Electricity price prediction method and system considering electricity-carbon real-time coupling transaction

Abstract

The invention discloses an electricity price prediction method and system considering electricity-carbon real-time coupling transaction, and belongs to the field of electricity price prediction, and the method comprises the following steps: S1, monitoring and recording the power consumption of each node in real time, and storing the power consumption data in a central database; s2, calculating the carbon emission of each node by using a preset carbon emission coefficient according to the power consumption data in the step S1, and tracking the carbon emission flow; s3, calculating the power utilization carbon emission intensity of each node in a specific time period based on the real-time monitored power consumption data in the step S1 and the tracked carbon emission flow data in the step S2; s4, on the basis of the collected carbon quota price data on the market, in combination with the real-time electricity utilization carbon emission intensity of the nodes, calculating the carbon price corresponding to electricity utilization of each unit, and obtaining the dynamic electricity utilization carbon price; and S5, electricity market transaction data and electricity demands of each node are collected in real time, the basic electricity price and the dynamic electricity utilization carbon price are embedded into the electricity price prediction model, and the coupled real-time electricity price is calculated. According to the electricity price prediction method and system considering the electricity-carbon real-time coupling transaction, the influence of the carbon emission cost on the electricity price can be reflected, more accurate price signals are provided for electricity users and market participants, and electricity transaction decisions and carbon emission management are optimized.

Description

Technical Field

[0001] The present invention relates to the field of electricity price forecasting, and in particular to an electricity price forecasting method and system taking into account real-time electricity-carbon coupling transactions. Background Art

[0002] As the world pays more attention to environmental protection and climate change, carbon emission control and management have become important issues for all countries. As an effective market mechanism, the carbon trading market promotes enterprises to reduce carbon emissions through the trading of carbon emission quotas.

[0003] Traditional electricity price forecasting methods mainly rely on factors such as the supply and demand relationship in the electricity market, fuel costs, and power generation efficiency, while ignoring the dynamic changes in carbon emission costs. At the same time, with the fluctuation of carbon quota prices, the impact of carbon emission costs on electricity prices has become increasingly significant, making it difficult for existing electricity price forecasting methods to provide accurate electricity price forecasts, thus affecting the decisions of electricity users and market participants. Summary of the invention

[0004] To solve the above problems, the present invention provides an electricity price prediction method and system taking into account real-time coupled trading of electricity and carbon, which can reflect the impact of carbon emission costs on electricity prices, and provide more accurate price signals for electricity users and market participants, thereby optimizing electricity trading decisions and carbon emission management.

[0005] To achieve the above object, the present invention provides an electricity price prediction method considering real-time coupling transaction of electricity and carbon, comprising the following steps:

[0006] S1, monitor and record the power consumption of each node in real time, and store the power consumption data in the central database;

[0007] S2. Calculate the carbon emissions of each node using a preset carbon emission coefficient according to the power consumption data in step S1, and track the carbon emission flow;

[0008] S3, based on the real-time monitoring power consumption data described in step S1 and the tracked carbon emission flow data described in step S2, calculating the carbon emission intensity of electricity consumption of each node in a specific time period;

[0009] S4. Based on the collected carbon quota price data in the market and the real-time carbon emission intensity of electricity consumption at the node, the carbon price corresponding to each unit of electricity consumption is calculated to obtain a dynamic electricity carbon price;

[0010] S5. Collect power market transaction data and power demand of each node in real time, embed the basic electricity price and dynamic electricity carbon price into the electricity price prediction model, and calculate the real-time electricity price after coupling.

[0011] Preferably, in step S1, based on the power trading contract, smart meter or sensor, the power consumption of each node is monitored and recorded in real time, and the collected power consumption data is transmitted to the central database through the wireless communication network for storage, and the data is calibrated and verified in real time, and a power consumption report is generated regularly;

[0012] The standard error correction method is used for error correction:

[0013] D_co=D_ra×(1+E_fa)

[0014] Wherein, D_co is the corrected data, D_ra is the original data, and E_fa is the error correction coefficient. Preferably, in step S2, the preset carbon emission coefficient calculation formula is as follows:

[0015] C_ad=C_ba×(1+E_ef)

[0016] In the formula, C_ad is the adjusted carbon emission coefficient, C_ba is the basic carbon emission coefficient, and E_ef is the equipment efficiency improvement coefficient.

[0017] Preferably, the tracking of carbon emission flows includes: using blockchain to record and store the carbon emission data of each node in real time, generating a carbon emission flow diagram for each node, and visually displaying the temporal and spatial distribution of carbon emissions.

[0018] Preferably, in step S3, data mining technology is first used to analyze the historical electricity consumption and carbon emission data of the node, and then a linear regression model is used to predict the future electricity carbon emission intensity. Combined with the node's electricity consumption pattern and seasonal changes, the node's electricity carbon emission intensity is dynamically adjusted and predicted to obtain the node's real-time electricity carbon emission intensity. Finally, the node's real-time electricity carbon emission intensity is calculated using the following formula:

[0019] l_c=R_tce / R_tec

[0020] In the formula, l_c is the real-time carbon emission intensity of electricity consumption at the node, R_tc is the real-time carbon emissions, and R_tec is the real-time electricity consumption.

[0021] Preferably, in step S4, firstly, the carbon quota prices of the international carbon market and the national carbon market are collected, and then the carbon quota prices of the international carbon market and the national carbon market are weighted averaged to form a unified carbon quota price benchmark, and then the carbon quota price benchmark data is subjected to time series analysis, and the future carbon quota price trend is predicted using ARIMA to obtain the carbon quota price, and finally the dynamic electricity carbon price is calculated using the following formula:

[0022] P_d=P_q×l_c

[0023] In the formula, P_d is the dynamic electricity carbon price, P_q is the carbon quota price, and l_c is the real-time electricity carbon emission intensity of the node.

[0024] Preferably, in step S4, it also includes real-time updating of the carbon quota price and the real-time electricity carbon emission intensity of the node, and using parallel computing technology to accelerate the calculation process of the dynamic electricity carbon price, using the Hadoop big data platform for distributed computing, and finally synchronizing the dynamic electricity carbon price results on multiple nodes.

[0025] Preferably, in step S5, blockchain technology is used to collect power market transaction data in real time, and the coupled real-time electricity price calculation formula is as follows:

[0026] R_tepa=B_ep+P_d

[0027] In the formula, R_tepa is the real-time electricity price after coupling, B_ep is the basic electricity price, and P_d is the dynamic electricity carbon price.

[0028] Preferably, in step S5, the coupled real-time electricity price is fed back to each node to support power trading decision-making and optimization.

[0029] The system of the electricity price prediction method considering the real-time coupling transaction of electricity and carbon comprises:

[0030] A central database for storing electricity consumption data, carbon emission data, carbon quota prices, and electricity market transaction data;

[0031] Data processing module, used to monitor and analyze power consumption in real time, calculate carbon emissions and carbon emission intensity of electricity consumption;

[0032] Carbon quota price analysis module, used to collect and analyze carbon quota prices in the market and calculate dynamic electricity carbon prices;

[0033] The electricity price calculation module is used to calculate the coupled real-time electricity price based on the basic electricity price and the dynamic electricity carbon price;

[0034] The feedback module is used to feed back the coupled real-time electricity price to each node to support power trading decision-making and optimization.

[0035] The present invention has the following beneficial effects:

[0036] By real-time monitoring of electricity consumption data and carbon emission data, using blockchain technology to ensure the transparency and immutability of data, and combining carbon quota prices, dynamically calculating electricity carbon prices, and embedding them into the electricity price prediction model, we can obtain coupled real-time electricity prices, which can reflect the impact of carbon emission costs on electricity prices. It can also provide more accurate price signals for electricity users and market participants, and optimize electricity trading decisions and carbon emission management.

[0037] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a flow chart of an electricity price prediction method considering real-time electricity-carbon coupling transaction of the present invention;

[0039] Figure 2 This is a structural block diagram of a system for an electricity price prediction method taking into account real-time coupled trading of electricity and carbon according to the present invention. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical scheme and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention are further described in detail in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the embodiments of the present invention and are not intended to limit the embodiments of the present invention. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements with the same or similar functions.

[0041] It should be noted that the terms "include", "have" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or devices.

[0042] Like reference numerals and letters denote similar items in the following drawings, and thus, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0043] like Figure 1 As shown, a method for predicting electricity prices considering real-time electricity-carbon coupling transactions includes the following steps:

[0044] S1, monitor and record the power consumption of each node in real time, and store the power consumption data in the central database;

[0045] In step S1, based on the power trading contract, smart meters or sensors, wherein the smart meters and sensors are installed at power users and production nodes, can measure power usage with high accuracy, monitor and record the power consumption of each node in real time, and transmit the collected power consumption data to a central database through a wireless communication network for storage, and perform real-time error calibration and verification on the data, and generate power consumption reports regularly;

[0046] The standard error correction method is used for error correction:

[0047] D_co=D_ra×(1+E_fa)

[0048] In the formula, D_co is the corrected data, D_ra is the original data, and E_fa is the error correction coefficient.

[0049] S2. Calculate the carbon emissions of each node using a preset carbon emission coefficient according to the power consumption data in step S1, and track the carbon emission flow;

[0050] In step S2, the preset carbon emission coefficient calculation formula is as follows:

[0051] C_ad=C_ba×(1+e_ef)

[0052] In the formula, C_ad is the adjusted carbon emission coefficient, C_ba is the basic carbon emission coefficient, and E_ef is the equipment efficiency improvement coefficient.

[0053] In this embodiment, the preset carbon emission coefficient can also be adjusted based on the following factors: 1. The carbon emission intensity of different power production methods is based on the standards issued by the International Energy Agency; 2. Real-time monitoring of the performance and maintenance status of power production equipment, and using EMS to monitor equipment operation in real time and perform predictive maintenance;

[0054] Tracking carbon emission flows includes: using blockchain to record and store carbon emission data of each node in real time, generating a carbon emission flow diagram for each node. If it is found that the carbon emissions of a certain node in a specific time period are significantly higher than those in other time periods, the peak period of carbon emissions of the node can be determined. Based on the analysis results, the temporal and spatial distribution of carbon emissions can be displayed in a visual way to facilitate analysis and decision-making.

[0055] S3, based on the real-time monitoring power consumption data described in step S1 and the tracked carbon emission flow data described in step S2, calculating the carbon emission intensity of electricity consumption of each node in a specific time period;

[0056] In step S3, data mining technology is first used to analyze the historical electricity consumption and carbon emission data of the node, and then a linear regression model is used to predict the future electricity carbon emission intensity. Combined with the node's electricity consumption pattern and seasonal changes, the node's electricity carbon emission intensity is dynamically adjusted and predicted to obtain the node's real-time electricity carbon emission intensity. Finally, the node's real-time electricity carbon emission intensity is calculated using the following formula:

[0057] l_c=R_tce / R_tec

[0058] In the formula, l_c is the real-time carbon emission intensity of electricity consumption at the node, R_tc is the real-time carbon emissions, and R_tec is the real-time electricity consumption.

[0059] S4. Based on the collected carbon quota price data in the market and the real-time carbon emission intensity of electricity consumption at the node, the carbon price corresponding to each unit of electricity consumption is calculated to obtain a dynamic electricity carbon price;

[0060] In step S4, the carbon quota prices of the international carbon market and the national carbon market are first collected, and then the carbon quota prices of the international carbon market and the national carbon market are weighted averaged to form a unified carbon quota price benchmark. Then, the carbon quota price benchmark data is analyzed in time series, and the future carbon quota price trend is predicted using ARIMA (the ARIMA model combines autoregression and moving average models, and can effectively capture the long-term trend and seasonal changes of the data by performing differential processing on the time series data) to obtain the carbon quota price. Finally, the dynamic electricity carbon price is calculated using the following formula:

[0061] P_d=P_q×l_c

[0062] In the formula, P_d is the dynamic electricity carbon price, P_q is the carbon quota price, and l_c is the real-time electricity carbon emission intensity of the node.

[0063] In step S4, it also includes real-time updating of carbon quota prices and real-time electricity carbon emission intensity of nodes, and the use of parallel computing technology to accelerate the calculation process of dynamic electricity carbon prices, and distributed computing using the Hadoop big data platform (the Hadoop platform can process massive data, support parallel computing, and improve computing efficiency and real-time performance), and finally synchronizes the dynamic electricity carbon price results on multiple nodes.

[0064] S5. Collect power market transaction data and power demand of each node in real time, embed the basic electricity price and dynamic electricity carbon price into the electricity price prediction model, and calculate the real-time electricity price after coupling.

[0065] In step S5, blockchain technology is used to collect power market transaction data in real time, and the coupled real-time electricity price calculation formula is as follows:

[0066] R_tepa=B_ep+P_d

[0067] In the formula, R_tepa is the real-time electricity price after coupling, B_ep is the basic electricity price, and P_d is the dynamic electricity carbon price.

[0068] Preferably, in step S5, the coupled real-time electricity price is fed back to each node to support power trading decision-making and optimization.

[0069] When calculating the real-time electricity price after coupling, the real-time transaction data of the electricity market and the electricity demand of each node are comprehensively considered. During peak hours of electricity consumption, the electricity demand increases and the basic electricity price may rise. At the same time, the carbon emission intensity of the node's electricity consumption is high, and the dynamic electricity carbon price will also increase accordingly. By combining the basic electricity price and the dynamic electricity carbon price, the coupled real-time electricity price can reflect the actual market supply and demand situation and carbon emission costs, and the coupled real-time electricity price calculation results are fed back to each node through the smart grid and communication network.

[0070] In this embodiment, the above data are collected using blockchain technology to ensure the transparency and non-tamperability of the data.

[0071] like Figure 2 As shown, the system of the electricity price prediction method considering the real-time coupling transaction of electricity and carbon includes:

[0072] A central database for storing electricity consumption data, carbon emission data, carbon quota prices, and electricity market transaction data;

[0073] Data processing module, used to monitor and analyze power consumption in real time, calculate carbon emissions and carbon emission intensity of electricity consumption;

[0074] Carbon quota price analysis module, used to collect and analyze carbon quota prices in the market and calculate dynamic electricity carbon prices;

[0075] The electricity price calculation module is used to calculate the coupled real-time electricity price based on the basic electricity price and the dynamic electricity carbon price;

[0076] The feedback module is used to feed back the coupled real-time electricity price to each node to support power trading decision-making and optimization.

[0077] This embodiment also includes: a smart meter and a sensor network for real-time monitoring and recording power consumption data of each node, and transmitting the data to a central database via a wireless communication network;

[0078] Wireless communication network, used to efficiently transmit the power consumption data of each node, ensuring the real-time and integrity of the power consumption data;

[0079] Machine learning module, used to optimize the electricity carbon emission intensity calculation model and electricity price prediction model, and improve calculation accuracy and real-time performance;

[0080] Blockchain technology module, used to ensure the transparency and immutability of power market transaction data;

[0081] The feedback module is used to feed back the coupled real-time electricity price to each node through the smart grid and communication network, and make power trading decisions and optimization based on the real-time electricity price.

[0082] Therefore, the present invention adopts the above-mentioned electricity price prediction method and system considering the real-time coupling transaction of electricity and carbon, which can reflect the impact of carbon emission costs on electricity prices, and provide more accurate price signals for electricity users and market participants, thereby optimizing electricity trading decisions and carbon emission management.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for predicting electricity prices considering real-time coupled trading of electricity and carbon, characterized by: The following steps are involved: S1, monitor and record the power consumption of each node in real time, and store the power consumption data in the central database; S2. Calculate the carbon emissions of each node using a preset carbon emission coefficient according to the power consumption data in step S1, and track the carbon emission flow; S3, based on the real-time monitoring power consumption data described in step S1 and the tracked carbon emission flow data described in step S2, calculating the carbon emission intensity of electricity consumption of each node in a specific time period; S4. Based on the collected carbon quota price data in the market and the real-time carbon emission intensity of electricity consumption at the node, the carbon price corresponding to each unit of electricity consumption is calculated to obtain a dynamic electricity carbon price; S5. Collect power market transaction data and power demand of each node in real time, embed the basic electricity price and dynamic electricity carbon price into the electricity price prediction model, and calculate the real-time electricity price after coupling.

2. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 1 is characterized in that: In step S1, based on the power trading contract, smart meter or sensor, the power consumption of each node is monitored and recorded in real time, and the collected power consumption data is transmitted to the central database through the wireless communication network for storage, and the data is calibrated and verified in real time, and a power consumption report is generated regularly; The standard error correction method is used for error correction: D_co=D_ra×(1+E_fa) In the formula, D_co is the corrected data, D_ra is the original data, and E_fa is the error correction coefficient.

3. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 2 is characterized in that: In step S2, the preset carbon emission coefficient calculation formula is as follows: C_ad=C_ba×(1+E_ef) In the formula, C_ad is the adjusted carbon emission coefficient, C_ba is the basic carbon emission coefficient, and E_ef is the equipment efficiency improvement coefficient.

4. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 3 is characterized by: Tracking carbon emission flows includes: using blockchain to record and store carbon emission data of each node in real time, generating a carbon emission flow diagram for each node, and visually displaying the temporal and spatial distribution of carbon emissions.

5. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 3 is characterized by: In step S3, data mining technology is first used to analyze the historical electricity consumption and carbon emission data of the node, and then a linear regression model is used to predict the future electricity carbon emission intensity. Combined with the node's electricity consumption pattern and seasonal changes, the node's electricity carbon emission intensity is dynamically adjusted and predicted to obtain the node's real-time electricity carbon emission intensity. Finally, the node's real-time electricity carbon emission intensity is calculated using the following formula: l_c=R_tce / R_tec In the formula, l_c is the real-time carbon emission intensity of electricity consumption at the node, R_tc is the real-time carbon emissions, and R_tec is the real-time electricity consumption.

6. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 5 is characterized by: In step S4, the carbon quota prices of the international carbon market and the national carbon market are first collected, and then the carbon quota prices of the international carbon market and the national carbon market are weighted averaged to form a unified carbon quota price benchmark. Then, a time series analysis is performed on the carbon quota price benchmark data, and ARIMA is used to predict the future carbon quota price trend to obtain the carbon quota price. Finally, the dynamic electricity carbon price is calculated using the following formula: P_d=P_q×l_c In the formula, P_d is the dynamic electricity carbon price, P_q is the carbon quota price, and l_c is the real-time electricity carbon emission intensity of the node.

7. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 1 is characterized by: In step S4, it also includes real-time updating of carbon quota prices and real-time electricity carbon emission intensity of nodes, and using parallel computing technology to accelerate the calculation process of dynamic electricity carbon prices, using the Hadoop big data platform for distributed computing, and finally synchronizing the dynamic electricity carbon price results on multiple nodes.

8. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 6 is characterized by: In step S5, blockchain technology is used to collect power market transaction data in real time, and the coupled real-time electricity price calculation formula is as follows: R_tepa=B_ep+R_d In the formula, R_tepa is the real-time electricity price after coupling, B_ep is the basic electricity price, and P_d is the dynamic electricity carbon price.

9. The method for predicting electricity prices considering real-time electricity-carbon coupling transactions according to claim 1 is characterized by: In step S5, the coupled real-time electricity price is fed back to each node to support power trading decision-making and optimization.

10. A system for predicting electricity prices considering real-time electricity-carbon coupling transactions as described in any one of claims 1 to 9, characterized in that: include: A central database for storing electricity consumption data, carbon emission data, carbon quota prices, and electricity market transaction data; Data processing module, used to monitor and analyze power consumption in real time, calculate carbon emissions and carbon emission intensity of electricity consumption; Carbon quota price analysis module, used to collect and analyze carbon quota prices in the market and calculate dynamic electricity carbon prices; The electricity price calculation module is used to calculate the coupled real-time electricity price based on the basic electricity price and the dynamic electricity carbon price; The feedback module is used to feed back the coupled real-time electricity price to each node to support power trading decision-making and optimization.

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