A method and system for comparing an electricity market economic model with a power grid model

Abstract

The application provides a comparison method of an electricity market economic model and a power grid model, comprising the following steps: extracting keywords from market economic model and power grid model names based on a semantic learning method; and comparing data in the market economic model and the power grid model based on the keywords and pre-set non-keyword information. The method solves the problem that the models of the medium and long-term market, the spot market and the safe operation of the power grid are inconsistent, which may cause the problem that the medium and long-term transaction and the spot transaction, safety checking and other businesses cannot be carried out seamlessly.

Description

Technical Field

[0001] This invention belongs to the fields of power automation and power market. It relates to a method and system for comparing data from power market economic models and power grid models. Background Technology

[0002] Electricity market economic models are the most important foundational data for carrying out medium- and long-term electricity market transactions, settlements, and other operational activities. Power grid models are important foundational data for dispatching operations such as safe production and spot market operations. They are the core model architectures for realizing electricity market operations.

[0003] With the ongoing reform of the electricity spot market, spot market operations and medium- and long-term market operations complement each other. Spot market operations are mainly based on the physical model of the power grid, while medium- and long-term market operations are based on the economic model of the electricity market. A consistent mapping relationship must be established between the two models to ensure the stable operation of electricity market operations. However, the existing mapping management is inefficient and has a high error rate. With a large number of market participants entering the market, it is difficult to support the operation through traditional management methods. Summary of the Invention

[0004] To address the shortcomings of existing mapping management methods, which primarily rely on manual comparison, mapping, and management, making it difficult for business personnel to support operations with the entry of a large number of market participants, this invention provides a method and system for comparing the economic model of the electricity market with the power grid model. This solves the problem that inconsistencies between the models of the medium- and long-term market, the spot market, and the safe operation of the power grid may prevent the seamless operation of medium- and long-term transactions, spot transactions, and safety verification.

[0005] This invention provides a method for comparing an economic model of the electricity market with a power grid model, the specific steps of which are as follows:

[0006] Keyword extraction from the names of market economy models and power grid models based on semantic learning methods;

[0007] The data in the market economy model and the power grid model are compared based on the keywords and pre-defined non-keyword information.

[0008] Preferably, the semantic learning-based method for extracting keywords from the names of the market economy model and the power grid model includes:

[0009] Based on pre-built keyword data information, keywords are extracted from the names of the market economy model data and the power grid physical model data, and organized according to a set format to obtain the keywords corresponding to each market economy model data and each power grid physical model data.

[0010] Preferably, the comparison of data in the market economy model and the power grid model based on the keywords and pre-defined non-keyword information includes:

[0011] Based on a pre-set matching ratio: the keywords of each market economy model data are compared with the corresponding keywords of each power grid physical model data, and the non-keywords of each market economy model data are matched with the non-keywords in each power grid physical model data. If the set matching ratio is reached, it is considered as passing; otherwise, it is considered as failing.

[0012] Based on the matching ratio, a mapping identifier is constructed between the market economy model and the power grid physical model data for keywords and non-keywords that meet the matching ratio.

[0013] For keywords and non-keywords that do not meet the matching ratio, a comparison is performed again based on the preset correlation information between the market economy model data and the power grid physical model data; if there are still data that fail to meet the matching ratio, the data is considered to be missing information, and a mapping identifier between the market economy model data and the power grid physical model data is constructed based on the human-machine interface.

[0014] Preferably, the step of constructing the mapping identifier between the market economy model and the power grid physical model data based on the human-machine interface includes:

[0015] Data that is highly relevant to missing information stored in the database is highlighted on the front-end system interface, generating missing data.

[0016] The missing data is supplemented with information, and the supplemented information is stored in the database;

[0017] Map the supplemented and completed market economy model data to the power grid physical model data;

[0018] The database includes a management database that unifies the storage of electricity market economic model data and power grid model data.

[0019] Preferably, the correlation information includes: model data information that matches the matching ratio with the market economy model data and the power grid physical model data.

[0020] Preferably, the matching ratio set for the keywords is ≥80% similarity.

[0021] The preferred method further includes: periodic self-checks;

[0022] The periodic self-check includes: setting the check time frequency and alarm level according to the data hierarchy classification relationship;

[0023] The comparison system's background program automatically checks whether the data in the market economy model and the data in the power grid model meet the matching ratio requirements;

[0024] An alarm will be issued if the data in the market economy model and the power grid model are updated and compared with the data in the database, and the results show that the matching ratio does not meet the requirements.

[0025] The alarm notification methods include: notifying the user via front-end interface, SMS, email or other messaging methods;

[0026] The data hierarchy classification relationships include: market members, generators, power plants, loads, tie lines, metering points, and gateways.

[0027] Based on the same inventive concept, this invention provides a comparison system for data from an economic model of the electricity market and a power grid model, comprising: a keyword module and a comparison module;

[0028] The keyword module is used to extract keywords from the names of market economy models and power grid models based on semantic learning methods.

[0029] The comparison module is used to compare data in the market economy model and the power grid model based on the keywords and pre-set non-keyword information.

[0030] Preferably, the keyword module includes: a keyword acquisition unit;

[0031] The keyword acquisition unit is used to extract keywords from the names of the market economy model data and the power grid physical model data based on pre-constructed keyword data information, and organize them according to a set format to obtain the keywords corresponding to each market economy model data and each power grid physical model data.

[0032] Preferably, the comparison module includes: a matching unit, a pass unit, and a fail unit;

[0033] The matching unit is used to compare the keywords of each market economy model data with the keywords corresponding to each power grid physical model data based on a pre-set matching ratio, and to match the non-keywords of each market economy model data with the non-keywords in each power grid physical model data. If the set matching ratio is reached, it is considered to pass; otherwise, it is considered to fail.

[0034] The passing unit is used to construct a mapping identifier between the market economy model and the power grid physical model data based on the matching ratio, for keywords and non-keywords that meet the matching ratio.

[0035] The rejection unit is used to compare keywords and non-keywords that do not meet the matching ratio again based on the preset correlation information between the market economy model data and the power grid physical model data; if there are still rejections, they are data with missing information, and a mapping identifier between the market economy model and the power grid physical model data is constructed based on the human-machine interface.

[0036] Compared with the closest existing technology, the present invention has the following beneficial effects:

[0037] This invention provides a method for comparing an economic model of the electricity market with a power grid model, comprising: extracting keywords from the names of the market economic model and the power grid model based on a semantic learning method; and comparing the data in the market economic model and the power grid model based on the keywords and pre-defined non-keyword information. This method solves the problem that inconsistencies between models of the medium- and long-term market, the spot market, and power grid safety operation may prevent seamless integration of medium- and long-term trading with spot trading, safety verification, and other operations. Attached Figure Description

[0038] Figure 1 A flowchart of a method for comparing an economic model of the electricity market and a power grid model provided by this invention.

[0039] Figure 2 The business process diagram provided for this invention;

[0040] Figure 3 This is a schematic diagram of the comparison method provided by the present invention;

[0041] Figure 4 A basic structural diagram of a comparison system between an economic model of the electricity market and a power grid model provided by the present invention;

[0042] Figure 5 A detailed structural diagram of a comparison system between an economic model of the electricity market and a power grid model provided by this invention. Detailed Implementation

[0043] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0044] Example 1:

[0045] This embodiment provides a method and system for comparing an economic model of the electricity market with a power grid model. The business process diagram for the comparison is as follows: Figure 2 As shown in the diagram, the comparison method is illustrated below. Figure 3As shown, after the comparison begins, the model being compared generates name keywords and performs the comparison. The keyword data information consists of keywords corresponding to historical market economy model data and keywords corresponding to each power grid physical model data. Models that pass the comparison continue to be mapped to key and non-key information, and then the mapping information is recorded in the database. Models that fail the comparison are then manually compared; models that still fail are generated to supplement missing model information before being mapped, and then the mapping information is recorded in the database. The specific technical solution is as follows: Figure 1 As shown:

[0046] Step 1: Extract keywords from the names of market economy models and power grid models using semantic learning methods;

[0047] Step 2: Compare the data in the market economy model and the power grid model based on the keywords and pre-defined non-keyword information.

[0048] Step 1: Extract keywords from the names of the market economy model and the power grid model based on semantic learning methods.

[0049] (1) Generate keywords for the names of the market economy model and the power grid physical model.

[0050] The background algorithm automatically extracts keywords from the names of the market economy model and the power grid physical model, and stores them in the model name keyword data information table, separated by ";". Taking the generator name "A Group B Second Thermal Power Plant #1 Unit" in the model as an example, the background algorithm automatically splits it into six name keywords: "A", "B", "Second", "Thermal", "Generation", and "#1".

[0051] Step 2: Compare the data in the market economy model and the power grid model based on the keywords and pre-defined non-keyword information:

[0052] (2) Comparison of market economy model and power grid physical model.

[0053] First, the two models can be automatically compared through a background program. This comparison is based on a comprehensive analysis of multiple information, including name keywords, type (e.g., power plant type, generator type, electricity user type), region (e.g., power plant region, electricity user region), capacity (e.g., installed capacity, transformer capacity), and voltage level, to determine whether a model passes the comparison. The program can be configured to specify the critical information required for comparison, such as name and capacity. For name keyword comparison, the pass rate can be configured; for example, 80% or more compliance is considered passing. The pass rate for other non-critical information combinations can also be set. Finally, if a model fails the comparison, the system can find and display several highly relevant models, allowing manual selection of a comparison method.

[0054] (3) Assisted generation of missing model information.

[0055] If any information models still fail the comparison, the system's backend program will identify them as missing model information, highlight the model information of the comparison end on the front-end system interface, and automatically generate the model data of the missing end. Business personnel can manually adjust relevant information such as name, type, region, capacity, and voltage level. After confirmation, the missing model information can be supplemented and then stored in the unified model database.

[0056] (4) Mapping between market economy model and power grid physical model.

[0057] By comparing or supplementing complete model information, unique identifiers, such as IDs or codes, can be set for the mapping between two models in the program. Based on the generation rules of the unique identifiers, a mapping relationship is automatically established for the two types of models. The mapping relationship is displayed through the system's front-end interface, and management functions are provided to adjust the model mapping relationship. After confirmation, the model mapping relationship information can be automatically stored in the database.

[0058] Take a generator as an example:

[0059] Generator information in the electricity market economic model

[0060]

[0061] Generator information in the power grid model

[0062]

[0063]

[0064] Generator model mapping information

[0065]

[0066] (5) Regular self-inspection

[0067] The tool also provides a configurable automatic check function. Based on the model hierarchy or classification relationship, such as market members, generators, loads, tie lines, etc., the check time frequency, alarm level, etc. can be set respectively. The background program automatically checks whether the comparison information of the two models is correct. If an inconsistency occurs after updating either model, an alarm will be issued. The alarm can be notified to the user through the front-end interface, SMS, email or other message methods.

[0068] The present invention has been described with reference to preferred embodiments. It should be understood that the above embodiments do not limit the present invention in any way. All technical solutions obtained by equivalent substitution or equivalent transformation fall within the protection scope of the present invention.

[0069] Example 2:

[0070] Based on the same inventive concept, this invention also provides a comparison system for data from an economic model of the power market and a power grid model. Since the principle of these devices in solving technical problems is similar to a test method for the fault ride-through performance of a reactive power compensation device, the repetitive parts will not be described again.

[0071] The following is combined with Figure 4 Basic structure diagram of the system and Figure 5 The system's detailed structure diagram is introduced, which is a comparison system for data from an economic model of the electricity market and a power grid model, including: a keyword module and a comparison module;

[0072] The keyword module is used to extract keywords from the names of market economy models and power grid models based on semantic learning methods.

[0073] The comparison module is used to compare data in the market economy model and the power grid model based on the keywords and pre-set non-keyword information.

[0074] The keyword module includes: a keyword acquisition unit;

[0075] The keyword acquisition unit is used to extract keywords from the names of the market economy model data and the power grid physical model data based on pre-constructed keyword data information, and organize them according to a set format to obtain the keywords corresponding to each market economy model data and each power grid physical model data.

[0076] The comparison module includes: a matching unit, a pass unit, and a fail unit;

[0077] The matching unit is used to compare the keywords of each market economy model data with the keywords corresponding to each power grid physical model data based on a pre-set matching ratio, and to match the non-keywords of each market economy model data with the non-keywords in each power grid physical model data. If the set matching ratio is reached, it is considered to pass; otherwise, it is considered to fail.

[0078] The passing unit is used to construct a mapping identifier between the market economy model and the power grid physical model data based on the matching ratio, for keywords and non-keywords that meet the matching ratio.

[0079] The rejection unit is used to compare keywords and non-keywords that do not meet the matching ratio again based on the preset correlation information between the market economy model data and the power grid physical model data; if there are still rejections, they are data with missing information, and a mapping identifier between the market economy model and the power grid physical model data is constructed based on the human-machine interface.

[0080] The non-passing unit includes: a missing data sub-unit, a supplementary sub-unit, and a mapping sub-unit;

[0081] The missing data subunit is used to generate missing data by highlighting data information that is highly relevant to the missing information stored in the database on the front-end system interface.

[0082] The supplementary subunit is used to supplement the missing data and store the supplemented information in the database;

[0083] The mapping subunit is used to map the supplemented and completed market economy model data with the power grid physical model data;

[0084] The database includes a management database that unifies the storage of electricity market economic model data and power grid model data.

[0085] The system also includes: a periodic self-test module;

[0086] The periodic self-test module is used to perform periodic self-tests on the comparison system. If the data update of either end of the data in the market economy model or the data in the power grid model is compared with the data in the database and a situation is found that does not meet the matching ratio requirements, an alarm will be issued.

[0087] The alarm notification methods include: front-end interface, SMS, email or other messaging methods to notify the user.

[0088] The periodic self-test module includes: a test setting unit, a comparison information test unit, and an alarm unit;

[0089] The inspection setting unit is used to set the inspection time frequency and alarm level according to the data hierarchy classification relationship;

[0090] The comparison information checking unit is used to automatically check whether the data in the market economy model and the data in the power grid model meet the matching ratio requirements of the comparison system background program.

[0091] The alarm unit is used to issue an alarm when the data in the market economy model and the data in the power grid model are updated and compared with the data in the database, and the results show that the matching ratio does not meet the requirements.

[0092] The data hierarchy classification relationship includes: market members, generators, power plants, loads, tie lines, metering points, and gateways.

[0093] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can 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, etc.) containing computer-usable program code.

[0094] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0095] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0097] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application and not to limit its protection scope. Although this application has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this application, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the application, but these changes, modifications or equivalent substitutions are all within the protection scope of the claims pending approval.

Claims

1. A method for comparing data from an electricity market economic model and a power grid model, characterized in that, include: Keyword extraction from the names of market economy models and power grid models based on semantic learning methods; The data in the market economy model and the power grid model are compared based on the keywords and pre-defined non-keyword information; The comparison of data in the market economy model and the power grid model based on the keywords and pre-defined non-keyword information includes: Based on a pre-set matching ratio: the keywords of each market economy model data are compared with the corresponding keywords of each power grid physical model data, and the non-keywords of each market economy model data are matched with the non-keywords in each power grid physical model data. If the set matching ratio is reached, it is considered as passing; otherwise, it is considered as failing. Based on the matching ratio, a mapping identifier is constructed between the market economy model and the power grid physical model data for keywords and non-keywords that meet the matching ratio. For keywords and non-keywords that do not meet the matching ratio, a comparison is performed again based on the preset correlation information between the market economy model data and the power grid physical model data; if there are still data that fail to meet the matching ratio, the data is considered to be missing information, and a mapping identifier between the market economy model data and the power grid physical model data is constructed based on the human-machine interface. The method also includes: periodic self-inspection; The periodic self-check includes: setting the check time frequency and alarm level according to the data hierarchy classification relationship; The comparison system's background program automatically checks whether the data in the market economy model and the data in the power grid model meet the matching ratio requirements; An alarm will be issued if the data in the market economy model and the power grid model are updated and compared with the data in the database, and the results show that the matching ratio does not meet the requirements. The alarm notification methods include: notifying the user via front-end interface, SMS, email or other messaging methods; The data hierarchy classification relationships include: market members, generators, power plants, loads, tie lines, metering points, and gateways.

2. The method as described in claim 1, characterized in that, The semantic learning-based method extracts keywords from the names of market economy models and power grid models, including: Based on pre-built keyword data information, keywords are extracted from the names of the market economy model data and the power grid physical model data, and organized according to a set format to obtain the keywords corresponding to each market economy model data and each power grid physical model data.

3. The method as described in claim 1, characterized in that, The mapping identifier between the market economy model and the power grid physical model data constructed based on the human-computer interface includes: Data that is highly relevant to missing information stored in the database is highlighted on the front-end system interface, generating missing data. The missing data is supplemented with information, and the supplemented information is stored in the database; Map the supplemented and completed market economy model data to the power grid physical model data; The database includes a management database that unifies the storage of electricity market economic model data and power grid model data.

4. The method as described in claim 1, characterized in that, The correlation information includes: model data information that matches the matching ratio with the market economy model data and the power grid physical model data.

5. The method as described in claim 1, characterized in that, The matching ratio set for the keywords is ≥80% similarity.

6. A system for comparing data from an electricity market economic model and a power grid model, characterized in that, include: Keyword module and comparison module; The keyword module is used to extract keywords from the names of market economy models and power grid models based on semantic learning methods. The comparison module is used to compare data in the market economy model and the power grid model based on the keywords and pre-set non-keyword information; The comparison module includes: a matching unit, a pass unit, and a fail unit; The matching unit is used to compare the keywords of each market economy model data with the keywords corresponding to each power grid physical model data based on a pre-set matching ratio, and to match the non-keywords of each market economy model data with the non-keywords in each power grid physical model data. If the set matching ratio is reached, it is considered to pass; otherwise, it is considered to fail. The passing unit is used to construct a mapping identifier between the market economy model and the power grid physical model data based on the matching ratio, for keywords and non-keywords that meet the matching ratio. The rejection unit is used to compare keywords and non-keywords that do not meet the matching ratio again based on the preset correlation information between the market economy model data and the power grid physical model data; if there are still rejections, they are data with missing information, and a mapping identifier between the market economy model and the power grid physical model data is constructed based on the human-machine interface. The system also includes: a periodic self-test module; The periodic self-test module is used to perform periodic self-tests on the comparison system. If the data update of either end of the data in the market economy model or the data in the power grid model is compared with the data in the database and a situation is found that does not meet the matching ratio requirements, an alarm will be issued. The alarm notification methods include: front-end interface, SMS, email or other messaging methods to notify the user. The periodic self-test module includes: a test setting unit, a comparison information test unit, and an alarm unit; The inspection setting unit is used to set the inspection time frequency and alarm level according to the data hierarchy classification relationship; The comparison information checking unit is used to automatically check whether the data in the market economy model and the data in the power grid model meet the matching ratio requirements of the comparison system background program. The alarm unit is used to issue an alarm when the data in the market economy model and the data in the power grid model are updated and compared with the data in the database, and the results show that the matching ratio does not meet the requirements. The data hierarchy classification relationship includes: market members, generators, power plants, loads, tie lines, metering points, and gateways.

7. The system as described in claim 6, characterized in that, The keyword module includes: a keyword acquisition unit; The keyword acquisition unit is used to extract keywords from the names of the market economy model data and the power grid physical model data based on pre-constructed keyword data information, and organize them according to a set format to obtain the keywords corresponding to each market economy model data and each power grid physical model data.

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