A distributed new energy collaborative control method and system

Abstract

The application discloses a kind of distributed new energy collaborative control method and system, method includes: according to the pre-set environmental state data subsequence, using preset sequence update strategy to update power generation data subsequence set, obtain target power generation data subsequence set;Determine the power generation change of a certain target power generation data subsequence Synergy degree, and according to a certain power generation change Synergy degree judges whether the collaborative state of each new energy power generation unit is abnormal at current time;If collaborative state is abnormal, then generate a certain collaborative instruction based on preset collaborative optimization strategy, and according to a certain collaborative instruction to each new energy power generation unit corresponding to a certain target power generation data subsequence is controlled.Can be identified in distributed new energy power generation unit, due to local environmental mutation or equipment failure causes group operation state to lose coordination, and on this basis, collaborative control strategy is implemented, to improve energy utilization efficiency.

Description

Technical Field

[0001] This invention belongs to the field of new energy power generation control technology, and in particular relates to a distributed new energy collaborative control method and system. Background Technology

[0002] Distributed renewable energy generation units are typically geographically dispersed, and their output exhibits significant intermittency, volatility, and randomness. These characteristics pose substantial challenges to the safe, stable, and economical operation of the power grid. While the fluctuations of a single renewable energy generation unit may be negligible, the aggregation effect of a large number of units can lead to problems such as power imbalance, voltage exceeding limits, and frequency fluctuations in the power grid.

[0003] To alleviate the aforementioned problems, it is typically necessary to treat geographically dispersed renewable energy power generation units as a whole for coordinated control and scheduling, forming a synergistic effect of a "virtual power plant." Traditional coordinated control methods mostly employ a centralized control architecture, where the operational data of all units are uploaded to a central controller, which then performs unified calculations and issues control commands. This approach suffers from high computational load, high communication bandwidth requirements, and the risk of single points of failure. Furthermore, a more critical limitation is that existing methods often treat the entire power plant as a black box or monitor only individual units independently, lacking a detailed perception and assessment of the operational coordination between different units within the power plant.

[0004] Specifically, existing technologies have the following shortcomings: First, they fail to fully consider the geographical distribution characteristics of power generation units and the local differences in environmental factors, making it impossible to accurately identify group-wide abnormal differentiation caused by local shadows, cloud cover, equipment failures, etc.; Second, monitoring strategies are mostly passive responses, usually taking control measures only after macroscopic parameters such as the total output power of the power plant or the voltage at the grid connection point exceed the limit, lacking the ability for early warning and proactive intervention; Third, control strategies are relatively crude, usually adopting a "one-size-fits-all" uniform adjustment without differentiated control based on the actual operating status and power generation potential of each unit, which not only results in low control efficiency but may also exacerbate equipment wear and tear on some units. Summary of the Invention

[0005] This invention provides a distributed new energy collaborative control method and system to solve the technical problem of low performance in existing distributed new energy collaborative control.

[0006] In a first aspect, the present invention provides a distributed new energy collaborative control method, comprising:

[0007] The power generation data and environmental status data of multiple new energy power generation units at the current moment are obtained to obtain the power generation data sequence and the environmental status data sequence.

[0008] According to the preset collaborative segmentation strategy, the power generation data sequence is divided to obtain a set of power generation data subsequences;

[0009] Based on a pre-defined environmental state data subsequence, the set of power generation data subsequences is updated using a preset sequence update strategy to obtain a target power generation data subsequence set, wherein the target power generation data subsequence set contains at least one target power generation data subsequence.

[0010] Determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and judge whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of a certain power generation change.

[0011] If the coordination status is abnormal, a certain coordination instruction is generated based on the preset coordination optimization strategy, and the new energy power generation units corresponding to the certain target power generation data subsequence are controlled according to the certain coordination instruction.

[0012] Secondly, the present invention provides a distributed new energy collaborative control system, comprising:

[0013] The acquisition module is configured to acquire power generation data and environmental status data of multiple new energy power generation units at the current moment, and obtain power generation data sequence and environmental status data sequence;

[0014] The partitioning module is configured to partition the power generation data sequence according to a preset collaborative segmentation strategy to obtain a set of power generation data subsequences;

[0015] The update module is configured to update the set of power generation data subsequences according to a preset environmental state data subsequence and a preset sequence update strategy to obtain a target power generation data subsequence set, wherein the target power generation data subsequence set contains at least one target power generation data subsequence.

[0016] The judgment module is configured to determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and to determine whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of the certain power generation change.

[0017] The control module is configured to generate a certain collaborative instruction based on a preset collaborative optimization strategy if the collaborative state is abnormal, and to control each new energy power generation unit corresponding to the certain target power generation data subsequence according to the certain collaborative instruction.

[0018] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the distributed new energy cooperative control method of any embodiment of the present invention.

[0019] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the distributed new energy cooperative control method of any embodiment of the present invention.

[0020] The distributed renewable energy collaborative control method and system of this application employs a sliding window with a preset step size and a first change threshold judgment to achieve dynamic adaptive segmentation of the power generation sequence. This enables precise capture of abrupt changes in power generation behavior within the group, thereby clearly defining the boundaries of abnormal areas. Furthermore, by introducing a minimum environmental state benchmark and comparing it with a second change threshold, the initial grouping is calibrated and subdivided, effectively isolating the influence of external environmental differences and the equipment's own state on power generation behavior. This achieves accurate assessment of the "adjustable power generation potential" of each unit. Subsequently, by calculating the degree of collaboration based on the average of the maximum fluctuations within the historical sliding window, a dynamic, data-driven benchmark for the normal fluctuation range is established, significantly improving the accuracy of anomaly diagnosis and effectively avoiding false alarms caused by normal environmental fluctuations. Finally, based on whether the power adjustment difference exceeds a preset threshold, the system intelligently switches between two control modes: "prioritizing the enhancement of high-potential units" or "selecting units with margin for precise fine-tuning," ensuring the safety, economy, and efficiency of the control strategy. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a distributed new energy collaborative control method according to an embodiment of the present invention;

[0023] Figure 2 This is a structural block diagram of a distributed new energy collaborative control system provided in an embodiment of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Please see Figure 1 The diagram shows a flowchart of a distributed new energy collaborative control method according to this application.

[0027] like Figure 1 As shown, the distributed new energy collaborative control method specifically includes the following steps:

[0028] Step S101: Obtain the power generation data and environmental status data of multiple new energy power generation units at the current moment to obtain the power generation data sequence and environmental status data sequence.

[0029] In this step, the power generation data and environmental status data of multiple new energy power generation units at the current moment are obtained, and the power generation data and environmental status data are sorted according to the geographical location of each new energy power generation unit in the distributed new energy power plant to obtain the power generation data sequence and the environmental status data sequence.

[0030] Suppose a distributed photovoltaic (PV) power station consists of 12 PV array units installed in different areas, scattered across a hilly region. Their geographical locations (latitude and longitude) are registered in the system. These units can be roughly divided into three areas: the north slope area (power generation data: PV-01, PV-02, PV-03, PV-04); the valley area (power generation data: PV-05, PV-06, PV-07, PV-08); and the south slope area (power generation data: PV-09, PV-10, PV-11, PV-12). Defining a north-to-south sorting rule, we obtain the power generation data sequence (PV-01, PV-02, PV-03, PV-04, PV-05, PV-06, PV-07, PV-08, PV-09, PV-10, PV-11, PV-12). Similarly, we can obtain the environmental status data sequence, where the environmental status data can be solar irradiance.

[0031] Step S102: According to the preset collaborative segmentation strategy, the power generation data sequence is divided to obtain a set of power generation data subsequences.

[0032] In this step, a sliding window with a preset step size is slid across the power generation data sequence. Each time the window slides, it is determined whether the change between the first power generation data and the second power generation data in the sliding window is greater than a preset first change threshold. The first power generation data and the second power generation data are two adjacent power generation data. If the change is greater than the preset first change threshold, the first power generation data and the second power generation data are divided into different power generation data subsequences. Otherwise, the first power generation data and the second power generation data are divided into the same power generation data subsequence, resulting in a set of power generation data subsequences.

[0033] In this embodiment, by determining whether the change between the first power generation data and the second power generation data in the sliding window is greater than a preset first change threshold during each sliding, dynamic and adaptive grouping can be performed based on the actual change behavior of power generation. This allows for more accurate location of the spatiotemporal point where significant changes in power generation occur, thereby determining the boundary of the abnormal region. This makes the power generation behavior of each power generation data in the same power generation data subsequence more consistent, facilitating the subsequent coordinated control of steps S103-S105.

[0034] Step S103: Based on the pre-set environmental state data subsequence, the set of power generation data subsequences is updated using a preset sequence update strategy to obtain a target power generation data subsequence set, wherein the target power generation data subsequence set contains at least one target power generation data subsequence.

[0035] In this step, the environmental state data sequence is divided based on the new energy power generation units corresponding to each power generation data subsequence, resulting in environmental state data subsequences aligned with each power generation data subsequence.

[0036] In a certain environmental state data subsequence, select the environmental state data with the smallest value, and determine the change in state data between the other environmental state data and the environmental state data. The other environmental state data are any environmental state data in the environmental state data subsequence after removing the environmental state data.

[0037] In this embodiment, the environmental state data with the smallest value is selected in order to obtain the lowest environmental state benchmark in a certain environmental state data subsequence. When the power generation data corresponding to the lowest environmental state benchmark is not much different from the power generation data corresponding to other environmental states, it indicates that the adjustment potential of subsequent power generation is greater. Therefore, this adjustment potential is further precisely divided according to the magnitude of the environmental state (the magnitude of solar irradiance).

[0038] Determine whether the change in each state data exceeds a preset second change threshold;

[0039] If the change in the first state data is greater than the preset second change threshold, the first environmental state data corresponding to the change in the first state data will be divided into a first environmental state data subsequence, wherein the change in the first state data is any change in the state data among the changes in the state data.

[0040] If the change in the first state data is not greater than the preset second change threshold, then the first environmental state data corresponding to the change in the first state data will be divided into the second environmental state data subsequence.

[0041] Based on the first environmental state data subsequence and the second environmental state data subsequence, a certain power generation data subsequence corresponding to a certain environmental state data subsequence is divided to obtain the first target power generation data subsequence and the second target power generation data subsequence.

[0042] In one specific embodiment, it is assumed that the subsequence of power generation data A (PV-01, PV-02, PV-03, PV-04) corresponds to the subsequence of environmental state data a (830W / m², 830W / m², 830W / m², 830W / m²).

[0043] The smallest environmental state data value in the environmental state data subsequence a1 is 830 W / m². No change needs to be calculated. This means that the power generation data subsequence A corresponds to a highly consistent environment and requires no updates.

[0044] In another scenario: Suppose a cloud drifts by, causing most units in the power generation data subsequence A to maintain an illuminance of 830 W / m², but the illuminance of PV-03 unit temporarily drops to 780 W / m². At this time, the environmental state data subsequence a corresponding to power generation data subsequence A becomes (830 W / m², 830 W / m², 780 W / m², 830 W / m²).

[0045] In this subsequence, the smallest environmental state data is 780 W / m².

[0046] Calculate the change in state data: Calculate the difference between other data and this minimum baseline, i.e., 830-780=50; 830-780=50; 830-780=50.

[0047] Assume the preset second change threshold is 40W / m².

[0048] The generating units are divided and updated based on the changes:

[0049] The calculated change is 50, which is greater than the threshold of 40.

[0050] Therefore, the data with an illumination of 830 W / m² was assigned to the first environmental state data subsequence (high illumination group), and the data with 780 W / m² was assigned to the second environmental state data subsequence (low illumination group).

[0051] Based on this environmental grouping, the original power generation data subsequence A is updated and divided into: the first target power generation data subsequence A1 (PV-01, PV-02, PV-04) and the second target power generation data subsequence A2 (PV-03).

[0052] The final output is the set of target power generation data subsequences: the first target power generation data subsequence A1 and the second target power generation data subsequence A2.

[0053] Step S104: Determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and determine whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of a certain power generation change.

[0054] In this step, historical power generation data of each new energy power generation unit within a historical time period are obtained, and the historical power generation data of the same new energy power generation unit are sorted according to the chronological order to obtain at least one historical power generation data sequence, wherein the historical time period is the time period before the current time; the at least one historical power generation data sequence is aligned, and a preset second sliding window is used to slide on the at least one historical power generation data sequence, and the maximum historical power generation change in the second sliding window is calculated each time it slides; the average value of each maximum historical power generation change is calculated to obtain the degree of coordination of power generation change.

[0055] It should be noted that the maximum historical power generation change is the difference between the maximum and minimum historical power generation within the second sliding window. By obtaining the actual power generation change between any two new energy power generation units at the current moment and determining whether this actual power generation change is greater than the maximum historical power generation change, it can be determined whether the coordination status of the two new energy power generation units is abnormal at the current moment. Specifically, the step size of the second sliding window is only one historical power generation data point.

[0056] In this embodiment, at the current moment, it is only necessary to compare the actual power generation change corresponding to a certain target power generation data subsequence with the degree of coordination of its associated power generation change. If the actual change is much greater than the historical benchmark, the coordination state of the group is determined to be "abnormal," indicating an unprecedented and drastic divergence beyond historical experience, which is highly likely to be due to local faults or abnormal occlusion. If the actual change is within the historical benchmark range, it is considered a normal fluctuation. This method greatly improves the identification rate of true anomalies and effectively suppresses false alarms caused by environmental changes, providing a crucial decision-making basis for achieving reliable distributed renewable energy coordinated control.

[0057] In one specific embodiment, after determining whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of a certain power generation change, if the coordination status is not abnormal, then there is no need to control each new energy power generation unit corresponding to the certain target power generation data subsequence.

[0058] Step S105: If the coordination state is abnormal, a certain coordination instruction is generated based on the preset coordination optimization strategy, and the new energy power generation units corresponding to the certain target power generation data subsequence are controlled according to the certain coordination instruction.

[0059] In this step, the new energy power generation units corresponding to each target power generation data in the subsequence of a certain target power generation data are clustered to obtain a set of a certain target new energy power generation units;

[0060] Based on the power generation size of each target power generation data in the target power generation data subsequence, sort each target new energy power generation unit in the target new energy power generation unit set to obtain a target new energy power generation unit sequence;

[0061] Obtain the total amount of electricity to be generated by the multiple new energy power generation units at the next moment, and determine the total amount of a certain electron to be generated in the target new energy power generation unit set based on the number of a certain new energy power generation unit in the target new energy power generation unit set;

[0062] Determine whether the power generation difference between the total amount of electrons to be generated and the current total amount of electrons generated in the target new energy power generation unit set is greater than a preset power generation threshold, wherein the current total amount of electrons generated is the sum of each target power generation data in the target power generation data subsequence;

[0063] If the difference in power generation is not greater than the preset power generation threshold, the ratio of the power generation difference to the number of new energy power generation units to be adjusted is calculated to obtain the power generation adjustment amount, and a certain collaborative instruction containing the power generation adjustment amount and the new energy power generation units to be adjusted is generated. The number of new energy power generation units to be adjusted is the total number of new energy power generation units to be adjusted, and the new energy power generation units to be adjusted are the target new energy power generation units whose deviation between the target power generation data and the rated power generation is greater than the average power generation adjustment amount.

[0064] If the power generation exceeds the preset threshold, a coordinated instruction is generated based on the sorting result of the target new energy power generation unit sequence to adjust each target new energy power generation unit to its rated power generation in sequence.

[0065] In one specific embodiment, Step 1: Clustering and Sorting (Determining the sequence of control objects)

[0066] Since the target power generation data subsequence X itself is an anomalous group obtained by clustering through the aforementioned steps, it is directly regarded as the target new energy power generation unit set: [PV-A, PV-B, PV-C, PV-D, PV-E].

[0067] The five units are sorted in descending order based on their power generation at time T, resulting in an ordered sequence. Assuming the ordered sequence is: (PV-C: 250kW, PV-A: 230kW, PV-D: 200kW, PV-B: 180kW, PV-E: 140kW), this sequence reflects the current power generation capacity of each unit under abnormal conditions. PV-C has the strongest capacity, and PV-E has the weakest.

[0068] Step 2: Calculate the total amount to be adjusted (determine the control target)

[0069] The system obtains the total amount of power to be generated by the entire power plant at the next time point (T+1) from the upper-level dispatch center or the local energy management system (EMS), assuming it is 5000kW.

[0070] Calculate the share (a certain total amount of electrons to be generated) that this anomalous group, the target power generation data subsequence X, needs to bear. A simple strategy is to distribute it evenly according to the number of units. Assuming there are 20 units in the entire station, the total amount of electrons to be generated for the target power generation data subsequence X (5 units) is: (5 / 20) 5000kW = 1250kW;

[0071] Calculate the difference in power generation that needs to be adjusted (ΔP): ΔP = Total amount of electrons to be generated - Total amount of electrons generated at present = 1250kW - 1000kW = +250kW.

[0072] This is a task that requires an additional 250kW of power generation.

[0073] Step 3: Determine the adjustment range (decision control strategy)

[0074] Determine whether the power generation difference |ΔP| (250kW) is greater than the preset power generation threshold (assuming it is set to 200kW).

[0075] For situations where the power generation exceeds a preset threshold, the system will adopt a conservative and safe strategy. Based on the previously obtained ranking sequence, the system will start with the unit with the strongest power generation capacity and sequentially increase its output power to its rated capacity until the demand ΔP is met.

[0076] Generate cooperative instructions:

[0077] Prioritize increasing the PV-C power from 250kW to its rated power of 280kW (an increase of 30kW).

[0078] The PV-A power was then increased from 230kW to its rated power of 260kW (an increase of 30kW).

[0079] At this point, 60kW has been added, but 190kW is still needed to reach the 250kW target. Continue to increase the PV-D from 200kW to the rated power of 250kW (an increase of 50kW).

[0080] The total power was increased by 110kW, but still 140kW was needed. Finally, the PV-B power was increased from 180kW to a rated power of 220kW (an increase of 40kW).

[0081] The total increase is (30+30+50+40)=150kW, which is still not enough. However, since the PV-E power generation capacity is already abnormally low and may be under severe shading, forcibly increasing it may be risky. The system may record this situation and report it, rather than forcibly adjusting it.

[0082] This strategy aims to prioritize the use of units with the greatest power generation potential and avoid making unreasonable adjustments to weaker units.

[0083] For cases where the power generation exceeds the preset threshold: calculate the power generation adjustment amount: 150kW / 5 units = 30kW / unit (average power generation adjustment amount).

[0084] Screening units to be adjusted: Check which units have a deviation of more than 30kW between their "current power generation" and "rated power generation". For example:

[0085] PV-C deviation: 280-250=30kW (equal to the average value, not included)

[0086] PV-A deviation: 260-230=30kW (equal to the average value, not included)

[0087] PV-D deviation: 250-200=50kW (>30kW, participates in regulation)

[0088] PV-B deviation: 220-180=40kW (>30kW, participates in regulation)

[0089] PV-E deviation: 200-140=60kW (>30kW, participates in regulation)

[0090] Therefore, the new energy power generation units to be adjusted are PV-D, PV-B, and PV-E, totaling three. The actual power generation adjustment is recalculated as follows: 150kW / 3 units = 50kW / unit.

[0091] Generate Coordination Command: Generate a command to instruct PV-D, PV-B, and PV-E units to each increase their power output by 50kW. PV-C and PV-A will maintain their current output.

[0092] In summary, the method of this application first constructs a power generation data sequence and an environmental status data sequence based on geographical location sorting, laying a structured data foundation for collaborative analysis. Then, it dynamically groups power generation units with similar operating states through sliding windows and changing threshold judgments, accurately identifying anomaly boundaries. Subsequently, environmental status data is introduced to calibrate and finely segment the groups, effectively distinguishing whether power generation behavior stems from external environmental constraints or the state of the equipment itself, achieving a key leap from "power generation behavior clustering" to "power generation potential clustering." Next, by calculating a dynamic quantitative collaborative degree benchmark based on historical group behavior characteristics, the identification rate of true anomalies is greatly improved, and false alarms are effectively suppressed. Finally, when facing anomalies, a hierarchical, refined, and safe collaborative optimization control strategy is adopted. Based on the magnitude of power adjustment, it intelligently selects whether to prioritize the use of high-potential units for rapid compensation or to precisely select units with margin for targeted fine-tuning, thereby eliminating anomalies with minimal disturbance and maximum efficiency, restoring the system to its predetermined target. Overall, this method effectively solves the challenges of "group behavior insight" and "collaborative control" in distributed renewable energy power plants, improves the stability, economy and reliability of system operation, and provides key technical support for realizing advanced applications such as smart grids and virtual power plants.

[0093] Please see Figure 2 The diagram shows a structural block diagram of a distributed new energy collaborative control system according to this application.

[0094] like Figure 2 As shown, the distributed new energy collaborative control system 200 includes an acquisition module 210, a partitioning module 220, an update module 230, a judgment module 240, and a control module 250.

[0095] The system includes the following modules: an acquisition module 210, configured to acquire power generation data and environmental status data of multiple new energy power generation units at the current moment, to obtain a power generation data sequence and an environmental status data sequence; a partitioning module 220, configured to partition the power generation data sequence according to a preset collaborative segmentation strategy, to obtain a set of power generation data sub-sequences; an update module 230, configured to update the set of power generation data sub-sequences according to a preset environmental status data sub-sequence using a preset sequence update strategy, to obtain a target power generation data sub-sequence set, wherein the target power generation data sub-sequence set contains at least one target power generation data sub-sequence; a judgment module 240, configured to determine the degree of collaboration of a certain power generation change in a certain target power generation data sub-sequence, and to determine whether the collaborative status of each new energy power generation unit is abnormal at the current moment based on the degree of collaboration of the certain power generation change; and a control module 250, configured to generate a certain collaborative instruction based on a preset collaborative optimization strategy if the collaborative status is abnormal, and to control each new energy power generation unit corresponding to the certain target power generation data sub-sequence according to the certain collaborative instruction.

[0096] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0097] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the distributed new energy cooperative control method in any of the above method embodiments.

[0098] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0099] The power generation data and environmental status data of multiple new energy power generation units at the current moment are obtained to obtain the power generation data sequence and the environmental status data sequence.

[0100] According to the preset collaborative segmentation strategy, the power generation data sequence is divided to obtain a set of power generation data subsequences;

[0101] Based on a pre-defined environmental state data subsequence, the set of power generation data subsequences is updated using a preset sequence update strategy to obtain a target power generation data subsequence set, wherein the target power generation data subsequence set contains at least one target power generation data subsequence.

[0102] Determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and judge whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of a certain power generation change.

[0103] If the coordination status is abnormal, a certain coordination instruction is generated based on the preset coordination optimization strategy, and the new energy power generation units corresponding to the certain target power generation data subsequence are controlled according to the certain coordination instruction.

[0104] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the distributed new energy collaborative control system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, and these remote memories may be connected to the distributed new energy collaborative control system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0105] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the distributed new energy collaborative control method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the distributed new energy collaborative control system. The output device 340 may include a display screen or other display device.

[0106] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0107] In one implementation, the aforementioned electronic device is used in a distributed new energy collaborative control system as a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0108] The power generation data and environmental status data of multiple new energy power generation units at the current moment are obtained to obtain the power generation data sequence and the environmental status data sequence.

[0109] According to the preset collaborative segmentation strategy, the power generation data sequence is divided to obtain a set of power generation data subsequences;

[0110] Based on a pre-defined environmental state data subsequence, the set of power generation data subsequences is updated using a preset sequence update strategy to obtain a target power generation data subsequence set, wherein the target power generation data subsequence set contains at least one target power generation data subsequence.

[0111] Determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and judge whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of a certain power generation change.

[0112] If the coordination status is abnormal, a certain coordination instruction is generated based on the preset coordination optimization strategy, and the new energy power generation units corresponding to the certain target power generation data subsequence are controlled according to the certain coordination instruction.

[0113] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A distributed new energy collaborative control method, characterized in that, include: The power generation data and environmental status data of multiple new energy power generation units at the current moment are obtained to obtain the power generation data sequence and the environmental status data sequence. According to the preset collaborative segmentation strategy, the power generation data sequence is divided to obtain a set of power generation data subsequences; Based on a pre-defined environmental state data subsequence, the power generation data subsequence set is divided using a preset sequence update strategy to obtain a target power generation data subsequence set. The target power generation data subsequence set contains at least one target power generation data subsequence. The process of dividing the power generation data subsequence set using a preset sequence update strategy to obtain the target power generation data subsequence set includes: Based on the new energy power generation units corresponding to each power generation data subsequence, the environmental state data sequence is divided to obtain environmental state data subsequences aligned with each power generation data subsequence; In a certain environmental state data subsequence, select the environmental state data with the smallest value, and determine the change in state data between the other environmental state data and the environmental state data. The other environmental state data are any environmental state data in the certain environmental state data subsequence after removing the environmental state data. Determine whether the change in each state data exceeds a preset second change threshold; If the change in the first state data is greater than the preset second change threshold, then the first environmental state data corresponding to the change in the first state data is divided into a first environmental state data subsequence, wherein the change in the first state data is any one of the state data changes. If the change in the first state data is not greater than the preset second change threshold, then the first environmental state data corresponding to the change in the first state data will be divided into the second environmental state data subsequence. Based on the first environmental state data subsequence and the second environmental state data subsequence, a certain power generation data subsequence corresponding to a certain environmental state data subsequence is divided to obtain a first target power generation data subsequence and a second target power generation data subsequence; Determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and judge whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of a certain power generation change. If the coordination status is abnormal, a certain coordination instruction is generated based on the preset coordination optimization strategy, and the new energy power generation units corresponding to the certain target power generation data subsequence are controlled according to the certain coordination instruction.

2. The distributed new energy collaborative control method according to claim 1, characterized in that, The step of dividing the power generation data sequence according to a preset collaborative segmentation strategy to obtain a set of power generation data sub-sequences includes: A first sliding window with a preset step size is slid over the power generation data sequence, and each time it is slid, it is determined whether the change between the first power generation data and the second power generation data in the first sliding window is greater than a preset first change threshold, wherein the first power generation data and the second power generation data are two adjacent power generation data; If the change exceeds a preset first threshold, the first power generation data and the second power generation data are divided into different power generation data subsequences; otherwise, the first power generation data and the second power generation data are divided into the same power generation data subsequence, resulting in a set of power generation data subsequences.

3. The distributed new energy collaborative control method according to claim 1, characterized in that, The determination of the degree of coordination of a certain power generation change in a certain target power generation data subsequence includes: The historical power generation data of each new energy power generation unit within a historical time period are obtained, and the historical power generation data of the same new energy power generation unit are sorted according to the chronological order to obtain at least one historical power generation data sequence, wherein the historical time period is the time period before the current time. Align the at least one historical power generation data sequence, slide it on the at least one historical power generation data sequence based on a preset second sliding window, and calculate the maximum historical power generation change in the second sliding window each time it slides. The average value of each maximum historical change in power generation is calculated to obtain the degree of coordination in power generation changes.

4. The distributed new energy collaborative control method according to claim 1, characterized in that, After determining whether the coordination status of each new energy power generation unit is abnormal at the current moment based on the degree of coordination of a certain power generation change, the method further includes: If the coordination status is not abnormal, then there is no need to control each new energy power generation unit corresponding to the target power generation data subsequence.

5. The distributed new energy collaborative control method according to claim 1, characterized in that, The generation of a cooperative instruction based on a preset cooperative optimization strategy includes: Cluster the new energy power generation units corresponding to each target power generation data in the subsequence of the target power generation data to obtain a set of new energy power generation units for a target. Based on the power generation size of each target power generation data in the target power generation data subsequence, sort each target new energy power generation unit in the target new energy power generation unit set to obtain a target new energy power generation unit sequence; Obtain the total amount of electricity to be generated by the multiple new energy power generation units at the next moment, and determine the total amount of a certain electron to be generated in the target new energy power generation unit set based on the number of a certain new energy power generation unit in the target new energy power generation unit set; Determine whether the power generation difference between the total amount of electrons to be generated and the current total amount of electrons generated in the target new energy power generation unit set is greater than a preset power generation threshold, wherein the current total amount of electrons generated is the sum of each target power generation data in the target power generation data subsequence; If the difference in power generation is not greater than the preset power generation threshold, the ratio of the power generation difference to the number of new energy power generation units to be adjusted is calculated to obtain the power generation adjustment amount, and a certain collaborative instruction containing the power generation adjustment amount and the new energy power generation units to be adjusted is generated. The number of new energy power generation units to be adjusted is the total number of new energy power generation units to be adjusted, and the new energy power generation units to be adjusted are the target new energy power generation units whose deviation between the target power generation data and the rated power generation is greater than the average power generation adjustment amount.

6. The distributed new energy collaborative control method according to claim 5, characterized in that, After determining whether the difference in power generation between the total amount of electrons to be generated and the current total amount of electrons generated in the target new energy power generation unit set is greater than a preset power generation threshold, the method further includes: If the power generation exceeds the preset threshold, a coordinated instruction is generated based on the sorting result of the target new energy power generation unit sequence to adjust each target new energy power generation unit to its rated power generation in sequence.

7. A distributed new energy collaborative control system, characterized in that, include: The acquisition module is configured to acquire power generation data and environmental status data of multiple new energy power generation units at the current moment, and obtain power generation data sequence and environmental status data sequence; The partitioning module is configured to partition the power generation data sequence according to a preset collaborative segmentation strategy to obtain a set of power generation data subsequences; The update module is configured to divide the power generation data subsequence set according to a preset environmental state data subsequence using a preset sequence update strategy to obtain a target power generation data subsequence set. The target power generation data subsequence set contains at least one target power generation data subsequence. The step of dividing the power generation data subsequence set according to the preset environmental state data subsequence using a preset sequence update strategy to obtain the target power generation data subsequence set includes: Based on the new energy power generation units corresponding to each power generation data subsequence, the environmental state data sequence is divided to obtain environmental state data subsequences aligned with each power generation data subsequence; In a certain environmental state data subsequence, select the environmental state data with the smallest value, and determine the change in state data between the other environmental state data and the environmental state data. The other environmental state data are any environmental state data in the certain environmental state data subsequence after removing the environmental state data. Determine whether the change in each state data exceeds a preset second change threshold; If the change in the first state data is greater than the preset second change threshold, then the first environmental state data corresponding to the change in the first state data is divided into a first environmental state data subsequence, wherein the change in the first state data is any one of the state data changes. If the change in the first state data is not greater than the preset second change threshold, then the first environmental state data corresponding to the change in the first state data will be divided into the second environmental state data subsequence. Based on the first environmental state data subsequence and the second environmental state data subsequence, a certain power generation data subsequence corresponding to a certain environmental state data subsequence is divided to obtain a first target power generation data subsequence and a second target power generation data subsequence; The judgment module is configured to determine the degree of coordination of a certain power generation change in a certain target power generation data subsequence, and to determine whether the coordination status of each new energy power generation unit is abnormal at the current time based on the degree of coordination of the certain power generation change. The control module is configured to generate a certain collaborative instruction based on a preset collaborative optimization strategy if the collaborative state is abnormal, and to control each new energy power generation unit corresponding to the certain target power generation data subsequence according to the certain collaborative instruction.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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