Day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition, and system

Abstract

The present invention relates to the technical field of power systems. Disclosed are a day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition, and a system. The method comprises: performing abnormal value detection and processing on historical energy consumption data of users; performing carbon emission decomposition on the basis of historical energy consumption curves of the users to obtain carbon emission baselines of different users; and establishing day-ahead electricity-carbon joint trading considering source-load uncertainty and intraday electricity-carbon joint trading considering the trading bias cost. In the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided by the present invention, carbon emissions of multiple entities in a park are decomposed to the hourly level, thereby optimizing carbon emission control accuracy in the park; by optimizing the power purchase and sale cost, the distributed energy cost, and the carbon trading cost, the trading bias caused by inaccurate prediction is reduced; a model that accounts for the trading bias cost is constructed, thereby improving the flexibility of response to the market. The present invention achieves better effects in terms of control accuracy, trading bias, and flexibility.

Description

A Method and System for Day-ahead and Intraday Electricity Carbon Joint Trading Based on Carbon Emission Baseline Decomposition Technical Field

[0001] This invention relates to the field of power system technology, specifically to a day-ahead and day-intraday electricity-carbon joint trading method based on carbon emission baseline decomposition. Background Technology

[0002] Carbon emission limits and carbon trading are important means of addressing climate change and have received widespread attention globally. With the introduction of carbon neutrality and emission reduction targets, the carbon emission management of the power industry, as one of the main sources of carbon emissions, has become increasingly important.

[0003] Traditional carbon trading markets operate on an annual or quarterly timeframe, while electricity spot markets typically operate on an hourly timeframe. This fails to effectively address the day-to-day and intraday carbon emission management needs within the power system. Furthermore, the electricity spot market involves multiple time scales, including day-to-day and intraday trading. Therefore, how to achieve orderly connection and coupling of electricity carbon trading at different stages has become a research focus.

[0004] Therefore, in order to solve the spatiotemporal coupling problem of carbon emission management in the power system, it is urgent to propose a day-ahead and intraday joint carbon trading method and system based on carbon emission baseline decomposition, so as to realize the reasonable decomposition of annual carbon quotas to the hourly level and organically connect carbon trading at different time scales.

[0005] This research was supported by the National Key Research and Development Program of China (No. 2022YFB2703500) and the Key Research and Development Program of Yunnan Province (No. 202303AC100003). Summary of the Invention

[0006] In view of the above-mentioned problems, the present invention is proposed.

[0007] Therefore, the technical problem solved by this invention is that existing electricity-carbon joint trading methods lack fine-grained spatiotemporal coupling in carbon emission management, have uncertainties in new energy and load forecasting, have not effectively controlled trading deviation costs, and have to optimize how to maximize economic and environmental benefits.

[0008] To address the aforementioned technical problems, this invention provides the following technical solution: a day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition, comprising: detecting and processing outliers in users' historical energy consumption data; decomposing carbon emissions according to users' historical energy consumption curves to obtain carbon emission baselines for different users; and establishing day-ahead electricity-carbon joint trading that considers source-load uncertainty and intraday electricity-carbon joint trading that considers trading deviation costs.

[0009] As a preferred embodiment of the day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition described in this invention, the outlier detection and processing of user historical energy consumption data includes outlier detection based on the K-means clustering algorithm. Objects are randomly selected from the historical energy consumption data as initial cluster centers. The distance from each sample to the cluster center is calculated, and the sample is assigned to the class containing the nearest cluster center. The distance from the data point to the cluster center is represented by Euclidean distance, expressed as:

[0010] Where k is the number of cluster centers, the mean of each newly formed cluster is calculated as the new cluster center, and the clustering is repeated until the criterion function no longer changes significantly or the clustered objects no longer change. The squared error criterion is used to express this as:

[0011] Where E is the sum of the root mean square deviations of all objects in the historical energy consumption database and their cluster centers, p is a point in the object space, and m i For clustering C i The mean, the threshold in the class is passed through 3σ X The criteria are set and expressed as follows:

[0012] Among them, X i For monitoring data points classified by clustering, Let σ be the mean of the monitoring data and σ be the standard deviation of the monitoring data. The ratio of the absolute value of the difference between each data point and the mean to the standard deviation is calculated as follows:

[0013] If q j >3. Consider the value as abnormal data and set a threshold for abnormal data between classes.

[0014] As a preferred embodiment of the day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition described in this invention, the outlier detection and processing of user historical energy consumption data further includes outlier processing based on Newton interpolation. Newton interpolation is introduced to replace outliers. The Newton interpolation function consists of an interpolation polynomial and an interpolation remainder term, which is ignored. The first-order mean difference is first calculated using the interpolation polynomial, and then successively calculated up to the k-th order mean difference. Finally, the interpolation result is calculated, expressed as: N n (x)=f(x0)+f[x0,x1](x-x0)+f[x0,x1,x2](x-x0)(x-x1)+...+f[x0,...,x n (x-x0)...(xx) n-1 )

[0015] Where, N n (x) is the interpolation polynomial.

[0016] As a preferred embodiment of the day-ahead and day-intraday carbon joint trading method based on carbon emission baseline decomposition described in this invention, the carbon emission decomposition based on users' historical energy consumption curves includes decomposition using decomposition factors for multiple user entities. The proportion of historical energy consumption to total energy consumption in each period constitutes the decomposition factor. The product of the user's annual carbon emission ceiling and the decomposition factor is the carbon baseline decomposition result, expressed as:

[0017] Among them, E i For user i's carbon emission baseline, For user i's annual carbon allowance, d t Let be the historical electricity consumption for the t-th time period, and α be the decomposition factor.

[0018] As a preferred embodiment of the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in this invention, the establishment of day-ahead electricity-carbon joint trading considering source-load uncertainty includes an objective function. The day-ahead trading aims to minimize the sum of electricity purchase and sale costs, distributed energy costs, and carbon trading costs. The objective function is expressed as: min F grid +F R +F C ΔE=E t -E i

[0019] Among them, F grid F R F C These are the costs of electricity purchase and sale between the park's electricity retailers and the external power grid, the costs of distributed energy resources, and the costs of carbon trading. These refer to the electricity purchase and sale price and the amount of electricity purchased and sold between the park's electricity retailers and the external power grid. The electricity price for distributed clean energy, This refers to the electricity sold by distributed clean energy resources to the external power grid. The base price for carbon trading is denoted by l, where l represents the interval length for carbon emissions. E represents the price growth rate, ΔE represents the carbon emissions trading volume, and E t Let t represent the total carbon emissions of the park at time t.

[0020] As a preferred embodiment of the day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition described in this invention, the establishment of day-ahead electricity carbon joint trading considering source-load uncertainty also includes day-ahead trading constraints, with distributed energy output constraints expressed as follows:

[0021] in, To contribute to distributed energy generator i at time t. The upper limit of output for distributed energy generators at any given time, and the user reporting constraint are expressed as follows:

[0022] in, and These are the upper and lower limits of the user's declared electricity consumption. and These represent the upper and lower limits for user-declared carbon emission reductions, respectively. The power balance constraint is expressed as follows:

[0023] in, For user i's load demand at time t, The carbon emissions of the park at time t for electricity purchased from the external network are expressed as follows:

[0024] in, Carbon emissions calculated for purchased electricity Carbon emission reductions resulting from distributed clean energy power generation.

[0025] As a preferred embodiment of the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in this invention, the intraday electricity-carbon joint trading considering trading deviation costs includes substituting the known quantities solved in the day-ahead trading into the intraday trading model for rolling calculations. Based on the latest new energy and load forecast information, the objective is to minimize the sum of the system's intraday energy purchase cost, carbon trading cost, and various trading deviation penalty costs. The objective function is expressed as: min F grid +F R +F C +F bias

[0026] Among them, F bias Penalty costs for transaction deviations and These are the unit adjustment costs for power interaction with the upper-level network and the unit adjustment costs for distributed clean energy power, respectively. and These are the power adjustment amounts for interaction with the upper-level network and the power adjustment amounts for distributed clean energy, respectively.

[0027] Another objective of this invention is to provide a day-ahead and day-intraday joint carbon trading system based on carbon emission baseline decomposition. This system can refine the carbon emissions of different users in the park to the hourly level through the carbon emission decomposition module, generating carbon emission baselines for different users, thus solving the problem of low accuracy in current carbon emission allocation.

[0028] As a preferred embodiment of the day-ahead and intraday electricity carbon joint trading system based on carbon emission baseline decomposition described in this invention, it includes: a detection and processing module, a carbon emission decomposition module, and a trading establishment module; the detection and processing module is used to detect and process outliers in users' historical energy consumption data; the carbon emission decomposition module is used to decompose carbon emissions according to users' historical energy consumption curves to obtain carbon emission baselines for different users; the trading establishment module is used to establish day-ahead electricity carbon joint trading considering source-load uncertainty and intraday electricity carbon joint trading considering trading deviation costs.

[0029] A computer device includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement a day-ahead and day-intraday electricity-carbon joint trading method based on a carbon emission baseline decomposition.

[0030] A computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of a day-ahead and day-intraday electricity-carbon joint trading method based on carbon emission baseline decomposition.

[0031] The beneficial effects of this invention are as follows: The day-ahead and intraday joint electricity-carbon trading method based on carbon emission baseline decomposition provided by this invention uses K-means clustering algorithm and Newton interpolation method for outlier detection and correction, ensuring the accuracy of the carbon emission baseline. It decomposes the carbon emissions of multiple entities in the park to the hourly level to obtain the carbon emission baseline of users, optimizes the carbon emission control precision of the park, and achieves optimal clearing of the electricity-carbon market by optimizing the cost of electricity purchase and sale, the cost of distributed energy, and the cost of carbon trading. It reduces the trading deviation caused by inaccurate prediction, and constructs a model that takes into account the cost of trading deviation, thereby improving the market's responsiveness and flexibility. This invention achieves better results in terms of control precision, trading deviation, and flexibility. Attached Figure Description

[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort. Wherein:

[0033] Figure 1 is an overall flowchart of the day-ahead and day-intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the first embodiment of the present invention.

[0034] Figure 2 is a diagram showing the detection and processing of outliers in historical energy consumption data for the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0035] Figure 3 is a graph showing the carbon baseline decomposition results based on historical energy consumption curves for the day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0036] Figure 4 shows the distributed photovoltaic and wind power forecast output diagram of the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0037] Figure 5 shows the load forecast of the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0038] Figure 6 is a diagram of the day-ahead phase power purchase structure of the day-ahead intraday electricity carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0039] Figure 7 is a graph showing the day-ahead carbon emissions and carbon emission factors of the day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0040] Figure 8 is a diagram of the intraday phase electricity purchase structure of the day-ahead intraday electricity carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0041] Figure 9 is a graph showing the intraday carbon emissions and carbon intensity factors of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0042] Figure 10 is a graph showing the user carbon emission reduction of the day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0043] Figure 11 is an overall module diagram of the day-ahead and day-intraday electricity carbon joint trading system based on carbon emission baseline decomposition provided in the third embodiment of the present invention. Detailed Implementation

[0044] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0045] Example 1

[0046] Referring to Figure 1, an embodiment of the present invention provides a day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition, including:

[0047] S1: Perform outlier detection and processing on users' historical energy consumption data.

[0048] Furthermore, outlier detection and processing of users' historical energy consumption data includes outlier detection based on the K-means clustering algorithm.

[0049] It should be noted that objects are randomly selected from historical energy consumption data as initial cluster centers. The distance from each sample to the cluster center is calculated, and the sample is assigned to the cluster containing the nearest cluster center. The distance from the data point to the cluster center is represented by Euclidean distance, as follows:

[0050] Where k is the number of cluster centers, the mean of each newly formed cluster is calculated as the new cluster center, and the clustering is repeated until the criterion function no longer changes significantly or the clustered objects no longer change. The squared error criterion is used to express this as:

[0051] Where E is the sum of the root mean square deviations of all objects in the historical energy consumption database and their cluster centers, p is a point in the object space, and m i For clustering C i The mean, the threshold in the class is passed through 3σ X The criteria are set and expressed as follows:

[0052] Among them, X i For monitoring data points classified by clustering, Let σ be the mean of the monitoring data and σ be the standard deviation of the monitoring data. The ratio of the absolute value of the difference between each data point and the mean to the standard deviation is calculated as follows:

[0053] If q j >3. Consider the value as abnormal data and set a threshold for abnormal data between classes.

[0054] Furthermore, outlier detection and processing of users' historical energy consumption data also includes outlier processing based on Newton interpolation.

[0055] It should be noted that Newton interpolation is introduced to replace outliers. The Newton interpolation function consists of two parts: an interpolation polynomial and an interpolation remainder term. The interpolation remainder term is ignored. The first-order mean difference is calculated first using the interpolation polynomial, and then successively calculated up to the k-th order mean difference. Finally, the interpolation result is calculated, expressed as: N n (x)=f(x0)+f[x0,x1](x-x0)+f[x0,x1,x2](x-x0)(x-x1)+...+f[x0,...,x n (x-x0)...(xx) n-1 )

[0056] Where, N n (x) is the interpolation polynomial.

[0057] S2: Decompose carbon emissions based on users' historical energy consumption curves to obtain carbon emission baselines for different users.

[0058] Furthermore, carbon emission decomposition based on users' historical energy consumption curves includes decomposition using decomposition factors for multiple user entities.

[0059] It should be noted that the historical energy consumption ratio for each period constitutes the decomposition factor. The product of the user's annual carbon emission ceiling and the decomposition factor is the carbon baseline decomposition result, expressed as:

[0060] Among them, E i For user i's carbon emission baseline, For user i's annual carbon allowance, d t Let be the historical electricity consumption for the t-th time period, and α be the decomposition factor.

[0061] Furthermore, establishing a day-ahead joint trading system for electricity and carbon that takes into account source-load uncertainties includes an objective function.

[0062] It should be noted that the day-ahead transaction aims to minimize the sum of electricity purchase and sale costs, distributed energy costs, and carbon trading costs. The objective function is expressed as: min F grid +F R +F C ΔE=E t -E i

[0063] Among them, F grid F R F C These are the costs of electricity purchase and sale between the park's electricity retailers and the external power grid, the costs of distributed energy resources, and the costs of carbon trading. These refer to the electricity purchase and sale price and the amount of electricity purchased and sold between the park's electricity retailers and the external power grid. The electricity price for distributed clean energy, This refers to the electricity sold by distributed clean energy resources to the external power grid. The base price for carbon trading is denoted by l, where l represents the interval length for carbon emissions. E represents the price growth rate, ΔE represents the carbon emissions trading volume, and E t Let t represent the total carbon emissions of the park at time t.

[0064] S3: Establish day-ahead joint trading of electricity and carbon that takes into account source-load uncertainty and intraday joint trading of electricity and carbon that takes into account trading deviation costs.

[0065] Furthermore, establishing a day-ahead carbon joint trading system that takes into account source-load uncertainties also includes constraints on day-ahead trading.

[0066] It should be noted that the output constraint of distributed energy resources is expressed as follows:

[0067] in, To contribute to distributed energy generator i at time t. The upper limit of output for distributed energy generators at any given time, and the user reporting constraint are expressed as follows:

[0068] in, and These are the upper and lower limits of the user's declared electricity consumption. and These represent the upper and lower limits for user-declared carbon emission reductions, respectively. The power balance constraint is expressed as follows:

[0069] in, For user i's load demand at time t, The carbon emissions of the park at time t for electricity purchased from the external network are expressed as follows:

[0070] in, Carbon emissions calculated for purchased electricity Carbon emission reductions resulting from distributed clean energy power generation.

[0071] It should also be noted that in practice, there is a certain forecasting error between the output values ​​of new energy sources and loads and the day-ahead forecast values. Taking into account the forecasting error, the actual output values ​​of new energy sources and loads are expressed as follows:

[0072] in, The actual output of distributed clean energy source i at time t. For users' actual load requirements at any given time, The predicted output of distributed clean energy source i at time t. For the predicted load demand of user i at time t, the power balance constraint including random variables, when considering prediction error, is expressed as:

[0073] Where, ω R,t and ω D,t These are the predicted output errors for new energy sources and loads, respectively.

[0074] Furthermore, intraday electricity-carbon joint trading that takes into account trading deviation costs includes substituting known quantities solved in day-ahead trading into the intraday trading model for rolling calculations.

[0075] It should be noted that, based on the latest forecast information on new energy sources and load, the objective function, which aims to minimize the sum of the system's daily energy purchase cost, carbon trading cost, and penalty costs for each trading deviation, is expressed as: min F grid +F R +F C +F bias

[0076] Among them, F bias Penalty costs for transaction deviations and These are the unit adjustment costs for power interaction with the upper-level network and the unit adjustment costs for distributed clean energy power, respectively. and These are the power adjustment amounts for interaction with the upper-level network and the power adjustment amounts for distributed clean energy, respectively.

[0077] It should also be noted that the constraints for intraday carbon trading also include basic power balance equation constraints, output unit and user reporting constraints, as well as inequality constraints on power interaction with the upstream network, which are basically the same as those for day-ahead carbon trading.

[0078] It should also be noted that, in the MATLAB environment, a two-stage joint trading model for electricity and carbon in the park was established using the Yalmip platform, and the Cplex solver was called through the Yalmip toolbox for rapid solution.

[0079] Example 2

[0080] Referring to Figures 2-10, an embodiment of the present invention provides a day-ahead and day-intraday electricity-carbon joint trading method based on carbon emission baseline decomposition. To verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculations and simulation experiments.

[0081] Taking the annual historical energy consumption data of a user in a certain industrial park as an example, outlier data is detected based on the K-means algorithm, and outlier data is replaced using the Newton interpolation method to clean the historical energy consumption data. The outlier detection and processing results are shown in Figure 2. The blue line is the original annual historical energy consumption curve of the user, the red dots are the detected outlier data, and the green dots are the data after the outlier replacement. The processed user historical energy consumption data is decomposed into a unified carbon emission baseline to obtain the carbon emission baselines of different users in the park. The decomposition results are shown in Figure 3. It can be seen that the trend of the carbon baseline decomposition results is roughly the same as the trend of the user's historical energy consumption curve. In the day-ahead phase, electricity carbon trading is carried out based on the output and load forecast of distributed clean energy, with a time scale of 1 hour. In the intraday phase, the forecast deviation and trading deviation cost are considered, with a time scale of 15 minutes. Users adjust their electricity purchase behavior and obtain carbon trading benefits through emission reduction behaviors such as purchasing green electricity and demand response. The output of distributed photovoltaic and wind power in the day-ahead phase is shown in Figure 4. The predicted values ​​are shown in Figure 5. The user electricity purchase structure in the day-ahead phase is shown in Figure 6. From the results of the electricity carbon trading, it can be seen that in the electricity carbon joint market, due to the low-carbon attributes of green electricity, photovoltaic and wind power are prioritized for purchase, followed by other power generation energy with relatively low carbon emission factors. The user carbon emissions and carbon emission intensity in the day-ahead phase are shown in Figure 7. From the carbon emission intensity factor, it can be seen that at noon when photovoltaic power generation is at its peak, the main source of electricity purchased by users is photovoltaic, resulting in a lower carbon emission intensity factor for users. The user electricity purchase structure, carbon emissions, and carbon emission intensity factor in the intraday phase are shown in Figures 8 and 9, respectively. Users prioritize purchasing green electricity to avoid the costs of curtailing wind and solar power. Considering the trading volume deviation penalty, the trading volume in the intraday and day-ahead phases should be as similar as possible. When clean energy power generation is at its peak, users reduce their carbon emissions by purchasing green electricity, thereby reducing their carbon emission factor. Figure 10 shows the user's carbon emission reduction. It can be seen that the main source of carbon emission reduction is the purchase of green electricity, while the carbon emission reduction due to demand response is relatively small.

[0082] Example 3

[0083] Referring to Figure 11, an embodiment of the present invention provides a day-ahead and day-intraday joint carbon trading system based on carbon emission baseline decomposition, including: a detection and processing module, a carbon emission decomposition module, and a trading establishment module.

[0084] The detection and processing module is used to detect and process outliers in users' historical energy consumption data; the carbon emission decomposition module is used to decompose carbon emissions based on users' historical energy consumption curves to obtain carbon emission baselines for different users; and the transaction establishment module is used to establish day-ahead joint electricity-carbon transactions that take into account source-load uncertainties and intraday joint electricity-carbon transactions that take into account transaction deviation costs.

[0085] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0086] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0087] More specific examples (a non-exhaustive list) of computer-readable media include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0088] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc. It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition, characterized in that, include: Detect and process outliers in users' historical energy consumption data; Carbon emissions are decomposed based on users’ historical energy consumption curves to obtain carbon emission baselines for different users. Establish day-ahead joint trading of electricity and carbon that takes into account source-load uncertainties and intraday joint trading of electricity and carbon that takes into account trading deviation costs.

2. The day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition as described in claim 1, characterized in that: The outlier detection and processing of historical energy consumption data includes outlier detection based on the K-means clustering algorithm. Objects are randomly selected from the historical energy consumption data as initial cluster centers. The distance from each sample to the cluster center is calculated, and the sample is assigned to the cluster containing the nearest cluster center. The distance from the data point to the cluster center is represented by Euclidean distance, as follows: Where k is the number of cluster centers, the mean of each newly formed cluster is calculated as the new cluster center, and the clustering is repeated until the criterion function no longer changes significantly or the clustered objects no longer change. The squared error criterion is used to express this as: Where E is the sum of the root mean square deviations of all objects in the historical energy consumption database and their cluster centers, p is a point in the object space, and m i For clustering C i The mean, the threshold in the class is passed through 3σ X The criteria are set and expressed as follows: Among them, X i For monitoring data points classified by clustering, Let σ be the mean of the monitoring data and σ be the standard deviation of the monitoring data. The ratio of the absolute value of the difference between each data point and the mean to the standard deviation is calculated as follows: If q j >3. Consider the value as abnormal data and set a threshold for abnormal data between classes.

3. The day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition as described in claim 2, characterized in that: The outlier detection and processing of historical energy consumption data also includes outlier handling based on Newton interpolation. Newton interpolation is introduced to replace outliers. The Newton interpolation function consists of an interpolation polynomial and an interpolation remainder term, which is ignored. The first-order mean difference is calculated first using the interpolation polynomial, and then successively calculated up to the k-th order mean difference. Finally, the interpolation result is calculated, expressed as: N n (x)=f(x0)+f[x0,x1](x-x0)+f[x0,x1,x2](x-x0)(x-x1)+...+f[x0,...,x n (x-x0)...(xx) n-1 ) Where, N n (x) is the interpolation polynomial.

4. The day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition as described in claim 3, characterized in that: The carbon emission decomposition based on users' historical energy consumption curves includes decomposition using decomposition factors for multiple users. The proportion of historical energy consumption to total energy consumption in each period constitutes the decomposition factor. The product of the user's annual carbon emission ceiling and the decomposition factor is the carbon baseline decomposition result, expressed as: Among them, E i For user i's carbon emission baseline, For user i's annual carbon allowance, d t Let be the historical electricity consumption for the t-th time period, and α be the decomposition factor.

5. The day-ahead and intraday carbon joint trading method based on carbon emission baseline decomposition as described in claim 4, characterized in that: The establishment of a day-ahead electricity-carbon joint trading system considering source-load uncertainty includes an objective function. The day-ahead trading aims to minimize the sum of electricity purchase and sale costs, distributed energy costs, and carbon trading costs. The objective function is expressed as: min F grid +F R +F C Among them, F grid F R F C These are the costs of electricity purchase and sale between the park's electricity retailers and the external power grid, the costs of distributed energy resources, and the costs of carbon trading. These refer to the electricity purchase and sale price and the amount of electricity purchased and sold between the park's electricity retailers and the external power grid. The electricity price for distributed clean energy, This refers to the electricity sold by distributed clean energy resources to the external power grid. The base price for carbon trading is denoted by l, where l represents the interval length for carbon emissions. E represents the price growth rate, ΔE represents the carbon emissions trading volume, and E t Let t represent the total carbon emissions of the park at time t.

6. The day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition as described in claim 5, characterized in that: The establishment of a day-ahead electricity-carbon joint trading system that considers source-load uncertainty also includes day-ahead trading constraints, with distributed energy output constraints expressed as follows: in, To contribute to distributed energy generator i at time t. The upper limit of output for distributed energy generators at any given time, and the user reporting constraint are expressed as follows: in, and These are the upper and lower limits of the user's declared electricity consumption. and These represent the upper and lower limits for user-declared carbon emission reductions, respectively. The power balance constraint is expressed as follows: in, For user i's load demand at time t, The carbon emissions of the park at time t for electricity purchased from the external network are expressed as follows: in, Carbon emissions calculated for purchased electricity Carbon emission reductions resulting from distributed clean energy power generation.

7. The day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition as described in claim 6, characterized in that: The intraday electricity-carbon joint trading system considering trading deviation costs involves substituting known quantities solved in day-ahead trading into the intraday trading model for rolling calculations. Based on the latest renewable energy and load forecasts, the objective is to minimize the sum of the system's intraday energy purchase cost, carbon trading cost, and various trading deviation penalty costs. The objective function is expressed as: min F grid +F R +F C +F bias Among them, F bias Penalty costs for transaction deviations and These are the unit adjustment costs for power interaction with the upper-level network and the unit adjustment costs for distributed clean energy power, respectively. and These are the power adjustment amounts for interaction with the upper-level network and the power adjustment amounts for distributed clean energy, respectively.

8. A system employing the day-ahead and intraday electricity carbon joint trading method based on carbon emission baseline decomposition as described in any one of claims 1 to 7, characterized in that: It includes a detection and processing module, a carbon emission decomposition module, and a transaction establishment module; The detection and processing module is used to detect and process outliers in the user's historical energy consumption data. The carbon emission decomposition module is used to decompose carbon emissions based on the user's historical energy consumption curve to obtain carbon emission baselines for different users. The transaction establishment module is used to establish day-ahead joint electricity and carbon transactions that take into account source-load uncertainties and intraday joint electricity and carbon transactions that take into account transaction deviation costs.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the day-ahead and day-intraday electricity-carbon joint trading method based on carbon emission baseline decomposition as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the day-ahead and day-intraday electricity-carbon joint trading method based on carbon emission baseline decomposition as described in any one of claims 1 to 7.

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