Electricity-carbon cooperative operation trading platform of power system

Abstract

The invention discloses an electricity-carbon collaborative operation transaction platform of an electric power system, relates to the technical field of data management, and solves the technical problem of low data sharing efficiency caused by lack of a classification strategy and priority management based on an aging threshold in a hybrid data transmission sharing process. Data are divided into high timeliness and low timeliness, a transmission strategy is optimized in a targeted mode, key data transmission priorities such as a priority value is calculated in a weighted mode by combining fault influence duration, timeliness attenuation rate and broadband occupancy rate, real-time carbon price and the like are improved, fragment length is dynamically adjusted according to a network MTU, and carbon transaction data transmission delay is reduced through reverse-order offset and multi-channel transmission. Meanwhile, timestamps and serial numbers are added, outdated data packets are rejected, power and carbon market data coding rules are unified based on a cross-domain data dictionary, standard format specification storage is adopted, and the data fusion efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the field of data management technology, and in particular to a power system electricity-carbon collaborative operation trading platform. Background Art

[0002] The need for data integration between power systems and carbon markets is becoming increasingly urgent.

[0003] However, in some existing technologies, the power sector mostly monitors real-time values, while the carbon market involves transaction records and accounting data. The coding rules and format standards of the two are not unified, resulting in low efficiency in cross-domain data sharing. At the same time, real-time power data needs to be updated in seconds, while the carbon market data is updated less frequently. Traditional processing solutions are difficult to take into account different timeliness requirements. Then, large-capacity data lacks dynamic encryption and sharded transmission mechanisms during the sharing process, and small-capacity data is susceptible to replay attacks or timing disorders. Finally, faced with network fluctuations during the mixed transmission of power and carbon market data, existing solutions cannot dynamically adjust transmission strategies based on timeliness and data characteristics, resulting in delays or loss of key data. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides a power system electricity-carbon collaborative operation trading platform, which solves the problem of low data sharing efficiency caused by the lack of classification strategy and priority management based on time thresholds in the hybrid data transmission and sharing process.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a power system electricity-carbon collaborative operation trading platform, comprising: The data verification and analysis module is used to encode and standardize the power system data and carbon market data transmitted by the power data acquisition module, verify the data integrity, timeliness and consistency, correct abnormal data, classify it, and transmit it to the shared analysis and processing module; The shared analysis and processing module is used to analyze the acquired classified data. If the network is congested, it quantifies the fault impact duration, timeliness sensitivity, and bandwidth occupancy rate of high-timeliness data, calculates the weighted priority processing value and sorts it, and outputs the priority sorting information to the shared encryption processing module. The shared encryption processing module is used to encrypt and transmit high-timeliness data based on the obtained priority information. Based on the priority information, the high-timeliness data is encrypted. If the data capacity exceeds the threshold, it is split into fixed-length segments and transmitted through multiple channels after adding random offsets; if it does not exceed the threshold, the timestamp and serial number are added for encryption, and the encrypted data is finally transmitted to the data management information output module.

[0006] As a further solution of the present invention, it also includes a power data acquisition module and a data management information output module; The power data acquisition module is used to collect power system data and carbon market data in the power system and transmit them to the data verification and analysis module. The power system data includes generator operating parameters, grid topology, real-time load data, and renewable energy output forecasts, while the carbon market data includes carbon quota allocation results, corporate carbon emission monitoring data, carbon trading history records, and carbon price fluctuation curves. The data management information output module is used to perform shared encryption processing on high-timeliness data based on the acquired shared encryption information.

[0007] As a further solution of the present invention, the specific method of classification after the data verification and analysis module corrects the abnormal data is as follows: Unify the coding and formatting of power system data and carbon market data to obtain pre-processed data, and verify the integrity, timeliness, and consistency of the pre-processed data; If everything is normal, a correct data verification result is generated. If any dimension is abnormal, an abnormal verification result is generated and the abnormal data is automatically corrected. The preprocessed data is divided into real-time power production data, carbon quota allocation data, and corporate carbon emission data by type, and the sharing scope of each type of data is marked and transmitted to the shared analysis and processing module.

[0008] As a further solution of the present invention, the specific manner in which the shared analysis and processing module analyzes the acquired classification data is as follows: All classified data is reclassified based on the timeliness of the classified data. Based on the data delay requirements, a timeliness threshold is set. Data with a delay below the threshold is marked as high-timeliness data, and data with a delay exceeding the threshold is marked as low-timeliness data. Taking time T as a cycle, calculate the average network speed within the cycle, count the number of times the difference between the network speed per unit time and the average exceeds the limit, calculate the proportion of these times, and compare them with the threshold set by the operator. If the proportion is greater than the threshold, the network is determined to be congested and a network congestion sharing analysis signal is generated. If the proportion is less than the threshold, the network is determined to be smooth and a network smoothness sharing analysis signal is generated. The two are analyzed separately.

[0009] As a further solution of the present invention, the specific manner in which the shared analysis processing module analyzes the network congestion and smooth shared analysis signals is as follows: Analyze the generated network smooth sharing analysis signal, use lightweight communication protocol to replace the traditional HTTP protocol, and lightweight communication protocol such as MQTT, AMQP, and generate smooth sharing analysis information, and transmit it to the data management information output module; Analyze the generated network speed sharing analysis signal, obtain all time-sensitive data, and label them with i, where i = 1, 2, ..., j, where j represents the number of time-sensitive data. Then, quantify the fault impact duration, time-sensitive sensitivity, and bandwidth utilization rate to obtain corresponding quantitative indicators. The fault impact duration T, attenuation rate α, and broadband occupancy rate B are weighted and the priority processing value Q corresponding to the high-timeliness data is calculated according to the formula Q=T×a1+α×a2+B×a3, where a1, a2, and a3 are the corresponding weight coefficients, and a1+a2+a3=1. The specific value is set by the operator according to the actual situation. At the same time, the data are sorted from large to small according to the priority processing value Q to generate priority sorting information and transmit it to the shared encryption processing module.

[0010] As a further solution of the present invention, the specific manner in which the shared analysis and processing module quantifies the fault impact duration, timeliness sensitivity, and bandwidth occupancy rate is as follows: Analyze the duration of the fault impact, obtain the fault processing window period T0 corresponding to the high-timeliness data i, and the proportion of data processing link time z, and then use the formula Calculate the fault impact duration T; Quantitative analysis of timeliness sensitivity is carried out according to the quantitative model α=e -λt The corresponding decay rate α is calculated, where e is a natural constant with a value of 2.71, λ is the decay constant, and t is the delay time; Analyze the broadband occupancy rate, obtain the data transmission rate corresponding to the high-timeliness data i, and obtain the total link bandwidth corresponding to the current shared network, and calculate it according to the formula The broadband occupancy rate B is calculated.

[0011] As a further solution of the present invention, the specific manner in which the shared encryption processing module performs encryption transmission processing on high-timeliness data is as follows: According to the priority sorting information, the corresponding high-timeliness data i is obtained, and the corresponding data capacity Li is obtained at the same time, and the data capacity Li is compared with the capacity threshold Ly. If the data capacity Li is greater than the capacity threshold Ly, it means that the corresponding high-timeliness data is large-capacity data, and a split encryption processing signal is generated. On the contrary, if the data capacity Li is less than the capacity threshold Ly, it means that the corresponding high-timeliness data is small-capacity data, and a timing addition processing signal is generated, and then the two are analyzed and processed separately.

[0012] As a further solution of the present invention, the specific manner in which the shared encryption processing module analyzes the split encryption processing signal and the timing addition processing signal is as follows: Analyze the generated segmented encryption processing signal to obtain the corresponding large-capacity data, split it into segments according to a fixed byte length, then add random sequence offsets to the obtained segments, and simultaneously add the obtained random sequence offsets to the segments in reverse order of the segment numbers. Then, perform multi-channel transmission on the added high-timeliness data to generate corresponding shared encryption information, and transmit it to the data management information output module; The generated timing is added and processed to analyze the signal, small-capacity data is obtained and timestamps and sequence numbers are added to the data packets, and shared encryption information is generated and transmitted to the data management information output module.

[0013] The present invention provides a trading platform for the coordinated operation of electricity and carbon in power systems. Compared with existing technologies, it has the following advantages: The present invention unifies the coding rules of electricity and carbon market data based on a cross-domain data dictionary, adopts standard format storage, improves data fusion efficiency, automatically corrects abnormal data through integrity, consistency, and timeliness verification, and reduces the amount of manual review.

[0014] Based on the data delay requirements, the present invention divides data into two categories: high / low timeliness, optimizes the transmission strategy in a targeted manner, and calculates the priority value based on the weighted calculation of the fault impact duration, timeliness decay rate, and broadband occupancy rate. It improves the data transmission priority of key data such as real-time carbon prices, dynamically adjusts the fragment length according to the network MTU, reduces the carbon trading data transmission delay through reverse offset + multi-channel transmission, and adds timestamps and sequence numbers to reject outdated data packets. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a schematic diagram of data classification of the present invention. DETAILED DESCRIPTION

[0016] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0017] See also Figure 1 This application provides a power system electric carbon collaborative operation trading platform, including power data acquisition module, data verification and analysis module, shared analysis and processing module, shared encryption processing module and data management information output module, and combined with Figure 1It can be known that the functional modules are electrically connected in a unidirectional manner.

[0018] The power data acquisition module is used to collect power system data and carbon market data in the power system, and transmit both to the data verification and analysis module. The power system data includes the operating parameters of the generator set (output, coal consumption, oil consumption, etc.), the grid topology, real-time load data, and renewable energy output forecasts, while the carbon market data includes carbon quota allocation results, corporate carbon emission monitoring data, carbon trading history records, and carbon price fluctuation curves.

[0019] A data verification and analysis module is used to uniformly encode the acquired power system data and carbon market data, and standardize their data formats. Specifically, based on a cross-domain data dictionary, the module unifies the encoding rules for power and carbon market data (for example, unifying the unit of "carbon emission factor" to "tons of CO2 / 10,000 kWh"), and uses JSON / XML format to standardize data storage and transmission to obtain pre-processed data. This module then performs verification analysis on the obtained pre-processed data, and the verification analysis specifically includes integrity, timeliness, and consistency. Completeness check: Ensure that there are no missing fields in power operation data (such as unit start and stop status) and carbon accounting data (such as fuel consumption); Consistency check: Verify the logical consistency of power generation energy consumption data and carbon emission calculation results (e.g. coal consumption × carbon emission factor = carbon emissions); Timeliness verification: Set the data update frequency (e.g., real-time power data is updated in seconds, carbon market data is synchronized in minutes), and establish a data delay warning mechanism, as shown in the following data delay warning mechanism table: If the integrity, timeliness and consistency are all normal during the verification process, it means that the pre-processed data is correct, and a correct data verification result is generated. On the contrary, if any of the integrity, timeliness and consistency are abnormal during the verification process, it means that the pre-processed data is abnormal, and a data verification abnormal result is generated. Further, the abnormal data is automatically corrected based on the generated verification abnormal result. For the missing data that can be deduced, the mean interpolation, regression prediction and other algorithms are used to automatically fill in the data. For example, if the carbon emission data for a certain period is missing, it can be estimated based on the average carbon emission intensity of the same type of units. When it is found that the power generation energy consumption is inconsistent with the carbon emissions, the original data source is checked first. If it is a calculation error, the formula parameters are automatically corrected; if it is a data entry error, it is marked for manual review; Then the preprocessed data is obtained and classified. Specifically, the preprocessed data is classified according to the data type to obtain classified data, such as real-time data on power production, carbon quota allocation data and corporate carbon emission data. At the same time, the classified data is marked with a shared range and transmitted to the shared analysis and processing module.

[0020] And the shared range allocation table is as follows: Shared analysis and processing module, which is used to share and manage the acquired classified data, obtain all classified data, and perform secondary classification based on the timeliness of the classified data to obtain high-timeliness data and low-timeliness data. The timeliness classification is specifically determined based on the data delay required by the data, and a corresponding threshold is set. If the threshold is exceeded, it is classified as low-timeliness data, and if it is below the threshold, it is classified as high-timeliness data; Taking time T1 as a period, analyze the current shared network situation, calculate the average network speed within time period T, and calculate the difference between the network speed per unit time and the average to obtain the network difference. Then filter the number of times the network difference is greater than the preset difference, calculate the corresponding number of times ratio, and compare the obtained number of times ratio with the ratio threshold. The specific value of the ratio threshold is set by the operator; If the number of times is greater than the ratio threshold, it means that the current shared network is congested, and a network congestion sharing analysis signal is generated. Conversely, if the number of times is less than the ratio threshold, it means that the current shared network is not congested, and a network smooth sharing analysis signal is generated. For example, the module obtains three types of data: real-time carbon price updates (delay requirement ≤ 500ms), corporate carbon quota declarations (delay requirement ≤ 2s), and monthly carbon trading statistics (delay requirement ≤ 10min). According to the threshold (the high timeliness threshold is set to 1s), real-time carbon price updates are divided into high timeliness data, and the other two categories are low timeliness data.

[0021] Set the time period T to 5 minutes, the preset difference to 0.8Mbps, and the proportion threshold to 30%. Monitoring found that network speed fluctuated frequently during peak trading hours, with abnormal fluctuations accounting for 45% of the time, generating a network congestion sharing analysis signal.

[0022] Analyze the generated network smooth sharing analysis signal, use lightweight communication protocol to replace the traditional HTTP protocol, and lightweight communication protocol such as MQTT, AMQP, and generate smooth sharing analysis information, and transmit it to the data management information output module; Analyze the generated network speed sharing analysis signal, obtain all time-sensitive data, and label them with i, where i = 1, 2, ..., j, where j represents the number of time-sensitive data. Then, prioritize the time-sensitive data based on three aspects: fault impact duration, time-sensitive sensitivity, and bandwidth utilization. Analyze the duration of the fault impact, obtain the fault processing window period T0 corresponding to the high-timeliness data i, and the proportion of data processing link time z, and then use the formula Calculate the fault impact duration T; Quantitative analysis of timeliness sensitivity is carried out according to the quantitative model α=e -λt The corresponding decay rate α is calculated, where e is a natural constant with a value of 2.71, λ is the decay constant, and t is the delay time; Analyze the broadband occupancy rate, obtain the data transmission rate corresponding to the high-timeliness data i, and obtain the total link bandwidth corresponding to the current shared network, and calculate it according to the formula Calculate the broadband occupancy rate B; The fault impact duration T, attenuation rate α, and broadband occupancy rate B are weighted and the priority processing value Q corresponding to the high-timeliness data is calculated according to the formula Q=T×a1+α×a2+B×a3, where a1, a2, and a3 are the corresponding weight coefficients, and a1+a2+a3=1. The specific value is set by the operator according to the actual situation. At the same time, the data are sorted from large to small according to the priority processing value Q to generate priority sorting information and transmit it to the shared encryption processing module.

[0023] A shared encryption processing module is used to encrypt and transmit high-timeliness data based on the obtained priority sorting information. The module obtains the corresponding high-timeliness data i according to the priority sorting information, and simultaneously obtains its corresponding data capacity Li. The data capacity Li is compared with the capacity threshold Ly, and the specific value of the capacity threshold is set by the operator. If the data capacity Li is greater than the capacity threshold Ly, it indicates that the corresponding high-timeliness data is large-capacity data, and a split encryption processing signal is generated. Conversely, if the data capacity Li is less than the capacity threshold Ly, it indicates that the corresponding high-timeliness data is small-capacity data, and a time-series addition processing signal is generated. The two are then analyzed and processed separately; The generated split encryption processing signal is analyzed to obtain the corresponding large-capacity data, and the data is split according to a fixed byte length to obtain split segments. The specific value of the fixed byte length is adjusted according to the network MTU (maximum transmission unit), such as 1KB or 4KB. Then, a random sequence offset is added to the obtained split segments. The random sequence offset is generated by using a cryptographically secure random number generator, such as Python's secrets module to generate an unpredictable offset sequence. At the same time, a corresponding index header is generated according to the obtained random offset, and the index header is located at the receiving end. At the same time, the obtained random sequence offset is added to the split segments, and the specific adding method is to add the data in reverse order according to the number of the split segments. Then, the added high-timeliness data is transmitted through multiple channels to generate the corresponding shared encryption information, and the information is transmitted to the data management information output module. When an enterprise conducts a 100,000-ton carbon quota transaction, the transaction instructions and detailed data volume is about 5MB. To prevent tampering or interception by middlemen, the fragment length is set to 2KB and split into 2500 fragments. Each fragment is encrypted with a different AES-256 key, the offset array length is 2500, and the index header is encrypted using the SM2 algorithm; the fragments are transmitted through three different 5G slice channels and decrypted and verified after reassembly at the receiving end.

[0024] The generated timing is added with processing signals for analysis, small-capacity data is obtained, and timestamps and serial numbers are added to the data packets. The receiving end verifies the validity of the timing, rejects outdated or duplicate data packets, prevents replay attacks such as forging historical carbon trading instructions, and generates shared encryption information, which is then transmitted to the data management information output module.

[0025] The data management information output module is used to perform shared encryption processing on high-timeliness data based on the acquired shared encryption information.

[0026] Some of the data in the above formulas are calculated based on their numerical values ​​and are not substituted into parameter units for calculation. At the same time, the contents not described in detail in this specification belong to the existing technology known to those skilled in the art.

[0027] The above embodiments are only used to illustrate the technical method of the present invention and are not intended to limit the present invention. 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 method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. The power system electricity-carbon collaborative operation trading platform is characterized by: include: The data verification and analysis module is used to encode and standardize the power system data and carbon market data transmitted by the power data acquisition module, verify the data integrity, timeliness and consistency, correct abnormal data and classify it, and transmit the classified data to the shared analysis and processing module; The shared analysis and processing module is used to analyze the acquired classified data. If the network is congested, it quantifies the fault impact duration, timeliness sensitivity, and bandwidth occupancy rate of high-timeliness data, calculates the weighted priority processing value and sorts it, and outputs the priority sorting information to the shared encryption processing module. The shared encryption processing module is used to encrypt and transmit high-timeliness data based on the obtained priority information. Based on the priority information, the high-timeliness data is encrypted. If the data capacity exceeds the threshold, it is split into fixed-length segments and transmitted through multiple channels after adding random offsets; if it does not exceed the threshold, the timestamp and serial number are added for encryption, and the encrypted data is finally transmitted to the data management information output module.

2. The power system electricity-carbon collaborative operation trading platform according to claim 1 is characterized in that: It also includes a power data acquisition module and a data management information output module; The power data acquisition module is used to collect power system data and carbon market data in the power system and transmit them to the data verification and analysis module; The power system data includes generator operating parameters, grid topology, real-time load data, and renewable energy output forecasts; the carbon market data includes carbon quota allocation results, corporate carbon emission monitoring data, carbon trading history records, and carbon price fluctuation curves; The data management information output module is used to perform shared encryption processing on high-timeliness data based on the acquired shared encryption information.

3. The power system electricity-carbon collaborative operation trading platform according to claim 1 is characterized in that: The specific method of classification after the data verification and analysis module corrects abnormal data is as follows: Unify the coding and formatting of power system data and carbon market data to obtain pre-processed data, and verify the integrity, timeliness, and consistency of the pre-processed data; If everything is normal, a correct data verification result is generated. If any dimension is abnormal, an abnormal verification result is generated and the abnormal data is automatically corrected. The preprocessed data is divided into real-time power production data, carbon quota allocation data, and corporate carbon emission data by type, and the sharing scope of each type of data is marked and transmitted to the shared analysis and processing module.

4. The power system electricity-carbon collaborative operation trading platform according to claim 1 is characterized in that: The specific method in which the shared analysis and processing module analyzes the acquired classification data is as follows: All classified data is reclassified based on the timeliness of the data. A timeliness threshold is set based on the data latency requirements. If the data latency is lower than the timeliness threshold, it is marked as high-timeliness data. If the data latency exceeds the timeliness threshold, it is marked as low-timeliness data. Taking time T1 as the cycle, calculate the average network speed within the cycle, count the number of times the difference between the network speed per unit time and the average exceeds the limit, calculate the proportion of the number of times it exceeds the limit, and compare it with the threshold set by the operator. If the proportion is greater than the threshold, it is determined that the network is congested and a network congestion sharing analysis signal is generated. If the proportion is less than the threshold, it is determined that the network is smooth and a network smooth sharing analysis signal is generated. The two are analyzed separately.

5. The power system electricity-carbon collaborative operation trading platform according to claim 4 is characterized in that: The specific method in which the shared analysis processing module analyzes the network congestion shared analysis signal and the smooth shared analysis signal is as follows: Analyze the generated network smooth sharing analysis signal, use a lightweight communication protocol to replace the traditional HTTP protocol, generate smooth sharing analysis information, and transmit it to the data management information output module; Analyze the generated network speed sharing analysis signal, obtain all time-sensitive data and label them as i, where i = 1, 2, ..., j, where j represents the number of time-sensitive data. Then, quantify the fault impact duration, time-sensitive sensitivity, and bandwidth utilization rate to obtain corresponding quantitative indicators. The fault impact duration T, attenuation rate α, and broadband occupancy rate B are weighted and the priority processing value Q corresponding to the high-timeliness data is calculated according to the formula Q=T×a1+α×a2+B×a3, where a1, a2, and a3 are the corresponding weight coefficients. At the same time, the data is sorted from large to small according to the priority processing value Q to generate priority sorting information, which is then transmitted to the shared encryption processing module.

6. The power system electricity-carbon collaborative operation trading platform according to claim 5 is characterized in that: The specific method in which the shared analysis and processing module performs quantitative analysis on the fault impact duration, timeliness sensitivity, and bandwidth occupancy rate is as follows: Quantify the duration of the fault impact and obtain the fault processing window period T0 corresponding to the high-timeliness data i, as well as the proportion of the data processing link time z. Then, according to the formula Calculate the fault impact duration T; Quantitative analysis of timeliness sensitivity is carried out according to the quantitative model α=e -λt The corresponding decay rate α is calculated, where e is a natural constant with a value of 2.71, λ is the decay constant, and t is the delay time; Quantitatively analyze the broadband occupancy rate to obtain the data transmission rate corresponding to the high-time data i, and at the same time obtain the total link bandwidth corresponding to the current shared network, and calculate it according to the formula The broadband occupancy rate B is calculated.

7. The power system electricity-carbon collaborative operation trading platform according to claim 1 is characterized in that: The specific method in which the shared encryption processing module performs encryption transmission processing on high-timeliness data is as follows: According to the priority sorting information, the corresponding high-timeliness data i is obtained, and the corresponding data capacity Li is obtained at the same time, and the data capacity Li is compared with the capacity threshold Ly. If the data capacity Li is greater than the capacity threshold Ly, it means that the corresponding high-timeliness data is large-capacity data, and a split encryption processing signal is generated. On the contrary, if the data capacity Li is less than the capacity threshold Ly, it means that the corresponding high-timeliness data is small-capacity data, and a timing addition processing signal is generated, and then the two are analyzed and processed separately.

8. The power system electricity-carbon coordinated operation trading platform according to claim 7 is characterized in that: The specific method in which the shared encryption processing module analyzes the split encryption processing signal and the timing addition processing signal is as follows: Analyze the generated split encryption processing signal to obtain the corresponding large-capacity data, split it into segments according to a fixed byte length, then add random sequence offsets to the obtained segments, and add the random sequence offsets to the segments in reverse order of the segment numbers. Then, transmit them through multiple channels to generate corresponding shared encryption information, and transmit it to the data management information output module; The generated timing is added and processed to analyze the signal, small-capacity data is obtained and timestamps and sequence numbers are added to the data packets, and shared encryption information is generated and transmitted to the data management information output module.

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