Marketized electricity selling data processing system and method

Abstract

The invention discloses a marketization electricity selling data processing system and method, and relates to the technical field of electricity selling data, and the system comprises a data collection module, a data analysis module and a strategy adjustment module, the data acquisition module is used for acquiring electricity selling data including intelligent electric meter reading, power generation enterprise data and electricity transaction market information; and the data analysis module is used for analyzing the electricity selling data. According to the invention, automatic data acquisition and processing are realized, manual intervention is reduced, working efficiency and electricity selling efficiency are improved, rapid and accurate data analysis is realized, timely support is provided for electricity selling decision making, reasonable electricity selling prices and marketing strategies can be formulated, market dynamics can be mastered in real time, the electricity selling strategies can be adjusted in time, market changes are adapted, and the market competitiveness is improved. And the market application range is wide, energy-saving strategy suggestions are provided, reasonable utilization of energy is promoted, intelligent power grid construction is supported, supply and demand interaction is achieved, and the efficiency and stability of an energy system are improved.

Description

Technical Field

[0001] The present invention relates to the field of electricity sales data technology, and in particular to a market-oriented electricity sales data processing system and method. Background Art

[0002] As the power market is gradually liberalized, competition in the power sales market is becoming increasingly fierce, and power sales companies are emerging as new market players. In order to adapt to this new market structure, it is necessary to establish an efficient market-oriented power sales data processing system to support the business operations of power sales companies, accurately process and analyze data such as power purchase, sale, and transaction, so that power sales companies can better participate in market competition and provide users with high-quality power services.

[0003] The rapid development of information technology has provided technical support for the establishment of a market-oriented electricity sales data processing system. The application of cloud computing, big data, the Internet of Things and other technologies has made the storage, processing and transmission of electricity sales data more efficient, accurate and secure. The continuous maturity and application of these technologies have promoted the development and improvement of the market-oriented electricity sales data processing system.

[0004] At present, the market-oriented electricity sales data processing system only has basic data storage and query functions, which may not only be insufficient in supporting business processes, but also fail to meet the complex business needs of power sales companies. For example, in terms of contract management, electricity bill settlement, user services, etc., the system's functions may not be flexible enough, resulting in cumbersome and inefficient business processes, and lack of in-depth analysis and mining capabilities for data. For example, it is impossible to predict users' electricity demand and analyze market trends based on historical data, making it difficult to meet the decision-making needs of power sales companies.

[0005] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0006] In view of the problems in the related technology, the present invention proposes a market-oriented electricity sales data processing system and method to overcome the above-mentioned technical problems existing in the existing related technology.

[0007] The technical solution of the present invention is achieved in this way:

[0008] In one aspect, the present invention:

[0009] A market-oriented electricity sales data processing system includes: a data acquisition module, a data analysis module and a strategy adjustment module, wherein;

[0010] The data collection module is used to collect electricity sales data, including: smart meter readings, power generation enterprise data, and power trading market information;

[0011] The data analysis module is used to analyze the electricity sales data, including: conducting electricity consumption trend analysis and generating electricity consumption analysis results, conducting load forecasting and generating load analysis results, and conducting price fluctuation analysis and generating price analysis results based on fluctuation indicators;

[0012] The strategy adjustment module is used to adjust the electricity sales strategy according to the electricity consumption analysis results, load analysis results and price analysis results.

[0013] Further, the steps include: a data storage module, a transaction management module, a user service module and a system management module, wherein;

[0014] A data storage module for storing the collected electricity sales data;

[0015] The transaction management module is used to manage the nodes of power sales transactions and realize automated transactions and information sharing, including: contract management, electricity settlement, and transaction monitoring;

[0016] User service module, used to provide users with personalized services, including: electricity usage query, bill management, and energy-saving suggestions;

[0017] The system management module is responsible for system configuration, maintenance and monitoring to ensure the stable operation of the system.

[0018] Further, performing power consumption trend analysis and generating power consumption analysis results include the following steps:

[0019] Obtain historical electricity consumption data in advance based on electricity sales data, including electricity consumption in different time periods, peak and trough period data, and electricity consumption data of different user types, and collect data on external factors that affect electricity consumption trends as influencing factors, including seasonal changes, temperature, economic development conditions, and industry dynamics;

[0020] Conduct regression analysis on electricity consumption and influencing factors to predict future electricity consumption trends, including: establishing a regression model of electricity consumption and influencing factors, analyzing the impact of changes in influencing factors on electricity consumption, and generating electricity consumption analysis results.

[0021] Further, the steps of performing load forecasting and generating load analysis results include the following steps:

[0022] Obtain historical load data in advance based on electricity sales data, including load curves, maximum load and minimum load in different time periods.

[0023] According to the electricity sales data, the load-related factor data is obtained as the influencing factors, and the influencing factors include: temperature, humidity, and holidays.

[0024] Based on historical load data, the exponential smoothing method is used to perform trend forecasting and generate load analysis results.

[0025] Further, the price fluctuation analysis is performed and the price analysis results are generated according to the fluctuation index, including the following steps:

[0026] The historical electricity price data is obtained in advance based on the electricity sales data, including: the changes in electricity prices in different time periods, the differences in electricity prices in different regions, and the data of factors affecting electricity prices as influencing factors. The influencing factors include: energy market prices, such as: coal, natural gas, oil, and electricity supply and demand conditions.

[0027] Conduct statistical analysis on historical electricity price data, calculate fluctuation indicators, and generate price analysis results based on fluctuation indicators. The fluctuation indicators include: average electricity price indicator, electricity price fluctuation range indicator, and standard deviation indicator.

[0028] Furthermore, the strategy adjustment module includes the following steps:

[0029] Determine whether the power consumption analysis results are long-term trends or periodic trends, including:

[0030] If the power consumption analysis result is a long-term trend, then determine whether the power demand is continuously increasing, stable, or decreasing. If it is continuously increasing, increase the power purchase volume or invest in new power generation facilities; if it is a stable power consumption trend, optimize operating costs; if it is a decreasing power consumption trend, explore other industry customers or develop distributed energy projects;

[0031] If the power consumption analysis result shows a cyclical trend, obtain the cyclical changes in power consumption, and increase the price during peak hours and reduce the price during off-peak hours;

[0032] Determine whether the load analysis result is a short-term load or a medium- to long-term load, including:

[0033] Among them, if the load analysis result is a short-term load, based on the short-term load forecast, electricity is purchased from the wholesale market or backup power generation facilities are started before the expected load peak.

[0034] If the load analysis result is a medium- to long-term load, a long-term power procurement and investment plan is formulated based on the medium- to long-term load forecast;

[0035] Calibrate price analysis results and adjust pricing strategies, including:

[0036] Among them, during the period of low prices, preferential packages are launched or prices are lowered, and long-term contract signing strategies are adopted;

[0037] Among them, during peak price periods, the pricing strategy is increased.

[0038] Another aspect of the present invention is:

[0039] A method for processing market-oriented electricity sales data comprises the following steps:

[0040] Collect smart meter readings, power generation enterprise data and power trading market information in advance as power sales data;

[0041] Analysis of electricity sales data, including: respectively analyzing electricity consumption trends and generating electricity consumption analysis results, performing load forecasting and generating load analysis results, and performing price fluctuation analysis and generating price analysis results based on fluctuation indicators;

[0042] Adjust the electricity sales strategy based on the results of electricity consumption analysis, load analysis and price analysis.

[0043] Beneficial effects of the present invention:

[0044] The market-oriented electricity sales data processing system and method of the present invention collects smart meter readings, power generation enterprise data and power trading market information as electricity sales data in advance, performs electricity consumption trend analysis on the electricity sales data and generates electricity consumption analysis results, performs load forecasting and generates load analysis results, and performs price fluctuation analysis and generates price analysis results according to fluctuation indicators, and adjusts the electricity sales strategy according to the electricity consumption analysis results, load analysis results and price analysis results. It not only automates data collection and processing, reduces manual intervention, improves work efficiency and improves electricity sales efficiency, but also provides timely support for electricity sales decision-making through fast and accurate data analysis, can formulate reasonable electricity sales prices and marketing strategies, realize real-time grasp of market dynamics, timely adjust electricity sales strategies, and adapt to market changes, but also has wide market applications, provides energy-saving strategy suggestions, promotes the rational use of energy, supports the construction of smart grids, realizes supply and demand interaction, and improves the efficiency and stability of energy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 It is a schematic diagram of the principle of a market-oriented electricity sales data processing system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the accompanying drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0048] According to an embodiment of the present invention, a market-based electricity sales data processing system is provided.

[0049] like Figure 1 As shown, the market-oriented electricity sales data processing system according to an embodiment of the present invention includes: a data acquisition module 1, a data analysis module 3 and a strategy adjustment module 4, wherein;

[0050] The data collection module 1 collects electricity sales data in advance, including: smart meter readings, power generation enterprise data, and power trading market information.

[0051] Data storage module 2 stores the collected electricity sales data.

[0052] This technical solution, when applied, uses database technology, such as relational database or distributed database. And establishes data indexes to facilitate quick query and retrieval of data. At the same time, ensures the security and integrity of data, and takes backup and recovery measures.

[0053] Data analysis module 3, used to analyze the electricity sales data, including: performing electricity consumption trend analysis and generating electricity consumption analysis results, performing load forecasting and generating load analysis results, and performing price fluctuation analysis and generating price analysis results based on fluctuation indicators;

[0054] The above-mentioned power consumption trend analysis includes the following steps:

[0055] Obtain historical electricity consumption data in advance based on electricity sales data, including electricity consumption in different time periods, such as day, week, month, season, and year, peak and valley period data, and electricity consumption data of different user types, such as residential, commercial, and industrial. At the same time, collect data on external factors that affect electricity consumption trends as influencing factors, including seasonal changes, temperature, economic development conditions, and industry dynamics;

[0056] Conduct regression analysis on electricity consumption and influencing factors to predict future electricity consumption trends, including: establishing a regression model of electricity consumption and influencing factors, analyzing the impact of changes in influencing factors on electricity consumption, and generating electricity consumption analysis results;

[0057] The above load forecasting includes the following steps:

[0058] Obtain historical load data in advance based on electricity sales data, including load curves, maximum load, and minimum load in different time periods.

[0059] According to the electricity sales data, the load-related factor data is obtained as the influencing factors, and the influencing factors include: temperature, humidity, and holidays.

[0060] Based on historical load data, exponential smoothing method is used to predict trends and generate load analysis results;

[0061] The above price fluctuation analysis includes the following steps:

[0062] Obtain historical electricity price data in advance based on electricity sales data, including: electricity price changes in different time periods and electricity price differences in different regions;

[0063] And obtain the data of factors affecting electricity prices as influencing factors, and its influencing factors include: energy market prices, such as: coal, natural gas, oil, electricity supply and demand conditions.

[0064] Conduct statistical analysis on historical electricity price data, calculate fluctuation indicators, and generate price analysis results based on fluctuation indicators. The fluctuation indicators include: average electricity price indicator, electricity price fluctuation range indicator, and standard deviation indicator.

[0065] The strategy adjustment module 4 adjusts the power selling strategy according to the power consumption analysis results, the load analysis results and the price analysis results, including the following steps:

[0066] Calibrate whether the power consumption analysis results are long-term trends or periodic trends, including:

[0067] If the power consumption analysis result is a long-term trend, then it is determined whether the power demand is growing, stable or declining. If it is growing, increase the amount of power purchased or invest in new power generation facilities. For example, in a rapidly developing industrial park, the power consumption trend continues to rise. The power sales company can establish a long-term cooperative relationship with the park enterprises and provide customized power solutions. If the power consumption trend is stable, optimize operating costs, improve service quality, and maintain market share through differentiated competition. For example, provide more efficient customer service, launch energy-saving consulting services, etc. If the power consumption trend is declining, explore other industry customers or develop distributed energy projects to make up for the shrinking traditional market.

[0068] If the power consumption analysis result is a cyclical trend, obtain the cyclical changes in power consumption. For example, the peak power consumption of air conditioning in summer and heating in winter, as well as the changes in power consumption during holidays. According to these changes, formulate seasonal pricing strategies, increase prices during peak hours and reduce prices during off-peak hours, guide customers to arrange power consumption time reasonably, and balance power supply and demand.

[0069] At the same time, in response to cyclical changes, electricity procurement and reserves are planned in advance to ensure sufficient electricity supply during peak periods of electricity consumption. At the same time, flexible contracts are signed with power generation companies to adapt to electricity demand in different cycles.

[0070] Determine whether the load analysis result is a short-term load or a medium- to long-term load, including:

[0071] Among them, if the load analysis result is a short-term load, based on the short-term load forecast, electricity is purchased from the wholesale market or backup power generation facilities are started in advance before the expected load peak.

[0072] This technical solution can optimize the distributed energy generation plan according to the load forecast results and improve energy utilization efficiency for distributed energy resources. For example, during peak load periods, the output of distributed photovoltaic and wind power generation can be increased to reduce dependence on traditional power grids.

[0073] Among them, if the load analysis result is a medium- and long-term load, a long-term power procurement and investment plan is formulated based on the medium- and long-term load forecast. If it is predicted that the future load will increase significantly, a long-term power purchase contract can be signed with the power generation company in advance to ensure a stable power supply. At the same time, consider investing in new power generation projects or energy storage facilities to meet future market demand. At the same time, the medium- and long-term load forecast can also evaluate market potential and formulate market expansion strategies. For example, in areas with greater load growth potential, market promotion efforts can be increased to attract more customers.

[0074] Calibrate price analysis results and adjust pricing strategies, including:

[0075] Among them, during periods of low prices, preferential packages can be launched or prices can be lowered to attract more customers to sign long-term contracts.

[0076] Among them, during peak price periods, prices will be appropriately increased, but at the same time, value-added services such as energy-saving consulting and power quality optimization will be provided to increase customer acceptance.

[0077] In addition, dynamic pricing strategies can be considered when applying it, and different price plans can be implemented for different customer groups based on real-time market prices and load conditions. For example, higher prices can be implemented for high-load customers during peak hours, while more favorable prices can be provided for low-load customers or customers with adjustable loads, to encourage customers to optimize their electricity usage behavior.

[0078] The transaction management module 5 is used to manage the nodes of power sales transactions and realize automated transactions and information sharing, including contract management, electricity settlement, and transaction monitoring.

[0079] The user service module 6 is used to provide users with personalized services, such as electricity usage inquiry, bill management, energy-saving suggestions, etc.

[0080] This technical solution, when applied, communicates with users through multiple channels, such as websites, mobile applications, customer service hotlines, etc.

[0081] The system management module 7 is responsible for the configuration, maintenance and monitoring of the system to ensure the stable operation of the system.

[0082] These include: user authority management, data security management, etc.

[0083] According to an embodiment of the present invention, a method for processing market-based electricity sales data is provided.

[0084] A method for processing market-oriented electricity sales data comprises the following steps:

[0085] Collect smart meter readings, power generation enterprise data and power trading market information in advance as power sales data;

[0086] Analysis of electricity sales data, including: respectively analyzing electricity consumption trends and generating electricity consumption analysis results, performing load forecasting and generating load analysis results, and performing price fluctuation analysis and generating price analysis results based on fluctuation indicators;

[0087] Adjust the electricity sales strategy based on the results of electricity consumption analysis, load analysis and price analysis.

[0088] It should be noted that in this technical solution, with the continuous deepening of power market reform, the market-oriented power sales data processing system will play an increasingly important role in the future energy field. It will provide power sales companies with powerful data analysis and management tools to help them stand out in the fierce market competition. At the same time, it will also provide users with more convenient and efficient services and promote the sustainable development of energy.

[0089] In summary, with the help of the above-mentioned technical scheme of the present invention, by pre-collecting smart meter readings, power generation enterprise data and power trading market information as power sales data, power consumption trend analysis is performed on the power sales data and power consumption analysis results are generated, load forecasting is performed and load analysis results are generated, and price fluctuation analysis is performed and price analysis results are generated according to fluctuation indicators. At the same time, the power sales strategy is adjusted according to the power consumption analysis results, load analysis results and price analysis results. Not only does it automate data collection and processing, reduce manual intervention, improve work efficiency and improve power sales efficiency, but fast and accurate data analysis also provides timely support for power sales decisions, and can formulate reasonable power sales prices and marketing strategies, realize real-time grasp of market dynamics, timely adjust power sales strategies, and adapt to market changes. It also has a wide market application, provides energy-saving strategy suggestions, promotes the rational use of energy, supports the construction of smart grids, realizes supply and demand interaction, and improves the efficiency and stability of the energy system.

[0090] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. After considering the disclosure of the specification and the examples, those skilled in the art will easily think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the art that are not disclosed in the present disclosure. The specification and the examples are only regarded as exemplary, and the true scope and spirit of the present disclosure are indicated by the claims.

[0091] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A market-oriented electricity sales data processing system, characterized in that: include: A data collection module (1), a data analysis module (3) and a strategy adjustment module (4), wherein; The data collection module (1) is used to collect electricity sales data, including: smart meter readings, power generation enterprise data, and electricity trading market information; The data analysis module (3) is used to analyze the electricity sales data, including: performing electricity consumption trend analysis and generating electricity consumption analysis results, performing load forecasting and generating load analysis results, and performing price fluctuation analysis and generating price analysis results according to fluctuation indicators; The strategy adjustment module (4) is used to adjust the electricity sales strategy according to the electricity consumption analysis results, the load analysis results and the price analysis results.

2. The market-oriented electricity sales data processing system according to claim 1, characterized in that: The following steps are also included: A data storage module (2), a transaction management module (5), a user service module (6) and a system management module (7), wherein; The data storage module (2) stores the collected electricity sales data; The transaction management module (5) is used to manage the nodes of power sales transactions and realize automated transactions and information sharing, including: contract management, electricity settlement, and transaction monitoring; The user service module (6) is used to provide personalized services to users, including: electricity usage inquiry, bill management, and energy-saving suggestions; The system management module (7) is used to be responsible for the configuration, maintenance and monitoring of the system to ensure the stable operation of the system.

3. The market-oriented electricity sales data processing system according to claim 1, characterized in that: The method of performing power consumption trend analysis and generating power consumption analysis results includes the following steps: Obtain historical electricity consumption data in advance based on electricity sales data, including electricity consumption in different time periods, peak and trough period data, and electricity consumption data of different user types, and collect data on external factors that affect electricity consumption trends as influencing factors, including seasonal changes, temperature, economic development conditions, and industry dynamics; Conduct regression analysis on electricity consumption and influencing factors to predict future electricity consumption trends, including: establishing a regression model of electricity consumption and influencing factors, analyzing the impact of changes in influencing factors on electricity consumption, and generating electricity consumption analysis results.

4. The market-oriented electricity sales data processing system according to claim 3 is characterized in that: The steps of performing load forecasting and generating load analysis results include the following steps: Obtain historical load data in advance based on electricity sales data, including load curves, maximum load and minimum load in different time periods. According to the electricity sales data, the load-related factor data is obtained as the influencing factors, and the influencing factors include: temperature, humidity, and holidays. Based on historical load data, the exponential smoothing method is used to perform trend forecasting and generate load analysis results.

5. The market-oriented electricity sales data processing system according to claim 4 is characterized in that: The process of performing price fluctuation analysis and generating price analysis results according to fluctuation indicators includes the following steps: The historical electricity price data is obtained in advance based on the electricity sales data, including: the changes in electricity prices in different time periods, the differences in electricity prices in different regions, and the data of factors affecting electricity prices as influencing factors. The influencing factors include: energy market prices, such as: coal, natural gas, oil, and electricity supply and demand conditions. Conduct statistical analysis on historical electricity price data, calculate fluctuation indicators, and generate price analysis results based on fluctuation indicators. The fluctuation indicators include: average electricity price indicator, electricity price fluctuation range indicator, and standard deviation indicator.

6. The market-oriented electricity sales data processing system according to claim 5, characterized in that: The strategy adjustment module (4) comprises the following steps: Determine whether the power consumption analysis results are long-term trends or periodic trends, including: If the power consumption analysis result is a long-term trend, then determine whether the power demand is continuously increasing, stable, or decreasing. If it is continuously increasing, increase the power purchase volume or invest in new power generation facilities; if it is a stable power consumption trend, optimize operating costs; if it is a decreasing power consumption trend, explore other industry customers or develop distributed energy projects; If the power consumption analysis result shows a cyclical trend, obtain the cyclical change of power consumption, and increase the price during peak hours and reduce the price during off-peak hours; Determine whether the load analysis result is a short-term load or a medium- to long-term load, including: Among them, if the load analysis result is a short-term load, based on the short-term load forecast, electricity is purchased from the wholesale market or backup power generation facilities are started before the expected load peak. If the load analysis result is a medium- to long-term load, a long-term power procurement and investment plan is formulated based on the medium- to long-term load forecast; Calibrate price analysis results and adjust pricing strategies, including: Among them, during the period of low prices, preferential packages are launched or prices are lowered, and long-term contract signing strategies are adopted; Among them, during peak price periods, the pricing strategy is improved.

7. A method for processing market-based electricity sales data, used in the processing method of the market-based electricity sales data processing system according to any one of claims 1 to 6, characterized in that: The following steps are involved: Collect smart meter readings, power generation enterprise data and power trading market information in advance as power sales data; Analysis of electricity sales data, including: respectively analyzing electricity consumption trends and generating electricity consumption analysis results, performing load forecasting and generating load analysis results, and performing price fluctuation analysis and generating price analysis results based on fluctuation indicators; Adjust the electricity sales strategy based on the results of electricity consumption analysis, load analysis and price analysis.

Images