Distributed new energy aggregation model construction method and device, equipment and medium

Abstract

The invention belongs to the technical field of new energy, and particularly relates to a distributed new energy aggregation model construction method and device, equipment and a medium. According to the method, firstly, distributed new energy is classified according to voltage grades and aggregators, wherein the distributed new energy comprises first voltage grade distributed new energy, aggregator distributed new energy and direct control condition new energy; secondly, determining output models, including a power model, a climbing rate model and an output upper and lower limit model, for each type of distributed new energy, and aggregating the output models in the same type into a virtual generator model; then, an information physical model is obtained, and the information physical model comprises physical information of the distributed new energy and basic information of an aggregator; and finally, according to the category of the distributed new energy, associating the virtual generator model with the corresponding information physical model to form various distributed new energy aggregation models. According to the method, effective integration and management of distributed new energy can be realized, and the stability and reliability of a power system are improved.

Description

Technical Field

[0001] The present invention belongs to the field of new energy technology, and specifically relates to a distributed new energy aggregation model construction method, device, equipment and medium. Background Art

[0002] With the transformation of the global energy structure and the in-depth implementation of the sustainable development strategy, distributed new energy, as a clean, efficient and flexible energy form, is gradually becoming an important part of the power system. Distributed new energy mainly includes solar photovoltaic power generation, wind power generation, small hydropower, biomass power generation and other forms. It is usually distributed on the user side or close to the load center, and can provide electricity and heat energy nearby, effectively reducing dependence on long-distance power transmission and improving energy utilization efficiency.

[0003] However, the access to distributed renewable energy has also brought many challenges to the traditional power system. On the one hand, the output of distributed renewable energy is intermittent and uncertain. Its power generation is affected by many factors such as weather, season, and geographical location, making it difficult to accurately predict and control, which brings great difficulties to the stable operation and dispatch management of the power system. On the other hand, the access to distributed renewable energy has changed the power flow distribution of the power system, which may lead to problems such as voltage fluctuations, harmonic pollution, and three-phase imbalance in the local power grid, affecting the safe operation of the power grid and the quality of power.

[0004] In order to meet these challenges and fully utilize the potential of distributed renewable energy, it is necessary to establish a scientific and reasonable distributed renewable energy aggregation model. Summary of the invention

[0005] The purpose of the present invention is to provide a distributed new energy aggregation model construction method, device, equipment and medium to solve the problem of distributed new energy aggregation model construction in the prior art.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for constructing a distributed new energy aggregation model, comprising the following steps: Determine the categories of each distributed renewable energy source; wherein the categories of the distributed renewable energy sources are classified according to voltage levels and aggregators; Determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model; Acquire a pre-built information-physical model; wherein the information-physical model includes physical information of each distributed new energy source and basic information of each aggregator; According to the categories of distributed renewable energy, the virtual generator model is associated with the corresponding cyber-physical model to obtain various distributed renewable energy aggregation models.

[0007] Furthermore, the categories of distributed renewable energy are classified according to voltage levels and aggregators, specifically including: Dividing the distributed renewable energy according to the voltage level to obtain the first voltage level distributed renewable energy and the second voltage level distributed renewable energy; The second voltage level distributed new energy is divided according to the aggregators to obtain aggregator distributed new energy and direct control condition new energy.

[0008] Furthermore, the output upper and lower limit models are determined as follows: Determine the time interval for scheduling moments; Determine the upward climbing rate and the upper limit of the electric output of each distributed energy source, as well as the downward climbing rate and the lower limit of the electric output of each distributed energy source; Determine the distributed energy power output at the last dispatching moment; According to the distributed energy power output at the previous scheduling moment, the time interval at the scheduling moment, the upward climbing rate and the upper limit of the power output, the upper limit of the power output at the current scheduling moment is corrected; according to the distributed energy power output at the previous scheduling moment, the time interval at the scheduling moment, the upward climbing rate and the lower limit of the power output, the lower limit of the power output at the current scheduling moment is corrected.

[0009] Further, according to the distributed energy power output at the last dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the power output upper limit, the power output upper limit at the current dispatching moment is corrected; according to the distributed energy power output at the last dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the power output lower limit, the power output lower limit at the current dispatching moment is corrected, specifically including: If satisfied , then the upper limit of the power output at the current dispatch time is If satisfied , then the upper limit of the power output at the current dispatch time is ; If satisfied , then the lower limit of the power output at the current dispatch time is If satisfied , then the lower limit of the power output at the current dispatch time is ; in, It is the distributed energy power output at the last dispatching moment; is the time interval of the scheduling time; is the upward climbing rate of distributed energy i; The upper limit of the electric output of distributed energy; is the downward climbing rate of distributed energy i; It is the lower limit of the electric output of distributed energy.

[0010] Furthermore, the information-physical model includes the physical information of each distributed renewable energy source and the basic information of each aggregator, specifically including: The physical information of distributed renewable energy includes: device ID, device name, total number of devices, device type and rated power; The basic information of the aggregator includes: aggregator ID, aggregator name, maximum power generation, total number of devices, power type and control type.

[0011] In a second aspect, the present invention provides a distributed new energy aggregation model construction device, comprising: A first determination module is used to determine the category of each distributed renewable energy source; wherein the category of the distributed renewable energy source is divided according to voltage level and aggregator; The second determination module is used to determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model; A data acquisition module, used to acquire a pre-built information-physical model; wherein the information-physical model includes the physical information of each distributed new energy source and the basic information of each aggregator; The model generation module is used to associate the virtual generator model with the corresponding information-physical model according to the category of distributed renewable energy, so as to obtain various distributed renewable energy aggregation models.

[0012] Furthermore, in the second determination module, the output upper and lower limit models are determined in the following manner: Determine the time interval for scheduling moments; Determine the upward climbing rate and the upper limit of the electric output of each distributed energy source, as well as the downward climbing rate and the lower limit of the electric output of each distributed energy source; Determine the distributed energy power output at the last dispatching moment; According to the distributed energy power output at the previous scheduling moment, the time interval at the scheduling moment, the upward climbing rate and the upper limit of the power output, the upper limit of the power output at the current scheduling moment is corrected; according to the distributed energy power output at the previous scheduling moment, the time interval at the scheduling moment, the upward climbing rate and the lower limit of the power output, the lower limit of the power output at the current scheduling moment is corrected.

[0013] Further, according to the distributed energy power output at the last dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the power output upper limit, the power output upper limit at the current dispatching moment is corrected; according to the distributed energy power output at the last dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the power output lower limit, the power output lower limit at the current dispatching moment is corrected, specifically including: If satisfied , then the upper limit of the power output at the current dispatch time is If satisfied , then the upper limit of the power output at the current dispatch time is ; If satisfied , then the lower limit of the power output at the current dispatch time is If satisfied , then the lower limit of the power output at the current dispatch time is ; in, It is the distributed energy power output at the last dispatching moment; is the time interval of the scheduling time; is the upward climbing rate of distributed energy i; The upper limit of the electric output of distributed energy; is the downward climbing rate of distributed energy i; It is the lower limit of the electric output of distributed energy.

[0014] In a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distributed new energy aggregation model construction method as described above.

[0015] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distributed new energy aggregation model construction method as described above is implemented.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This scheme proposes a method for constructing a distributed new energy aggregation model. The present invention classifies distributed renewable energy sources in a refined manner and divides them according to voltage levels and aggregators, so that the characteristics of each type of distributed renewable energy sources are clearer and easier to model and control later. At the same time, by constructing an output model including a power model, a ramp rate model, and an upper and lower limit model of output, the output characteristics of distributed renewable energy sources can be described more accurately, and the prediction accuracy and practicality of the model can be improved. It provides strong technical support for the widespread application of distributed renewable energy sources. By constructing a scientific and reasonable distributed renewable energy aggregation model, it is possible to effectively integrate and manage distributed renewable energy sources and promote their access to the power system. It is conducive to optimizing the energy structure, improving energy utilization efficiency, and promoting the implementation of a green and sustainable development strategy. The construction device, electronic device, and computer-readable storage medium of a distributed renewable energy aggregation model provided by the present invention also solve the problems raised in the background technology section.

[0017] The present invention proposes to aggregate the distributed renewable energy output models under the same category into a virtual generator model, which simplifies the regulation and management of a large number of distributed renewable energy. The virtual generator model retains the main output characteristics of distributed renewable energy, facilitates seamless connection with the existing power system, and improves the compatibility and flexibility of the system.

[0018] The present invention pre-builds a cyber-physical model and associates the virtual generator model with the corresponding cyber-physical model, thus realizing the deep integration of distributed new energy physical information and regulation information. This improves the accuracy and reliability of the model and provides strong data support for subsequent power trading, dispatching and management.

[0019] To sum up, the distributed new energy aggregation model construction method of the present invention has significant advantages and effects in refined classification and modeling, innovative application of virtual generator models, deep integration of information-physical models, improving system stability and power quality, and promoting the widespread application of distributed new energy, and has important practical application value and strategic significance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of a method for constructing a distributed new energy aggregation model according to an embodiment of the present invention; Figure 2 This is a structural block diagram of a distributed new energy aggregation model building device according to an embodiment of the present invention; Figure 3 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0022] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0023] Resource modeling: refers to building models to describe various distributed resources. Its description methods can be divided into physical modeling and data modeling. Physical modeling models resources based on the physical properties of the resources themselves, while data modeling builds models based on the historical operation data of resources to describe resource characteristics.

[0024] Resource aggregation: refers to the aggregation of massive distributed resources from spatial and temporal dimensions to form resource clusters based on resource modeling and different control objectives. Resource aggregation methods usually use optimization technology, propose different requirements for aggregation parameters for different scenarios, and build a dynamic aggregation model of resources.

[0025] Example 1 like Figure 1 As shown, a method for constructing a distributed new energy aggregation model includes the following steps: S1. Determine the categories of each distributed renewable energy source; wherein the categories of the distributed renewable energy sources are classified according to voltage levels and aggregators.

[0026] In this plan, the categories of distributed renewable energy are divided according to voltage levels and aggregators, specifically including: dividing distributed renewable energy according to voltage levels to obtain distributed renewable energy of the first voltage level and distributed renewable energy of the second voltage level; dividing distributed renewable energy of the second voltage level according to aggregators to obtain distributed renewable energy of aggregators and new energy under direct control conditions.

[0027] In one embodiment, the hierarchical aggregation principle of distributed renewable energy follows: the aggregation subjects are within the same distribution network area, and the distributed power generation cannot be reversed. From the bottom up, a multi-level aggregation architecture of distribution network, distribution station area, distributed aggregator, and distributed equipment is formed to form resource clusters of different capacities and scales.

[0028] This embodiment takes distributed renewable energy below 35kv as an example, which is mainly divided into the following categories.

[0029]

[0030] In the above scheme, when classifying distributed renewable energy, the voltage level is first divided into categories, where the voltage level of 10-35kV is one category, and the voltage level of 220-380V is another category; for the distributed renewable energy of voltage level 220-380V, it is further divided according to whether it is an aggregator. A total of three categories of distributed renewable energy are obtained, and model aggregation is performed for these three categories of distributed renewable energy.

[0031] S2. Determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model.

[0032] In this scheme, the output upper and lower limit model is determined in the following manner: determine the time interval of the scheduling moment; determine the upward climbing rate and the upper limit of the electric output of each distributed energy, as well as the downward climbing rate and the lower limit of the electric output of each distributed energy; determine the electric output of the distributed energy at the last scheduling moment; according to the electric output of the distributed energy at the last scheduling moment, the time interval of the scheduling moment, the upward climbing rate and the upper limit of the electric output, correct the upper limit of the electric output at the current scheduling moment; according to the electric output of the distributed energy at the last scheduling moment, the time interval of the scheduling moment, the upward climbing rate and the lower limit of the electric output, correct the lower limit of the electric output at the current scheduling moment.

[0033] In one embodiment, the upper and lower limits of the electric output at the current scheduling time are determined as follows: If satisfied , then the upper limit of the power output at the current dispatch time is If satisfied , then the upper limit of the power output at the current dispatch time is ; If satisfied , then the lower limit of the power output at the current dispatch time is If satisfied , then the lower limit of the power output at the current dispatch time is ; in, It is the distributed energy power output at the last dispatching moment; is the time interval of the scheduling time; is the upward climbing rate of distributed energy i; The upper limit of the electric output of distributed energy; is the downward climbing rate of distributed energy i; It is the lower limit of the electric output of distributed energy.

[0034] In one embodiment, a simplified power model of distributed renewable energy is as follows: (1) Where: is the output power of distributed generation resources; is the resource intensity, such as sunlight intensity in photovoltaic power generation or wind speed in wind power generation; is the conversion coefficient; The scale of resources.

[0035] The distributed new energy in this solution can include photovoltaic and wind power systems.

[0036] The output power of photovoltaic power generation is mainly affected by light intensity, temperature and the area of ​​the power generation panel. The mathematical model used to calculate the photovoltaic power generation power can be expressed on the basis of formula (1) as follows: (2) In the formula, The photovoltaic array is under the light intensity Output power at , , Standard test environment: 1000(W / m 2 ), light intensity, PV array temperature and maximum output power at 25°C; is the temperature coefficient, generally ; for The surface temperature of the photovoltaic array at the moment.

[0037] Wind power systems can be divided into asynchronous power generation systems and synchronous power generation systems according to the type of generator. The actual wind speed The relationship between them is as follows: (3) in, is the rated power of the wind turbine, , , They are cut-in wind speed, cut-out wind speed and rated wind speed.

[0038] In the above scheme, after the power models of photovoltaic and wind power are determined, the distributed renewable energy outputs under the corresponding categories are summed up as the power model of the virtual generator model.

[0039] In one embodiment, the ramp rate model includes an upward ramp rate and a downward ramp rate.

[0040] Optionally, the ramp-up rate of the virtual generator model may be determined as follows: (4) In the above formula: is the ramp-up rate of the virtual generator model, Is the serial number The upward climbing rate of the distributed generator, is the number of distributed generators in the current category.

[0041] Optionally, the ramp-down rate of the virtual generator model may be determined as follows: (5) In the above formula: is the ramp-down rate of the virtual generator model, Is the serial number The ramp-up rate of the distributed generators, is the number of distributed generators in the current category.

[0042] In one embodiment, the output upper and lower limit models of the virtual generator model may be calculated as follows: First, calculate the upper limit of the power output of the virtual generator model: Set the sequence number to The initial power output of the distributed generator is , the upper limit of power output is , for serial number Distributed generators at each dispatching time of the power grid The upper limit of power output Make corrections, including , the process is as follows: ① Order scheduling time ; ②If , is the time interval of the dispatching time, then the upper limit of the power output of the distributed generator at the dispatching time is ; ③If , then the upper limit of the power output of the distributed generator at the dispatching time is ; ④ Make , return to step 1), until all scheduling times are calculated, and all The upper limit of the power output of a distributed generator at each dispatching time ; Secondly, all The upper limit of the power output of a distributed generator at each dispatching time Add together to get the upper limit of the power output of the virtual generator model at each dispatching time: (6) Then, the lower limit of the power output of the virtual generator model is calculated as follows: Set the sequence number to The initial power output of the distributed generator is , the lower limit of power output is , for serial number Distributed generators at each dispatching time of the power grid The lower limit of power output Make corrections, including , the process is as follows: ① Order scheduling time ; ②If , then the lower limit of the power output of the distributed generator at the dispatching time is ; ③If , then the lower limit of the power output of the distributed generator at the dispatch time is ; ④ Make , return to step 1), until all scheduling times are calculated, and all The upper limit of the power output of a distributed generator at each dispatching time ; Finally, all I The upper limit of the power output of a distributed generator at each dispatching time Add together to get the upper limit of the power output of the virtual generator model at each dispatching time: (7) S3. Obtain a pre-built information-physical model; wherein the information-physical model includes the physical information of each distributed new energy source and the basic information of each aggregator.

[0043] In this solution, the physical information of distributed renewable energy includes: device ID, device name, total number of devices, device type and rated power; the basic information of the aggregator includes: aggregator ID, aggregator name, maximum power generation, total number of devices, power type and control type.

[0044] In one embodiment, the classification of distributed renewable energy sources according to the above step S1 includes distributed renewable energy sources of the first voltage level, aggregator distributed renewable energy sources and directly controlled renewable energy sources.

[0045] When electricity trading is conducted, distributed renewable energy is aggregated by aggregation service providers, such as aggregator distributed renewable energy; the remaining distributed renewable energy is directly controlled by the grid dispatching agency or directly controlled by system zone IV.

[0046] Then, the basic information attributes of distributed energy aggregators are as follows:

[0047] The physical information of distributed energy is as follows:

[0048] S4. According to the categories of distributed renewable energy, the virtual generator model is associated with the corresponding cyber-physical model to obtain various types of distributed renewable energy aggregation models.

[0049] Example 2 like Figure 3 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a distributed new energy aggregation model construction device, including: A first determination module is used to determine the category of each distributed renewable energy source; wherein the category of the distributed renewable energy source is divided according to voltage level and aggregator; The second determination module is used to determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model; A data acquisition module, used to acquire a pre-built information-physical model; wherein the information-physical model includes the physical information of each distributed new energy source and the basic information of each aggregator; The model generation module is used to associate the virtual generator model with the corresponding information-physical model according to the category of distributed renewable energy, so as to obtain various distributed renewable energy aggregation models.

[0050] In the second determination module, the output upper and lower limit models are determined as follows: Determine the time interval for scheduling moments; Determine the upward climbing rate and the upper limit of the electric output of each distributed energy source, as well as the downward climbing rate and the lower limit of the electric output of each distributed energy source; Determine the distributed energy power output at the last dispatching moment; According to the distributed energy power output at the previous scheduling moment, the time interval at the scheduling moment, the upward climbing rate and the upper limit of the power output, the upper limit of the power output at the current scheduling moment is corrected; according to the distributed energy power output at the previous scheduling moment, the time interval at the scheduling moment, the upward climbing rate and the lower limit of the power output, the lower limit of the power output at the current scheduling moment is corrected.

[0051] According to the distributed energy output at the last dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the upper limit of the output, the upper limit of the output at the current dispatching moment is corrected; according to the distributed energy output at the last dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the lower limit of the output, the lower limit of the output at the current dispatching moment is corrected, specifically including: If satisfied , then the upper limit of the power output at the current dispatch time is If satisfied , then the upper limit of the power output at the current dispatch time is ; If satisfied , then the lower limit of the power output at the current dispatch time is If satisfied , then the lower limit of the power output at the current dispatch time is ; in, It is the distributed energy power output at the last dispatching moment; is the time interval of the scheduling time; is the upward climbing rate of distributed energy i; The upper limit of the electric output of distributed energy; is the downward climbing rate of distributed energy i; It is the lower limit of the electric output of distributed energy.

[0052] Example 3 like Figure 3 As shown, the present invention also provides an electronic device 100 for implementing a distributed new energy aggregation model construction method; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0053] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a distributed new energy aggregation model construction method in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0054] The memory 101 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 (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0055] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0056] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a distributed new energy aggregation model construction method, and the processor 102 can execute the plurality of instructions to implement: Determine the categories of each distributed renewable energy source; wherein the categories of the distributed renewable energy sources are classified according to voltage levels and aggregators; Determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model; Acquire a pre-built information-physical model; wherein the information-physical model includes physical information of each distributed new energy source and basic information of each aggregator; According to the categories of distributed renewable energy, the virtual generator model is associated with the corresponding cyber-physical model to obtain various distributed renewable energy aggregation models.

[0057] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0058] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0060] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0061] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0062] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for constructing a distributed new energy aggregation model, characterized in that: The steps include: Determine the categories of each distributed renewable energy source; wherein the categories of the distributed renewable energy sources are classified according to voltage levels and aggregators; Determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model; Acquire a pre-built information-physical model; wherein the information-physical model includes physical information of each distributed new energy source and basic information of each aggregator; According to the categories of distributed renewable energy, the virtual generator model is associated with the corresponding cyber-physical model to obtain various distributed renewable energy aggregation models.

2. The distributed new energy aggregation model construction method according to claim 1 is characterized in that: The categories of distributed renewable energy are divided according to voltage levels and aggregators, including: Dividing the distributed renewable energy according to the voltage level to obtain the first voltage level distributed renewable energy and the second voltage level distributed renewable energy; The second voltage level distributed new energy is divided according to the aggregators to obtain aggregator distributed new energy and direct control condition new energy.

3. The distributed new energy aggregation model construction method according to claim 1 is characterized in that: The output upper and lower limit models are determined as follows: Determine the time interval for scheduling moments; Determine the upward climbing rate and the upper limit of the electric output of each distributed energy source, as well as the downward climbing rate and the lower limit of the electric output of each distributed energy source; Determine the distributed energy power output at the last dispatching moment; According to the distributed energy power output at the previous dispatching moment, the time interval of the dispatching moment, the upward climbing rate and the power output upper limit, the power output upper limit at the current dispatching moment is corrected; According to the distributed energy power output at the previous scheduling moment, the time interval of the scheduling moment, the upward climbing rate and the lower limit of the power output, the lower limit of the power output at the current scheduling moment is corrected.

4. The distributed new energy aggregation model construction method according to claim 3 is characterized in that: According to the distributed energy power output at the previous dispatching moment, the time interval of the dispatching moment, the upward climbing rate and the power output upper limit, the power output upper limit at the current dispatching moment is corrected; According to the distributed energy output at the previous dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the lower limit of the output, the lower limit of the output at the current dispatching moment is corrected, specifically including: If satisfied , then the upper limit of the power output at the current dispatch time is If satisfied , then the upper limit of the power output at the current dispatch time is ; If satisfied , then the lower limit of the power output at the current dispatch time is If satisfied , then the lower limit of the power output at the current dispatch time is ; in, It is the distributed energy power output at the last dispatching moment; is the time interval of the scheduling time; is the upward climbing rate of distributed energy i; The upper limit of the electric output of distributed energy; is the downward ramp rate of distributed energy i; It is the lower limit of the electric output of distributed energy.

5. The method for constructing a distributed new energy aggregation model according to claim 1, characterized in that: The cyber-physical model includes the physical information of each distributed renewable energy source and the basic information of each aggregator, specifically including: The physical information of distributed renewable energy includes: device ID, device name, total number of devices, device type and rated power; The basic information of the aggregator includes: aggregator ID, aggregator name, maximum power generation, total number of devices, power type and control type.

6. A distributed new energy aggregation model construction device, characterized in that: include: A first determination module is used to determine the category of each distributed renewable energy source; wherein the category of the distributed renewable energy source is divided according to voltage level and aggregator; The second determination module is used to determine the output model of each distributed renewable energy source, and aggregate the output models of each distributed renewable energy source belonging to the same category into a virtual generator model; wherein the output model includes a power model, a ramp rate model, and an output upper and lower limit model; A data acquisition module, used to acquire a pre-built information-physical model; wherein the information-physical model includes the physical information of each distributed new energy source and the basic information of each aggregator; The model generation module is used to associate the virtual generator model with the corresponding information-physical model according to the category of distributed renewable energy, so as to obtain various distributed renewable energy aggregation models.

7. The distributed new energy aggregation model construction device according to claim 6 is characterized in that: In the second determination module, the output upper and lower limit models are determined as follows: Determine the time interval for scheduling moments; Determine the upward climbing rate and the upper limit of the electric output of each distributed energy source, as well as the downward climbing rate and the lower limit of the electric output of each distributed energy source; Determine the distributed energy power output at the last dispatching moment; According to the distributed energy power output at the previous dispatching moment, the time interval of the dispatching moment, the upward climbing rate and the power output upper limit, the power output upper limit at the current dispatching moment is corrected; According to the distributed energy power output at the previous scheduling moment, the time interval of the scheduling moment, the upward climbing rate and the lower limit of the power output, the lower limit of the power output at the current scheduling moment is corrected.

8. The distributed new energy aggregation model construction device according to claim 7 is characterized in that: According to the distributed energy power output at the previous dispatching moment, the time interval of the dispatching moment, the upward climbing rate and the power output upper limit, the power output upper limit at the current dispatching moment is corrected; According to the distributed energy output at the previous dispatching moment, the time interval at the dispatching moment, the upward climbing rate and the lower limit of the output, the lower limit of the output at the current dispatching moment is corrected, specifically including: If satisfied , then the upper limit of the power output at the current dispatch time is If satisfied , then the upper limit of the power output at the current dispatch time is ; If satisfied , then the lower limit of the power output at the current dispatch time is If satisfied , then the lower limit of the power output at the current dispatch time is ; in, It is the distributed energy power output at the last dispatching moment; is the time interval of the scheduling time; is the upward climbing rate of distributed energy i; The upper limit of the electric output of distributed energy; is the downward ramp rate of distributed energy i; It is the lower limit of the electric output of distributed energy.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement the distributed new energy aggregation model construction method as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, it implements the distributed new energy aggregation model construction method as described in any one of claims 1 to 5.

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