and obtains a conflict-free energy storage unit set;

[0027] The priority generating submodule counts the active number of each energy storage unit in the dispatch time slice according to the conflict-free energy storage unit set, records the station number, the number of participating time slices and the number of allocated tasks, accumulates the number of dispatch tasks, and sorts all energy storage units according to the call frequency and the dispatch saturation degree of the belonging station to generate an energy storage dispatch priority list.

[0028] As a further scheme of the present application, the system further comprises:

[0029] The collaborative regulation instruction arrangement module establishes a unified instruction sequence according to the energy storage dispatch priority list, performs period coverage verification on all dispatch instructions, partitions and outputs according to the station dimension, and generates a multi-station wind-solar-storage joint dispatch instruction;

[0030] The wind-solar-storage joint dispatch instruction comprises an AGC instruction set, an AVC instruction set, a period deduplication result and a station regulation instruction output set.

[0031] As a further scheme of the present application, the collaborative regulation instruction arrangement module comprises

[0032] The standard instruction generating submodule extracts the dispatch time slice data, the belonging station number and the dispatch type of each unit according to the energy storage dispatch priority list, generates a standard control instruction format for each dispatch segment, maps different types of tasks to various regulation target parameters, uniformly encapsulates the dispatch instruction structure according to the specification, and establishes a standard control instruction set;

[0033] The conflict period elimination sub-module performs time period cross verification on all control instructions based on the standard control instruction set in the field station dimension, extracts the control instructions of the same energy storage unit and wind, light and photovoltaic unit in adjacent time periods, eliminates instruction conflict entries, and obtains a non-conflict regulation and control instruction set;

[0034] The field station instruction collection sub-module classifies each instruction according to the field station number according to the non-conflict regulation and control instruction set, merges and outputs the instructions in the same field station, arranges them in order according to the instruction number, and uniformly forms a cross-field station scheduling instruction set integration area to generate a multi-field station wind, light and photovoltaic storage joint scheduling instruction.

[0035] Compared with the prior art, the advantages and positive effects of the present application are that:

[0036] In the present application, by extracting the wind power climbing extreme value and the photovoltaic descending section, the matching relationship between the power generation complementarity and the energy storage response time is established, the time sequence scheduling accuracy is enhanced, the SOC and the running state are fused for multi-dimensional screening of the energy storage unit, the non-adjustable resources are excluded, the execution reliability is improved, the power fluctuation and the energy storage window intersection are constructed, the standardized scheduling time slice is generated, the task is uniformly located, the energy storage resource grouping and conflict elimination are completed based on the available time length, the priority is sorted, the scheduling coordination is ensured, the redundant conflict items are removed, the instruction flow integrity and the control coverage are improved, and the execution efficiency and the system stability of the multi-field station collaborative scheduling are enhanced. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The system flowchart of the present application is shown in the figure;

[0038] Figure 2 The wind and light output change identification module flowchart of the present application is shown in the figure;

[0039] Figure 3 The energy storage window state screening module flowchart of the present application is shown in the figure;

[0040] Figure 4 The linkage section intersection calibration module flowchart of the present application is shown in the figure;

[0041] Figure 5 The multi-field station energy storage priority allocation module flowchart of the present application is shown in the figure;

[0042] Figure 6 The collaborative regulation and control instruction arrangement module flowchart of the present application is shown in the figure. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical scheme and advantages of the present application clearer and more apparent, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0044] In the description of the present application, it should be understood that the terms "length", "width", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, in the description of the present application, the meaning of "a plurality of" is two or more, unless otherwise explicitly and specifically limited.

[0045] Please refer to Figure 1 The multi-station wind and light coordinated energy storage optimization control system comprises:

[0046] The wind and light output change identification module obtains the output sequence of the wind and light power generation units of multiple stations, extracts the wind power output climbing rate extreme point (the maximum value monitoring point of the power change rate of the wind turbine unit per unit time) and the photovoltaic output power drop duration (the time interval during which the output power of the photovoltaic power generation system continuously decreases) according to the internal output change trend of each station, determines the power generation complementary period (the time period during which the wind and photovoltaic power generation presents complementary output characteristics), and collects and marks the extraction results of each station internally to generate a wind and light fluctuation timestamp data set of each station;

[0047] The energy storage window state screening module identifies the energy storage units that can enter the dispatching state according to the multi-station energy storage SOC state information (State of Charge data of the battery energy storage system) and the running state identifier (device running state code conforming to the IEC61850 standard), screens out energy storage devices in the maintenance locking state (the protection state of the device in the planned maintenance) and the task exclusion state (the state of the energy storage system being unavailable due to the execution of other dispatching tasks), extracts the corresponding available section as a regulation reference time period, and generates a set of available time windows of the energy storage of each station;

[0048] The linkage section intersection calibration module performs time window overlap analysis (time relationship analysis method based on Allen interval algebra) on the complementary section and the available section of each station based on the wind and light fluctuation timestamp data set of each station and the set of available time windows of the energy storage of each station, marks the overlapping time range with linkage relationship, and performs period UUID coding (unique period identifier generated by adopting the RFC4122 standard), to generate a set of multi-station dispatching time slices;

[0049] The multi-station energy storage priority allocation module groups resources (resource classification method according to power grid dispatching regulations) according to the dispatch time slice set for all time slices with linkage relationship, and cross- verifies the available duration of the energy storage units associated with each type of time slice, screens the energy storage units without resource conflict records, and records the station attribution and dispatch task counter, to generate a multi-station energy storage dispatch priority list;

[0050] The collaborative regulation instruction arrangement module establishes a unified AGC / AVC instruction sequence (standardized instruction format of automatic generation control / automatic voltage control) according to the energy storage dispatch priority list, and performs period coverage verification on all dispatch instructions, removes period overlapping items and instruction conflict detection, and outputs by station dimension, to generate a multi-station wind-solar-storage joint dispatch instruction.

[0051] The wind-solar fluctuation timestamp data set includes a ramp rate extreme value mark, a power drop period mark, a power generation complementary period identifier, and a station output trend summary. The energy storage available time window set includes an SOC filtering result, an operating state screening result, a controllable time period identifier, and an energy storage unit regulation qualification mark. The dispatch time slice set includes a linkage overlap time range mark, a period unique identifier, a cross-site linkage section mapping table, and controllable section time alignment information. The energy storage dispatch priority list includes a resource grouping label, an energy storage unit availability verification result, a resource conflict screening record, and a dispatch task count identifier. The wind-solar-storage joint dispatch instruction includes an AGC instruction set, an AVC instruction set, a period deduplication result, and a station regulation instruction output set.

[0052] Please refer to Figure 2 , the wind-solar output change recognition module includes

[0053] The output sequence extraction submodule obtains the output sequence of the wind power and photovoltaic power generation units of multiple stations, extracts the unit time output power data and time sequence data of each wind power and photovoltaic power generation unit, synchronously organizes them by station, aligns the sequences according to the time step, and generates a unified time scale output sequence matrix.

[0054] The output sequence of multiple wind power stations and photovoltaic power generation units is obtained by first collecting the original power output data of multiple wind power stations and photovoltaic stations in the same observation period. For each station, the output power record in unit time is extracted. In the example, the sampling interval is set to 1 minute. Taking the observation interval from 10:00 to 12:00 as an example, the output power values of wind power station W1 and photovoltaic station S1 in each minute are collected. The output power of wind power station W1 from 10:00 to 10:05 is 320, 335, 355, 360, 370, and 378 kW, respectively. The power of photovoltaic station S1 during the same period is 210, 205, 198, 190, 180, and 170 kW, respectively. The above data is arranged in an initial data matrix according to the minute level, with time as the index. The data of each station is synchronized and aligned, and a horizontal arrangement data structure is formed according to the uniform time step. Then, the synchronized data is structured. According to the station category, wind power output matrix and photovoltaic power output matrix are generated. Each row in the matrix represents a time point, and each column represents a specific station. The unified data structure is shown in the following table:

[0055] Table 1: Wind power and photovoltaic power output sequence table

[0056]

[0057] As shown in Table 1, the output sequence data matrix formed after synchronization can be used for subsequent fluctuation analysis and processing to obtain a unified time scale output sequence matrix.

[0058] 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 where the wind turbine output change rate exceeds the output change rate threshold, and marks them as extreme points to obtain the wind turbine output change rate extreme point set.

[0059] Based on the unified time scale output sequence matrix, the power change rate of each wind turbine is calculated by continuously differentiating the power in unit time step. The formula is wherein, represents the unit time power change rate of the wind power in the first period, represents the power value of the wind power in the first period, represents the power value of the wind power in the first period, represents the time of the first period, represents the time of the first The time period is calculated step by step for W1 in Table 1 in the time period from 10:00 to 10:05, and the results are as follows: 10:00-10:01, 15 kW / min; 10:01-10:02, 20 kW / min; 10:02-10:03, 5 kW / min; 10:03-10:04, 10 kW / min; 10:04-10:05, 8 kW / min. Based on the power change rate analysis, the time period with a change rate greater than the set threshold is determined as the extreme point. In this embodiment, the threshold is set to 12 kW / min. The basis for setting the threshold is that the power change rate of the wind turbine in the normal operation mode usually maintains in the range of 6-11 kW / min. When a fluctuation greater than about 10% of the upper limit of the range occurs, it is considered to enter the non-steady-state climbing zone. Therefore, the threshold is set in the manner of adding 1.2 times the standard deviation to the sample mean of the wind power output change rate, and combined with the actual fluctuation amplitude, it is set to 12 kW / min to balance the sensitivity and false rejection rate. This value will change with the capacity of the wind turbine and the fluctuation gradient of the wind speed, but it will not be linearly upward with the expansion of the instantaneous maximum output, so the time periods of 10:00-10:01 and 10:01-10:02 meet the conditions, and the corresponding change rates are 15 and 20 kW / min. The time points are marked as 10:01 and 10:02, which are used as the wind power output change rate extreme points for subsequent matching with the photovoltaic descending trend. Finally, the wind power output change rate extreme point set is obtained.

[0060] The complementary time period marking submodule analyzes the time coincidence relationship between the wind power output change rate extreme points and the photovoltaic output descending interval based on the wind power output change rate extreme point set and the time interval of the photovoltaic output power continuous decline, and uses the formula:

[0061] ;

[0062] Calculate the complementary index value of each coincidence section , filter the time period with a complementary index value greater than the complementary threshold, and mark it to obtain the wind-solar complementary marked time period set, wherein, represents the unit time power change rate of the wind power in the first time period, represents the time span corresponding to the first complementary section, represents the total amplitude of the power decline of the photovoltaic in the first complementary section, represents the duration of the photovoltaic descending section, represents the mean value of the time span of all coincidence sections, represents the total number of complementary sections;

[0063] Based on the extreme point set of wind power output change rate and combined with the time interval of continuous decline in photovoltaic power output, it is necessary to analyze the overlap relationship between the rise in wind power and the decline in photovoltaic power, and to statistically analyze the total amount and duration of the decline in photovoltaic power output. For example, in Table 1, the output power of S1 continuously decreased from 210kW to 170kW between 10:00 and 10:05, with a total decrease of 40kW and a duration of 5 minutes. The strength of the photovoltaic downward trend is calculated. Meanwhile, the wind power output in this segment increased from 320 kW to 378 kW, a difference of 58 kW, with a rate of change of 11.6 kW / min per unit time. This is combined with the average time span. Minutes, substituting into the complementarity index formula:

[0064] ;

[0065] In this embodiment, the complementarity determination threshold is set to 20. This threshold is based on the distribution characteristics analysis of the complementarity index values ​​obtained after performing index calculations on multiple sets of wind and solar power output over various time periods. The data shows that... When the value is greater than 20, the time overlap and output complementarity between the wind power ramp-up phase and the photovoltaic ramp-down phase are both over 90%, and the index distribution shows a clear discontinuity. The slope changes inflection point between 18 and 20, so this critical value is used as the standard and 20 is set as the judgment threshold. This value varies with the time span. The fluctuation range increases and tends to rise, but its sensitivity to changes in the downward trend itself is relatively small. Therefore, as an overall judgment indicator, it has good stability and adaptability. If this value meets the conditions, it is determined that this time period constitutes a wind-solar complementary relationship, and the time period from 10:00 to 10:05 is finally added to the complementary time period set. Further analysis of multiple sample segments is as follows:

[0066] Table 2. Analysis Data of Wind-Solar Hybridization

[0067]

[0068] As shown in Table 2, the complementary indices of the sample data for different time periods were calculated using the formula, and the results were 55.5, 41.735, and 23.0, respectively. All of these indices exceeded the threshold of 20. Therefore, the corresponding time periods of 10:00-10:05, 11:20-11:26, and 12:15-12:19 were marked as wind-solar complementary time periods, effectively filtering out time periods with significant complementarity, and finally obtaining the set of wind-solar complementary time periods.

[0069] The complementary index value is a quantitative index for measuring whether the rising trend of wind power output and the falling trend of photovoltaic output form an effective complementary relationship in the same time period. The greater the value, the more the increment of wind power can cover the decrement of photovoltaic, and the higher the coincidence degree of time. The index comprehensively considers the increment formed by the change rate of wind power output and the time span, and the difference value comparison of the falling intensity of photovoltaic in the same time, and gives normalization processing combined with the time offset factor, so as to reflect the synchronization and amplitude compensation relationship of the output fluctuations of the two. By setting the complementary judgment threshold, if the complementary index of a certain period of time exceeds the value, it can be judged that the two types of energy form a relatively coordinated complementary operation state in the period of time, which is the core quantitative basis for identifying complementary behavior in wind-solar output regulation and joint scheduling.

[0070] The formula aims to evaluate the complementary matching degree of wind power rising and photovoltaic falling in a specific period. The numerator part represents the total output increment of wind power in the complementary period, which is obtained by multiplying the change rate per unit time by the time span to get the total power rising amount; and represents the falling trend intensity of photovoltaic output, which is obtained by multiplying the total amount of photovoltaic falling power by the duration and then taking the square root, so that the value reflects the falling amplitude and persistence and enhances the sensitivity to extreme falling situations, and enhances the trend significance representation. The difference between the two reflects whether the increment of wind power is sufficient to compensate for the intensity of photovoltaic decline, and the absolute value is taken to eliminate directional bias and focus on the compensation matching degree itself. The denominator part is the time deviation penalty term, which adjusts the deviation of the actual complementary span from the mean value by weighting, and gives a moderate penalty when the time span deviates from the overall trend, avoiding the interference of isolated segments on the overall complementary determination. The whole formula is normalized by weighted difference value, which comprehensively evaluates multiple time periods at the summation level, ensuring that the complementary degree measurement has both time continuity sensitivity and trend intensity discrimination.

[0071] Please refer to Figure 3 The energy storage window state screening module comprises:

[0072] The SOC state extraction submodule acquires the SOC state information of the energy storage of multiple stations, collects the current SOC value, nominal capacity and available capacity of each energy storage unit, calculates the SOC change trend, the difference between the minimum SOC threshold and the current state in the observation interval, judges whether it is in the discharge capacity interval allowed to be dispatched, and generates a set of energy storage SOC screening states;

[0073] The SOC state information of multiple stations is acquired, the data source of the accessed battery energy storage system is sampled at a minute level, the current SOC value, nominal capacity and available capacity of each energy storage unit are collected, and in the example, three energy storage units E1, E2 and E3 are taken as examples, the current SOC values of which are 63%, 58% and 39% respectively, the corresponding nominal capacities are 100 kWh, 120 kWh and 90 kWh, and the available capacities are 52 kWh, 66 kWh and 29 kWh. The SOC values are normalized and the deviation between the normalized SOC values and the set scheduling participation threshold is calculated. The scheduling participation threshold is set to 45%, and the setting basis is that the lithium battery system operating in a normal environment is prone to over-discharge loss risk when the SOC is lower than 40%, and has scheduling release capacity around 50% and retains a certain redundancy, so the median of the interval is taken as the judgment criterion, and the lower limit of the available capacity is set to 30% of the nominal capacity, that is, if the available capacity of a unit is lower than this proportion, it cannot be included in the scheduling available range. According to the data in the above table, the SOC values of E1 and E2 are higher than 45%, and the available capacities are 52% and 55% respectively, both of which meet the conditions. E3 is excluded because the SOC is 39% and the available capacity is 32.2%, and the SOC value does not meet the scheduling conditions. Therefore, E1 and E2 are determined as scheduling response units, forming a preliminary screening state set, and the specific data is summarized as follows:

[0074] Table 3: SOC state data table of energy storage unit

[0075]

[0076] 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, and the energy storage SOC screening state set is obtained.

[0077] The running state elimination submodule reads the current running state identification information of each energy storage unit based on the energy storage SOC screening state set, analyzes the state code field, identifies the maintenance lock identifier and task mutual exclusion identifier in the state code, and eliminates the corresponding unit to obtain a running state available unit set.

[0078] Based on the energy storage SOC screening state set, the running state identification field information of each energy storage unit is called to make state judgment, the state code in the field that meets the IEC61850 standard is extracted and its meaning is analyzed. The maintenance lock state in the state code is usually marked as 105, indicating that the current energy storage unit is in the planned maintenance stage. The task 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 state code of E1 and E2 is analyzed. The current state code of E1 is 101 (normal operation), and the current state code of E2 is 109 (task occupation). Therefore, E2 should be excluded from the candidate set in this step, and only E1 is retained. The exclusion type is task exclusion, and the current judgment window is marked with a timestamp as 09:30-10:00. The matching table of time field and unit index is established, and the running state energy storage unit that can enter the scheduling in the current period is finally screened out, only containing E1. The running state available unit set is finally obtained.

[0079] The available window generation submodule establishes the available segment index according to the unit index and the time field based on the running state available unit set, and counts the continuous available duration and the minimum continuous available segment of each unit in the given scheduling period. The fragmented segments below 10 minutes are screened out, and the remaining segments are continuously marked and start and end time numbered. The formula is:

[0080] ;

[0081] The window effective index value of the first energy storage unit in the scheduling period is calculated , and the time period with an index value greater than the set window scheduling threshold is output as the available time period to generate the energy storage available time window set, wherein, represents the maximum available energy of the first unit in the first available time period, represents the minimum energy threshold required in the corresponding segment, represents the length of the first available time period, represents the average length of all available time periods of the first energy storage unit, represents the discharge capacity value corresponding to the first available time period, represents the number of all available time periods of the first energy storage unit identified in the scheduling period;

[0082] According to the running state available unit set, the E1 unit that has been confirmed to be adjustable is analyzed for all available periods in the scheduling period. The time period that continuously meets the SOC threshold and the running state in the historical running interval is 09:15 to 09:35, lasting for 20 minutes. The maximum available energy in this period is obtained , the minimum energy threshold , the discharge capacity , the average duration is calculated by combining all available segments (only one segment in this example) minutes, and the formula is used for calculation. Since , the denominator is 1, and the final calculation is:

[0083] ;

[0084] The scheduling window threshold is set to 25. This value is set based on the lower limit requirement of the response ability of the energy storage to the scheduling task in the system. This value has been verified by the scheduling power load calculation and is used as the identification threshold of the scheduling control unit. Therefore, when is greater than this value, it is considered as an available window segment. For example, the current E1 value is 56.71, which is much higher than the 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 related parameters are summarized as follows:

[0085] Table 4 Energy storage window effectiveness parameter table

[0086]

[0087] As shown in Table 4, the E1 unit has strong discharge capacity and persistence in the corresponding segment, and the index value exceeds the threshold. Finally, it is included in the scheduling available window, and the energy storage available time window set is obtained.

[0088] The window effectiveness index value is a numerical index that measures the comprehensive scheduling ability of the energy storage unit in a specific scheduling period. This index not only reflects the net energy that can be released in the time period, but also integrates the time period duration stability and the discharge capacity strength performance. The higher the value, the stronger the scheduling response ability and time adaptability of the energy storage unit in the time period, and thus it is more suitable for being included in the load regulation or energy balance task. The construction of this value makes the scheduling system no longer rely on SOC or duration as the basis for judging the available window, but integrates multiple performance factors to generate a numerical reference standard with significant differentiation and screening direction, providing a clear preferred sorting basis for the scheduling strategy.

[0089] The operation logic of the formula is to comprehensively evaluate the performance of each available time period of the energy storage unit in the scheduling period in the three dimensions of energy, time stability and capacity strength. Specifically, the numerator This represents the net adjustable energy value of the energy storage unit during the available time period. It is the difference between the maximum releaseable energy of the energy storage unit and the minimum safety threshold value of that period. It reflects the dispatch release potential of that period. The larger this value, the stronger the dispatch flexibility. The denominator is... The introduction of absolute value calculation is used to measure the difference between the current time period length and the average adjustable time period of that unit. If the time period is close to the average, the closer the denominator is to 1, the smaller the impact on the net energy index. If the deviation is large, it acts as a penalty weight, causing periods with high volatility to be downweighted, thus favoring stable and continuous periods. The right-hand side... The discharge capacity term within this period is compressed by taking the square root to reduce the impact of extreme capacity data, maintaining a moderate value while exhibiting an increasing trend. This characterizes the enhancing contribution of energy storage intensity to window scheduling capability. Finally, the corrected net energy index and capacity impact are weighted and superimposed to comprehensively evaluate the availability value of the window. The overall formula design unifies the three dimensions of time stability, energy adjustability, and intensity capability through a combination of linear and nonlinear approaches, improving the rationality and universal adaptability of the selection results.

[0090] Please see Figure 4 The linkage section intersection calibration module includes:

[0091] The time overlap determination submodule is based on the wind and 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 and solar complementary segment and the start and end times of the corresponding energy storage unit's adjustable window for each station, determines the relative order of the start and end of the time period, identifies and marks the overlapping segments, and obtains the wind, solar and energy storage time overlap segment set.

[0092] Based on the wind and solar power fluctuation timestamp dataset and the energy storage availability time window set for each station, it is necessary to match the wind-solar complementary sections and energy storage scheduling window sections for each station one by one, extract the start and end timestamps of each group of sections, and uniformly adopt the RFC1123 time format standard. Let the wind-solar complementary section of station A be from 10:20 to 10:45 on May 15, 2024, and the energy storage window section be from 10:30 to 11:00. The start times are respectively identified as "Thu, 15May2024 10:20:00GMT" and "Thu, 15May2024 10:30:00GMT", and the end times are "Thu, 15May2024 10:45:00GMT" and "Thu, 15May2024 11:00:00GMT". The time field "00GMT" is used to compare the relative positions of two segments on the time axis. Allen's interval algebra model is used to analyze the temporal relationship between the two segments, and each pair is matched to determine the relationship type as "overlap," indicating partial overlap between the two time periods. According to the model definition, relationships such as "during," "starts," "finishes," and "equals" can also be identified. Only those types that satisfy the intersection are retained. If a site's wind-solar hybrid segment is 11:10-11:20 and the energy storage available segment is 10:50-11:05, the segment relationship is "before," which does not meet the screening criteria and is removed. Time periods that satisfy the overlap relationship are numbered and recorded, and summarized to form the first set of overlapping segments. To enhance implementation clarity, the following are the overlap determination data for three example sites:

[0093] Table 5. Time Overlap Relationship Judgment Table

[0094]

[0095] As shown in Table 5, it was finally determined that there is a time overlap between the sections of station A1 and A3, and a set of time overlap segments for wind, solar and energy storage was generated.

[0096] The overlapping calculation submodule calculates the start and end overlap lengths, intersection center time, and expected joint output for each overlapping segment based on the set of overlapping time segments of wind, solar, and energy storage, and obtains the scheduling potential range for each overlapping segment using the following formula:

[0097] ;

[0098] Calculate the first The overlap strength index of each overlapping segment Retain overlapping segments with index values ​​below a set intensity threshold, and establish a set of overlapping intensity screening intervals, where... Indicates the duration of the overlapping section. This indicates the total adjustable power capacity of the combined wind, solar, and energy storage system within the corresponding section. Indicates the first Duan Di The combined output value at a given time point This represents the average combined output of the corresponding section. This indicates the load forecasting error weight for the hour corresponding to the center time of the corresponding section.

[0099] Based on the time overlap segment set of wind-solar-storage energy, the overlap capacity of all overlapping segments is evaluated sequentially. It is necessary to extract the duration of each overlapping segment, the load prediction error of the hourly segment at the center time point, and the average power value and deviation of the combined output sequence. Let K1 correspond to the overlapping segment of station A1, with a start and end time of 10:30 to 10:45, 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, 120 kW. The calculated combined average output is approximately 112.5 kW. Assuming a prediction error of 6%, [the following values ​​are assigned]. Then, extract their maximum capacity of 120kW and total deviation of 25kW respectively, and further process the numbers K2 and K3. The comprehensive parameters are shown in the table below:

[0100] Table 6 Key Parameters of Overlapping Sections

[0101]

[0102] Substitute the values ​​into the formula to calculate the overlap strength:

[0103] K1:

[0104] ;

[0105] K2:

[0106] ;

[0107] K3:

[0108] ;

[0109] Based on the intensity threshold set to 30 (this value is set based on the comparison between the degree of fluctuation of the joint response within the system and the boundary of the scheduling safety range; those with an intensity index below 30 have advantages in output stability and scheduling matching), it was finally determined that K1 and K2 met the retention criteria, and the overlapping intensity screening interval set was obtained.

[0110] The overlap intensity index is a numerical assessment result used to measure the coordinated scheduling capability of wind-solar hybrid periods and available energy storage sections within the same time window. Its core significance lies in quantifying the comprehensive coordination between the sustainability of multi-source output, capacity support level, and output volatility within this time period. The smaller the index, the closer the duration of this period is to the actual output capacity of adjustable capacity, and the more stable the joint output tends to be. It is less affected by load forecasting errors and is therefore more suitable as a priority time slice for system scheduling tasks. Conversely, if the index value is large, it indicates that the adjustable capacity and continuous demand are mismatched or the output volatility is large within this period, which is not conducive to forming a stable energy allocation response section and its scheduling priority should be reduced. Therefore, the overlap intensity index is essentially a composite scheduling adaptability assessment index that integrates the three characteristics of "duration adaptability", "capacity saturation" and "fluctuation stability".

[0111] The formula's operational logic involves a composite quantitative evaluation of the structural characteristics and force output behavior during overlapping time periods. Firstly, through... The item reflects the degree of matching between scheduling persistence and capacity balance, among which Indicates the duration of the overlapping period. The nonlinear reduction index representing the combined power capacity expresses the degree of deviation between the duration of scheduling demand and capacity supply through the absolute value of the difference between the two values. The closer the two values ​​are, the smaller the value of this index, indicating higher scheduling stability for that segment. Then, through... This value characterizes the fluctuation of combined wind, solar, and energy storage output during the specified period, indicating the degree of power output dispersion or non-steady-state behavior. A larger value indicates more severe fluctuations, which is detrimental to dispatching and therefore needs to be included as a risk indicator. This fluctuation value is deducted from the denominator. The correction, This indicates the load forecast error weight corresponding to the hour at the center time of the overlapping segment. It is used to express the need to aggravate fluctuations when forecast uncertainty is high, ensuring the robustness of the dispatch assessment. The sum of the above two items constitutes the total dispatch intensity index. The smaller the value, the more suitable it is as a priority segment for scheduling. Therefore, the formula as a whole combines the two attributes of "time-capacity matching" and "output fluctuation correction" to form a comprehensive judgment standard for the quality of scheduling segments.

[0112] 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 segment, 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.

[0113] Based on the overlap intensity, the interval set is filtered, and the fields of segment number, station ID, start and end time and overlap intensity are called. A unique identifier UUIDv4 format identifier is generated for the numbers K1 and K2 respectively. The string value is randomly generated using the UUID module in the Python standard library.

[0114] Assign the value “0d6f8894-5a61-4c7f-b390-1cf0bcd1b2f0” to K1;

[0115] Assign the value “a3897f12-28e0-4bce-94d3-cd6f4e44f781” to K2;

[0116] 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:

[0117] Table 7. Schematic diagram of scheduling time slice structure

[0118]

[0119] As shown in Table 7, both overlapping segments are uniquely identified and integrated with time fields to form a segment-level control task package that can be called by the system for scheduling, and finally a set of multi-site scheduling time slices is established.

[0120] Please see Figure 5 The multi-site energy storage priority dispatch module includes:

[0121] The time slice grouping submodule obtains a set of time slices for scheduling multiple sites, extracts the site 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 scheduling area, and establishes three logical scheduling groups: frequency regulation, peak regulation and standby, and generates scheduling time slice grouping results.

[0122] A set of time slices for multi-site scheduling is obtained. For each time slice record, the start and end times, task type, and associated energy storage unit number are extracted item by item. The time slices are then divided into regions according to the regional scheduling principles in the State Grid scheduling regulations. For example, S01 and S02 belong to the Northwest and Central China scheduling regions, respectively. Based on this, the time slices are further divided into three categories—frequency regulation, peak regulation, and reserve—according to the scheduling task type field, and numbered T1, T2, T3, etc., respectively. Time slices belonging to the same site and having the same task attributes are grouped and identified, forming physical scheduling subgroups and task type logical subgroups. A multi-level scheduling index is established for structured management. Simultaneously, 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:

[0123] Table 8 Time Slice Scheduling Group Structure

[0124]

[0125] As shown in Table 8, multiple time slices have been effectively grouped according to the scheduling objectives and their respective stations, and the scheduling time slice grouping results have been obtained.

[0126] The duration verification submodule extracts the energy storage unit number in each group based on the scheduling time slice grouping results. Combined with the available time length data of the energy storage unit within the scheduling cycle, it determines whether each energy storage unit can completely cover the start and end time periods corresponding to the time slice, detects scheduling conflict status and removes them to obtain a set of conflict-free energy storage units.

[0127] Based on the scheduling time slice grouping results, all energy storage unit numbers in the table are extracted one by one. Cross-validation is then performed using previously recorded energy storage availability time window information to determine whether the adjustable time period covers the allocated time slice requirements. Records with continuous scheduling but a total available time of less than 15 minutes are marked as conflicting. For example, E2 is allocated to T2 and T5, with only a 3-minute interval between time periods, but T2 lasts for 8 minutes and T5 lasts for 5 minutes. Although the total duration is 13 minutes, it is less than the set threshold. Therefore, E2 is marked as having a scheduling conflict and removed. Meanwhile, E1 and E3 are retained as conflict-free units. The following conflict verification data table is established:

[0128] Table 9. Verification Table of Energy Storage Dispatch Duration

[0129]

[0130] As shown in Table 9, it was finally confirmed that E1 and E3 met the continuous scheduling requirements. E2, which had scheduling overlap but did not meet the time base, was removed, and a set of conflict-free energy storage units was obtained.

[0131] 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 site number, the number of time slices it participates in, and the number of tasks it is assigned. It accumulates the number of scheduling tasks and sorts all energy storage units according to their call frequency and the scheduling saturation of their respective sites to generate an energy storage scheduling priority list.

[0132] Based on the set of conflict-free energy storage units, the frequency of each unit's appearance in the task table is recorded, i.e., the number of adjustable time slices. Simultaneously, the scheduling activity is calculated by combining its associated site number and its proportion in the total number of time slices. Assuming a total of 5 scheduling task segments, E1 appears in 4 segments with an activity of 0.8; E2 appears twice with an activity of 0.4 but has been removed; and E3 appears three times with an activity of 0.6. Further analysis is conducted to determine if scheduling conflicts exist, constructing the following energy storage scheduling index set:

[0133] Table 10 Energy Storage Unit Scheduling Attributes Table

[0134]

[0135] As shown in Table 10, E1 and E3 are assigned priority values ​​based on scheduling frequency, scheduling consistency and the saturation level of their respective regions, and a scheduling order list is generated from high to low priority to finally obtain the energy storage scheduling priority list.

[0136] Please see Figure 6 The coordinated control instruction orchestration module includes

[0137] The standard instruction generation submodule extracts the scheduling time slice data, the site number and scheduling type of each unit according to 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 encapsulates them into a scheduling instruction structure in a unified manner according to the specifications to establish a standard control instruction set.

[0138] After obtaining the energy storage scheduling priority list, it is necessary to extract the task type of each energy storage unit in its respective time slice, and construct standardized instruction structures based on the two control requirements of AGC (Automatic Generation Control) and AVC (Automatic Voltage Control). AGC instructions need to calculate the target power based on the average output and adjustable range of the energy storage unit. For example, the scheduling target for energy storage unit E1 during the time period 09:00 to 09:15 is 112.5kW. Meanwhile, AVC instructions are set with the reference voltage at the access point ±0.02kV. For example, the adjustment target for instruction CMD002 is 0.98kV, with a start and end time of 09:20 to 09:40. Additionally, the AGC control instruction numbered CMD003 in station S02 has a target power of 108.0kW, with 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:

[0139] Table 11 Structure of Control Instructions for Each Station

[0140]

[0141] As shown in Table 11, all instruction format fields have been aligned with the scheduling template requirements to establish a standard control instruction set.

[0142] The conflict period elimination submodule is based on the standard control instruction set. It performs cross-validation of all control instructions by time period according to the site dimension, extracts the control instructions of the same energy storage unit and wind power photovoltaic unit in adjacent time periods. If there is an overlap in the start and end time of the instructions, the intersection duration is calculated and it is determined whether it exceeds the scheduling tolerance threshold. If the intersection exceeds the threshold, it is regarded as an instruction conflict, and subsequent instruction entries are eliminated to obtain a conflict-free control instruction set.

[0143] Based on the standard control instruction set, 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 type. First, the start and end time periods of all instructions are extracted. Multiple instructions under the same power station are compared pairwise. Suppose CMD001 and CMD002 control power station S01 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 from 09:10 to 09:25, which has a 5-minute overlap with CMD001, then a time conflict elimination judgment is performed. The maximum allowable overlap of the scheduling system is set to 3 minutes. The current overlap exceeds the threshold, so CMD004 with the later time needs to be eliminated. The same treatment is applied to the case of inconsistent types. For example, if CMD005 issues AGC and AVC instructions on the same energy storage device for the same time period, then the AGC instruction is retained according to the time period priority identifier. Finally, conflicting instructions are removed and the instruction status is summarized to obtain a conflict-free control instruction set.

[0144] 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, and arranges them in the order of instruction number to form a unified cross-station dispatch instruction integration area, generating multi-station wind, solar and storage joint dispatch instructions.

[0145] Based on the conflict-free control instruction set, each scheduling instruction needs to be categorized and output according to the station number. The AGC and AVC instructions for each station are stored separately in the station's control task queue. The scheduling center reads the task queue data through the scheduling execution engine and issues control instructions. All instructions are grouped according to the station number. AGC and AVC tasks within the same station are arranged in ascending order of instruction number, written into the scheduling cache structure, and the following aggregation view is formed, ultimately constructing a complete cross-station instruction integration system:

[0146] Table 12 Summary Table of Multi-Station Control Tasks

[0147]

[0148] As shown in Table 12, the control commands for multiple stations have been integrated and categorized by region, and a joint scheduling command for wind, solar and energy storage at multiple stations has been successfully established.

[0149] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A multi-site wind-solar coordinated energy storage optimization control system, characterized in that, The system includes: The wind and solar power output change recognition 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 power output decline, determines the complementary power generation period, and generates a wind and solar power fluctuation timestamp dataset for each station. The energy storage window status filtering module identifies energy storage units that can enter the scheduling state based on the energy storage SOC status information and operation status identifiers of multiple sites, filters out energy storage devices that are in maintenance lock-in state or task mutual exclusion state, extracts the corresponding available segment as the control reference time period, and generates a set of available time windows for energy storage at each site. The linkage section intersection calibration module, based on the wind and solar fluctuation timestamp dataset of each station and the energy storage available time window set of each station, performs time window overlap analysis on complementary segments and available intervals for each station, marks the overlapping time range, performs time period encoding, and generates a set of multi-site scheduling time slices. The multi-site energy storage priority allocation module groups resources according to the set of scheduling time slices, performs cross-validation of available time, filters energy storage units with no resource conflict records, records site affiliation and scheduling task counters, and generates a multi-site energy storage scheduling priority list. The energy storage window status filtering module includes: The SOC status extraction submodule acquires SOC status information of energy storage at multiple sites, 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, the difference between the minimum SOC threshold and the current status, determines whether it is within the allowable discharge capacity range, and generates an energy storage SOC screening status set. The operation status elimination submodule filters the status set based on the energy storage SOC, reads the current operation status identification information of each energy storage unit, parses the status 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 operation status. The available window generation submodule establishes an available segment index based on the available unit set in the operating status, according to the unit index and time field, counts the continuous available duration and minimum continuous available segment of each unit in a given scheduling period, calculates the window effective index value of the energy storage unit in the scheduling period, and outputs the time period with the index value greater than the set window scheduling threshold as the available time period, thereby generating a set of available time windows for energy storage.

2. The multi-site wind-solar coordinated energy storage optimization control system according to claim 1, characterized in that, The wind and solar fluctuation timestamp dataset includes ramp rate extreme value markers, power decline period markers, power generation complementarity period identifiers, and power station output trend summaries. The energy storage available time window set includes SOC filtering results, operating status screening results, adjustable time period identifiers, and energy storage unit control eligibility markers. The scheduling time slice set includes linkage overlapping time range markers, time period unique identifiers, cross-site linkage segment mapping tables, and adjustable segment time alignment information. The energy storage scheduling priority list includes resource grouping labels, energy storage unit availability verification results, resource conflict screening records, and scheduling task count identifiers.

3. The multi-site wind-solar coordinated energy storage optimization control system according to claim 1, 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 from multiple power plants, extracts the unit time output power data and time series data of each wind power and photovoltaic power generation unit, synchronizes and organizes them by power plant, aligns the sequences according to the time step, and generates a unified time scale output sequence matrix. The trend identification submodule detects the maximum value of the power change rate in wind power output based on the unified time scale power output sequence matrix, filters the time points in the power change rate of wind turbine units that exceed the power change rate threshold, and marks them as extreme points to obtain the set of extreme points of wind power output change rate. The complementary time period labeling submodule analyzes the time overlap between the extreme points of the wind power output change rate and the photovoltaic power decline interval based on the set of extreme points of the wind power output change rate and the photovoltaic power decline interval. It calculates the complementarity index value of each overlapping segment, filters out the time periods with the complementarity index value greater than the complementarity threshold, and marks them to obtain the wind-solar complementary labeling time period set.

4. The multi-site wind-solar coordinated energy storage optimization control system according to claim 1, characterized in that, The linkage segment intersection calibration module includes: The time overlap determination submodule, based on the wind and solar fluctuation timestamp dataset of each station and the set of available energy storage time windows, calls the start and end times of the wind-solar complementary segment of each station and the start and end times of the adjustable window of the corresponding energy storage unit, determines the relative order of the start and end of the time period, determines and marks the overlapping segments, and obtains the set of wind-solar-energy storage time overlap segments. The overlapping calculation submodule calculates the start and end overlap lengths, intersection center time, and expected joint output for each overlapping segment based on the set of overlapping wind-solar-storage time segments, obtains the scheduling potential range for each overlapping segment, and calculates the first... The overlap intensity index of each overlapping segment is used to retain overlapping segments with index values ​​lower than a set intensity threshold, and an overlap intensity screening interval set is established. 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 segment, 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.

5. The multi-site wind-solar coordinated energy storage optimization control system according to claim 1, characterized in that, The multi-site energy storage priority allocation module includes: The time slice grouping submodule obtains the set of scheduling time slices for the multiple sites, extracts the site number, start and end time, scheduling type and energy storage unit number corresponding to each time slice in sequence, divides all time slices according to scheduling area, classifies and establishes three logical scheduling groups: frequency regulation, peak regulation and standby, and generates scheduling time slice grouping results; The duration verification submodule extracts the energy storage unit number in each group based on the scheduling time slice grouping results, and combines the available time length data of the energy storage unit within the scheduling cycle to determine whether each energy storage unit can completely cover the start and end time periods corresponding to the time slice, detects scheduling conflict status and removes them to obtain a set of conflict-free energy storage units. 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 site number, the number of time slices it participates in, and the number of tasks it is assigned to, accumulates the number of scheduling tasks, and sorts all energy storage units according to their call frequency and the scheduling saturation of their respective sites to generate an energy storage scheduling priority list.

6. The multi-site wind-solar coordinated energy storage optimization control system according to claim 1, characterized in that, The system also includes: The coordinated control instruction orchestration module establishes a unified instruction sequence based on the energy storage scheduling priority list, performs time period coverage verification on all scheduling instructions, outputs them in partitions according to the site dimension, and generates joint scheduling instructions for wind, solar and energy storage at multiple sites. The joint wind-solar-storage scheduling instructions include the AGC instruction set, the AVC instruction set, time period deduplication results, and the station control instruction output set.

7. The multi-site wind-solar coordinated energy storage optimization control system according to claim 6, characterized in that, The coordinated control command arrangement module includes The standard instruction generation submodule extracts the scheduling time slice data, the site number and scheduling type of each unit according to 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 encapsulates them into a scheduling instruction structure in a unified manner according to the specifications to establish a standard control instruction set. The conflict period elimination submodule, based on the standard control instruction set, performs cross-validation of all control instructions by time period according to the site dimension, extracts the control instructions of the same energy storage unit and wind power photovoltaic unit in adjacent time periods, eliminates conflicting instruction entries, and obtains a conflict-free control instruction set. The station instruction collection submodule categorizes each instruction according to the station number based on the conflict-free control instruction set, merges and outputs instructions within the same station, and arranges them in order of instruction number to form a unified cross-station scheduling instruction integration area, generating multi-station wind, solar and energy storage joint scheduling instructions.

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