CN113312748A - Online modeling method and system for load model - Google Patents
Online modeling method and system for load model Download PDFInfo
- Publication number
- CN113312748A CN113312748A CN202110429654.6A CN202110429654A CN113312748A CN 113312748 A CN113312748 A CN 113312748A CN 202110429654 A CN202110429654 A CN 202110429654A CN 113312748 A CN113312748 A CN 113312748A
- Authority
- CN
- China
- Prior art keywords
- load
- data
- model
- modeling
- power supply
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000004458 analytical method Methods 0.000 claims abstract description 32
- 230000003068 static effect Effects 0.000 claims abstract description 22
- 230000002776 aggregation Effects 0.000 claims abstract description 20
- 238000004220 aggregation Methods 0.000 claims abstract description 20
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 8
- 238000009826 distribution Methods 0.000 claims description 21
- 230000008859 change Effects 0.000 claims description 18
- 238000005259 measurement Methods 0.000 claims description 10
- 238000005094 computer simulation Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 10
- 239000000203 mixture Substances 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 7
- 238000004590 computer program Methods 0.000 description 7
- 238000004088 simulation Methods 0.000 description 7
- 238000001308 synthesis method Methods 0.000 description 6
- 238000012986 modification Methods 0.000 description 5
- 230000004048 modification Effects 0.000 description 5
- 230000002159 abnormal effect Effects 0.000 description 4
- 238000011835 investigation Methods 0.000 description 4
- 238000012545 processing Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 238000012937 correction Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013523 data management Methods 0.000 description 1
- 239000006185 dispersion Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000001960 triggered effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an online modeling method and system for a load model, and belongs to the technical field of power system simulation modeling. The method comprises the following steps: acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule, and generating classified load data; generating load modeling basic data; analyzing a power supply area network topological structure aiming at load modeling basic data, collecting and sorting all load data of the load stations collected at each moment, and comprehensively analyzing the types and the ratios of load equipment, the motor load ratio, static load frequency characteristics and the types and the ratios of distributed new energy; determining load model parameters by adopting an online load aggregation equivalent algorithm; and establishing a load model according to the load model parameters. The invention realizes the normalized load analysis and modeling work and improves the timeliness and the accuracy of load modeling.
Description
Technical Field
The present invention relates to the field of power system simulation modeling technology, and more particularly, to an online modeling method and system for a load model.
Background
The construction and development of the intelligent power grid put higher requirements on the accuracy of real-time simulation calculation analysis of the power system. In order to meet the requirements of operation and control of the smart power grid, the safety and stability analysis of the power system needs dynamic model parameters capable of reflecting the actual characteristics of the power grid more accurately, and a load model is one of the most critical simulation models.
However, due to the geographical dispersion and random time-varying property of the load model itself, it is difficult to obtain a comprehensive load model for reasonably describing each load node, so that it becomes an important factor influencing the improvement of the simulation accuracy of the power system.
In recent years, with the rapid development of load modeling research work at home and abroad, researchers have proposed that various measurement means are adopted for load modeling, and a parameter identification load modeling method based on an identification theory and a statistical comprehensive load modeling method based on survey statistics are also more researched methods. However, both modeling approaches suffer from inherent drawbacks and modeling difficulties. The problem of the parameter identification method is that the physical significance of model parameters is not clear, and the generated load model can only correspond to the identified measured sample in principle, and when the model is applied to different transformer substations and even different time periods of the same transformer substation, the problems of insufficient model coverage and weak adaptability of the established model can be faced, so that the model cannot be applied to the simulation calculation of the power system.
The method based on survey statistics faces two major problems in practical application: firstly, the time and labor spent on investigation and statistics are huge, and moreover, due to the limitation of numerous conditions, the accuracy of investigation results is difficult to ensure; and secondly, the load composition of the power utilization industry and the power utilization industry composition survey of the transformer substation can only be static, the composition characteristics of the actual comprehensive load change along with time and have randomness, the characteristics of the change along with the time are hardly reflected on the basis of the survey statistics, the time-varying property of the load cannot be considered, and the dynamic process of the load cannot be accurately simulated.
Disclosure of Invention
In order to solve the above problems, the present invention provides an online modeling method for a load model, comprising:
acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule, and generating classified load data;
carrying out integrity analysis on the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data to generate load modeling basic data;
analyzing a power supply area network topological structure aiming at load modeling basic data, collecting and sorting load data of load stations acquired at each moment, and determining the type, the proportion, the motor load proportion, the static load frequency characteristic, the type and the proportion of distributed new energy;
calculating the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the distributed new energy type and the occupation ratio of the load equipment by adopting an online load aggregation equivalent algorithm, and determining load model parameters;
and establishing a load model according to the load model parameters.
Optionally, the multi-source load data includes: model data, network topology data and real-time data of a technical support system, a power distribution automation system transformer substation and a power distribution network in a target power supply area network, and industry load classification data and load actual measurement data of a marketing comprehensive data platform.
Optionally, after integrity analysis is performed on the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data, if the data are incomplete, an alarm is sent out and the data are supplemented, and load modeling basic data is generated for the supplemented data.
Optionally, the load characteristic variation trend includes: the method comprises the steps of load classification distribution trend, motor load proportion time interval change trend, static load frequency characteristic change trend and analysis of distributed new energy trend.
Optionally, the load model is established, specifically: and carrying out comprehensive aggregation equivalence on the load model parameters to generate a load model.
Optionally, after the load model is established, a model file in a PSD-BPA format is created, and the load model parameters are generated into a model file conforming to the PSD-BPA data format and stored in a database.
Optionally, the load model parameters are model parameters of different load nodes;
the load node is a substation.
The invention also provides an online modeling system for the load model, which comprises:
the data acquisition unit is used for acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule and generating classified load data;
the analysis unit is used for carrying out integrity analysis on the power grid model data, the network topology data, the load active power real-time data, the distributed power supply real-time data and the classified load data to generate load modeling basic data;
the computing unit analyzes a power supply area network topological structure aiming at load modeling basic data, collects and arranges the load data of the load stations collected at each moment, and determines the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the type and the occupation ratio of distributed new energy;
the parameter determination unit is used for calculating the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the distributed new energy type and the occupation ratio of the load equipment by adopting an online load aggregation equivalent algorithm to determine load model parameters;
and the modeling unit is used for establishing a load model according to the load model parameters.
Optionally, the multi-source load data includes: model data, network topology data and real-time data of a technical support system, a power distribution automation system transformer substation and a power distribution network in a target power supply area network, and industry load classification data and load actual measurement data of a marketing comprehensive data platform.
Optionally, after integrity analysis is performed on the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data, if the data are incomplete, an alarm is sent out and the data are supplemented, and load modeling basic data is generated for the supplemented data.
Optionally, the load characteristic variation trend includes: the method comprises the steps of load classification distribution trend, motor load proportion time interval change trend, static load frequency characteristic change trend and analysis of distributed new energy trend.
Optionally, the load model is established, specifically: and carrying out comprehensive aggregation equivalence on the load model parameters to generate a load model.
Optionally, after the load model is established, a model file in a PSD-BPA format is created, and the load model parameters are generated into a model file conforming to the PSD-BPA data format and stored in a database.
Optionally, the load model parameters are model parameters of different load nodes;
the load node is a substation.
The invention realizes the on-line modeling function of the dynamic load model, achieves the purpose of modeling the load node in quasi-real time, full coverage and more fit with the actual operation condition, improves the accuracy of the simulation calculation of the power grid, and ensures the safe, reliable and economic operation of the power grid;
the invention realizes the normalized load analysis and modeling work, and improves the timeliness and the accuracy of load modeling;
the invention changes the traditional dependence on manual census, typical load station detailed investigation, data arrangement and error correction, and effectively reduces the implementation difficulty and workload of load analysis and modeling.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of a method of the present invention;
FIG. 3 is a flow chart of integrity check of data according to the method of the present invention;
fig. 4 is a block diagram of the system of the present invention.
Detailed Description
The exemplary embodiments of the present invention will now be described with reference to the accompanying drawings, however, the present invention may be embodied in many different forms and is not limited to the embodiments described herein, which are provided for complete and complete disclosure of the present invention and to fully convey the scope of the present invention to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the accompanying drawings is not intended to be limiting of the invention. In the drawings, the same units/elements are denoted by the same reference numerals.
Unless otherwise defined, terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Further, it will be understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense.
The invention provides an online modeling method for a load model, as shown in fig. 1, comprising the following steps:
acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule, and generating classified load data;
carrying out integrity analysis on the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data to generate load modeling basic data;
analyzing a power supply area network topological structure aiming at load modeling basic data, collecting and sorting load data of load stations acquired at each moment, and determining the type, the proportion, the motor load proportion, the static load frequency characteristic, the type and the proportion of distributed new energy;
calculating the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the distributed new energy type and the occupation ratio of the load equipment by adopting an online load aggregation equivalent algorithm, and determining load model parameters;
and establishing a load model according to the load model parameters.
Wherein, multisource load data includes: model data, network topology data and real-time data of a technical support system, a power distribution automation system transformer substation and a power distribution network in a target power supply area network, and industry load classification data and load actual measurement data of a marketing comprehensive data platform.
After integrity analysis is carried out on power grid model data, network topology data, load active power real-time data, distributed power supply real-time data and classified load data, if the data are not complete, an alarm is sent out, the data are supplemented, and load modeling basic data are generated for the supplemented data.
Wherein, the load characteristic variation trend includes: the method comprises the steps of load classification distribution trend, motor load proportion time interval change trend, static load frequency characteristic change trend and analysis of distributed new energy trend.
The method comprises the following steps of establishing a load model, specifically: and carrying out comprehensive aggregation equivalence on the load model parameters to generate a load model.
After the load model is built, a model file in a PSD-BPA format is created, and the load model parameters are generated into the model file which conforms to the PSD-BPA data format and are stored in a database.
Wherein, the load model parameters are model parameters of different load nodes;
the load node is a substation.
The invention is further illustrated by the following examples:
as shown in fig. 2, the system collects model data, network topology data and real-time data of the scheduling technology support system, the distribution automation system substation and the distribution network on line; acquiring the industry load classification and load actual measurement data of the marketing comprehensive data platform on line;
automatically classifying the marketing load classification into a modeling system load classification; setting a load composition identification rule through a data management module, and automatically matching corresponding load classification according to the rule aiming at load data which cannot distinguish load composition;
checking the integrity of the power supply area network topology data, analyzing the power supply area network topology data and the switch state, checking the integrity of the power supply area network topology data, triggering an alarm for abnormal conditions such as data loss and the like, and prompting a worker to confirm completion; the integrity of the model data is checked through a load modeling data integrity checking module, the model data is analyzed, the integrity of the model data is checked, an alarm is triggered on abnormal conditions such as data loss and the like, and workers are prompted to confirm completion;
analyzing the load characteristic change trend, analyzing the load classification distribution condition, analyzing the motor load proportion time interval change trend, analyzing the static load frequency characteristic change trend, analyzing the distributed new energy trend by using a statistical synthesis method according to the collected model data, network topology data, load industry classification and load actual measurement data, and managing and storing the comprehensive load model parameters;
creating a unified comprehensive load model, and carrying out comprehensive aggregation equivalence on load model parameters of different load nodes based on the load model parameters of the load nodes to form a comprehensive load model of the whole network, the whole province and the whole city; creating a model file in a PSD-BPA format through a load modeling module, automatically forming a model file which accords with the PSD-BPA data format by using the formed load model parameters, and storing the model file in a database for offline and online simulation calculation;
the load node is a 220kV substation.
Creating a load composition component definition table, defining load components and ratios, setting different load components and ratios according to different load use scenes, and automatically matching the load components and ratios of the scenes; setting an identification rule aiming at load data which cannot distinguish load constitution, and automatically matching corresponding load classification according to the rule; a marketing load classification matching function, which is used for actively classifying the target marketing load and classifying the load of the load modeling system on line based on the operation data; establishing a marketing load classification and load classification mapping relation of a load modeling system, and automatically matching the load classification of the load modeling system according to the acquired marketing load classification; the load classification of the load analysis and modeling system comprises four major categories of an industrial category, a commercial category, a residential category and an agricultural category, each major category is divided into specific load subtypes, and the load of the benchmarking system is automatically classified according to the industrial load classification.
As shown in fig. 3, checking the integrity of the data includes: calling a relational database interface, and reading model data, network topology data, switch state and load actual measurement data of a relational database; analyzing the power supply area network topology data according to the network topology data and the switch state, and checking the integrity of the power supply area topology structure; verifying the integrity of data required for generating the comprehensive load model, wherein the verifying the data comprises: transformer parameters, line parameters, reactive compensation parameters, distributed new energy parameters and load parameters; checking the limit value of the parameter for generating the comprehensive load model; and (4) triggering an alarm for abnormal conditions such as data loss and the like, and prompting a worker to confirm completion.
The modeling process comprises the following steps: firstly, modeling comprehensive loads of all stations, and carrying out comprehensive aggregation equivalence on load model parameters of 220kV transformer substations by utilizing a statistical synthesis method to form the load model parameters of all 220kV transformer substations; then, modeling the uniform comprehensive load of the whole city, and performing comprehensive aggregation equivalence on the load model parameters of all 220kV transformer substations in one city by using a statistical synthesis method to form uniform comprehensive load model parameters of the whole city; then, modeling the uniform comprehensive load of the whole province, and performing comprehensive aggregation equivalence on the load model parameters of each city of the province by using a statistical synthesis method to form the uniform comprehensive load model parameters of the whole province; then, modeling the whole network unified comprehensive load, and carrying out comprehensive aggregation equivalence on the load model parameters of each province by using a statistical synthesis method to form the whole network unified comprehensive load model parameters; and finally, automatically generating load model parameters PSD-BPA format data, and automatically forming a model which accords with the PSD-BPA data format by using the formed load model parameters.
The present invention also proposes an online modeling system 200 for a load model, as shown in fig. 4, comprising:
the data acquisition unit 201 is used for acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule and generating classified load data;
the analysis unit 202 is used for carrying out integrity analysis on the power grid model data, the network topology data, the load active power real-time data, the distributed power supply real-time data and the classified load data to generate load modeling basic data;
the calculation unit 203 analyzes the topological structure of the power supply area network aiming at the load modeling basic data, collects and arranges the load data of the load stations collected at each moment, and determines the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the type and the occupation ratio of the distributed new energy;
the parameter determination unit 204 is used for calculating the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the distributed new energy type and the occupation ratio of the load equipment by adopting an online load aggregation equivalent algorithm to determine load model parameters;
the modeling unit 205 builds a load model based on the load model parameters.
Wherein, multisource load data includes: model data, network topology data and real-time data of a technical support system, a power distribution automation system transformer substation and a power distribution network in a target power supply area network, and industry load classification data and load actual measurement data of a marketing comprehensive data platform.
After integrity analysis is carried out on power grid model data, network topology data, load active power real-time data, distributed power supply real-time data and classified load data, if the data are not complete, an alarm is sent out, the data are supplemented, and load modeling basic data are generated for the supplemented data.
Wherein, the load characteristic variation trend includes: the method comprises the steps of load classification distribution trend, motor load proportion time interval change trend, static load frequency characteristic change trend and analysis of distributed new energy trend.
The method comprises the following steps of establishing a load model, specifically: and carrying out comprehensive aggregation equivalence on the load model parameters to generate a load model.
After the load model is built, a model file in a PSD-BPA format is created, and the load model parameters are generated into the model file which conforms to the PSD-BPA data format and are stored in a database.
Wherein, the load model parameters are model parameters of different load nodes;
the load node is a substation.
The data identification module is in signal connection with the load modeling data integrity checking module, the load modeling data integrity checking module is in signal connection with the load analysis module, and the load analysis module is in signal connection with the load modeling module.
The data identification module has the marketing load classification automatic matching function, and actively classifies the marketing load and the modeling system load on line; the system also has the functions of identifying and defining load composition components, sets identification rules aiming at load data which cannot distinguish load composition, and automatically matches corresponding load classification according to the rules;
the load modeling data integrity checking module is provided with a typical checking strategy, realizes the integrity checking function of model data, network topology data and real-time data in a substation time-sharing manner, triggers an alarm for abnormal conditions such as data loss and the like, and prompts workers to confirm completion;
the load analysis module analyzes the topological data of the power supply area network, collects and arranges all the load data of the load station collected at each moment by adopting a statistical synthesis method, and comprehensively analyzes the load composition, the motor load ratio, the static load frequency characteristic and the change trend of the distributed new energy;
the load modeling module adopts an online load aggregation equivalence calculation algorithm, the substations classify in a time-sharing way to perform comprehensive aggregation equivalence, and the comprehensive load models of all load nodes are obtained through respective calculation; the load modeling module is loaded with a power system analysis software tool for automatically generating a PSD-BPA format model file.
The invention realizes the on-line modeling function of the dynamic load model, achieves the purpose of modeling the load node in quasi-real time, full coverage and more fit with the actual operation condition, improves the accuracy of the simulation calculation of the power grid, and ensures the safe, reliable and economic operation of the power grid;
the invention realizes the normalized load analysis and modeling work, and improves the timeliness and the accuracy of load modeling;
the invention changes the traditional dependence on manual census, typical load station detailed investigation, data arrangement and error correction, and effectively reduces the implementation difficulty and workload of load analysis and modeling.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the invention can be realized by adopting various computer languages, such as object-oriented programming language Java and transliterated scripting language JavaScript.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (14)
1. An online modeling method for a load model, the method comprising:
acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule, and generating classified load data;
carrying out integrity analysis on the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data to generate load modeling basic data;
analyzing a power supply area network topological structure aiming at load modeling basic data, collecting and sorting load data of load stations acquired at each moment, and determining the type, the proportion, the motor load proportion, the static load frequency characteristic, the type and the proportion of distributed new energy;
calculating the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the distributed new energy type and the occupation ratio of the load equipment by adopting an online load aggregation equivalent algorithm, and determining load model parameters;
and establishing a load model according to the load model parameters.
2. The method of claim 1, the multi-source load data, comprising: model data, network topology data and real-time data of a technical support system, a power distribution automation system transformer substation and a power distribution network in a target power supply area network, and industry load classification data and load actual measurement data of a marketing comprehensive data platform.
3. The method according to claim 1, wherein after integrity analysis is performed on the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data, if the data are not complete, an alarm is sent out and the data are supplemented, and load modeling basic data is generated on the supplemented data.
4. The method of claim 1, the load characteristic trend, comprising: the method comprises the steps of load classification distribution trend, motor load proportion time interval change trend, static load frequency characteristic change trend and analysis of distributed new energy trend.
5. The method according to claim 1, wherein the establishing of the load model specifically comprises: and carrying out comprehensive aggregation equivalence on the load model parameters to generate a load model.
6. The method of claim 1, wherein after the load model is built, a model file in a PSD-BPA format is created, and the load model parameters are generated into a model file conforming to the PSD-BPA data format and stored in a database.
7. The method of claim 1, the load model parameters are model parameters of different load nodes;
the load node is a transformer substation.
8. An online modeling system for a load model, the system comprising:
the data acquisition unit is used for acquiring multi-source load data of a target power supply area network, classifying the multi-source load data according to a preset identification rule and generating classified load data;
the analysis unit is used for carrying out integrity analysis on the power grid model data, the network topology data, the load active power real-time data, the distributed power supply real-time data and the classified load data to generate load modeling basic data;
the computing unit analyzes a power supply area network topological structure aiming at load modeling basic data, collects and arranges the load data of the load stations collected at each moment, and determines the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the type and the occupation ratio of distributed new energy;
the parameter determination unit is used for calculating the type, the occupation ratio, the motor load occupation ratio, the static load frequency characteristic, the distributed new energy type and the occupation ratio of the load equipment by adopting an online load aggregation equivalent algorithm to determine load model parameters;
and the modeling unit is used for establishing a load model according to the load model parameters.
9. The system of claim 8, the multi-source load data, comprising: model data, network topology data and real-time data of a technical support system, a power distribution automation system transformer substation and a power distribution network in a target power supply area network, and industry load classification data and load actual measurement data of a marketing comprehensive data platform.
10. The system of claim 8, wherein after integrity analysis of the power grid model data, the network topology data, the real-time load active power data, the real-time distributed power supply data and the classified load data is performed, if the data is incomplete, an alarm is issued and the data is supplemented, and load modeling basic data is generated for the supplemented data.
11. The system of claim 8, the load characteristic trend, comprising: the method comprises the steps of load classification distribution trend, motor load proportion time interval change trend, static load frequency characteristic change trend and analysis of distributed new energy trend.
12. The system according to claim 8, wherein the establishing of the load model specifically comprises: and carrying out comprehensive aggregation equivalence on the load model parameters to generate a load model.
13. The system of claim 8, wherein after the load model is built, a model file in a PSD-BPA format is created, and the load model parameters are generated into a model file conforming to the PSD-BPA data format and stored in a database.
14. The system of claim 8, the load model parameters are model parameters of different load nodes;
the load node is a transformer substation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110429654.6A CN113312748A (en) | 2021-04-21 | 2021-04-21 | Online modeling method and system for load model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110429654.6A CN113312748A (en) | 2021-04-21 | 2021-04-21 | Online modeling method and system for load model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113312748A true CN113312748A (en) | 2021-08-27 |
Family
ID=77372743
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110429654.6A Pending CN113312748A (en) | 2021-04-21 | 2021-04-21 | Online modeling method and system for load model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113312748A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113987848A (en) * | 2021-12-28 | 2022-01-28 | 中国电力科学研究院有限公司 | Intelligent load composition identification and accurate load modeling method and system |
-
2021
- 2021-04-21 CN CN202110429654.6A patent/CN113312748A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113987848A (en) * | 2021-12-28 | 2022-01-28 | 中国电力科学研究院有限公司 | Intelligent load composition identification and accurate load modeling method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110707692A (en) | Online load analysis and modeling system and method for power system | |
| CN111815132B (en) | Network security management information publishing method and system for power monitoring system | |
| CN106960103B (en) | Automatic generation method and device of power grid graph | |
| CN108345739B (en) | Method for identifying CIME and SVG model file information to realize protection fixed value on-line check function and obtain model | |
| AU2022204116A1 (en) | Verification method for electrical grid measurement data | |
| CN107797910A (en) | A kind of evaluation method of dispatch automated system software quality | |
| CN102931625A (en) | Online state maintenance intelligent decision analysis device used for relay protection device, and signal processing method and application thereof | |
| CN109742788A (en) | A kind of grid-connected Performance Evaluating Indexes modification method of new energy power station | |
| CN114548800A (en) | Future-state power grid maintenance risk identification method and device based on power grid knowledge graph | |
| CN113033617A (en) | Deep mining analysis method based on line loss data of big data transformer area | |
| CN113312748A (en) | Online modeling method and system for load model | |
| CN114529166A (en) | Power distribution network operation safety risk early warning method and system | |
| CN110490220A (en) | A kind of bus load discrimination method and system | |
| CN111552686B (en) | Power data quality assessment method and device | |
| Popovics et al. | ISA standard simulation model generation supported by data stored in low level controllers | |
| CN107146166B (en) | Distribution network planning auxiliary system | |
| CN107169180A (en) | A kind of power grid regulation model center system of two-stage deployment | |
| CN112467806B (en) | Method and system for determining power grid operation state based on DMN power grid regulation and control rule | |
| CN114205355A (en) | Method and system for testing performance of auxiliary equipment of power transformation gateway | |
| CN103166322A (en) | Monitoring method and system of under frequency load shedding device | |
| CN108830744B (en) | Dynamic verification method for external network equivalent model of designated area in power system | |
| CN110928855A (en) | Method and system for establishing power grid measurement database | |
| CN111310855B (en) | Multi-data source planning state data fusion method based on MapReduce | |
| Wang et al. | Research on line loss prediction model based on clustering and multi prediction model fusion | |
| CN111176257B (en) | New energy control system parameter debugging method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |