Province and region coordination control method and device for distributed new energy

Abstract

The invention relates to the technical field of power systems, and discloses a provincial and local coordination control method and device for distributed new energy, and the method comprises the steps: constructing a two-stage control architecture composed of a provincial master station and a local master station; establishing a data interaction channel between the provincial master station and the local master station through a preset communication protocol, performing two-way transmission on the global adjustment demand and the local adjustment demand through the data interaction channel, and verifying the transmission data based on a real-time verification rule; adopting a multi-objective optimization algorithm to generate a provincial and regional cooperative active power control strategy according to the verified global adjustment demand and the local adjustment demand; and decomposing the province-prefecture cooperative active power control strategy into a province-level regulation instruction and a prefecture-level regulation instruction, and performing province-prefecture two-level dynamic cooperative control according to the province-level regulation instruction and the prefecture-level regulation instruction. According to the invention, efficient dynamic adjustment of distributed new energy in provincial and local stages can be realized, and the stability and economy of power grid operation are improved.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a provincial and local coordinated control method and device for distributed renewable energy. Background Art

[0002] With the widespread integration of distributed renewable energy sources such as photovoltaics and wind power into distribution networks, their active power control faces numerous challenges. Distributed renewable energy sources are characterized by randomness and intermittency, and are widely distributed across diverse regions. Traditional active power control methods struggle to effectively coordinate renewable energy output across different regions. Different regions possess varying energy resource endowments, power generation characteristics, and load demands. Existing technologies lack a comprehensive provincial and regional coordination mechanism, hindering the full utilization of the complementary advantages of distributed renewable energy across different regions. Regarding data exchange, the collection, transmission, and sharing of distributed renewable energy generation data present difficulties. This data volume is vast and dispersed, and issues with communication technologies and protocols can lead to data delays, loss, or inaccuracies during data transmission, impacting the real-time and accuracy of active power control. Existing control strategies for distributed renewable energy are inadequate. Dynamically adjusting control strategies based on the complex and ever-changing grid operating conditions is difficult, and coordination between distributed renewable energy sources and traditional power sources and loads is insufficient, hindering overall grid operation efficiency and stability. Consequently, existing technologies exhibit significant deficiencies in cross-regional coordination, data exchange accuracy, and dynamic control strategy adjustment, failing to meet the requirements for stable and economical grid operation.

[0003] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0004] The embodiments of the present application provide a provincial and local coordinated control method and device for distributed renewable energy. By constructing a provincial and local two-level control architecture and data interaction channel, and using a multi-objective optimization algorithm to generate a collaborative control strategy, efficient dynamic regulation of distributed renewable energy at the provincial and local levels is achieved, thereby improving the stability and economy of power grid operation.

[0005] In a first aspect, an embodiment of the present application provides a provincial and local coordinated control method for distributed new energy, including:

[0006] Construct a two-level control architecture consisting of a provincial master station and a prefecture-level master station. The provincial master station integrates the province's distributed renewable energy operation data through a data acquisition module to generate global regulation requirements, while the prefecture-level master station generates local regulation requirements based on locally collected distributed renewable energy station data.

[0007] Establishing a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmitting the global adjustment demand and the local adjustment demand through the data exchange channel, and verifying the transmitted data based on real-time verification rules;

[0008] A multi-objective optimization algorithm is used to generate a provincial and local coordinated active power control strategy based on the verified global and local regulation requirements.

[0009] The provincial-local coordinated active power control strategy is decomposed into provincial-level regulation instructions and local-level regulation instructions, and dynamic coordinated control at the provincial and local levels is performed according to the provincial-level regulation instructions and the local-level regulation instructions.

[0010] Optionally, in some embodiments of the present application, the construction of a two-level control architecture consisting of a provincial master station and a prefecture-level master station includes:

[0011] In the provincial master station, each corresponding prefectural master station is abstracted as a virtual machine group object, and the properties, communication interface and data interaction logic of each prefectural master station are encapsulated through object-oriented programming technology to form a provincial data pool;

[0012] The local distributed new energy station operation data is collected in real time through the prefecture-level master station, and the operation data is uploaded to the provincial data pool. The operation data includes real-time active power output, upper and lower regulation limits, and grid-connected switch status;

[0013] The provincial master station calculates a priority ranking factor or a proportional allocation factor based on the virtual machine group object attributes in the provincial data pool, and generates a global adjustment demand including adjustment amount allocation.

[0014] Optionally, in some embodiments of the present application, establishing a data interaction channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmitting the global adjustment demand and the local adjustment demand through the data interaction channel, and verifying the transmitted data based on real-time verification rules, includes:

[0015] A communication link is established between the provincial master station and the prefectural master station using a preset communication protocol, and a data retransmission mechanism is configured;

[0016] When receiving the target active power instruction issued by the provincial master station through the prefectural master station, verifying the data integrity of the transmitted data through a cyclic redundancy check algorithm, and triggering a corresponding retransmission request if the check fails;

[0017] The provincial master station monitors the switch status of the group control stations uploaded by the prefectural master station in real time. When an abnormal state is detected, the current adjustment instruction is suspended and a manual evaluation process is triggered.

[0018] Optionally, in some embodiments of the present application, the real-time verification rule includes:

[0019] The control dead zone check is performed on the adjustment instruction uploaded by the station through the prefectural master station. If the adjustment amount is less than the preset dead zone threshold, the adjustment instruction is ignored and fed back to the provincial master station;

[0020] The provincial master station performs an operation limit check on the output data uploaded by the prefectural master station. If the limit range is exceeded, the instruction is automatically corrected and reissued.

[0021] Optionally, in some embodiments of the present application, the multi-objective optimization algorithm is used to generate a provincial and local coordinated active power control strategy according to the verified global regulation demand and local regulation demand, including:

[0022] Initialize the particle swarm. The position vector of each particle corresponds to the active power distribution value of each local virtual machine group, and the initial weight is set based on the real-time operation parameters of the power grid.

[0023] The objective function weights are dynamically adjusted during the iteration process, with priority given to the system frequency stability weight during peak power consumption periods.

[0024] By iteratively updating the particle positions and velocities multiple times, the optimal strategy with the smallest deviation in the interconnection line exchange power is screened out, and the optimal strategy is converted into executable provincial-level and prefecture-level regulation instructions.

[0025] Optionally, in some embodiments of the present application, the priority ranking factor is calculated as follows:

[0026] Determine the adjustment order of each distributed new energy station according to the preset station priority table;

[0027] Calculate the real-time adjustable output ratio of each distributed new energy station, and sort them from low to high according to the real-time adjustable output ratio, giving priority to distributed new energy stations with large adjustable space.

[0028] Optionally, in some embodiments of the present application, the decomposition of the provincial regulatory instructions includes:

[0029] The provincial master station distributes the regulation amount to each provincial virtual machine group according to the global regulation demand and the installed capacity ratio or the actual output ratio;

[0030] After the local master station receives the regulation amount, it allocates the regulation amount to the corresponding distributed new energy station in combination with the local feeder load rate or utilization hours.

[0031] Optionally, in some embodiments of the present application, performing the dynamic coordinated control at the provincial and prefectural levels according to the provincial regulation instructions and the prefectural regulation instructions includes:

[0032] The provincial master station issues the provincial adjustment instruction to the corresponding prefecture-level master station for cross-regional coordination;

[0033] The local-level adjustment instructions are sent by the local-level master station to the corresponding distributed new energy stations to perform active power output adjustment.

[0034] Optionally, in some embodiments of the present application, the dynamic collaborative control includes at least one of the following:

[0035] In the provincial-region coordinated real-time peak-shaving mode, the provincial master station sends the provincial regulation instruction to the prefectural master station in real time, so that the prefectural master station adjusts the local station output according to the provincial regulation instruction to maintain the tie line exchange power threshold;

[0036] In the prefectural section control mode, the prefectural master station monitors the load rate of the local distribution network equipment, generates adjustment requirements and feeds them back to the provincial master station, and the provincial master station reissues correction instructions after overall coordination;

[0037] In the free power generation mode, the provincial master station does not issue any instructions, and the prefectural master station operates autonomously according to the maximum adjustable power of the station, and uploads the operating data to the provincial master station in real time.

[0038] In a second aspect, an embodiment of the present application provides a distributed new energy provincial and local coordinated control device, including:

[0039] A construction module is used to construct a two-level control architecture consisting of a provincial master station and a prefecture-level master station. The provincial master station integrates the operating data of distributed renewable energy resources across the province through a data acquisition module to generate global regulation requirements, while the prefecture-level master station generates local regulation requirements based on locally collected distributed renewable energy station data.

[0040] a transmission module, configured to establish a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmit the global adjustment demand and the local adjustment demand through the data exchange channel, and verify the transmitted data based on real-time verification rules;

[0041] The strategy module is used to generate a provincial and local coordinated active power control strategy based on the verified global and local regulation requirements using a multi-objective optimization algorithm;

[0042] The control module is used to decompose the provincial-local coordinated active power control strategy into provincial-level regulation instructions and local-level regulation instructions, and perform dynamic coordinated control at the provincial and local levels according to the provincial-level regulation instructions and the local-level regulation instructions.

[0043] The present application provides a provincial and local coordinated control method and device for distributed renewable energy. First, a two-level control architecture consisting of a provincial master station and a prefectural master station is constructed to integrate the operating data of distributed renewable energy across the province and generate local regulation demands, thereby realizing cross-regional coordinated control of distributed renewable energy. Then, a data interaction channel is established between the provincial master station and the prefectural master station through a preset communication protocol. Global regulation demands and local regulation demands are bidirectionally transmitted through the data interaction channel, and the transmitted data is verified based on real-time verification rules to ensure the real-time and accuracy of the transmitted data, thereby providing a reliable basis for subsequent control strategies. Next, a multi-objective optimization algorithm is used to generate a provincial and local coordinated active power control strategy based on the verified global regulation demands and local regulation demands, so that the control strategy can be dynamically adjusted according to the real-time operating parameters of the power grid, thereby realizing coordinated interaction between distributed renewable energy and traditional power sources and loads, enhancing the stability of power grid operation, and improving the absorption capacity of distributed renewable energy. Finally, the provincial and local coordinated active power control strategy is decomposed into provincial regulation instructions and prefectural regulation instructions, and dynamic coordinated control of the two levels of province and locality is performed according to the provincial regulation instructions and prefectural regulation instructions, thereby realizing the complementary advantages of distributed renewable energy between different regions and improving the stability and economy of power grid operation. It can be seen that this application constructs a provincial and local two-level control architecture and data interaction channel, and uses a multi-objective optimization algorithm to generate a collaborative control strategy and then conducts dynamic collaborative control at the provincial and local levels, thereby realizing efficient dynamic regulation of distributed new energy at the provincial and local levels, promoting the efficient absorption of distributed new energy, and thus improving the stability and economy of power grid operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 This is a diagram of the application environment of the provincial and local coordinated control method of distributed new energy provided in an embodiment of the present application;

[0046] Figure 2 This is a flow chart of a provincial and local coordinated control method for distributed renewable energy provided by an embodiment of the present application;

[0047] Figure 3 This is a schematic diagram of the process of designing a provincial master station according to an embodiment of the present application;

[0048] Figure 4 This is a flow chart of data collection and acquisition provided by the embodiment of the present application;

[0049] Figure 5 This is a schematic diagram of the process of designing a prefecture-level master station according to an embodiment of the present application;

[0050] Figure 6 This is a flow chart of a method for implementing provincial and prefecture-level data interaction and verification provided by an embodiment of the present application;

[0051] Figure 7 This is a flow chart of the generation of the active power saving regulation strategy provided in the embodiment of the present application;

[0052] Figure 8 This is another flow chart of the provincial and local coordinated control method for distributed renewable energy provided by an embodiment of the present application;

[0053] Figure 9 This is a schematic diagram of the structure of a distributed new energy provincial and local coordinated control device provided in an embodiment of the present application;

[0054] Figure 10 It is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of systems and methods consistent with aspects of the present application, as detailed in the appended claims.

[0056] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive descriptions such as inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0057] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0058] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0059] In order to solve the above-mentioned technical problems and overcome the defects of the existing technology, the embodiment of the present application provides a provincial and local coordinated control method and device for distributed new energy. By constructing a provincial and local two-level control architecture and data interaction channel, and using a multi-objective optimization algorithm to generate a coordinated control strategy, dynamic coordinated control at the provincial and local levels is performed to achieve efficient dynamic regulation of distributed new energy at the provincial and local levels, promote the efficient absorption of distributed new energy, and thus improve the stability and economy of power grid operation.

[0060] Figure 1 FIG. 1 is an application environment diagram of a distributed new energy coordinated control method in an embodiment. Figure 1 The provincial and local coordinated control method for distributed renewable energy is applied to a provincial and local coordinated control system for distributed renewable energy. The provincial and local coordinated control system for distributed renewable energy includes a terminal 110 and a server 120. Terminal 110 and server 120 are connected via a network. Terminal 110 can be a desktop terminal or a mobile terminal. The mobile terminal can be at least one of a mobile phone, tablet computer, and laptop computer. Server 120 can be implemented as a standalone server or a server cluster consisting of multiple servers. Server 120 is used to build a two-level control architecture consisting of a provincial master station and a prefectural master station, wherein the provincial master station integrates the operating data of distributed new energy in the province through a data acquisition module to generate a global regulation demand, and the prefectural master station generates a local regulation demand based on the locally collected distributed new energy station data; a data interaction channel is established between the provincial master station and the prefectural master station through a preset communication protocol, and the global regulation demand and the local regulation demand are bidirectionally transmitted through the data interaction channel, and the transmitted data is verified based on real-time verification rules; a multi-objective optimization algorithm is used to generate a provincial-local coordinated active power control strategy based on the verified global regulation demand and local regulation demand; the provincial-local coordinated active power control strategy is decomposed into provincial regulation instructions and prefectural regulation instructions, and dynamic coordinated control of the provincial and local levels is performed according to the provincial regulation instructions and the prefectural regulation instructions.

[0061] See also Figure 2 , Figure 2This is a flow chart of a distributed renewable energy coordinated control method according to an embodiment of the present application. This embodiment mainly uses the distributed renewable energy coordinated control method applied to a computer device as an example to illustrate. The distributed renewable energy coordinated control method according to an embodiment of the present application may specifically include the following steps:

[0062] S1. Build a two-tier control architecture consisting of a provincial master station and a prefectural master station. The provincial master station uses a data acquisition module to integrate the province's distributed renewable energy operation data and generate global regulation requirements. Prefectural master stations generate local regulation requirements based on locally collected data from distributed renewable energy stations.

[0063] Specifically, for step S1, the provincial master station serves as the upper hub, and integrates the operating data of the province's distributed new energy through the data acquisition module, including but not limited to real-time active power output, upper and lower limits of regulation, etc., so as to grasp the operating status of the province's distributed new energy from a macro perspective. The prefectural master station focuses on the management of local distributed new energy sites, and generates local regulation requirements that meet the local power grid conditions based on locally collected site data, such as operating status, power generation, etc. When constructing a two-level control architecture, advanced computer technology and communication technology are used to ensure efficient connection and stable operation between the provincial master station and the prefectural master station. For example, high-speed communication networks are used to achieve rapid data transmission, and cloud computing technology is used to enhance the data processing capabilities of provincial master stations to better cope with the analysis and processing needs of massive data.

[0064] S2. Establish a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, transmit the global and local regulation requirements in both directions through the data exchange channel, and verify the transmitted data based on real-time verification rules;

[0065] Specifically, for step S2, a communication protocol that is compatible with both the provincial master station and the prefectural master station systems is selected, such as internationally accepted power system communication protocols such as IEC61850, to ensure the compatibility and stability of data transmission. During the transmission process, the two-way transmission mechanism enables the provincial master station to send global regulation requirements to the prefectural master station in a timely manner. At the same time, the prefectural master station can also upload local regulation requirements and station operation data to the provincial master station. Real-time verification rules cover multiple aspects such as data integrity, accuracy, and consistency. For example, a cyclic redundancy check algorithm is used to verify the data. Once errors or loss of data are detected, a retransmission mechanism is immediately triggered. In addition to the protocols and algorithms mentioned above, communication technologies and verification strategies can be further optimized according to actual needs. For example, encrypted communication technology is used to ensure the security of data transmission and prevent data tampering or theft; artificial intelligence algorithms are used to monitor and analyze transmitted data in real time to predict possible data anomalies in advance and take corresponding preventive measures.

[0066] S3. Generate a provincial and local coordinated active power control strategy based on the verified global and local regulation requirements using a multi-objective optimization algorithm;

[0067] Specifically, in step S3, the multi-objective optimization algorithm comprehensively considers multiple interrelated and potentially conflicting objective functions, such as minimizing tie-line exchange power deviation, maintaining system frequency stability, and maximizing renewable energy consumption. Taking the particle swarm optimization algorithm as an example, each possible active power regulation strategy is treated as a particle and searched in the solution space by simulating the foraging behavior of a flock of birds. The algorithm continuously evaluates the strengths and weaknesses of the regulation strategy represented by each particle based on its position and velocity, and dynamically adjusts the particle's position and velocity based on the evaluation results, ultimately finding the optimal active power regulation strategy that meets multiple objectives. The active power regulation strategy is optimized to minimize tie-line exchange power deviation and maintain system frequency stability. In addition to the particle swarm optimization algorithm, other multi-objective optimization algorithms suitable for this scenario, such as genetic algorithms and ant colony algorithms, can be explored and improved to enhance convergence speed and optimization accuracy. Furthermore, machine learning techniques can be incorporated to learn and analyze historical and real-time data to further improve the algorithm's performance and adaptability.

[0068] S4. Decompose the provincial-local coordinated active power control strategy into provincial-level regulation instructions and prefecture-level regulation instructions, and perform dynamic coordinated control at the provincial and prefecture levels based on the provincial-level regulation instructions and the prefecture-level regulation instructions;

[0069] Specifically, for step S4, the provincial master station decomposes the generated collaborative active power control strategy into specific provincial regulation instructions, clarifies the regulation tasks and goals that each region and each station should undertake, and issues these instructions to the corresponding prefectural master station. After receiving the instructions, the prefectural master station further refines them into prefectural regulation instructions based on local actual conditions, assigns them to specific distributed new energy stations, and instructs the stations to adjust their active power output, thereby achieving dynamic collaborative control at the provincial and local levels, and enabling distributed new energy to achieve optimized configuration and efficient operation across the province. In the process of instruction decomposition and issuance, an intelligent decision support system can be introduced to assist the provincial and prefectural master stations in decomposing and distributing instructions more scientifically and rationally, thereby improving the accuracy and timeliness of control decisions. At the same time, a real-time feedback mechanism is established to timely collect feedback information on the execution of instructions by stations and the operating status of the power grid, so as to dynamically adjust and optimize the control strategy.

[0070] This embodiment establishes a two-level control architecture of provincial master stations and prefecture-level master stations, establishes a stable and efficient data interaction channel, uses a multi-objective optimization algorithm to generate a collaborative active power control strategy and performs precise instruction decomposition and dynamic collaborative control, effectively solving problems such as cross-regional coordination difficulties, inaccurate and untimely data interaction, and imperfect control strategies of distributed renewable energy in the distribution network, comprehensively improving the operation and management level of distributed renewable energy, ensuring the stable, economical and reliable operation of the power grid, and is of great significance for promoting the efficient consumption of new energy and promoting the optimization and adjustment of the energy structure.

[0071] Optionally, in some embodiments, step S1 of "building a two-level control architecture consisting of a provincial master station and a prefecture-level master station" may specifically include:

[0072] S11. In the provincial master station, each corresponding prefectural master station is abstracted as a virtual machine group object, and the properties, communication interfaces and data interaction logic of each prefectural master station are encapsulated through object-oriented programming technology to form a provincial data pool;

[0073] Specifically, within the provincial master station, object-oriented programming technology is used to encapsulate each prefecture-level master station and its related attributes (such as installed capacity, real-time output, etc.) and behaviors (such as data uploading, receiving instructions, etc.) into an object, create a prefecture-level virtual machine group object instance, and then build a provincial data pool to achieve effective integration and management of prefecture-level master station-related data. As the scale of distributed new energy sites continues to expand and the data types become increasingly complex, the object-oriented programming model can be further optimized, and new attributes and behavior descriptions can be added to accommodate more diverse data management needs. At the same time, data mining technology is introduced to conduct in-depth analysis of the massive data in the provincial data pool, explore the potential patterns of the data, and provide a basis for more accurate adjustment and demand allocation.

[0074] S12. Real-time operation data of local distributed renewable energy stations is collected through prefecture-level master stations and uploaded to the provincial data pool. The operation data includes real-time active power output, upper and lower regulation limits, and grid-connected switch status.

[0075] Specifically, prefecture-level master stations collect real-time operational data, such as active power output, upper and lower regulation limits, and grid-connected switch status, through various monitoring devices and sensors installed at local distributed renewable energy sites. These data are then uploaded to the provincial data pool of the provincial master station using network communication technology, ensuring that the provincial master station can obtain the latest operating status of the local site in a timely manner. To ensure the accuracy and completeness of data collection, more advanced intelligent monitoring equipment can be deployed at the site, using multi-sensor data fusion technology to pre-process and verify the collected data and reduce data errors. At the same time, the communication network structure is optimized to improve the reliability of data transmission and reduce the risk of data loss and delay.

[0076] S13. Calculate the priority ranking factor or proportional allocation factor based on the provincial master station object properties in the virtual machine group in the provincial data pool to generate a global regulation demand including the allocation of the regulation amount;

[0077] Specifically, based on the attribute information of the virtual machine group objects in the provincial data pool, the provincial master station uses preset algorithms and rules to calculate priority sorting factors (such as specified priority sorting, real-time adjustable output ratio sorting, etc.) or proportional allocation factors (such as installed capacity ratio, actual output ratio, etc.). Based on these factors, the allocation order and proportion of each provincial master station in the global regulation are determined, thereby generating a global regulation demand including the allocation of regulation quantity. The algorithms for priority sorting and proportional allocation can be further studied and optimized, combined with machine learning technology, to automatically adjust the weights of factors based on historical data and real-time operating status, thereby improving the rationality and adaptability of regulation demand allocation. In addition, a simulation analysis platform for regulation demand allocation is established to simulate and evaluate different allocation schemes and select the optimal scheme to generate global regulation demand.

[0078] This embodiment forms a provincial data pool by encapsulating the virtual machine group objects of the prefectural master station in the provincial master station, thereby realizing the effective integration and centralized management of the province's distributed new energy data; the real-time data collection and upload of the prefectural master station ensures the dynamic grasp of the operating status of the local site by the provincial master station; the provincial master station realizes the optimized regulation and allocation of distributed new energy based on the global regulation demand generation mechanism of priority sorting and proportional allocation factors.

[0079] In a specific embodiment, the provincial master station serves as an upper-level hub, integrating distributed renewable energy data across the province for overall monitoring and macro-control. Leveraging its robust data processing and communication capabilities, it exchanges information with prefectural master stations to understand the province's operational status. Second, under the guidance of the provincial master station, the prefectural master station manages local renewable energy in a refined manner, collecting and analyzing local data, generating local strategies, and providing feedback, collaborating with the provincial master station to achieve two-level optimization. Simultaneously, provincial and prefectural data exchange and verification mechanisms ensure data accuracy and consistency through standard protocols and efficient networks, supporting strategy generation. Finally, the provincial and prefectural levels utilize intelligent algorithms to generate strategies based on data. The provincial master station establishes a macro-level framework, while the prefectural master station refines local strategies, jointly achieving the optimal configuration and efficient operation of distributed renewable energy within the provincial and prefectural power grids. Within the distributed renewable energy provincial and prefectural coordination and active power control system based on the distribution network OCS, the provincial master station is designed to provide macro-control of distributed renewable energy within the region, ensuring the safe and stable operation of the overall power grid and the efficient absorption of distributed renewable energy. The provincial master station construction includes data collection and acquisition, real-time monitoring of operating status, and overall regulation demand allocation strategies. Figure 3 , Figure 3 A schematic diagram of the process flow of the provincial master station design provided for this embodiment.

[0080] For data collection and acquisition at the provincial master station, the specific process is as follows: Figure 4 As shown in the figure, a prefecture-level virtual machine group is first established. Using object-oriented programming techniques, each prefecture-level master station and its associated attributes and behaviors are encapsulated as an object. A corresponding prefecture-level virtual machine group object instance is created in the provincial master station. Communication and data exchange between these objects enable effective integration and management of prefecture-level master station data. Data reception and processing then proceed. A network communication protocol is used to establish a stable data transmission channel between the provincial and prefecture-level master stations, ensuring real-time reception of data uploaded by the prefecture-level master stations. For numerical data such as the total real-time active output, upper and lower regulation limits of distributed renewable energy, a cyclic redundancy check (CRC) algorithm is used to verify data accuracy and prevent data transmission errors. For discrete data such as controllable signals and operating status, a state machine model is used to process them, triggering corresponding logical operations based on different signal and status values. Real-time database technology is used to store received core grid operation parameters, such as grid frequency and tie line exchange power, ensuring fast data reading and writing and real-time updates.

[0081] For real-time monitoring of operating status, such as Figure 3 As shown, data visualization technology is used to display system operating status, control mode, real-time frequency and other information to dispatchers in an intuitive manner, which is convenient for real-time monitoring and quick decision-making; a data-driven interface design method is adopted to dynamically update the monitoring interface according to the operating information of the dispatching units in various places, and display its operating status, controllable signals, real-time output and other detailed information in real time; a data early warning mechanism is established, and by setting the threshold range, when key parameters such as real-time frequency and interconnection line switching power exceed the normal range, an alarm signal is triggered to remind dispatchers to pay attention and deal with it in time.

[0082] For the total regulation demand allocation strategy, such as Figure 3 As shown in the figure, this allocation strategy determines how to distribute the total regulation demand to each participating distributed renewable energy station or control object, achieving effective active power regulation and making grid operation more stable and efficient. Two approaches can be considered for allocation: Priority-based allocation. This method determines the regulation order of control objects based on different operating indicators, distributing the regulation load sequentially until the total regulation load is fully allocated. Proportional allocation. This method calculates the sharing coefficient of each control object based on different reference quantities to determine the proportion of regulation load that each control object should bear.

[0083] ① The specific ranking factors assigned according to priority are as follows:

[0084] Priority Assignment: Each control object has a pre-set priority in the system. When allocating control variables, the control object with the highest priority will be assigned the control variable first according to the set adjustment step size.

[0085] Ranking by real-time adjustable output ratio: The prefectural master station calculates the real-time adjustable output ratio of each distributed renewable energy station by dividing the station's actual output by its upper limit of adjustable capacity. Stations are then ranked by this ratio. The lower the ratio, the greater the station's relative adjustable capacity, and the higher the adjustment step size, the higher the priority allocated to the station.

[0086] ② The proportionate distribution method is as follows:

[0087] Installed capacity ratio: When calculating the load sharing coefficient of each prefecture-level virtual machine group, the total installed capacity data of each prefecture-level virtual machine group is directly obtained, and a weighted average calculation is performed based on the installed capacity ratio to achieve a reasonable allocation of regional regulation needs.

[0088] Actual output ratio: Real-time statistics of the total output data of each level virtual unit are collected, and the sharing coefficient is calculated based on its proportion in the total output to ensure that the regulation amount matches the actual power generation contribution.

[0089] Manual sharing factor ratio: Read the pre-set manual sharing coefficient and use it as a weight in the calculation of the sharing coefficient to meet special control needs.

[0090] In a specific embodiment, the design of the prefecture-level master station is closely related to the provincial master station design, and together they form a two-level architecture for distributed renewable energy active power control. The construction of the prefecture-level master station also includes data collection and acquisition, real-time monitoring of operating status, and demand allocation strategy.

[0091] like Figure 5 As shown, Figure 5 A flow chart of the prefectural master station design provided for this embodiment. Data acquisition at the prefectural master station design focuses on real-time collection of detailed data on distributed renewable energy stations, including real-time active power output, upper and lower regulation limits, and grid-connected power switch status. The station also acquires grid frequency, control instructions, and control status from the upper-level distributed renewable energy active power control system, enabling precise control and management of the station. Real-time operational status monitoring is similar to that performed by the provincial master station, providing real-time insights into renewable energy output status. The prefectural-level total regulation demand allocation strategy also utilizes priority-based allocation and proportional allocation, but the factors involved differ.

[0092] The ranking factors assigned to the prefecture-level priority ranking are as follows:

[0093] Adjustment type sorting, the local master station should have the group adjustment and group control priority configuration function. When group adjustment priority is configured, active automatic control will give priority to stations that support group adjustment. If the adjustment still does not meet the demand, stations that support group control will be assigned; when group control priority is configured, active automatic control will give priority to stations that support group control. If the adjustment still does not meet the demand, stations that support group adjustment will be assigned;

[0094] Specify priority sorting, sorting according to the preset priority of each control object. The control object with high priority will bear the adjustment amount according to the adjustment step until the total adjustment demand reaches zero;

[0095] Real-time sorting of adjustable output ratios: The prefecture-level master station calculates the adjustable output ratio of each distributed renewable energy station (the ratio of the current distributed renewable energy station output to the upper limit of the renewable energy station's adjustable capacity) and sorts them by the adjustable output ratio. Distributed renewable energy stations with low adjustable output ratios will bear the adjustment amount according to the adjustment step until the total adjustment demand reaches zero.

[0096] Sorting by utilization hours: the local master station calculates the utilization hours of each distributed new energy station (the ratio of the current power generation of the distributed new energy station to the installed capacity of the new energy), and sorts them according to the utilization hours. The distributed new energy stations with high utilization hours bear the adjustment amount according to the adjustment step until the total adjustment demand is zero.

[0097] The sharing factors for prefecture-level proportional allocation are as follows:

[0098] Installed capacity ratio: Each control object is allocated regional regulation demand according to the ratio of total installed capacity;

[0099] Actual output ratio: Each control object is allocated regional regulation demand according to the total output ratio;

[0100] Consider the load ratio of feeders and distribution transformers: Each control object is allocated regional regulation demand according to the load ratio of its feeders and distribution transformers.

[0101] Proportion of artificial sharing factor: Each control object is allocated regional regulation demand according to the proportion of artificial sharing coefficient.

[0102] The process of designing a prefecture-level master station is shown in the figure below:

[0103] Optionally, in some embodiments, step S2 of "establishing a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmitting global adjustment requirements and local adjustment requirements through the data exchange channel, and verifying the transmitted data based on real-time verification rules" may specifically include:

[0104] S21. Use the preset communication protocol to build a communication link between the provincial master station and the prefecture-level master station, and configure a data retransmission mechanism;

[0105] Specifically, select communication protocols that comply with power system standards, such as IEC 61850 and DL / T634.5104, to establish stable communication links between provincial and prefectural master stations to ensure the compatibility, accuracy, and stability of data transmission. At the same time, configure a data retransmission mechanism in the communication link. When errors or loss occur in data transmission, retransmission can be automatically triggered to ensure data integrity. With the continuous development of communication technology, communication links can be further optimized, and communication technologies with higher bandwidth and lower latency, such as 5G communication, can be adopted to improve the efficiency and real-time performance of data transmission. In addition, encryption and identity authentication functions can be added to the communication link to ensure the security of data transmission and prevent data tampering or theft.

[0106] S22. When the provincial master station receives the target active power command issued by the prefectural master station, the data integrity of the transmitted data is verified by a cyclic redundancy check algorithm, and the corresponding retransmission request is triggered after the check fails;

[0107] Specifically, after receiving the target active power command from the provincial master station, the prefectural master station uses a cyclic redundancy check (CRC) algorithm to verify the data. This algorithm verifies the data's integrity by calculating a checksum. If the checksum does not match the expected value, the data integrity is deemed compromised and a retransmission request is triggered to ensure the accuracy of the received command. In addition to the CRC algorithm, other data verification algorithms, such as parity check and Hamming code check, can be combined to further improve data verification accuracy. Furthermore, the retransmission request mechanism can be optimized, for example by setting parameters such as the maximum number of retransmissions and the retransmission interval to avoid unlimited retransmissions due to network failures and other reasons, thereby improving the system's fault tolerance.

[0108] S23. The provincial master station monitors the switch status of the group control stations uploaded by the prefectural master station in real time. Upon detecting an abnormal state, the current adjustment command is suspended and the manual evaluation process is triggered;

[0109] Specifically, the provincial master station collects real-time switch status information from the group-controlled stations uploaded by the prefectural master station and uses a status monitoring algorithm to analyze the switch status in real time. Once an abnormal switch status is detected, such as abnormal opening and closing, the current regulation command is immediately suspended to prevent the station in the abnormal state from participating in regulation and causing adverse effects on the power grid. A manual assessment process is triggered, and professional operations and maintenance personnel will evaluate and address the abnormal situation. The switch status monitoring algorithm can be further optimized and combined with machine learning technology to learn and analyze historical switch status data to improve the accuracy of identifying abnormal conditions. At the same time, a comprehensive fault diagnosis and early warning system can be established to detect potential switch failure hazards in advance, take timely measures to address them, and reduce the probability of failure.

[0110] This embodiment ensures the integrity and reliability of data transmission between provincial and local master stations by building a stable and reliable communication link and configuring a data retransmission mechanism; the local master station performs data integrity verification on the received instructions to avoid control errors caused by data errors; the provincial master station's real-time monitoring of the switch status of the group control station and the exception handling mechanism effectively ensure the safe operation of the power grid.

[0111] Optionally, in some embodiments, the real-time verification rules may specifically include:

[0112] The control dead zone check is performed on the adjustment instructions uploaded by the station through the prefectural master station. If the adjustment amount is less than the preset dead zone threshold, the adjustment instruction is ignored and fed back to the provincial master station.

[0113] Specifically, after receiving a regulation command from a site, the prefectural master station first determines whether the adjustment amount is less than a preset deadband threshold. This deadband threshold is a minimum regulation amount standard set based on grid operation experience and equipment characteristics. If the adjustment amount is below this threshold, the impact of the regulation on the grid operation is negligible. The adjustment amplitude may even be too small, causing frequent equipment operation, which could affect the stability of the equipment and the grid. In this case, the prefectural master station will ignore the regulation command and report the situation to the provincial master station. The deadband threshold setting can be dynamically adjusted based on site type, equipment parameters, and the real-time grid operation. For example, when the grid load is light and equipment margin is large, the deadband threshold can be appropriately reduced to enable more precise control of renewable energy output. However, when the grid is operating under stress and equipment is near full load, the deadband threshold can be appropriately increased to prevent unnecessary small adjustments from disrupting grid operation. Furthermore, an intelligent algorithm is used to learn and analyze historical regulation commands and grid response data to further optimize the deadband threshold setting method and improve the accuracy and adaptability of deadband control verification.

[0114] The provincial master station performs operational limit checks on the output data uploaded by the prefectural master station. If the limit is exceeded, the command is automatically corrected and reissued.

[0115] Specifically, upon receiving the output data of distributed renewable energy stations uploaded by the prefectural master stations, the provincial master station immediately verifies the data against operating limits. Operating limits are pre-set upper and lower limits based on the requirements for safe and stable grid operation, as well as the rated capacity and permissible operating range of the equipment. The provincial master station compares the actual output data of the station with the corresponding operating limits. If the output data exceeds the limits, indicating that the station's output may pose a threat to grid security or equipment operation, the provincial master station automatically adjusts the station's output to a reasonable range and reissues the corrected instructions to the prefectural master station to ensure that the station's output remains within a safe range. The provincial master station can update the operating limits in real time based on the real-time operating status of the grid and the dynamic characteristics of the equipment. For example, in the event of a grid fault or equipment anomaly, the operating limits can be promptly tightened to limit the station's output and prevent the fault from escalating. After the grid returns to normal, the operating limits can be gradually restored based on actual conditions to fully utilize the generating capacity of the renewable energy station. At the same time, big data analysis and machine learning technologies are used to mine and analyze historical output data and power grid operation data, establish a correlation model between output data and power grid operation status, and further improve the accuracy and timeliness of operating limit verification.

[0116] This embodiment performs comprehensive and detailed verification and correction of the regulation instructions and output data of distributed new energy sites through control dead zone verification at the prefecture-level master station and operation limit verification at the provincial-level master station, effectively filtering out unreasonable and unsafe regulation instructions and output data, preventing problems such as small-scale regulation and output exceeding standards from causing adverse effects on the power grid and equipment, and further improving the safety and reliability of the distributed new energy provincial and local coordinated control system.

[0117] In a specific embodiment, for provincial and prefecture-level data interaction and verification, the provincial power department needs to obtain power load data, equipment operating status data, etc. from all prefecture-level cities in order to carry out province-wide power resource planning and optimized scheduling; while prefecture-level departments need to receive provincial-level policy instructions, technical standards, and macro-guidance data for power grid operation. The provincial master station achieves unified management of distributed renewable energy active power control in the entire region through data interaction and instruction transmission with the prefecture-level master station, solving problems such as the wide distribution of distributed renewable energy and difficulty in cross-regional coordination. Please refer to Figure 6 , Figure 6 The following is a flow chart of the provincial and prefecture-level data interaction and verification implementation method provided in this embodiment. The specific process is as follows:

[0118] 1) Provincial master station data interaction

[0119] Data Input: Key operational data is obtained from prefecture-level systems. Provincial master stations utilize a dedicated communications network to connect to prefecture-level systems, acquiring real-time data on prefecture-level virtual power units, including active power, upper and lower regulation limits, and controllable signals. Active power is used for power balance analysis and control decisions, upper and lower regulation limits ensure the rationality of instructions, and controllable signals determine effective control. Data transmission utilizes the IEC 61850 protocol to ensure integrity and accuracy, and a data retransmission mechanism is implemented. System frequency and tie-line exchange power data are acquired: Real-time system frequency is acquired to monitor grid stability, and the output of prefecture-level virtual power units is coordinated accordingly to maintain normal frequency. Analysis of tie-line exchange power optimizes regional power resource allocation, ensuring grid power balance and economical operation.

[0120] Data output: The target active power and power generation plan values ​​are sent to the prefecture-level master station. These data are calculated based on the province's power demand, grid status, and prefecture-level virtual unit characteristics, affecting the grid power balance and supply-demand matching. The prefecture-level master station controls and dispatches local distributed new energy stations accordingly.

[0121] 2) Data interaction with prefecture-level master stations

[0122] Data Input: Receives various data and instructions from the provincial master station. Prefectural master stations receive real-time information from the provincial master station via communication links, including target active power, planned generation values, distributed generation active power, regulation limits, and grid-connection switch telesignals. Target active power and planned generation values ​​are used for macroeconomic regulation. Distributed generation active power is used to monitor power generation conditions and regulate the regulation range. Grid-connection switch telesignals indicate connection status. CRC verification technology is used during data reception to ensure data accuracy. Any errors immediately trigger a request for retransmission from the provincial master station.

[0123] Data Output: Sending regulation instructions and planned power generation values ​​to the power stations. The prefectural master station configures the regulation method based on the station's regulation capabilities and business needs. Based on the provincial master station's requirements and the local grid status, it generates regulation instructions and sends them to the power stations, enabling real-time control. Simultaneously, it issues planned power generation values ​​to ensure stable and reliable regional power supply.

[0124] 3) Data verification and security strategy

[0125] ① Provincial master station verifies data sent by local virtual machine group:

[0126] Quality bit check: Check the data quality flag. If there is a problem that affects active power control, suspend or exit control. Re-evaluate and resume after the problem is resolved.

[0127] Range verification: Compare the measured quantity with the set value range, suspend or exit control in case of abnormality, and notify verification to ensure data accuracy.

[0128] Switch abnormality verification: Pay attention to the switch status of the group control station, suspend control in case of abnormality, and resume or switch to manual evaluation within the specified time.

[0129] Verification of opening and closing commands: Strict verification is performed before sending. If it fails, execution is prohibited to prevent malfunction of station equipment.

[0130] ② Verification of data sent by local master stations to distributed new energy stations:

[0131] Control dead zone check: When the adjustment amount is less than the dead zone, the command is suppressed, and the reasonable distribution of the adjustment amount is not guaranteed.

[0132] Adjustment step length verification: If the difference exceeds the step length, the execution will be refused, recorded and notified for inspection to ensure that subsequent instructions are reasonable.

[0133] Group control quantity verification: If the number of commands exceeds the upper limit, they will not be executed, and feedback will be provided for adjustment by the provincial master station.

[0134] Output data verification: If the total output exceeds the upper limit, manual confirmation will be required. The decision on whether to execute the data will be made based on various factors. Otherwise, the strategy will be adjusted.

[0135] Operation limit verification: Check whether the instructions are within the adjustment range. If they are exceeded, limit or correct them to ensure the stability of equipment and power grid.

[0136] Lock signal constraint check: Check whether the instruction direction conflicts with the lock signal. If there is a conflict, execution is prohibited.

[0137] Section constraint verification: The associated section stations check the target power, and make corrections and provide feedback if it may exceed the limit. The local master station coordinates accordingly.

[0138] Optionally, in some embodiments, step S3 of "using a multi-objective optimization algorithm to generate a provincial-local coordinated active power control strategy based on the verified global regulation demand and local regulation demand" may specifically include:

[0139] S31. Initialize the particle swarm, where the position vector of each particle corresponds to the active output distribution value of each level virtual machine group, and set the initial weight based on the real-time operation parameters of the power grid;

[0140] Specifically, at the provincial master station, a particle swarm is initialized based on the province-wide distribution of distributed renewable energy resources and the characteristics of each provincial-level virtual cluster. Each particle represents a possible active power allocation scheme, with the components of its position vector corresponding to the active power allocation values ​​of each provincial-level virtual cluster. Furthermore, based on the current real-time operating parameters of the power grid, such as load demand, frequency, and tie-line exchange power, initial weights are assigned to each particle, reflecting the importance of different objective functions within the current power grid operating state.

[0141] S32. Dynamically adjust the objective function weights during the iteration process, giving priority to allocating system frequency stability weights during peak power consumption periods;

[0142] Specifically, as the particle swarm optimization algorithm iterates, the weight of the objective function is dynamically adjusted based on changes in the grid's operating state. Especially during peak hours, when system frequency stability is crucial for ensuring grid security and power quality, the system frequency stability objective function is prioritized. This increases the weight of the system frequency stability objective function within the overall objective function, placing greater emphasis on ensuring system frequency stability during the optimization process.

[0143] S33. By iteratively updating the particle positions and velocities multiple times, the optimal strategy with the smallest tie-line exchange power deviation is selected, and the optimal strategy is converted into executable provincial and prefecture-level regulation instructions;

[0144] Specifically, during each iteration, the position and velocity of each particle are updated according to the particle swarm optimization algorithm's update rules, combining the objective function value with individual and global optimal information. After multiple iterations, the optimal strategy that minimizes the deviation in interconnection line exchange power is gradually identified. This optimal strategy represents the optimal provincial and local coordinated active power control scheme for the current grid operating state. This optimal strategy is then converted into specific provincial and local regulation instructions. The provincial regulation instructions guide the local master station in overall active power regulation, while the local regulation instructions direct specific distributed renewable energy stations to adjust their active power output.

[0145] This embodiment provides a good starting point for the optimization algorithm by initializing the particle swarm and reasonably setting the initial weights. The objective function weights are dynamically adjusted during the iteration process so that the control strategy can adapt to different grid operating states. Finally, the optimal strategy is screened and converted into executable adjustment instructions to achieve precise regulation of distributed renewable energy.

[0146] Optionally, in some embodiments, the calculation method of the priority ranking factor may specifically include:

[0147] Determine the adjustment order of each distributed new energy station according to the preset station priority table;

[0148] Calculate the real-time adjustable output ratio of each distributed new energy station, and sort them from low to high according to the real-time adjustable output ratio, giving priority to distributed new energy stations with large adjustable space.

[0149] Specifically, at provincial master stations, a station priority table is pre-established based on multiple dimensions of distributed renewable energy station information, including type, installed capacity, geographic location, grid connection time, and historical operating data. For example, wind farms are graded based on the stability of wind resources in their areas, and photovoltaic power plants are ranked based on the superiority of their lighting conditions. When determining the regulation order, stations with higher priority are ranked first and given priority in active power regulation. This ensures that when grid regulation needs arise, stations with the greatest regulation potential and stability are prioritized to respond, providing fast and reliable power support to the grid.

[0150] The real-time adjustable output ratio is calculated by comparing the current actual output of a distributed renewable energy station to its upper limit of adjustable capacity. For example, if a wind farm's current output is 10 MW and its upper limit of adjustable capacity is 20 MW, its adjustable output ratio is 0.5. The provincial master station calculates this ratio for each station in real time and sorts the stations in ascending order. The lower the ratio, the greater the station's remaining adjustable capacity under the current operating conditions, and thus receives priority for adjustment tasks. This ranking method fully utilizes the station's remaining adjustment capacity and enables refined management of active power regulation.

[0151] This embodiment provides an accurate decision-making basis for the active power regulation of distributed renewable energy by constructing a scientific and reasonable priority sorting and real-time adjustable output ratio sorting mechanism. In terms of priority sorting, it can quickly screen out the stations with the greatest regulation value to ensure the effective execution of the regulation tasks. In terms of real-time adjustable output ratio sorting, it can fully tap the remaining regulation capacity of the stations to achieve optimal allocation of regulation resources.

[0152] Optionally, in some embodiments, the decomposition of the provincial regulatory directive may specifically include:

[0153] Based on the global regulation needs, the provincial master station distributes the regulation amount to each provincial virtual machine group according to the installed capacity ratio or actual output ratio;

[0154] Specifically, after determining the overall regulation demand, the provincial master station calculates the regulation load to be borne by each unit based on the installed capacity or actual output of each prefecture-level virtual unit. For example, if the distribution is based on the proportion of installed capacity, and the installed capacity of a prefecture-level virtual unit accounts for 20% of the province's total distributed renewable energy installed capacity, then that unit will bear 20% of the total regulation load. If the distribution is based on the proportion of actual output, and the current actual output of a prefecture-level virtual unit accounts for 30% of the province's total distributed renewable energy output, then that unit will bear 30% of the total regulation load. In this way, the overall regulation demand is rationally broken down into each prefecture-level virtual unit.

[0155] After receiving the regulation amount, the local master station will allocate the regulation amount to the corresponding distributed new energy station based on the local feeder load rate or utilization hours.

[0156] Specifically, after receiving the regulation capacity allocated by the provincial master station, the prefectural master station first sorts the stations based on the local feeder load rate. For example, for stations with low feeder load rates, this indicates that the power supply in their area is relatively abundant, and they can be appropriately allocated more regulation capacity to reduce their output to balance the grid load. For stations with high feeder load rates, regulation capacity is allocated with caution to avoid power shortages caused by regulation. At the same time, stations can also be sorted based on their utilization hours, with regulation capacity being allocated preferentially to stations with lower utilization hours to balance equipment utilization across stations and extend their service life.

[0157] Optionally, in some embodiments, step S4 of "performing dynamic coordinated control at the provincial and prefectural levels according to the provincial and prefectural regulation instructions" may specifically include:

[0158] S41. The provincial master station issues provincial adjustment instructions to the corresponding prefecture-level master stations for cross-regional coordination;

[0159] Specifically, the provincial master station generates provincial regulation instructions based on the operating conditions and overall regulation needs of the provincial power grid, and sends these instructions to each prefectural master station via a preset communication link. These instructions contain the active power regulation targets and requirements that the prefectural master station needs to implement, such as adjusting the total output of renewable energy in a specific area and maintaining the exchange power of the interconnection line within a certain range, in order to achieve coordinated operation of the power grid across regions. Before issuing provincial regulation instructions, the provincial master station can use advanced forecasting technologies, such as deep learning algorithms, to make ultra-short-term forecasts of grid load and renewable energy output, and combine them with real-time grid topology analysis to pre-check and optimize the instructions. At the same time, a feedback mechanism for the issuance of instructions is established. After receiving the instructions, the prefectural master station will feedback the instruction reception status and preliminary execution plan to the provincial master station to ensure the effective communication and execution of the instructions.

[0160] S42. The local master station issues local adjustment instructions to the corresponding distributed new energy stations to perform active output adjustment;

[0161] Specifically, after receiving provincial regulation instructions from the provincial master station, the prefectural master station further refines them into specific prefectural regulation instructions based on the actual conditions of the local power grid and the operating status of distributed renewable energy stations, and then sends them to each station. These instructions directly instruct the station to adjust its active power output, such as requiring a wind farm to increase or decrease its output or a photovoltaic power station to maintain its current output, to meet the regulation requirements of the provincial master station and the operational needs of the local power grid. When issuing regulation instructions, the prefectural master station can comprehensively consider factors such as the operating status of the station's equipment, maintenance plans, and the power flow distribution of the local power grid to optimize the instructions. For example, for stations about to undergo equipment maintenance, the regulation amount in the instructions can be appropriately adjusted to avoid excessive equipment operation before maintenance. Simultaneously, an intelligent monitoring system monitors the stations' execution of the instructions in real time, promptly identifying and addressing any issues during execution to ensure effective regulation.

[0162] This embodiment builds a highly efficient, interconnected distributed renewable energy control system by issuing cross-regional coordination commands from provincial master stations and executing precise regulation commands from prefectural master stations. The provincial master station manages the active power balance of the province's power grid at a macro level, while the prefectural master station ensures effective execution of local grids and stations at a micro level. These two closely collaborate to effectively address the challenges of coordinated control of distributed renewable energy within the distribution network.

[0163] Optionally, in some embodiments, the dynamic collaborative control includes at least one of the following:

[0164] In the real-time peak-shaving mode of provincial-regional coordinated operation, the provincial master station issues a provincial regulation instruction to the prefectural master station in real time, so that the prefectural master station adjusts the output of the local station according to the provincial regulation instruction to maintain the interconnection line exchange power threshold;

[0165] Specifically, in real-time peak-shaving mode, the provincial master station calculates the required regulation instructions in real time based on the grid's real-time load and renewable energy output, and quickly transmits these instructions to the prefectural master station. Upon receiving the instructions, the prefectural master station immediately adjusts the output of each station based on the actual conditions of the local station, ensuring that the interconnection line exchange power remains within the set threshold, thereby achieving real-time power balance and stable operation of the provincial power grid.

[0166] In the prefectural section control mode, the prefectural master station monitors the load rate of local distribution network equipment, generates adjustment requirements and feeds them back to the provincial master station, which then reissues correction instructions after overall coordination.

[0167] Specifically, in prefecture-level section control mode, the prefecture-level master station monitors the load rate of local distribution network equipment in real time. When it detects that the equipment load rate is too high or too low, it generates corresponding regulation requirements based on preset rules. These regulation requirements include information on the operating status of the local power grid and the required regulation amount. The prefecture-level master station promptly feeds back the regulation requirements to the provincial master station. The provincial master station, after comprehensively considering the operating status of the provincial power grid and the requirements of other prefecture-level master stations, reissues revised regulation instructions to effectively control the load rate of local distribution network equipment.

[0168] In the free power generation mode, the provincial master station does not issue any instructions, and the prefecture-level master station operates autonomously according to the maximum adjustable power of the station, and uploads the operating data to the provincial master station in real time;

[0169] Specifically, in free power generation mode, provincial master stations do not issue unified regulation commands. Instead, prefectural master stations independently determine the operating mode and output level of their local stations based on their maximum adjustable power. Each station is encouraged to generate as much power as possible, without exceeding the permitted capacity of its equipment, to maximize the utilization of renewable energy. Simultaneously, prefectural master stations upload real-time operational data, including output and equipment status, to the provincial master station, enabling it to maintain abreast of the province's distributed renewable energy operations.

[0170] This embodiment provides flexible and diverse solutions for controlling the active power of distributed renewable energy through three modes: real-time peak-shaving through coordinated provincial and local governments, prefectural-level section control, and free generation. Real-time peak-shaving allows for rapid response to grid load changes and maintains grid power balance. Prefectural-level section control effectively addresses load anomalies in local distribution equipment, ensuring safe grid operation. Free generation fully taps into the power generation potential of renewable energy and improves its utilization.

[0171] In a specific embodiment, for the generation and control of provincial and municipal active power control strategies, the generation and implementation of active power control strategies involve close cooperation between provincial master stations and municipal master stations, and effective control of distributed new energy sites and virtual machine groups is achieved through multiple control modes and advanced algorithms.

[0172] 1) Provincial control model

[0173] Provincial and local coordinated real-time peak-shaving mode: The provincial master station obtains precise adjustment instructions through real-time calculation or manual setting and sends them to the local master station in a timely manner, ensuring that the exchange power of the interconnection line or the transmission section is always maintained within the normal range, thereby ensuring the stable operation of the power grid.

[0174] Provincial and local coordinated planned value peak-shaving mode: The provincial master station will accurately send the carefully formulated power generation plan value to the prefecture-level master station, providing a clear target for the active power control of the prefecture-level master station to maintain the interconnection line exchange power or transmission section within the normal range.

[0175] Free power generation mode: The provincial master station sends the control mode information to the prefectural master station. It can choose not to send the adjustment instruction, or set the adjustment instruction to the maximum adjustable power of the prefectural virtual machine group, giving the prefectural master station a certain amount of autonomous control space.

[0176] 2) Local level control mode

[0177] Provincial and local coordinated real-time peak-shaving mode: The local master station sends the real-time total output, upper and lower limits, controllable signals and other data of the locally managed distributed renewable energy to the provincial master station. The provincial master station obtains the adjustment instructions through real-time calculation or manual setting and sends them to the local master station to maintain the unified load, system frequency or interconnection line exchange power within the normal range.

[0178] Provincial and local coordinated planned value peak-shaving mode: After the prefecture-level master station receives the power generation plan value issued by the provincial master station, it distributes the power generation plan value to the site station to maintain the interconnection line exchange power or transmission section within the normal range.

[0179] Local-level section control mode: The local-level master station monitors the distribution network operation status in real time. When the distribution network equipment is overloaded, it issues a real-time alarm and adjusts the output of the associated distributed new energy stations. When overload and current reversal occur, the distributed new energy and other adjustable resources are actively adjusted to maintain the distribution network section within the normal range.

[0180] Free power generation mode: The local master station sends the regulation mode to the station without issuing any regulation instructions; or the regulation instruction is the maximum adjustable power of the distributed new energy station.

[0181] 3) Active power control strategy generation

[0182] During real-time calculations, provincial master stations collect province-wide grid operation data, including active power, upper and lower regulation limits, controllable signals, system frequency, and tie-line exchange power information for each virtual unit at each level. Using this data, a particle swarm optimization algorithm is employed for calculations. Simulating the foraging behavior of a flock of birds, the particle swarm optimization algorithm treats each possible active power regulation strategy as a particle and searches for the optimal solution in the solution space based on the particle's position (representing a different regulation strategy) and velocity (representing the direction and magnitude of the strategy's adjustment). This process continuously evaluates the performance of each regulation strategy represented by each particle based on pre-defined objective functions (such as minimizing tie-line exchange power deviation or maintaining system frequency stability). The particle's position and velocity are then updated, ultimately finding the optimal active power regulation strategy as the regulation instruction. Manual setting is used in special circumstances, such as when a grid emergency occurs or when planned adjustments are required, when dispatchers directly set regulation instructions based on their experience and grid operating conditions.

[0183] like Figure 7 As shown, Figure 7 The flow chart generated for the active power regulation strategy is as follows:

[0184] 1. Determine the particle swarm size: Reasonably set the number of particles, usually in the range of 50-100. Each particle represents a potential active power control strategy to ensure sufficient search range in the solution space.

[0185] 2. Initialize particle position and velocity: Randomly assign an initial position to each particle. Each dimension of the position vector corresponds to the active power distribution value of each local virtual machine group.

[0186] 3. Set algorithm parameters:

[0187] Inertia weight: controls the influence of the particle's previous velocity on this velocity update.

[0188] Learning factor: The cognitive learning factor guides the particle to its own historical optimal position, and the social learning factor prompts the particle to move toward the group's optimal position. The two work together to accelerate the particle's convergence to a better solution space.

[0189] 4. Clarify multiple objectives: Based on the actual needs of grid operation, determine the core optimization goals as minimizing the deviation of interconnection line exchange power, maintaining system frequency stability, and reducing power generation costs.

[0190] 5. Weighting: Dynamic weights are assigned to each objective based on the focus of different operating periods and the current grid control priorities. For example, maintaining system frequency stability during peak demand periods could be weighted 0.45, while minimizing tie-line exchange power deviation could be weighted 0.4.

[0191] 6. Strategy simulation calculation: For each particle in the particle swarm, based on the active power allocation strategy represented by its current position, combined with the detailed characteristic data of each provincial virtual machine group stored in the provincial master station, using tools such as the power grid flow calculation model, the operating status of the provincial power grid under this strategy is simulated and calculated.

[0192] 7. Evaluation and update of advantages and disadvantages:

[0193] Individual optimal update: Compare the objective function value calculated under the current particle strategy with the particle's own historical optimal value. If the new value is better, update the current optimal position.

[0194] Group optimal update: If a particle strategy is better than the group optimal, it is updated to the group optimal position.

[0195] 8. Speed ​​and position update:

[0196] Set the maximum number of iterations to ensure that valid results are obtained within an acceptable computing time.

[0197] According to the updated individual optimum, group optimum and the established algorithm parameters, the classic speed update formula and position update formula of the particle swarm optimization algorithm are used to calculate and update the speed and position of each particle.

[0198] 9. Convergence judgment and strategy output:

[0199] When the convergence condition is met, the active power allocation strategy corresponding to the optimal position of the group is obtained.

[0200] The determined optimal strategy is converted according to the communication protocol format to generate specific adjustment instructions.

[0201] In order to facilitate understanding of the provincial and local coordinated control method of distributed renewable energy provided by this application, Figure 8 As shown, this embodiment also provides a specific implementation method of a provincial and local coordinated control method for distributed new energy. Figure 8 As can be seen from the above-mentioned embodiments, this embodiment constructs a two-level architecture for the active power control of distributed renewable energy, consisting of provincial and prefectural master stations. It also details the provincial and prefectural data exchange and verification mechanisms, as well as the generation and control mechanisms for active power control strategies. The provincial master station uses object-oriented programming and other technologies to collect and acquire data, employs data visualization and other means to monitor operational status, and determines the total regulation demand allocation plan based on strategies such as priority sorting and proportional allocation. Prefectural master stations, operating under a similar architecture, set data collection, operation monitoring, and demand allocation strategies based on their own characteristics. Data exchange between the provincial and prefectural levels is carried out through specific communication protocols and verification technologies, while strictly enforcing data verification and security policies covering aspects such as quality level, range, switch anomalies, and opening and closing commands. Regarding active power control strategies, the provincial and prefectural master stations each have collaborative real-time peak-shaving, planned peak-shaving, and free power generation modes. A particle swarm optimization algorithm is used in real-time calculations to generate the optimal regulation strategy, ensuring stable grid operation and efficient absorption of distributed renewable energy.

[0202] Compared with existing technologies, the provincial and regional coordinated control method for distributed renewable energy provided in this embodiment constructs a comprehensive architecture including provincial and regional master stations, fully considering the widespread distribution of distributed renewable energy. It can adapt to various types of distributed renewable energy and the complex grid characteristics of different regions. Compared with the photovoltaic storage VSG control strategy based on active differential compensation and virtual inertia adaptation, it has stronger system adaptability and versatility, and can effectively cope with various complex operating conditions in actual power grids. The provincial and regional data interaction and verification mechanism established in this embodiment uses advanced communication technology and data verification methods to ensure accurate and timely data transmission, providing a reliable basis for active power control. Compared with the wind power cluster active power control strategy that relies on data accuracy and considers the differences in wind farm grid connection performance, which is easily affected by the influence, this embodiment can effectively avoid control errors caused by data problems and achieve more accurate active power control. When generating the active power control strategy, this embodiment comprehensively considers the relationship between distributed renewable energy and traditional power sources and load feedback in a larger area, achieving overall optimization at the grid level. Compared with strategies that mainly focus on optimizing the photovoltaic storage system itself, it can better leverage the complementary advantages of distributed renewable energy in the entire distribution network, improve the economy and stability of grid operation, and promote synergistic interaction among multiple energy sources.

[0203] It should be understood that although Figure 2 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. In addition, Figure 2 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0204] To facilitate better implementation of the distributed renewable energy provincial and local coordinated control method of the present application embodiment, the present invention also provides a distributed renewable energy provincial and local coordinated control device based on the above-mentioned distributed renewable energy provincial and local coordinated control method. The meanings of the terms herein are the same as those in the above-mentioned distributed renewable energy provincial and local coordinated control method. For specific implementation details, please refer to the description in the method embodiment.

[0205] See also Figure 9 , Figure 9This is a schematic diagram of the structure of a distributed renewable energy provincial and regional coordinated control device provided in an embodiment of the present application. The distributed renewable energy provincial and regional coordinated control device may specifically include a construction module 201, a transmission module 2022, a strategy module 203, and a control module 204, which may be specifically as follows:

[0206] Construction module 201 is used to build a two-level control architecture consisting of a provincial master station and a prefectural master station. The provincial master station integrates the operating data of distributed renewable energy resources across the province through the data acquisition module to generate global regulation requirements. The prefectural master station generates local regulation requirements based on the locally collected distributed renewable energy station data.

[0207] Transmission module 202 is used to establish a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmit global adjustment requirements and local adjustment requirements through the data exchange channel, and verify the transmitted data based on real-time verification rules;

[0208] Strategy module 203, for generating a provincial-local coordinated active power control strategy based on the verified global regulation demand and local regulation demand using a multi-objective optimization algorithm;

[0209] The control module 204 is used to decompose the provincial-local coordinated active power control strategy into provincial-level regulation instructions and local-level regulation instructions, and perform dynamic coordinated control at the provincial and local levels according to the provincial-level regulation instructions and local-level regulation instructions.

[0210] For the specific definition of the provincial and local coordinated control device for distributed new energy, please refer to the definition of the provincial and local coordinated control method for distributed new energy above, which will not be repeated here. The various modules in the provincial and local coordinated control device for distributed new energy can be implemented in whole or in part by software, hardware and their combination. The above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0211] The provincial and local coordinated control device for distributed new energy provided in this embodiment constructs a provincial and local two-level control architecture and data interaction channel, and uses a multi-objective optimization algorithm to generate a coordinated control strategy and then perform provincial and local dynamic coordinated control, thereby realizing efficient dynamic regulation of distributed new energy at the provincial and local levels, promoting the efficient absorption of distributed new energy, and thus improving the stability and economy of power grid operation.

[0212] In addition, the present invention also provides an electronic device, such as Figure 10 , which shows a schematic diagram of the structure of the electronic device involved in the embodiment of the present application, specifically:

[0213] The electronic device may include one or more processors 301 of processing cores, one or more computer-readable storage media memories 302, a power supply 303, an input unit 304 and other components. Those skilled in the art will appreciate that Figure 10 The electronic device structure shown in the figure does not constitute a limitation of the electronic device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0214] The processor 301 is the control center of the electronic device. It connects all parts of the electronic device using various interfaces and lines. By running or executing software programs and / or modules stored in the memory 302 and accessing data stored in the memory 302, it performs various functions of the electronic device and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 301 may include one or more processing cores; preferably, the processor 301 may integrate an application processor and a modem processor, wherein the application processor primarily processes the operating system, user interface, and application programs, while the modem processor primarily handles wireless communications. It is understood that the modem processor may not be integrated into the processor 301.

[0215] The memory 302 can be used to store software programs and modules. The processor 301 executes various functional applications and distributed new energy coordinated control methods by running the software programs and modules stored in the memory 302. The memory 302 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data created according to the use of the electronic device, etc. In addition, the memory 302 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage device. Accordingly, the memory 302 may also include a memory controller to provide the processor 301 with access to the memory 302.

[0216] The electronic device also includes a power supply 303 for supplying power to various components. Preferably, the power supply 303 can be logically connected to the processor 301 via a power management system, thereby enabling the power management system to manage charging, discharging, and power consumption. The power supply 303 can also include one or more DC or AC power supplies, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and other arbitrary components.

[0217] The electronic device may further include an input unit 304, which may be configured to receive input digital or character information and generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control.

[0218] Although not shown, the electronic device may further include a display unit, etc., which will not be described in detail here. Specifically, in this embodiment, the processor 301 in the electronic device will load the executable files corresponding to the processes of one or more application programs into the memory 302 according to the following instructions, and the processor 301 will run the application programs stored in the memory 302 to implement various functions as follows:

[0219] A two-level control architecture consisting of a provincial master station and a prefectural master station is constructed, wherein the provincial master station integrates the operating data of distributed renewable energy throughout the province through a data acquisition module to generate global regulation requirements, and the prefectural master station generates local regulation requirements based on the locally collected distributed renewable energy station data; a data interaction channel is established between the provincial master station and the prefectural master station through a preset communication protocol, and the global regulation requirements and local regulation requirements are bidirectionally transmitted through the data interaction channel, and the transmitted data is verified based on real-time verification rules; a multi-objective optimization algorithm is used to generate a provincial-local coordinated active power control strategy based on the verified global regulation requirements and local regulation requirements; the provincial-local coordinated active power control strategy is decomposed into provincial regulation instructions and prefectural regulation instructions, and dynamic coordinated control of the two levels of province and city is carried out according to the provincial regulation instructions and prefectural regulation instructions.

[0220] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0221] The embodiment of the present application constructs a provincial and local two-level control architecture and data interaction channel, and uses a multi-objective optimization algorithm to generate a collaborative control strategy and then perform dynamic collaborative control at the provincial and local levels, thereby achieving efficient dynamic regulation of distributed new energy at the provincial and local levels, promoting the efficient absorption of distributed new energy, and thus improving the stability and economy of power grid operation.

[0222] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be accomplished by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0223] To this end, an embodiment of the present application provides a storage medium storing a plurality of instructions that can be loaded by a processor to execute the steps of any of the distributed renewable energy coordinated control methods provided in the embodiments of the present application. For example, the instructions can execute the following steps:

[0224] A two-level control architecture consisting of a provincial master station and a prefectural master station is constructed, wherein the provincial master station integrates the operating data of distributed renewable energy throughout the province through a data acquisition module to generate global regulation requirements, and the prefectural master station generates local regulation requirements based on the locally collected distributed renewable energy station data; a data interaction channel is established between the provincial master station and the prefectural master station through a preset communication protocol, and the global regulation requirements and local regulation requirements are bidirectionally transmitted through the data interaction channel, and the transmitted data is verified based on real-time verification rules; a multi-objective optimization algorithm is used to generate a provincial-local coordinated active power control strategy based on the verified global regulation requirements and local regulation requirements; the provincial-local coordinated active power control strategy is decomposed into provincial regulation instructions and prefectural regulation instructions, and dynamic coordinated control of the two levels of province and city is carried out according to the provincial regulation instructions and prefectural regulation instructions.

[0225] The specific implementation of the above operations can be found in the previous embodiments and will not be repeated here.

[0226] The storage medium may include a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0227] Since the instructions stored in the storage medium can execute the steps in any of the distributed new energy resource-saving and local coordinated control methods provided in the embodiments of the present application, the beneficial effects that can be achieved by any of the distributed new energy resource-saving and local coordinated control methods provided in the embodiments of the present application can be achieved. Please see the previous embodiments for details and will not be repeated here.

[0228] The above is a detailed introduction to a provincial and local coordinated control method and device for distributed new energy provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, based on the ideas of the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A distributed new energy coordinated control method, characterized in that: The steps include: Construct a two-level control architecture consisting of a provincial master station and a prefecture-level master station. The provincial master station integrates the province's distributed renewable energy operation data through a data acquisition module to generate global regulation requirements, while the prefecture-level master station generates local regulation requirements based on locally collected distributed renewable energy station data. Establishing a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmitting the global adjustment demand and the local adjustment demand through the data exchange channel, and verifying the transmitted data based on real-time verification rules; A multi-objective optimization algorithm is used to generate a provincial and local coordinated active power control strategy based on the verified global and local regulation requirements. The provincial-local coordinated active power control strategy is decomposed into provincial-level regulation instructions and local-level regulation instructions, and dynamic coordinated control at the provincial and local levels is performed according to the provincial-level regulation instructions and the local-level regulation instructions.

2. The distributed new energy coordinated control method according to claim 1 is characterized in that: The two-level control architecture consisting of a provincial master station and a prefecture-level master station includes: In the provincial master station, each corresponding prefectural master station is abstracted as a virtual machine group object, and the properties, communication interface and data interaction logic of each prefectural master station are encapsulated through object-oriented programming technology to form a provincial data pool; The local distributed new energy station operation data is collected in real time through the prefecture-level master station, and the operation data is uploaded to the provincial data pool. The operation data includes real-time active power output, upper and lower regulation limits, and grid-connected switch status; The provincial master station calculates a priority ranking factor or a proportional allocation factor based on the virtual machine group object attributes in the provincial data pool, and generates a global adjustment demand including adjustment amount allocation.

3. The distributed new energy coordinated control method according to claim 1 is characterized in that: The step of establishing a data interaction channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmitting the global adjustment demand and the local adjustment demand through the data interaction channel, and verifying the transmitted data based on real-time verification rules includes: A communication link is established between the provincial master station and the prefectural master station using a preset communication protocol, and a data retransmission mechanism is configured; When receiving the target active power instruction issued by the provincial master station through the prefectural master station, verifying the data integrity of the transmitted data through a cyclic redundancy check algorithm, and triggering a corresponding retransmission request if the check fails; The provincial master station monitors the switch status of the group control stations uploaded by the prefectural master station in real time. When an abnormal state is detected, the current adjustment instruction is suspended and a manual evaluation process is triggered.

4. The distributed new energy coordinated control method according to claim 3 is characterized in that: The real-time verification rules include: The control dead zone check is performed on the adjustment instruction uploaded by the station through the prefectural master station. If the adjustment amount is less than the preset dead zone threshold, the adjustment instruction is ignored and fed back to the provincial master station; The provincial master station performs an operation limit check on the output data uploaded by the prefectural master station. If the limit range is exceeded, the instruction is automatically corrected and reissued.

5. The distributed new energy coordinated control method according to claim 1 is characterized in that: The multi-objective optimization algorithm is used to generate a provincial and local coordinated active power control strategy based on the verified global regulation demand and local regulation demand, including: Initialize the particle swarm. The position vector of each particle corresponds to the active power output distribution value of each local virtual machine group, and the initial weight is set based on the real-time operation parameters of the power grid. The objective function weights are dynamically adjusted during the iteration process, with priority given to the system frequency stability weight during peak power consumption periods. By iteratively updating the particle positions and velocities multiple times, the optimal strategy with the smallest deviation in the interconnection line exchange power is screened out, and the optimal strategy is converted into executable provincial-level and prefecture-level regulation instructions.

6. The distributed new energy coordinated control method according to claim 5 is characterized in that: The priority ranking factor is calculated as follows: Determine the adjustment order of each distributed new energy station according to the preset station priority table; Calculate the real-time adjustable output ratio of each distributed new energy station, and sort them from low to high according to the real-time adjustable output ratio, giving priority to distributed new energy stations with large adjustable space.

7. The distributed new energy coordinated control method according to claim 1 is characterized in that: The breakdown of the provincial regulatory directives includes: The provincial master station distributes the regulation amount to each provincial virtual machine group according to the global regulation demand and the installed capacity ratio or the actual output ratio; After the local master station receives the regulation amount, it allocates the regulation amount to the corresponding distributed new energy station in combination with the local feeder load rate or utilization hours.

8. The distributed new energy coordinated control method according to claim 1 is characterized in that: The performing of dynamic coordinated control at the provincial and prefectural levels according to the provincial regulation instructions and the prefectural regulation instructions includes: The provincial master station issues the provincial adjustment instruction to the corresponding prefecture-level master station for cross-regional coordination; The local-level adjustment instructions are sent by the local-level master station to the corresponding distributed new energy stations to perform active power output adjustment.

9. The distributed new energy coordinated control method according to claim 1 is characterized in that: The dynamic collaborative control includes at least one of the following: In the provincial-region coordinated real-time peak-shaving mode, the provincial master station sends the provincial regulation instruction to the prefectural master station in real time, so that the prefectural master station adjusts the local station output according to the provincial regulation instruction to maintain the tie line exchange power threshold; In the prefectural section control mode, the prefectural master station monitors the load rate of the local distribution network equipment, generates adjustment requirements and feeds them back to the provincial master station, and the provincial master station reissues correction instructions after overall coordination; In the free power generation mode, the provincial master station does not issue any instructions, and the prefectural master station operates autonomously according to the maximum adjustable power of the station, and uploads the operating data to the provincial master station in real time.

10. A distributed new energy coordinated control device, characterized in that: include: A construction module is used to construct a two-level control architecture consisting of a provincial master station and a prefecture-level master station. The provincial master station integrates the operating data of distributed renewable energy resources across the province through a data acquisition module to generate global regulation requirements, while the prefecture-level master station generates local regulation requirements based on locally collected distributed renewable energy station data. a transmission module, configured to establish a data exchange channel between the provincial master station and the prefectural master station through a preset communication protocol, bidirectionally transmit the global adjustment demand and the local adjustment demand through the data exchange channel, and verify the transmitted data based on real-time verification rules; The strategy module is used to generate a provincial and local coordinated active power control strategy based on the verified global and local regulation requirements using a multi-objective optimization algorithm; The control module is used to decompose the provincial-local coordinated active power control strategy into provincial-level regulation instructions and local-level regulation instructions, and perform dynamic coordinated control at the provincial and local levels according to the provincial-level regulation instructions and the local-level regulation instructions.

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