Carbon emission baseline decomposition-based day-ahead and intra-day electricity-carbon combined transaction method and system

Abstract

The invention discloses a day-ahead and intra-day electricity-carbon combined transaction method and system based on carbon emission baseline decomposition, and relates to the technical field of electric power systems, and the method comprises the steps: carrying out the abnormal value detection and processing of historical energy consumption data of a user; performing carbon emission decomposition according to the historical energy consumption curve of the user to obtain carbon emission baselines of different users; and establishing a day-ahead electricity-carbon joint transaction considering the source-load uncertainty and an intra-day electricity-carbon joint transaction considering the transaction deviation cost. According to the day-ahead and intra-day electricity and carbon combined transaction method based on carbon emission baseline decomposition, the carbon emission of multiple subjects in a park is decomposed to the hour level, the carbon emission control precision of the park is optimized, the transaction deviation caused by inaccurate prediction is reduced through optimization of the electricity purchase and sale cost, the distributed energy cost and the carbon transaction cost, and the transaction efficiency is improved. A model considering the transaction deviation cost is constructed, the response flexibility of the market is improved, and better effects are achieved in the aspects of control precision, transaction deviation and flexibility.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and specifically to a day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition. Background Art

[0003] The traditional carbon trading market has an annual or quarterly time span, while the electricity spot market usually has an hourly time span, which fails to effectively respond to the carbon emission management needs of the power system within the day before the day. In addition, the electricity spot market has multiple time scales such as the day before the day. 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 problem of spatiotemporal coupling of carbon emission management in the power system, it is urgent to propose a day-ahead and intraday electricity-carbon joint trading method and system based on the decomposition of carbon emission baseline, so as to realize the reasonable decomposition of annual carbon quota to the hourly level and organically connect electricity-carbon transactions at different time scales. Summary of the invention

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

[0007] Therefore, the technical problem solved by the present invention is: the existing electricity-carbon joint trading method lacks fine-grained spatiotemporal coupling in carbon emission management, new energy and load forecasts are uncertain, transaction deviation costs are not effectively controlled, and there is an optimization problem of how to maximize economic and environmental benefits.

[0008] To solve the above technical problems, the present invention provides the following technical solutions: a method for day-ahead and intraday electricity-carbon joint trading based on carbon emission baseline decomposition, including outlier detection and processing of users' historical energy consumption data; decomposing carbon emissions according to users' historical energy consumption curves to obtain carbon emission baselines for different users; establishing day-ahead electricity-carbon joint trading that considers source and load uncertainty and intraday electricity-carbon joint trading that considers transaction deviation costs.

[0009] As a preferred solution of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in the present invention, the outlier detection and processing of the user's historical energy consumption data includes outlier detection based on the K_means clustering algorithm, randomly selecting objects from the historical energy consumption data as the initial cluster center, calculating the distance from each sample to the cluster center, and classifying the sample into the class where the cluster center closest to it is located, and expressing the distance from the data point to the cluster center by the Euclidean distance, which is expressed as:

[0010]

[0011] Among them, k is the number of cluster centers. The means of the newly formed classes are calculated as new cluster centers, and clustering is repeated until the criterion function no longer changes significantly or the clustered objects no longer change. The square error criterion is expressed as:

[0012]

[0013] Where E is the sum of the mean square errors between all objects in the historical energy consumption database and the cluster center, p is a point in the object space, and m i is cluster C i The mean of the class threshold is 3σ X The criteria setting is expressed as:

[0014]

[0015] Among them, X i is the monitoring data point for cluster classification, is the average value of the monitoring data, σ is the mean square error of the monitoring data, and the ratio of the absolute value of each data in the class from the average value to the mean square error is calculated, expressed as:

[0016]

[0017] If q j >3, the value is considered abnormal data, and the threshold of abnormal data between classes is set

[0018] As a preferred solution of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in the present invention, the detection and processing of outliers in the user's historical energy consumption data also includes outlier processing based on the Newton interpolation method, and the Newton interpolation method is introduced to replace the outliers. The Newton interpolation function consists of an interpolation polynomial and an interpolation remainder. The interpolation remainder is ignored. The first-order mean difference is first calculated by the interpolation polynomial, and the k-order mean difference is calculated successively. Finally, the interpolation result is calculated, which is expressed as:

[0019] N n (x)=f(x0)+f[x0,x1](x-x0)+f[x0,x1,x2](x-x0)(x-x1)+...

[0020] +f[x0,...,x n ](x-x0)...(xx n-1 )

[0021]

[0022] Among them, N n(x) is the interpolation polynomial.

[0023] As a preferred solution of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in the present invention, the carbon emission decomposition according to the user's historical energy consumption curve includes decomposing multiple subject users using decomposition factors, the proportion of historical energy consumption in each period to total energy consumption constitutes the decomposition factor, and the product of the user's annual carbon emission limit and the decomposition factor is the carbon baseline decomposition result, which is expressed as:

[0024]

[0025] Among them, E i is the carbon emission baseline of user i, is the annual carbon quota of user i, d t is the historical electricity consumption in the tth period, and α is the decomposition factor.

[0026] As a preferred solution of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in the present invention, the day-ahead electricity-carbon joint trading considering source-load uncertainty includes an objective function, and the day-ahead trading takes the minimum sum of the electricity purchase and sales cost, the distributed energy cost and the carbon trading cost as the optimization target, and the objective function is expressed as:

[0027] min F grid +F R +F C

[0028]

[0029] Among them, F grid 、F R 、F C They are the electricity purchase and sales costs, distributed energy costs and carbon trading costs of the interaction between the park's electricity sellers and the external power grid. They are the electricity purchase and sales price and the electricity purchase and sales volume of the park electricity sellers interacting with the external power grid. is the electricity selling price of distributed clean energy, The electricity sold by distributed clean energy to external power grids. is the carbon trading benchmark price, l is the interval length of carbon emissions, is the price growth rate, ΔE is the carbon emission trading volume, E t is the total carbon emissions of the park at time t.

[0030] As a preferred solution of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in the present invention, wherein: the establishment of the day-ahead electricity-carbon joint trading considering the uncertainty of source and load also includes the constraint conditions of the day-ahead trading, and the distributed energy output constraint is expressed as:

[0031]

[0032] in, is the output of distributed energy generator i at time t, is the upper limit of the output of distributed energy generators at any given moment, and the user reporting constraint is expressed as:

[0033]

[0034] in, and Declare the upper and lower limits of electricity consumption for users respectively. and The upper and lower limits of carbon emission reduction are declared for users respectively, and the power balance constraint is expressed as:

[0035]

[0036] in, is the load demand of user i at time t, is the electricity purchased from the external network at time t, and the carbon emissions of the park are expressed as:

[0037]

[0038] in, Carbon emissions calculated for purchased electricity, Carbon emissions reductions from distributed clean energy generation.

[0039] As a preferred solution of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition described in the present invention, the day-ahead electricity-carbon joint trading considering transaction deviation costs includes substituting the known quantities solved in the day-ahead transaction into the intraday transaction model for rolling calculation, and according to the latest forecast information of new energy and load, the goal is to minimize the sum of the system's intraday energy purchase cost, carbon transaction cost and penalty costs of each transaction deviation, and the objective function is expressed as:

[0040] min F grid +F R +F C +F bias

[0041]

[0042] Among them, F bias Penalize costs for trading deviations, and They are the power unit adjustment cost for interaction with the upper network and the power unit adjustment cost for distributed clean energy. and They are the upper-level network interaction power adjustment amount and the distributed clean energy power adjustment amount respectively.

[0043] Another object of the present invention is to provide a day-ahead and intraday electricity-carbon joint trading system based on carbon emission baseline decomposition, which can break down the carbon emissions of different users in the park to the hourly level through the carbon emission decomposition module, generate carbon emission baselines for different users, and solve the current problem of low accuracy of carbon emission allocation.

[0044] As an optimal solution of the day-ahead and intraday electricity-carbon joint trading system based on carbon emission baseline decomposition described in the present invention, 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 according to 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 electricity-carbon joint trading that considers source-load uncertainty and intraday electricity-carbon joint trading that considers transaction deviation costs.

[0045] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and wherein the processor executes the computer program to implement a step of a day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition.

[0046] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of a day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition are implemented.

[0047] Beneficial effects of the present invention: The day-ahead and intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided by the present invention performs outlier detection and correction based on the K_means clustering algorithm and the Newton interpolation method, thereby ensuring the accuracy of the carbon emission baseline, decomposing the carbon emissions of multiple entities in the park to the hourly level, obtaining the user's carbon emission baseline, and optimizing the carbon emission control accuracy of the park. By optimizing the electricity purchase and sales costs, distributed energy costs, and carbon trading costs, the optimal clearing of the electricity-carbon market is achieved, the trading deviation caused by inaccurate predictions is reduced, a model taking into account the trading deviation costs is constructed, and the market's response flexibility is improved. The present invention achieves better results in terms of control accuracy, trading deviation, and flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor. Among them:

[0049] Figure 1 This is an overall flow chart of the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the first embodiment of the present invention.

[0050] Figure 2 A diagram of anomaly detection and processing of historical energy consumption data for a day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0051] Figure 3 A graph of carbon baseline decomposition results based on historical energy consumption curves for the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0052] Figure 4 A distributed photovoltaic and wind power forecast output diagram for a day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0053] Figure 5 A load forecast value diagram 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.

[0054] Figure 6 This is a diagram of the electricity purchase structure in the day-ahead phase 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.

[0055] Figure 7 A graph of carbon emissions and carbon emission factors in the day-ahead phase 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.

[0056] Figure 8 This is a diagram of the intraday 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.

[0057] Fig. 9 A graph of intraday carbon emissions and carbon emission intensity factors for the day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition provided in the second embodiment of the present invention.

[0058] Fig.10 A user carbon emission reduction graph 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.

[0059] Fig.11 This is an overall module diagram of the day-ahead and intraday electricity-carbon joint trading system based on carbon emission baseline decomposition provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0061] Example 1

[0062] Reference Figure 1 , which is an embodiment of the present invention, provides a day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition, including:

[0063] S1: Detect and process outliers in the user’s historical energy consumption data.

[0064] Furthermore, outlier detection and processing of the user's historical energy consumption data includes outlier detection based on the K_means clustering algorithm.

[0065] It should be noted that objects are randomly selected from the historical energy consumption data as the initial cluster center, the distance from each sample to the cluster center is calculated, and the sample is classified into the class where the cluster center closest to it is located. The distance from the data point to the cluster center is represented by the Euclidean distance, which is expressed as:

[0066]

[0067] Among them, k is the number of cluster centers. The means of the newly formed classes are calculated as new cluster centers, and clustering is repeated until the criterion function no longer changes significantly or the clustered objects no longer change. The square error criterion is expressed as:

[0068]

[0069] Where E is the sum of the mean square errors between all objects in the historical energy consumption database and the cluster center, p is a point in the object space, and m i is cluster C i The mean of the class threshold is 3σ X The criteria setting is expressed as:

[0070]

[0071] Among them, X i is the monitoring data point for cluster classification, is the average value of the monitoring data, σ is the mean square error of the monitoring data, and the ratio of the absolute value of each data in the class from the average value to the mean square error is calculated, expressed as:

[0072]

[0073] If q j >3, the value is considered abnormal data, and the threshold of abnormal data between classes is set

[0074] Furthermore, outlier detection and processing of historical energy consumption data of users also includes outlier processing based on Newton interpolation method.

[0075] It should be noted that the Newton interpolation method is introduced to replace the outliers. The Newton interpolation function consists of two parts: the interpolation polynomial and the interpolation remainder. The interpolation remainder is ignored. The first-order mean difference is calculated by the interpolation polynomial, and then the k-order mean difference is calculated successively. Finally, the interpolation result is calculated, which is expressed as:

[0076] 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 )

[0077]

[0078] Among them, N n (x) is the interpolation polynomial.

[0079] S2: Decompose carbon emissions according to the user's historical energy consumption curve to obtain the carbon emission baseline of different users.

[0080] Furthermore, decomposing carbon emissions according to the user's historical energy consumption curve includes decomposing multiple subject users using decomposition factors.

[0081] It should be noted that the proportion of historical energy consumption in each period to total energy consumption constitutes the decomposition factor, and the product of the user's annual carbon emission limit and the decomposition factor is the carbon baseline decomposition result, expressed as:

[0082]

[0083] Among them, E i is the carbon emission baseline of user i, is the annual carbon quota of user i, d t is the historical electricity consumption in the tth period, and α is the decomposition factor.

[0084] Furthermore, a day-ahead electricity-carbon joint transaction is established that takes into account source-load uncertainty, including an objective function.

[0085] It should be noted that the optimization objective of day-ahead trading is to minimize the sum of the cost of electricity purchase and sale, distributed energy cost and carbon trading cost. The objective function is expressed as:

[0086] min F grid +F R +F C

[0087]

[0088] Among them, F grid 、F R 、F C They are the electricity purchase and sales costs, distributed energy costs and carbon trading costs of the interaction between the park's electricity sellers and the external power grid. They are the electricity purchase and sales price and the electricity purchase and sales volume of the park electricity sellers interacting with the external power grid. is the electricity selling price of distributed clean energy, The electricity sold by distributed clean energy to external power grids. is the carbon trading benchmark price, l is the interval length of carbon emissions, is the price growth rate, ΔE is the carbon emission trading volume, E t is the total carbon emissions of the park at time t.

[0089] S3: Establish day-ahead electricity-carbon joint trading that considers source-load uncertainty and intraday electricity-carbon joint trading that considers transaction deviation costs.

[0090] Furthermore, establishing a day-ahead electricity-carbon joint transaction that takes into account source-load uncertainty also includes constraints on day-ahead transactions.

[0091] It should be noted that the distributed energy output constraint is expressed as:

[0092]

[0093] in, is the output of distributed energy generator i at time t, is the upper limit of the output of distributed energy generators at any given moment, and the user reporting constraint is expressed as:

[0094]

[0095] in, and Declare the upper and lower limits of electricity consumption for users respectively. and The upper and lower limits of carbon emission reduction are declared for users respectively, and the power balance constraint is expressed as:

[0096]

[0097] in, is the load demand of user i at time t, is the electricity purchased from the external network at time t, and the carbon emissions of the park are expressed as:

[0098]

[0099] in, Carbon emissions calculated for purchased electricity, Carbon emissions reductions from distributed clean energy generation.

[0100] It should also be noted that in practice, there is a certain prediction error between the output value of new energy and load and the day-ahead prediction value. Taking into account the prediction error value, the actual output value of new energy and load is expressed as:

[0101]

[0102] in, is the actual output of distributed clean energy i at time t, The actual load demand of the user at the moment. The predicted output of distributed clean energy i at time t, is the predicted load demand of user i at time t. When the prediction error is considered, the power balance constraint containing random variables is expressed as:

[0103]

[0104] Among them, ω R,t and ω D,t are the predicted output errors of new energy and load respectively.

[0105] Furthermore, intraday electricity-carbon joint trading that takes into account transaction deviation costs includes substituting the known quantities solved in the day-ahead transaction into the intraday trading model for rolling calculations.

[0106] It should be noted that, based on the latest forecast information of new energy and load, the goal is to minimize the sum of the system's daily energy purchase cost, carbon trading cost, and penalty costs for each transaction deviation. The objective function is expressed as:

[0107] min F grid +F R +F C +F bias

[0108]

[0109] Among them, F bias Penalize costs for trading deviations, and They are the power unit adjustment cost for interaction with the upper network and the power unit adjustment cost for distributed clean energy. and They are the upper-level network interaction power adjustment amount and the distributed clean energy power adjustment amount respectively.

[0110] It should also be noted that the constraints of intraday electricity carbon trading also include basic power balance equation constraints, output units, user reporting constraints, and inequality constraints on interactive power constraints with the upper-level network, which are basically the same as the day-ahead electricity carbon trading.

[0111] It should also be noted that in the MATLAB environment, the Yalmip platform was used to establish a two-stage day-ahead and day-intraday electricity-carbon joint trading model for the industrial park, and the Cplex solver was called through the Yalmip toolbox to quickly solve it.

[0112] Example 2

[0113] Reference Figure 2-10 , which is an embodiment of the present invention, provides a day-ahead intraday electricity-carbon joint trading method based on carbon emission baseline decomposition. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0114] Taking the annual historical energy consumption data of a certain park user as an example, the outlier data is detected based on the K_means algorithm, and the 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. Figure 2 As shown, the blue line is the original user's annual historical energy consumption curve, the red dots are the detected abnormal data, and the green dots are the data after the abnormal values ​​are interpolated and replaced. The processed user historical energy consumption data is subjected to a unified carbon emission decomposition to obtain the carbon emission baselines of different users in the park. The decomposition results are shown in Figure 3 As shown in the figure, it can be seen that the trend of the carbon baseline decomposition result is roughly the same as the trend of the user's historical energy consumption curve. In the day-ahead stage, electricity carbon trading is carried out based on the distributed clean energy output and load forecast value, with a time scale of 1 hour. In the intraday stage, the forecast deviation and transaction deviation cost are considered, with a time scale of 15 minutes. Users adjust their electricity purchasing behavior and obtain carbon trading benefits through emission reduction behaviors such as purchasing green electricity and demand response. The distributed photovoltaic and wind power output in the day-ahead stage is as follows: Figure 4 As shown in Figure 2, the load demand forecast value is as follows: Figure 5 As shown in the figure, the electricity purchasing structure of users in the day-ahead stage is as follows: Figure 6 As shown in the figure, the results of the electricity-carbon trading show that in the electricity-carbon joint market, due to the low-carbon nature of green electricity, photovoltaic and wind power will be purchased first, followed by other power generation energy sources with relatively low carbon emission factors. The carbon emissions and carbon emission intensity of users in the day-ahead stage are shown in Figure 2. Figure 7As shown in the figure, it can be seen from the carbon emission intensity factor that at noon when photovoltaic power generation is high, the main source of electricity purchased by users is photovoltaic power, resulting in a lower carbon emission intensity factor for users. The electricity purchase structure, carbon emissions and carbon emission intensity factor of users in the intraday stage are shown as follows: Figure 8 and Fig. 9 As shown in the figure, users give priority to purchasing green electricity to avoid the cost of wind and solar power abandonment. Under the condition of considering the penalty of transaction volume deviation, the transaction volume during the day and the day before is as close as possible. When the clean energy power generation is large, users reduce carbon emissions by purchasing green electricity, and thus the carbon emission factor is reduced, as shown in the figure. Fig.10 The figure shows the carbon emission reduction of users. It can be found that the main source of carbon emission reduction is the purchase of green electricity, while the carbon emission reduction caused by demand response is relatively small.

[0115] Example 3

[0116] Reference Fig.11 , which is an embodiment of the present invention, provides a day-ahead intraday electricity-carbon joint trading system based on carbon emission baseline decomposition, including: a detection and processing module, a carbon emission decomposition module, and a transaction establishment module.

[0117] 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 according to the user's historical energy consumption curve to obtain the carbon emission baseline of different users; the transaction establishment module is used to establish day-ahead electricity-carbon joint transactions considering source-load uncertainty and intraday electricity-carbon joint transactions considering transaction deviation costs.

[0118] If the function is implemented in the form of 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 the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0119] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0120] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0121] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0122] It should be noted that the above embodiments are only used to illustrate the technical solutions 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 the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A day-ahead 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; Decompose carbon emissions based on the user's historical energy consumption curve to obtain carbon emission baselines for different users; Establish day-ahead electricity-carbon joint trading that takes into account source and load uncertainty, and intraday electricity-carbon joint trading that takes into account transaction deviation costs.

2. The method for day-ahead intraday electricity-carbon joint trading based on carbon emission baseline decomposition according to claim 1 is characterized by: The outlier detection and processing of the user's historical energy consumption data includes outlier detection based on the K-means clustering algorithm, randomly selecting objects from the historical energy consumption data as the initial cluster center, calculating the distance from each sample to the cluster center, and classifying the sample into the class where the cluster center closest to it is located. The distance from the data point to the cluster center is represented by the Euclidean distance, which is expressed as: Among them, k is the number of cluster centers. The means of the newly formed classes are calculated as new cluster centers, and clustering is repeated until the criterion function no longer changes significantly or the clustered objects no longer change. The square error criterion is expressed as: Where E is the sum of the mean square errors between all objects in the historical energy consumption database and the cluster center, p is a point in the object space, and m i is cluster C i The mean of the class threshold is 3σ X The criteria setting is expressed as: Among them, X i is the monitoring data point for cluster classification, is the average value of the monitoring data, σ is the mean square error of the monitoring data, and the ratio of the absolute value of each data in the class from the average value to the mean square error is calculated, expressed as: If q j >3, the value is considered abnormal data, and the threshold of abnormal data between classes is set 3. The method for day-ahead intraday electricity-carbon joint trading based on carbon emission baseline decomposition according to claim 2 is characterized by: The outlier detection and processing of the user's historical energy consumption data also includes outlier processing based on the Newton interpolation method. The Newton interpolation method is introduced to replace the outliers. The Newton interpolation function consists of an interpolation polynomial and an interpolation remainder. The interpolation remainder is ignored. The first-order mean difference is first calculated through the interpolation polynomial, and the k-order mean difference is calculated successively. Finally, the interpolation result is calculated, which is 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 ) Among them, N n (x) is the interpolation polynomial.

4. The method for day-ahead intraday electricity-carbon joint trading based on carbon emission baseline decomposition according to claim 3 is characterized by: The carbon emission decomposition based on the user's historical energy consumption curve includes decomposing multiple subject users using decomposition factors. The ratio of historical energy consumption in each period to total energy consumption constitutes the decomposition factor. The product of the user's annual carbon emission limit and the decomposition factor is the carbon baseline decomposition result, which is expressed as: Among them, E i is the carbon emission baseline of user i, is the annual carbon quota of user i, d t is the historical electricity consumption in the tth period, and α is the decomposition factor.

5. The method for day-ahead intraday electricity-carbon joint trading based on carbon emission baseline decomposition according to claim 4 is characterized by: The establishment of the day-ahead electricity-carbon joint transaction considering the uncertainty of source and load includes an objective function. The day-ahead transaction takes the minimum sum of the purchase and sale cost of electricity, the cost of distributed energy and the carbon transaction cost as the optimization goal. The objective function is expressed as: min F grid +F R +F C ΔE=E t -E i Among them, F grid 、F R 、F C They are the electricity purchase and sales costs, distributed energy costs, and carbon trading costs of the interaction between the park's electricity sellers and the external power grid. They are the electricity purchase and sales price and the electricity purchase and sales volume of the park electricity sellers interacting with the external power grid. is the electricity selling price of distributed clean energy, The electricity sold by distributed clean energy to external power grids. is the carbon trading benchmark price, l is the interval length of carbon emissions, is the price growth rate, ΔE is the carbon emission trading volume, E t is the total carbon emissions of the park at time t.

6. The method for day-ahead intraday electricity-carbon joint trading based on carbon emission baseline decomposition according to claim 5 is characterized by: The establishment of the day-ahead electricity-carbon joint transaction considering the uncertainty of source and load also includes the constraint conditions of the day-ahead transaction. The output constraint of distributed energy is expressed as: in, is the output of distributed energy generator i at time t, is the upper limit of the output of distributed energy generators at any given moment, and the user reporting constraint is expressed as: in, and Declare the upper and lower limits of electricity consumption for users respectively. and The upper and lower limits of carbon emission reduction are declared for users respectively, and the power balance constraint is expressed as: in, is the load demand of user i at time t, is the electricity purchased from the external network at time t, and the carbon emissions of the park are expressed as: in, Carbon emissions calculated for purchased electricity, Carbon emissions reductions from distributed clean energy generation.

7. The method for day-ahead intraday electricity-carbon joint trading based on carbon emission baseline decomposition according to claim 6 is characterized by: The intraday electricity-carbon joint transaction considering transaction deviation costs includes substituting the known quantities solved in the day-ahead transaction into the intraday transaction model for rolling calculation. According to the latest forecast information of new energy and load, the goal is to minimize the sum of the system's intraday energy purchase cost, carbon transaction cost and penalty costs of each transaction deviation. The objective function is expressed as: min F grid +F R +F C +F bias Among them, F bias Penalize costs for trading deviations, and They are the power unit adjustment cost for interaction with the upper network and the power unit adjustment cost for distributed clean energy. and They are the upper-level network interaction power adjustment amount and the distributed clean energy power adjustment amount respectively.

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

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

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

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