New energy electric power spot transaction auxiliary decision-making system and method

Abstract

The invention discloses a new energy electric power spot transaction auxiliary decision-making system and method, and the system comprises a data collection module which is used for collecting the related data of the new energy electric power transaction in real time; the data processing module is used for classifying the original data acquired by the data acquisition module and then preprocessing and storing the original data; the analysis and prediction module is used for monitoring new energy output data of an own power grid and performing power consumption prediction on market power consumption of each power consumer; and the transaction strategy generation module is used for generating an allowable transaction strategy for the new energy electric power spot goods, the allowable transaction strategy comprises a relationship between the transaction electric power spot goods quantity of each electric power user and the corresponding allowable transaction price, and the allowable transaction price has a maximum value and a minimum value. According to the invention, data of various data platforms and sources can be collected and processed in time, a user is helped to respond to market changes in time, and the transaction risk is reduced.

Description

Technical Field

[0001] The present invention belongs to the field of new energy power market and transaction technology, and relates to a new energy power spot transaction auxiliary decision-making system and method. Background Art

[0002] With the advancement of science and technology and the acceleration of global energy transformation, the proportion of renewable energy in the power market is increasing. However, due to its dependence on natural conditions, the intermittent and uncertain nature of renewable energy has brought great challenges to spot trading. Traditional trading decision-making methods are difficult to adapt to the characteristics of renewable energy, resulting in market participants being unable to make accurate and timely decisions when faced with rapidly changing market supply and demand relationships. Therefore, it is particularly important to develop a system and method that can provide intelligent and automated decision-making assistance for renewable energy. Summary of the invention

[0003] An object of the present invention is to solve at least the above problems and / or disadvantages and to provide at least the advantages which will be described hereinafter.

[0004] Another object of the present invention is to provide a new energy power spot trading auxiliary decision-making system to provide efficient and accurate spot trading strategy recommendations for the new energy power market, so as to optimize resource allocation, improve transaction efficiency and reduce market risks.

[0005] Another object of the present invention is to provide a new energy power spot transaction decision-making auxiliary device.

[0006] To this end, the technical solution provided by the present invention is: New energy power spot trading auxiliary decision-making system, which includes: The data collection module is used to collect in real time the data related to the transaction of new energy power within a certain period. The data related to the transaction of new energy power includes the market spot data of new energy power, the existing forecast data of new energy power generation of others, the current market demand data of each power user and the market demand data of the same period in history, as well as the meteorological forecast data and the meteorological data of the same period in history. The certain period can be based on actual needs, such as 12 months, 6 months, 3 months, 30 days, etc.

[0007] A data processing module, which is used to classify the raw data collected by the data collection module, pre-process and store it, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; The analysis and prediction module includes a new energy power generation monitoring module and a market demand prediction module. The new energy power generation monitoring module is used to monitor the new energy output data of the self-owned power grid, and the market demand prediction module is used to predict the power consumption of each power user market according to the data of each power user market demand data category combined with the meteorological data category; A trading strategy generation module is used to generate an allowable trading strategy for the new energy power spot according to the monitored new energy output data, the power consumption forecast of each power user, and the preset maximum profit value and minimum profit value. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and the corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value. The generation method of the allowable trading strategy includes: when the new energy output data is greater than or equal to the sum of the power consumption forecasts of each power user, a allowable trading strategy that meets the demand for the power consumption forecast data of each power user is generated; when the new energy output data is less than the sum of the power consumption forecasts of each power user, the new energy output data is allocated to each power user according to the percentage of each power user, and the power data shortage of the sum of the power consumption forecast of the power user and the new energy output data is obtained at the same time. After obtaining the shortage of new energy output data of the power data shortage from other new energy output units, the shortage of new energy output data is allocated to each power user again according to the percentage of each power user to meet the user's needs. Among them, the percentage of each power user is the proportion of the power consumption data of each power user to the total power consumption forecast.

[0008] Preferably, the new energy power spot transaction auxiliary decision-making system further includes a large language library module, and the large language library module includes: An extraction module, which is used to extract keywords from the data in each category of the data processing module, and form a vocabulary of the corresponding category with the extracted keywords; The training module is used to input the professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary, and simultaneously learn the logical relationship between multiple categories of vocabulary to generate a large language library in the field of new energy and electricity; The data processing module uses the large language library in the field of renewable energy and electricity to perform searches and collect data related to renewable energy and electricity transactions.

[0009] Preferably, in the new energy power spot trading auxiliary decision-making system, the training module also uses the knowledge graph in the field of new energy power as an external knowledge source and integrates it into the training of the large language library.

[0010] Preferably, the new energy power spot transaction decision-making auxiliary system further includes: The data storage module is used to store data from the data acquisition module, data processing module, analysis and prediction module, and trading strategy generation module.

[0011] Preferably, the new energy power spot transaction decision-making auxiliary system further includes: The user interaction module is used to allow the user to customize the relevant parameters of the trading strategy generation module and to display the allowed trading strategies.

[0012] A new energy power spot transaction decision-making auxiliary method comprises the following steps: Real-time collection of new energy power transaction-related data, including market new energy power spot data, existing other people's new energy power generation forecast data, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; The collected raw data is classified, pre-processed and stored, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; Monitor the renewable energy output data of the company’s own power grid. The market demand forecasting module is used to forecast the power consumption of each power user market according to the data of each power user market demand data category combined with the meteorological data category; Based on the monitored new energy output data, the electricity consumption forecast of each power user, and the preset maximum profit value and minimum profit value, an allowable trading strategy is generated for the new energy power spot. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value.

[0013] Preferably, the new energy power spot transaction decision-making assistance method further includes the following steps: Extract keywords from the data in each category, and form a vocabulary of the corresponding category with the extracted keywords; The professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary input into the training module are learned at the same time, and the logical relationship between multiple categories of vocabulary is learned to generate a large language library in the field of new energy and electricity; Then, the large language library in the field of renewable energy and electricity is used for retrieval to collect data related to renewable energy and electricity transactions.

[0014] Preferably, in the new energy power spot trading decision-making assistance method, the training module also uses the knowledge graph in the field of new energy power as an external knowledge source and integrates it into the training of the large language library.

[0015] Preferably, the new energy power spot trading decision-making assistance method also includes a step of storing data.

[0016] Preferably, in the new energy power spot trading auxiliary decision-making method, users are allowed to customize relevant parameters of the trading strategy generation module, and are used to display allowed trading strategies.

[0017] The present invention has at least the following beneficial effects: The data acquisition module of the present invention collects the relevant data of the new energy power transaction in real time, and can obtain the latest information. The data processing module performs classification, cleaning and integration, etc., and divides it into different categories. The analysis and prediction module monitors the new energy output data of the self-owned power grid, and generates the power consumption forecast of each power user market by analyzing the corresponding data. Finally, the trading strategy generation module generates the allowed trading strategy for the new energy power spot according to the monitored new energy output data, the power consumption forecast of each power user, and the preset maximum profit value and minimum profit value. The allowed trading strategy includes the relationship between the trading power spot quantity of each power user and the corresponding allowed trading price. The allowed trading price has a maximum value and a minimum value, so that the relationship between the electricity price and the amount of electricity sold can be made intelligently according to the current situation, meeting the needs of each power user. Therefore, the new energy power spot transaction auxiliary decision-making system and method of the present invention provide a set of efficient and intelligent auxiliary decision-making tools for participants in the new energy power market. The system can significantly improve the accuracy and timeliness of trading decisions, help users grasp market opportunities, avoid risks, and optimize resource allocation, thereby occupying a competitive advantage in the new energy power market. The present invention can collect and process data from multiple data platforms and sources in a timely manner, helping users to respond to market changes in a timely manner and reduce transaction risks.

[0018] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. DETAILED DESCRIPTION

[0019] The present invention is described in further detail below so that those skilled in the art can implement it according to the description.

[0020] It should be understood that terms such as “having”, “including” and “comprising” used herein do not specify the existence or addition of one or more other elements or combinations thereof.

[0021] The present invention provides a new energy power spot transaction decision-making auxiliary system, comprising: A data collection module is used to collect data related to new energy power transactions in real time within a certain period, wherein the data related to new energy power transactions include market new energy power spot data, existing forecast data on new energy power generation by others, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; A data processing module, which is used to classify the raw data collected by the data acquisition module and then pre-process and store it. The classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category. The pre-processing includes data cleaning and sorting, etc.; the new energy power data category comes from the market new energy power spot data and the existing new energy power generation forecast data of others, the market demand data category comes from the current market demand data of each power user and the historical market demand data of the same period, and the meteorological data category comes from the meteorological forecast data and the historical meteorological data of the same period; an analysis and prediction module, which includes a new energy power generation monitoring module and a market demand prediction module. The new energy power generation monitoring module is used to monitor the new energy output data of the self-owned power grid, and the market demand prediction module is used to predict the power consumption of each power user market according to the data of the market demand data category of each power user combined with the meteorological data category; The trading strategy generation module is used to generate an allowable trading strategy for the new energy power spot according to the monitored new energy output data and the power consumption forecast of each power user, combined with the preset maximum profit value and minimum profit value. The allowable trading strategy includes the relationship between the trading power spot volume of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value. That is, the maximum and minimum values ​​of the allowable trading price generated corresponding to the maximum profit value and the minimum profit value. The trading strategy generation module also needs to consider the uncertainty of new energy power for risk management and strategy optimization.

[0022] The data acquisition module of the present invention collects the relevant data of the new energy power transaction in real time, and can obtain the latest information. The data processing module performs classification, cleaning and integration, etc., and divides it into different categories. The analysis and prediction module monitors the new energy output data of the self-owned power grid, and generates the power consumption forecast of each power user market by analyzing the corresponding data. Finally, the trading strategy generation module generates the allowed trading strategy for the new energy power spot according to the monitored new energy output data, the power consumption forecast of each power user, and the preset maximum profit value and minimum profit value. The allowed trading strategy includes the relationship between the trading power spot quantity of each power user and the corresponding allowed trading price. The allowed trading price has a maximum value and a minimum value, so that the relationship between the electricity price and the amount of electricity sold can be made intelligently according to the current situation, meeting the needs of each power user. Therefore, the new energy power spot transaction auxiliary decision-making system and method of the present invention provide a set of efficient and intelligent auxiliary decision-making tools for participants in the new energy power market. The system can significantly improve the accuracy and timeliness of trading decisions, help users grasp market opportunities, avoid risks, and optimize resource allocation, thereby occupying a competitive advantage in the new energy power market. The present invention can collect and process data from multiple data platforms and sources in a timely manner, helping users to respond to market changes in a timely manner and reduce transaction risks.

[0023] In the above scheme, as a preference, a large language library module is further included, and the large language library module includes: An extraction module, which is used to extract keywords from the data in each category of the data processing module, and form a vocabulary of the corresponding category with the extracted keywords; The training module is used to input the professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary, and learn the logical relationship between multiple categories of vocabulary to generate a large language library in the field of new energy and electricity; Bert and other related models can be used for training.

[0024] The data processing module uses the large language library in the field of new energy and electricity to search and collect data related to new energy and electricity transactions. The large language library produced after training and learning with the collected energy and electricity transaction data is used in subsequent data collection, which can greatly improve the accuracy and real-time performance of information search and help users respond to market changes accurately and timely to make strategic changes.

[0025] In the above solution, preferably, the training module also incorporates the knowledge graph in the field of renewable energy and electricity as an external knowledge source into the training of the large language library, thereby enriching the representation and reasoning capabilities of knowledge in the field of renewable energy and electricity.

[0026] In one of the solutions of the present invention, as a preferred embodiment, the data storage module is used to store the data of the data acquisition module, the data processing module, the analysis and prediction module and the trading strategy generation module. It is also used for backup and data query, recovery and security management to ensure the security and traceability of the data.

[0027] In one of the solutions of the present invention, as a preferred embodiment, it also includes: a user interaction module, which is used to allow users to customize the relevant parameters of the trading strategy generation module and to display the allowed trading strategies. The strategy suggestions are displayed through the user interaction interface to support user decision-making and collect user feedback for system optimization.

[0028] The present invention also provides a new energy power spot transaction decision-making auxiliary method, comprising the following steps: Real-time collection of new energy power transaction-related data within a certain period, including market new energy power spot data, existing other people's new energy power generation forecast data, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; The collected raw data is classified, pre-processed and stored, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; Monitor the renewable energy output data of the company’s own power grid. The market demand forecasting module is used to forecast the power consumption of each power user market according to the data of each power user market demand data category combined with the meteorological data category; Based on the monitored new energy output data, the electricity consumption forecast of each power user, and the preset maximum profit value and minimum profit value, an allowable trading strategy is generated for the new energy power spot. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value.

[0029] In one of the schemes of the present invention, as preferred, the following steps are also included: Extract keywords from the data in each category, and form a vocabulary of the corresponding category with the extracted keywords; The professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary input into the training module are learned at the same time, and the logical relationship between multiple categories of vocabulary is learned to generate a large language library in the field of new energy and electricity; Then, the large language library in the field of renewable energy and electricity is used for retrieval to collect data related to renewable energy and electricity transactions.

[0030] In one of the solutions of the present invention, preferably, the training module also incorporates the knowledge graph in the field of renewable energy and electricity as an external knowledge source into the training of the large language library.

[0031] In one of the solutions of the present invention, preferably, the user is allowed to customize the relevant parameters of the trading strategy generation module, and the parameters are used to display the allowed trading strategies.

[0032] In one of the schemes of the present invention, preferably, a step of storing data is also included.

[0033] In order to enable those skilled in the art to better understand the technical solution of the present invention, the following embodiments are provided for illustration: A new energy power spot trading decision-making auxiliary system, comprising: A data collection module is used to collect in real time data related to the transaction of new energy power (including solar energy, wind power, and photovoltaic power output) within a certain period. The new energy power transaction-related data includes market new energy power spot data, existing new energy power generation forecast data of others, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; A data processing module, which is used to classify the raw data collected by the data collection module, pre-process and store it, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; The analysis and prediction module includes a new energy power generation monitoring module and a market demand prediction module. The new energy power generation monitoring module is used to monitor the new energy output data of the self-owned power grid, and the market demand prediction module is used to predict the power consumption of each power user market according to the data of each power user market demand data category combined with the meteorological data category; A trading strategy generation module is used to generate an allowable trading strategy for the new energy power spot according to the monitored new energy output data and the power consumption forecast of each power user, combined with the preset maximum profit value and minimum profit value. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value.

[0034] A large language library module, the large language library module comprising: An extraction module, which is used to extract keywords from the data in each category of the data processing module, and form a vocabulary of the corresponding category with the extracted keywords; The training module is used to input the professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary, and simultaneously learn the logical relationship between multiple categories of the vocabulary to generate a large language library in the field of renewable energy and electricity; the training module also uses the knowledge graph in the field of renewable energy and electricity as an external knowledge source and integrates it into the training of the large language library.

[0035] The data processing module uses the large language library in the field of renewable energy and electricity to perform searches and collect data related to renewable energy and electricity transactions.

[0036] The data storage module is used to store data from the data acquisition module, data processing module, analysis and prediction module, and trading strategy generation module.

[0037] The user interaction module is used to allow the user to customize the relevant parameters of the trading strategy generation module and to display the allowed trading strategies.

[0038] A new energy power spot transaction decision-making auxiliary method comprises the following steps: Real-time collection of new energy power transaction-related data within a certain period, including market new energy power spot data, existing other people's new energy power generation forecast data, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; The collected raw data is classified, pre-processed and stored, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; Monitor the renewable energy output data of the company’s own power grid. The market demand forecasting module is used to forecast the power consumption of each power user market according to the data of each power user market demand data category combined with the meteorological data category; Based on the monitored new energy output data, the electricity consumption forecast of each power user, and the preset maximum profit value and minimum profit value, an allowable trading strategy is generated for the new energy power spot. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value.

[0039] Extract keywords from the data in each category, and form a vocabulary of the corresponding category with the extracted keywords; The professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary input into the training module are used to simultaneously learn the logical relationship between multiple categories of the vocabulary to generate a large language library in the field of renewable energy and electricity. The training module also uses the knowledge graph in the field of renewable energy and electricity as an external knowledge source and integrates it into the training of the large language library.

[0040] Then, the large language library in the field of renewable energy and electricity is used for retrieval to collect data related to renewable energy and electricity transactions.

[0041] Allows users to customize the relevant parameters of the trading strategy generation module and is used to display allowed trading strategies.

[0042] Also included is the step of storing the data.

[0043] The present invention also provides an electronic device, comprising: at least one processor, and a memory connected to the at least one processor in communication, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method described. When the processor runs the computer program stored in the memory, it can perform the various operations and functions described in the embodiments of the present invention to ensure the speed and scale of data processing.

[0044] The present invention also provides a storage medium on which a computer program is stored, and when the program is executed by a processor, the method described above is implemented. The specific implementation can be found in the method embodiment, which will not be described in detail here.

[0045] The number of modules and processing scale described here are used to simplify the description of the present invention. The application, modification and variation of the new energy power spot transaction decision-making support system of the present invention are obvious to those skilled in the art.

[0046] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the embodiments shown and described herein.

Claims

1. New energy power spot trading decision-making support system, characterized by: include: A data collection module is used to collect data related to new energy power transactions in real time within a certain period, wherein the data related to new energy power transactions include market new energy power spot data, existing forecast data on new energy power generation by others, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; A data processing module, which is used to classify the raw data collected by the data collection module, pre-process and store it, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; The analysis and prediction module includes a new energy power generation monitoring module and a market demand prediction module. The new energy power generation monitoring module is used to monitor the new energy output data of the self-owned power grid, and the market demand prediction module is used to predict the power consumption of each power user market according to the data of each power user market demand data category combined with the meteorological data category; A trading strategy generation module is used to generate an allowable trading strategy for the new energy power spot according to the monitored new energy output data and the power consumption forecast of each power user, combined with the preset maximum profit value and minimum profit value. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value.

2. The new energy power spot transaction decision-making auxiliary system according to claim 1, characterized in that: It also includes a large language library module, which includes: An extraction module, which is used to extract keywords from the data in each category of the data processing module, and form a vocabulary of the corresponding category with the extracted keywords; The training module is used to input the professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary, and simultaneously learn the logical relationship between multiple categories of vocabulary to generate a large language library in the field of new energy and electricity; The data processing module uses the large language library in the field of renewable energy and electricity to perform searches and collect data related to renewable energy and electricity transactions.

3. The new energy power spot transaction decision-making auxiliary system according to claim 2, characterized in that: The training module also incorporates the knowledge graph in the field of new energy and electricity as an external knowledge source into the training of the large language library.

4. The new energy power spot transaction decision-making auxiliary system according to claim 1, characterized in that: Also includes: The data storage module is used to store data from the data acquisition module, data processing module, analysis and prediction module, and trading strategy generation module.

5. The new energy power spot transaction decision-making auxiliary system according to claim 1, characterized in that: Also includes: The user interaction module is used to allow the user to customize the relevant parameters of the trading strategy generation module and to display the allowed trading strategies.

6. A new energy power spot trading decision-making auxiliary method, characterized in that: The steps include: Real-time collection of new energy power transaction-related data, including new energy power spot data in the market within a certain period, existing new energy power generation forecast data of others, current market demand data of various power users and historical market demand data for the same period, as well as meteorological forecast data and historical meteorological data for the same period; The collected raw data is classified, pre-processed and stored, and the classification of the data processing module includes: new energy power data category, market demand data category and meteorological data category; Monitor the renewable energy output data of the own power grid. The market demand forecasting module is used to forecast the electricity consumption of each power user market according to the data of each power user market demand data category in combination with the meteorological data category. According to the monitored new energy output data, the electricity consumption forecast of each power user, combined with the preset maximum profit value and minimum profit value, an allowable trading strategy is generated for the new energy power spot. The allowable trading strategy includes the relationship between the trading power spot quantity of each power user and its corresponding allowable trading price, and the allowable trading price has a maximum value and a minimum value.

7. The new energy power spot transaction decision-making auxiliary method according to claim 6, characterized in that: The following steps are also included: Extract keywords from the data in each category, and form a vocabulary of the corresponding category with the extracted keywords; The professional vocabulary, knowledge structure and logical relationship of each category of the vocabulary input into the training module are learned at the same time, and the logical relationship between multiple categories of vocabulary is learned to generate a large language library in the field of new energy and electricity; Then, the large language library in the field of renewable energy and electricity is used for retrieval to collect data related to renewable energy and electricity transactions.

8. The new energy power spot transaction decision-making auxiliary method according to claim 7, characterized in that: The training module also incorporates the knowledge graph in the field of new energy and electricity as an external knowledge source into the training of the large language library.

9. The new energy power spot transaction decision-making auxiliary method according to claim 6, characterized in that: Also included is the step of storing the data.

10. The new energy power spot transaction decision-making auxiliary method according to claim 6, characterized in that: Allows users to customize the relevant parameters of the trading strategy generation module and is used to display allowed trading strategies.