A green electricity data processing method, device, equipment, medium and product

Abstract

The application discloses a green electricity data processing method, device, equipment, medium and product, comprising: acquiring the buyer bidding interval of a green electricity demand side and the seller offer of a green electricity supply side, and determining a target green electricity supply side and a plurality of candidate green electricity demand sides matched with the price of the target green electricity supply side; acquiring the production cost data and carbon emission reduction data of the target green electricity supply side; determining the incentive cost data of each candidate green electricity demand side according to the carbon emission reduction data and the attribute information of the demand side, and determining the transaction cost data of each candidate green electricity demand side according to the incentive cost data and the production cost data; determining the target green electricity demand side from the plurality of candidate green electricity demand sides according to the transaction cost data, and performing green electricity data interaction processing based on the target green electricity supply side and the target green electricity demand side. Through the embodiment of the application, the matching efficiency of the buyer and the seller in the transaction process to deliver the satisfied green electricity quantity and green electricity price is improved.

Description

Technical Field

[0001] This invention relates to the field of green electricity technology, specifically to a method, apparatus, equipment, medium, and product for green electricity data processing. Background Technology

[0002] Currently, renewable green energy is widely used in industrial production and daily life to replace fossil fuels that easily produce greenhouse gases or pollutants.

[0003] In related technologies, green electricity trading has become the core link connecting new energy power generation and end-use low-carbon energy. By establishing a tradable carbon emission quota market and setting an upper limit on total emissions, companies can buy and sell quotas based on their actual emissions, thereby achieving the overall emission reduction target at the lowest social cost.

[0004] However, in the process of green electricity trading, green electricity suppliers and green electricity demanders are mostly operating independently. The former focuses on electricity delivery and green certificate accounting, while the latter relies on electricity price signals or load regulation guidelines. In the trading process, they simply obtain the corresponding green energy price data for carbon emission reduction through electricity prices, resulting in a lack of feedback and coordination based on green electricity demanders. This makes it impossible for both buyers and sellers to achieve the optimal transaction cost amount and the quantity of green electricity delivered. Summary of the Invention

[0005] In view of the above problems, a method, apparatus, electronic device, storage medium, and program product for processing green electricity data are proposed to overcome or at least partially solve the above problems, including:

[0006] A method for processing green electricity data, the method comprising:

[0007] Obtain the bid range of green electricity demanders and the bids of green electricity suppliers;

[0008] Based on the buyer's bidding range and the seller's offer, a target green energy supplier and multiple candidate green energy demanders whose prices match the target green energy supplier are determined;

[0009] Obtain the production cost data and carbon emission reduction data of the target green electricity supplier;

[0010] Determine the demander attribute information for each candidate green energy demander;

[0011] Based on the carbon emission reduction data and the demand-side attribute information, determine the incentive cost data for each candidate green electricity demander, and based on the incentive cost data and the production cost data, determine the transaction cost data for each candidate green electricity demander.

[0012] Based on the transaction cost data, a target green electricity demander is determined from the plurality of candidate green electricity demanders, and green electricity data interaction processing is performed based on the target green electricity supplier and the target green electricity demander.

[0013] Optionally, obtaining the bid range of green electricity demanders and the bid price of green electricity suppliers includes:

[0014] Determine the amount of green electricity generated by the green electricity supplier;

[0015] Based on the green electricity generation, determine the carbon emission reduction data of the green electricity supplier;

[0016] Based on the carbon emission reduction data, regional benchmark electricity price, and premium factor, the seller's offer price from the green electricity supplier is determined.

[0017] Optionally, determining the incentive cost data for each candidate green electricity demander based on the carbon emission reduction data and the demand-side attribute information includes:

[0018] Determine the carbon emission data for the target region where each candidate green energy demander is located;

[0019] Obtain the green energy consumption of each candidate green energy demander;

[0020] Based on the carbon emission data and the green electricity consumption, the target feedback coefficient is determined;

[0021] Based on the target feedback coefficient, the carbon emission reduction data, and the demand-side attribute information, the incentive cost data for each candidate green electricity demander is determined.

[0022] Optionally, determining the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demand-side attribute information includes:

[0023]

[0024] in, Incentive cost data for each candidate green electricity demander, For the carbon emission reduction data, For the aforementioned demand-side attribute information, Let be the target feedback coefficient, j be the j-th candidate green electricity demander, and t be a preset time period.

[0025] Optionally, determining the target feedback coefficient based on the carbon emission data and the green electricity consumption includes:

[0026] Based on the carbon emission data and the regional carbon emission threshold, a first feedback coefficient is determined;

[0027] Obtain the green electricity consumption of each candidate green electricity demander and the green electricity generation of the target area where each candidate green electricity demander is located;

[0028] The second feedback coefficient is determined based on the green electricity generation and the green electricity consumption.

[0029] The target feedback coefficient is determined based on the first feedback coefficient and the second feedback coefficient.

[0030] Optionally, determining the carbon emission data of the region where each candidate green electricity demander is located includes:

[0031] Obtain the total power generation of the target area where each candidate green energy demander is located;

[0032] The first carbon emission data is determined based on the total power generation.

[0033] When there is cross-regional power transmission in the target area where each candidate green electricity demander is located, the second carbon emission data is determined based on the cross-regional power difference.

[0034] Based on the first carbon emission data and the second carbon emission data, determine the carbon emission data of the target area where each candidate green electricity demander is located.

[0035] Optionally, after the green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander, the method further includes:

[0036] Determine the transmission lines from the target green energy supplier to the target green energy demander;

[0037] Obtain the rated capacity of the power transmission line of the transmission line;

[0038] Based on the rated capacity and safety margin coefficient of the transmission line, determine the maximum amount of green electricity that can be transmitted in real time to the target green electricity demander in the target green electricity supply direction;

[0039] Based on the maximum amount of green electricity transmitted, control the maximum upper limit of the amount of green electricity supplied in real time to the target green electricity demander from the target green electricity supply direction.

[0040] Optionally, after the green electricity trading process is performed based on the target green electricity supplier and the target green electricity demander, the process further includes:

[0041] Determine the power shortage rate of the target area where each candidate green energy demander is located;

[0042] Based on the power shortage rate, the electricity consumption results of the target area where each candidate green electricity demander is located are determined;

[0043] Based on the electricity consumption results, the bid range for buyers of green electricity demand will be adjusted.

[0044] An apparatus for green electricity data processing, comprising:

[0045] The quotation and bidding module is used to obtain the bid range of green electricity demanders and the bids of green electricity suppliers.

[0046] The seller and buyer candidate module is used to determine the target green electricity supplier and multiple candidate green electricity demanders whose prices match the target green electricity supplier's price, based on the buyer's bidding range and the seller's quotation.

[0047] The supply information acquisition module is used to acquire the production cost data and carbon emission reduction data of the target green electricity supplier.

[0048] The demand information determination module is used to determine the demander attribute information for each candidate green electricity demander;

[0049] The total transaction cost determination module is used to determine the incentive cost data of each candidate green electricity demander based on the carbon emission reduction data and the demander attribute information, and to determine the transaction cost data of each candidate green electricity demander based on the incentive cost data and the production cost data.

[0050] The green electricity interaction processing module is used to determine the target green electricity demander from the multiple candidate green electricity demanders based on the transaction cost data, and to perform green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander.

[0051] An electronic device includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method described above.

[0052] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0053] A computer program product, wherein the computer program, when executed by a processor, implements the method described above.

[0054] The embodiments of the present invention have the following advantages:

[0055] In this embodiment of the invention, the following steps are taken: First, the bid range of green electricity demanders and the seller's price quote of green electricity suppliers are obtained. Second, based on the bid range and the seller's price quote, a target green electricity supplier and multiple candidate green electricity demanders matching the target green electricity supplier's price are determined. Third, the production cost data and carbon emission reduction data of the target green electricity supplier are obtained. Fourth, the demand attribute information of each candidate green electricity demander is determined. Fifth, based on the carbon emission reduction data and the demand attribute information, the incentive cost data of each candidate green electricity demander is determined, and based on the incentive cost data and the production cost data, the transaction cost data of each candidate green electricity demander is determined. Sixth, based on the transaction cost data, a target green electricity demander is determined from the multiple candidate green electricity demanders, and green electricity data interaction processing is performed based on the target green electricity supplier and the target green electricity demander. This approach first matches potential buyers and sellers, then obtains data on the green electricity production costs of green electricity suppliers and the incentive costs of green electricity demanders to calculate the optimal transaction cost. This effectively solves the problem of lack of coordination between green electricity suppliers and demanders, and improves the matching efficiency of buyers and sellers in delivering satisfactory quantities and prices of green electricity during the transaction process. Attached Figure Description

[0056] Figure 1 This is a flowchart of a method for processing green electricity data disclosed in an embodiment of the present invention;

[0057] Figure 2 This is a matching algorithm logic diagram of a green electricity data processing method disclosed in an embodiment of the present invention;

[0058] Figure 3 This is a flowchart of another method for processing green electricity data disclosed in an embodiment of the present invention;

[0059] Figure 4 This is a flowchart of another method for processing green electricity data disclosed in an embodiment of the present invention;

[0060] Figure 5 This is a structural block diagram of a green electricity data processing device disclosed in an embodiment of the present invention. Detailed Implementation

[0061] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] A carbon footprint is the total amount of greenhouse gas emissions that an individual, organization, product, service, or activity directly or indirectly produces throughout its entire life cycle. It is usually measured in carbon dioxide equivalents (CO2). A carbon footprint is a quantitative representation of the total amount of carbon emissions.

[0063] Reference Figure 1 The diagram illustrates a flowchart of a method for processing green electricity data according to an embodiment of the present invention, which may specifically include the following steps:

[0064] Step 101: Obtain the bid range of green electricity demanders and the bid price of green electricity suppliers.

[0065] In some examples, the green electricity supplier can be a unit capable of producing and providing new or renewable energy, and the green electricity demander can be a unit that requires new or renewable energy to meet its needs.

[0066] Through a blockchain trading platform for green electricity transactions, it is possible to obtain units in a preset area that produce new energy or renewable energy and can provide new energy or renewable energy to other units, determine the seller's price for the new energy or renewable energy provided by the units, and then obtain green electricity demand users in the preset area who generate carbon emissions and need new energy or renewable energy to achieve carbon emission balance, and determine the buyer's bidding range issued by the green electricity demand users.

[0067] In a practical application, taking a cross-provincial photovoltaic green electricity transaction scenario in a certain summer as an example, through the blockchain trading platform for green electricity transactions, a photovoltaic power station in area A (green electricity supplier I1, installed capacity 100MW) was acquired. The seller's bid for the photovoltaic power station in area A was... The seller's quoted price for the B-zone wind power station (green electricity supplier I2, installed capacity 80MW) is... Area A includes chemical enterprises (green electricity demanders, industrial user J1, with a buyer's bidding range of 0.72-0.85 yuan / kWh) and commercial complexes (green electricity demanders, commercial user J2, with a buyer's bidding range of 0.59-0.65 yuan / kWh); Area B includes residential communities (green electricity demanders, residential user J3). The grid parameters are as follows: Area A's average carbon emission factor f(A) = 0.9 kgCO2 / kWh (85% thermal power), Area B's f(B) = 0.5 kgCO2 / kWh (40% wind and solar power); the inter-regional transmission line capacity P... 输max =50MW, transmission loss rate γ=4%; Carbon market data: Carbon quota price in area A is C 配额 (A) = 85 yuan / ton CO2, and the carbon quota price in area B is C. 配额 (B) = 70 yuan / ton CO2.

[0068] Furthermore, at the edge processing layer, by preprocessing the data and generating dynamic carbon footprint labels, the data can be tagged. Blockchain is used to manage the data with the tags throughout its entire lifecycle, and smart contracts are used to complete the issuance of green certificates and the green electricity transactions between buyers and sellers.

[0069] In practical applications, by setting up tables such as Table 1 and Table 2, the data in the calculation process is divided into multiple tables according to different quantity types, and stored in the blockchain based on the mapping method between the table headers and table contents:

[0070] Table 1

[0071]

[0072] Table 2

[0073]

[0074] Step 102: Based on the buyer's bidding range and the seller's quotation, determine the target green electricity supplier and multiple candidate green electricity demanders whose prices match the target green electricity supplier.

[0075] In some examples, the buyer's bidding range can be the demand bidding range set by the candidate buyer's willingness to reduce carbon emissions.

[0076] Based on the buyer's bidding range and the seller's quotation, when the obtained seller's quotation falls within the buyer's bidding range set by the candidate buyer's willingness to reduce carbon emissions, the seller corresponding to the current seller's quotation can be identified as the target green electricity supplier, while the green electricity demanders who set the bidding range are identified as multiple candidate green electricity demanders.

[0077] In practical applications, the bidding range for green electricity demanders can be determined by the regional benchmark electricity price of the second region where the buyer is located, the amount of carbon emission reductions, and the buyer's carbon cost affordability coefficient. Specifically:

[0078]

[0079] j represents the user input, and t represents the time. Let t be the regional benchmark electricity price. λ represents the instantaneous carbon emission reduction. j Let be the carbon cost affordability coefficient for the j-th user (0.3-0.5 for industrial users, 0.2-0.3 for commercial users).

[0080] It should be noted that industrial users (especially high-energy-consuming industries such as steel, chemicals, and cement) are the main contributors to carbon emissions, with carbon costs accounting for 3%-5% of their production costs. They have a rigid demand for the "low-carbon value" of green electricity, facing high carbon cost pressures and a strong willingness to pay. In contrast, commercial users (such as supermarkets, office buildings, and hotels) have much lower carbon emission intensity than industrial users, with carbon costs accounting for less than 1% of their revenue. Their green electricity purchases are more driven by "ESG image building needs," with lower low-carbon cost pressures and relatively limited willingness to pay. Commercial users' annual carbon emissions... With approximately 0.5-1 tonnes (CO2 / 10,000 RMB) in revenue, carbon allowance costs account for only 0.5%-1% of revenue, indicating a weak "rigid emission reduction demand" for green electricity. Furthermore, electricity costs for commercial users account for 8%-12% of operating costs (lower than industrial costs), resulting in narrow profit margins (e.g., supermarket gross profit margins are approximately 10%-15%). Excessive green electricity premiums would lead to increased operating costs; therefore, the upper limit of the coefficient is set at 0.3 (corresponding to a premium of approximately 0.04-0.06 RMB / kWh), matching the cost affordability of commercial users. Thus, the carbon cost affordability coefficients set for different user types (attribute information of green electricity demanders) are not the same. The highest bid from the buyer will be lower than the buyer's budget limit and demand urgency weight, but the specific values ​​are not specifically limited in the embodiments of this invention.

[0081] For industrial user J1 (Area A): Carbon cost affordability coefficient λ1 = 0.4 (high energy-consuming enterprise). Substitute the parameters into the above model formula:

[0082]

[0083] Actual declared price range: 0.72-0.85 yuan / kWh.

[0084] Commercial user J2 (Area A): λ2 = 0.25 (commercial complex), substitute the parameters according to the above model formula:

[0085]

[0086] Actual declared price range: 0.59-0.65 yuan / kWh.

[0087] In some embodiments of the present invention, obtaining the buyer's bidding range for green electricity demanders and the seller's bid for green electricity suppliers includes:

[0088] Sub-step 11: Determine the amount of green electricity generated by the green electricity supplier.

[0089] In some examples, the green electricity generation may include one or more of the following: data on new energy generation from photovoltaic, wind, and hydropower.

[0090] A data perception layer can be constructed by deploying distributed acquisition terminals, including photovoltaic irradiance and wind speed sensors of new energy power plants and energy consumption monitoring devices of transmission lines, to monitor the green electricity generation generated by the green electricity supplier.

[0091] Sub-step 12: Determine the carbon emission reduction data of the green electricity supplier based on the green electricity generation.

[0092] In some examples, carbon reduction data can be the amount of carbon emissions reduced over a period of time compared to the amount of fossil fuels consumed in green electricity generation.

[0093] By determining the instantaneous output data of the green electricity supplier, the instantaneous carbon emission reduction within a preset time period is determined based on the instantaneous output data. Based on the instantaneous carbon emission reduction, a daily cumulative calculation method is used to determine the instantaneous carbon emission reduction as the carbon emission reduction over a period of time.

[0094] In practical applications, the instantaneous carbon emission reduction can be determined by the instantaneous power output (instantaneous power generation data), the grid average carbon emission factor, the power generation carbon emission factor, and the inter-regional transmission loss rate generated during the process. :

[0095]

[0096] t represents time (h), and Δt represents the calculation period (taken as 0.25 hours, i.e. 15 minutes).

[0097] Let t be the instantaneous power output (MW) at time t;

[0098] Let x be the average carbon emission factor of the regional power grid at time t (x kg CO2 / kWh).

[0099] The carbon emission factor for new energy power generation is 0.03 kg CO2 / kWh for photovoltaic power and 0.02 kg CO2 / kWh for wind power.

[0100] The cross-regional power transmission loss rate (%) at time t;

[0101] It should be noted that the calculation period Δt does not necessarily have to be 0.25 hours (15 minutes). The specific time value can be set according to the actual situation. In this embodiment of the invention, July 15 is selected as a typical day, and 96 calculation periods (t=1 to 96) are divided at 15-minute intervals. The focus is on showing the calculation process of the period from 10:00 to 10:15 (t=41).

[0102] Therefore, we can conclude that: Photovoltaic power station in area A:

[0103]

[0104] Wind power station in Zone B (local consumption with no cross-regional losses):

[0105]

[0106] Sub-step 13: Determine the seller's offer price from the green electricity supplier based on the carbon emission reduction data, regional benchmark electricity price, and premium coefficient.

[0107] In some examples, the regional benchmark electricity price can be the benchmark level of the on-grid electricity price for power generation companies within a specific region; the premium factor can be determined based on the volatility of carbon emission reductions.

[0108] The seller's bid for green electricity can be calculated using instantaneous carbon emission reductions, regional benchmark electricity prices, and corresponding premium coefficients.

[0109] In practical applications, instantaneous carbon emission reduction (instantaneous carbon emission reduction) is used to measure carbon emission reduction. The regional benchmark electricity price and the corresponding premium factor are used to calculate the seller's offer price for green electricity suppliers. :

[0110]

[0111] Let x be the regional benchmark electricity price at time t (e.g., x yuan / kWh).

[0112] Let be the premium coefficient for the i-th renewable energy power station;

[0113] This is the regional carbon factor correction coefficient;

[0114] It should be noted that the premium coefficient is set based on the volatility of carbon emission reductions and is generally taken as 1.0-1.5. The regional carbon factor correction coefficient is obtained based on the "Guidelines for the Compilation of Provincial Greenhouse Gas Inventories" and local carbon market data. The specific values ​​of the premium coefficient and the regional carbon factor correction coefficient can be set according to actual conditions. The embodiments of the present invention do not impose specific limitations.

[0115] For photovoltaic power plants in Area A (green electricity supplier, seller I1): the benchmark electricity price P 基 (A) = 0.38 yuan / kWh, premium coefficient α1 = 1.2, high carbon region β 区 = 1.2;

[0116]

[0117] For wind power plants in Zone B (green electricity supplier, seller I2): the benchmark electricity price P 基 (B) = 0.42 yuan / kWh, α2 = 1.1, β in the low-carbon region 区 = 0.8;

[0118]

[0119] Step 103: Obtain the production cost data and carbon emission reduction data of the target green electricity supplier.

[0120] In some examples, the production cost data can be the resource cost required for the green electricity resources produced by the target green electricity supplier within a preset time period.

[0121] By deploying distributed acquisition terminals, the amount of green electricity resources generated by the target green electricity supplier within a preset time period can be collected, thereby calculating the corresponding production cost data. At the same time, by determining the unit instantaneous output green electricity resource data of the green electricity supplier, the instantaneous carbon emission reduction within a preset time period can be determined based on the instantaneous output green electricity resource data, and the instantaneous carbon emission reduction is determined as the carbon emission reduction data of the target green electricity supplier.

[0122] Step 104: Determine the demander attribute information for each candidate green electricity demander.

[0123] In some examples, the demand-side attribute information can be user demand information for carbon emission reduction;

[0124] The user type of each candidate green electricity demander is determined based on the industry or electricity consumption nature of each candidate green electricity demander.

[0125] In some examples, the nature of the electricity use is commercial electricity use and residential electricity use; the industry can be an industry distinguished by carbon emission intensity.

[0126] Based on the carbon emission intensity of each candidate green electricity demander, the industry or electricity consumption nature is distinguished, and each candidate green electricity demander is determined to be either an industrial user with major carbon emissions or a commercial user with relatively low carbon emissions. According to different user types, the demander attribute information of each candidate green electricity demander is determined, and the demander attribute information is mapped to the corresponding demander attribute information data.

[0127] Step 105: Based on the carbon emission reduction data and the demand side attribute information, determine the incentive cost data for each candidate green electricity demander, and based on the incentive cost data and the production cost data, determine the transaction cost data for each candidate green electricity demander.

[0128] In some examples, the transaction cost data can be the green electricity supplier and the green electricity demander of the candidate transaction, assuming that the transaction incurs costs when it occurs.

[0129] Based on the carbon emission reduction data of the region where each candidate green electricity demander is located and the attribute information of each candidate green electricity demander, the incentive cost data of each candidate green electricity demander can be calculated. Based on the calculated incentive cost data and the production cost data of the green electricity produced by the candidate green electricity supplier, the transaction cost data of each candidate green electricity demander can be determined.

[0130] In practical applications, the incentive cost for users can be calculated based on the demander attribute information of each candidate green electricity demander, the carbon emission reduction data, and the actual electricity load adjustment amount. The actual electricity load adjustment amount can be the deviation of the user's actual electricity consumption behavior from historical normal conditions under the guidance of the policy during daily electricity use. Specifically, it can be achieved through:

[0131] Determine the user's incentive cost by identifying user j and the time at which the calculation is required. :

[0132]

[0133] k j This is the user type coefficient (demand-side attribute information, linked to the user's carbon cost affordability coefficient).

[0134] δ(j,t) represents the response completion degree, 0 ≤ δ ≤ 1, calculated based on the actual load adjustment.

[0135] In some embodiments of the present invention, determining the incentive cost data for each candidate green electricity demander based on the carbon emission reduction data and the demander attribute information includes:

[0136] Sub-step 21: Determine the carbon emission data of the target area where each candidate green electricity demander is located.

[0137] In some examples, the carbon emission data can be real-time carbon emission levels corresponding to a certain amount of electricity supplied.

[0138] By deploying distributed data acquisition terminals, which include photovoltaic irradiance and wind speed sensors from new energy power plants and energy consumption monitoring devices for transmission lines, the power supply of each candidate green electricity demander in the target area can be monitored and acquired. Based on the amount of electricity supplied, the corresponding real-time carbon emission level (real-time carbon emission data) can be determined.

[0139] In practical applications, the first real-time power supply of all thermal power plants in the target area where each candidate green electricity demander is located and the instantaneous output data of green electricity produced by all green electricity suppliers are determined as the second real-time power supply. Then, based on the average carbon emission benchmark value (the average carbon emission benchmark value for green electricity is 0, and the average carbon emission benchmark value for fossil energy is the regional carbon emission factor), the first carbon emission footprint of the first real-time power supply and the second real-time power supply is calculated. When there is cross-regional power transmission in the target area where each candidate green electricity demander is located, the cross-regional power transmission is monitored, and the cross-regional difference in power between input power and output current in the cross-regional power transmission is calculated. Based on the cross-regional difference in power, the second carbon emission footprint of the cross-regional difference in power is calculated.

[0140] Specifically, it is necessary to further consider the impact of changes in the carbon footprint of energy storage charging and discharging and the total regional power supply on the real-time carbon emission footprint. Therefore, it is necessary to determine the changes in the carbon footprint of energy storage charging and discharging and the total regional power supply, and then calculate the real-time carbon emission footprint of the candidate buyer's region. .

[0141]

[0142] P 本地火电 (t) represents the actual power supply from all thermal power plants in this region at time t;

[0143] P 绿电 (t) represents the total actual power supply from photovoltaic, wind power, hydropower, and other power sources in this region at time t;

[0144] P 跨区 (t) represents the difference between the input electricity and the cross-regional output electricity for each candidate green electricity demander at time t, i.e., P. 跨区 (t)=P 输进 (t) P 输出 (t).

[0145] f 跨区 (t) represents the unit carbon intensity corresponding to the net cross-regional electricity input (carbon intensity represents the amount of carbon dioxide emissions generated per unit of economic activity or unit of energy / electricity), which is only related to the "electricity source region".

[0146] ΔC 储能 (t) represents the change in carbon footprint (kgCO2) of energy storage charging and discharging at time t: carbon emissions from the power grid are included during charging, but not during discharging (which can directly offset the carbon cost of charging).

[0147] P 总供电 (t) represents the total power supply in the region at time t, which is obtained by the total power output in the region + cross-regional input power - cross-regional output power.

[0148] Sub-step 22: Obtain the green electricity consumption of each candidate green electricity demander.

[0149] In some instances, the green electricity consumption can be the actual green electricity consumption consumed in real time by each candidate green electricity demander within a preset time period.

[0150] The green energy consumption of the candidate buyer can be predicted based on the candidate buyer's historical green energy consumption.

[0151] In some examples, the historical green energy consumption of each candidate green energy demander can be the amount of green energy consumed by each candidate green energy demander within a certain preset time period, and the green energy consumption can be the actual green energy consumption consumed by each candidate green energy demander in real time within a certain preset time period.

[0152] In practical applications, based on meteorological data, the instantaneous output data of green energy produced by the green energy supplier in the target area where each candidate green energy demander is located is determined. The power generation in the next 24 hours is predicted by a random forest model. The amount of green energy consumed by the candidate green energy demander in a certain preset time period is determined. Based on the predicted green energy generation and historical green energy consumption, the green energy consumption of the candidate buyer is predicted.

[0153] Sub-step 23: Determine the target feedback coefficient based on the carbon emission data and the green electricity consumption.

[0154] In some examples, the target feedback coefficient can be the carbon emission data of the target area where each candidate green electricity demander is located and the feedback index of each candidate green electricity demander on the amount of green electricity consumed.

[0155] The target feedback coefficient can be determined by adding the calculated carbon emission data and green electricity consumption.

[0156] Sub-step 24: Determine the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demander attribute information.

[0157] The target feedback coefficient can be multiplied by the carbon emission reduction data and the demand-side attribute information to calculate the incentive cost data for each candidate green electricity demander.

[0158] In some embodiments of the present invention, determining the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demander attribute information includes:

[0159]

[0160] in, Incentive cost data for each candidate green electricity demander, For the carbon emission reduction data, For the aforementioned demand-side attribute information, Let be the target feedback coefficient, j be the j-th candidate green electricity demander, and t be a preset time period.

[0161] In some embodiments of the present invention, determining the target feedback coefficient based on the carbon emission data and the green electricity consumption includes:

[0162] Sub-step 31: Determine the first feedback coefficient based on the carbon emission data and the regional carbon emission threshold.

[0163] In some examples, the regional real-time carbon footprint threshold can be a characterizing upper limit of carbon emission intensity set for a specific region.

[0164] Based on the real-time carbon emission footprint and the regional real-time carbon footprint threshold, a first feedback coefficient is calculated according to the ratio of the real-time carbon emission footprint to the regional real-time carbon footprint threshold.

[0165] In practical applications, the first feedback coefficient can be used to characterize the current proportion of fossil energy in the power grid. If the first feedback coefficient is greater than 1, it can be determined that the current proportion of fossil energy in the power grid is too high, and a response needs to be triggered to reduce carbon emissions.

[0166] Sub-step 32: Obtain the green electricity consumption of each candidate green electricity demander and the green electricity generation of the target area where each candidate green electricity demander is located.

[0167] The green electricity consumption of the candidate buyers can be predicted based on their historical green electricity consumption, thus obtaining the green electricity consumption of each candidate green electricity demander; the green electricity suppliers in the target area where each candidate green electricity demander is located can be obtained, and the amount of green electricity supplied by the green electricity suppliers can be monitored by deploying distributed acquisition terminals.

[0168] Sub-step 33: Determine the second feedback coefficient based on the green electricity generation and the green electricity consumption.

[0169] The regional green energy absorption rate can be determined by the ratio of green energy consumption to green energy generation; the second feedback coefficient can be determined by the ratio of the regional green energy absorption rate to the regional green energy consumption rate threshold.

[0170] In some examples, the green energy consumption rate can be the proportion of electricity effectively utilized in the power system, and the regional green energy consumption rate threshold can be the maximum proportion of electricity that needs to be effectively utilized in the power system of that region.

[0171] By determining the ratio of green electricity generation to green electricity consumption, the regional green electricity absorption rate of the target area where each candidate green electricity demander is located is determined. Then, based on the ratio of the regional green electricity absorption rate to the regional green electricity consumption rate threshold, a second feedback coefficient is calculated.

[0172] In practical applications, when the second feedback coefficient is less than 1, it can indicate that there is a risk of green electricity curtailment, and guidelines or measures are needed to improve the demand-side response to enhance the consumption (absorption) capacity of green electricity.

[0173] Sub-step 34: Determine the target feedback coefficient based on the first feedback coefficient and the second feedback coefficient.

[0174] The target feedback coefficient is calculated by adding the first feedback coefficient and the second feedback coefficient.

[0175] In practical applications, it is also necessary to further consider the cross-regional carbon costs that may result from cross-regional carbon trading, which can be calculated. Calculating cross-regional carbon costs .in, For transmission loss rate, and These represent regional carbon emission factors for high and low carbon emissions, respectively (regional carbon emission factors are traditional static regional carbon emission factors that characterize the average level of carbon emissions in an industry). After determining the cross-regional carbon cost, a third feedback coefficient is determined based on the ratio of the cross-regional carbon cost to the cross-regional carbon cost threshold. When the third feedback coefficient is greater than 1, it indicates that the carbon cost of cross-regional transportation is too high, and the direction of green current needs to be adjusted.

[0176] Furthermore, the first feedback coefficient, the second feedback coefficient, and the third feedback coefficient can be added together to calculate the target feedback coefficient (K(t)).

[0177]

[0178] Where ω1, ω2, and ω3 are weight coefficients, and satisfy ω1 + ω2 + ω3 = 1;

[0179] Carbon emission data for the target region where each candidate green energy demander is located;

[0180] Green energy consumption rate in the target area where each candidate green energy demander is located;

[0181] The cross-regional carbon cost generated for each candidate green electricity demander in the target region.

[0182] It should be noted that the weight coefficient is a weight that can be set according to actual needs, and the embodiments of the present invention do not make specific limitations. T1 is the regional real-time carbon footprint threshold (kgCO2 / kWh), set to 0.5 kgCO2 / kWh; when F(t) > T1, it indicates that the proportion of fossil energy in the current power grid is too high, and a response needs to be triggered to reduce carbon emissions. T2: The threshold of green power consumption rate (%), corresponding to the requirement that the green power consumption rate needs to be ≥ 85%, is set to 85%. When R(t) < T2, it indicates that there is a risk of abandoning green power, and the consumption capacity needs to be improved through response. T3: The cross-regional carbon cost threshold (yuan / kgCO2), according to relevant guidelines, combined with the cross-regional power transmission loss (4%) and the regional carbon factor difference (high-carbon region 0.9 kgCO2 / kWh, low-carbon region 0.5 kgCO2 / kWh), the carbon cost of cross-regional power transmission can be deduced as , and after rounding, it is set to 0.05 yuan / kgCO2.

[0183] For the period of Area A (t = 41):

[0184] Among them, F(t) = 0.62 kgCO2 / kWh (real-time carbon emissions in Area A), T1 = 0.5 kgCO2 / kWh, R(t) = 78%, T2 = 85%, C(t) = 0.06 yuan / kgCO2, T3 = 0.05 yuan / kgCO2, weight ω1 = 0.4, ω2 = 0.3, ω3 = 0.3, substituting the parameters into the above formula:

[0185]

[0186] The regional carbon footprint is 0.62 kgCO2 / kWh (higher than T1 = 0.5), the green power consumption rate is 78% (lower than T2 = 85%), and the cross-regional carbon cost is 0.06 yuan / kgCO2 (higher than T3 = 0.05), indicating that the proportion of fossil energy in the current power grid is too high, there is a risk of abandoning green power, a response needs to be triggered to reduce carbon emissions, the consumption capacity needs to be improved through response, and at the same time, the carbon cost of cross-regional transmission is too high, and the flow direction of green power needs to be adjusted or a local response needs to be triggered.

[0187] Step 106, determine the target green power demand side from the multiple candidate green power demand sides according to the transaction cost data, and perform green power data interaction processing based on the target green power supply side and the target green power demand side.

[0188] It is possible to determine the transaction cost data between multiple candidate green power demand sides and multiple target green power demand sides. Based on the minimum transaction cost data, determine the target green power demand side from the multiple candidate green power demand sides, and at the same time perform green power data interaction processing based on the target green power supply side and the target green power demand side.

[0189] In practical applications, when multiple target green energy suppliers' seller quotes all fall within the bidding range of the target green energy demander, the cloud-based decision-making layer, for example... Figure 2 As shown, the Hungarian algorithm is used to find the globally optimal match with the minimum total transaction cost, identifying the target green electricity supplier and the target green electricity demander that meet the minimum total transaction cost. Simultaneously, the quantity and price of green electricity to be traded by the target green electricity supplier are determined. The supply and demand balance is solved using the Hungarian algorithm, and the matching result is as follows: Photovoltaic power plants in area A supply 40MW to J1 (transaction price 0.82 yuan / kWh), supply 20MW to J2 (transaction price 0.65 yuan / kWh), and the remaining 15MW of photovoltaic power is transmitted to area B via inter-regional lines (satisfying Ptransmission = 15MW ≤ 50MW). Wind power in area B is entirely supplied to local residential user J3 (transaction price 0.84 yuan / kWh). It should be noted that if prices cannot be matched, suppliers in the same area as the demander will be prioritized, which can reduce cross-regional power loss and computational costs.

[0190] Furthermore, it is necessary to consider the possibility of carbon deficits among some target green energy demanders, meaning that there may be a gap between the actual carbon emission reductions and the regional carbon quota requirements. Therefore, it is still necessary to obtain green energy from green energy suppliers in other regions to make up for this gap. In this case, it is also necessary to calculate the carbon emission reduction deficit cost generated by this deficit, using a specific formula to calculate the carbon emission deficit cost generated by the carbon deficit:

[0191] in The total carbon allowance requirement (kgCO2) for regions A and B.

[0192] The average price of regional carbon quotas (yuan / kgCO2);

[0193] If ΔC_total ≥ C_quota, then this item is 0.

[0194] Based on the example above, we can conclude that:

[0195]

[0196] because (Carbon quota constraints), therefore:

[0197]

[0198] At this point, the cross-regional green current direction optimization model can be further solved using the Alternating Direction Multiplier Method (ADMM).

[0199] Using a multi-objective optimization function, the minimum total objective cost minZ is obtained:

[0200]

[0201] The ultimate optimization of cross-regional green energy trading is achieved by using the total cost Z.

[0202] Set μ1=0.3 (transaction cost), μ2=0.5 (carbon emission reduction gap), and μ3=0.2 (power supply reliability) to satisfy μ1+μ2+μ3=1.

[0203]

[0204] By solving the multi-objective optimization function using the Alternating Directional Multiplier Method (ADMM), it can be determined that transmitting 15MW of photovoltaic power from area A to area B can meet the load gap of residents in area B. Furthermore, according to this optimization, the total carbon emission reduction can reach its maximum: ΔC_total = 15.66 (area A) + 4.8 (area B) = 20.46 tons of CO2 (t=41 time period).

[0205] In some embodiments of the present invention, after the green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander, the method further includes:

[0206] Sub-step 41: Determine the transmission line from the target green electricity supplier to the target green electricity demander.

[0207] The transaction identifies the target green energy supplier and the target green energy demander. Based on their different locations in the actual area, the transmission line from the target green energy supplier to the target green energy demander is determined.

[0208] Sub-step 42: Obtain the rated capacity of the transmission line of the transmission line.

[0209] In some examples, the rated capacity of the transmission line can be the maximum rated capacity for transmitting electrical energy.

[0210] Sub-step 43: Based on the rated capacity and safety margin coefficient of the transmission line, determine the maximum amount of green electricity to be transmitted in real time to the target green electricity demander in the target green electricity supply direction.

[0211] Sub-step 44: Based on the maximum amount of green electricity transmitted, control the maximum upper limit of the amount of real-time green electricity supplied by the target green electricity supply direction to the target green electricity demand side.

[0212] In some examples, the safety margin factor can be a proportional value that sets the available capacity of the line below its theoretical maximum carrying capacity.

[0213] In practical applications, the transmission of green electricity after a transaction from the seller to the buyer is carried out through transmission lines. However, during the transmission process, it is also necessary to consider the rated capacity of the transmission lines.

[0214] Rated capacity (kWh) of the transmission line from region i to region j;

[0215] θ(t) is the safety margin coefficient (the safety margin coefficient can be 0.9 for the time period t=41).

[0216] It should be noted that the rated capacity of the transmission line is determined based on the actual rated capacity of the transmission lines between regions, while the safety margin factor is actually set according to a specific time period. During different time periods in each season, temperature differences may affect the heat dissipation of the transmission line. Therefore, a safety margin factor is determined for a certain period of time to ensure the safety of the overall transmission process.

[0217] When considering the safety of cross-regional power transmission lines, it is also necessary to further consider the supply and demand balance within the region, that is, to satisfy: the total power consumption of region k = local power supply + total external power transmission - total external power transmission.

[0218]

[0219] Furthermore, while ensuring the safety of power transmission lines across regions and meeting the power constraints of the region, it is also necessary to ensure that carbon quotas do not exceed the prescribed carbon quota limits. Specifically, the following measures are adopted:

[0220]

[0221] in, For green electricity to be transmitted; η=0.95 (with a 5% margin of safety).

[0222] This means that the carbon emission reduction quota obtained from the green electricity that is ultimately transmitted will be less than the limit of the prescribed carbon quota.

[0223] In some embodiments of the present invention, after the green electricity trading process based on the target green electricity supplier and the target green electricity demander, the method further includes:

[0224] Sub-step 51: Determine the power shortage rate of the target area where each candidate green electricity demander is located.

[0225] In some examples, the power shortage rate can be power shortage data that characterizes the target area where the candidate green energy demander is located.

[0226] Without human intervention, first determine the original electricity consumption of the region under normal conditions, and then confirm the reduced electricity consumption of each candidate green electricity demander in the target area after the user in the region is adjusted through human guidance or other response instructions after the transaction adjustment. Based on the original electricity consumption and the reduced electricity consumption under normal conditions, determine the actual electricity consumption of each candidate green electricity demander in the target area.

[0227] Next, calculate the total power generation of all power plants in the target area where each candidate green electricity demander is located within the time period. Then, calculate the power shortage rate of the target area where each candidate green electricity demander is located based on the power generation and actual power consumption.

[0228] In practical applications, the power shortage rate of a region can be determined by calculating its actual electricity consumption and total power generation. Further consideration is needed when cross-regional power transmission and the region's energy reserves are involved in calculating the power shortage rate.

[0229]

[0230] in, = - ;

[0231] This represents the region's original electricity consumption.

[0232] To reduce electricity consumption in response to demand;

[0233] = + + - ;

[0234] This refers to the total output of local power (total electricity generation), of which For cross-regional input / output power, This refers to the amount of energy stored and discharged.

[0235] Sub-step 52: Based on the power shortage rate, determine the power consumption result of the target area where each candidate green electricity demander is located.

[0236] The power shortage rate of a region can be used to characterize whether there is a power shortage in the region. Therefore, the power shortage rate can be used to determine whether the power supply in the region is operating normally.

[0237] The actual electricity consumption of the region can be calculated by combining the region's original electricity consumption with the reduced electricity consumption in the demand-side response. Then, by further combining the input and output with the local power supply and energy storage to determine the actual electricity load provided by the power shortage, the current power shortage rate of the region can be determined.

[0238]

[0239]

[0240] The final calculation showed that L = 0, meaning there was no power shortage.

[0241] Sub-step 53: Adjust the bid range for green electricity demanders based on the electricity consumption results.

[0242] When the electricity consumption result is normal, there is no need to adjust the buyer bidding range of the green electricity demander. When the electricity consumption result is a large surplus or indicates a shortage of electricity, it is necessary to further adjust the buyer bidding range of the green electricity demander to purchase more or less cross-regional electricity.

[0243] After the green electricity transaction processing based on the target green electricity supplier and the target green electricity demander, the process further includes:

[0244] Adjustment information is sent to the target green electricity demanders based on the target feedback coefficient;

[0245] The target green electricity demander responds by implementing electricity consumption regulation based on the regulation information.

[0246] In practical applications, the target feedback coefficient can be used to determine whether there is a risk of green energy curtailment in the current region or whether the buyer needs to improve its green energy absorption capacity. Adjustment information is then sent to the buyer. Based on the adjustment information, the buyer will respond by implementing the adjustment information to adjust the electricity consumption. The adjustment also has execution constraints to avoid over-adjustment, which could lead to the opposite of the original intention.

[0247] Specifically, demand-side response execution constraints (user adjustment capability limitations) can be imposed on the target green electricity demand side:

[0248]

[0249] : Load (kWh) of user j before and after response;

[0250] Maximum adjustment amount.

[0251] In this embodiment of the invention, the following steps are taken: First, the bid range of green electricity demanders and the seller's price quote of green electricity suppliers are obtained. Second, based on the bid range and the seller's price quote, a target green electricity supplier and multiple candidate green electricity demanders matching the target green electricity supplier's price are determined. Third, the production cost data and carbon emission reduction data of the target green electricity supplier are obtained. Fourth, the demand attribute information of each candidate green electricity demander is determined. Fifth, based on the carbon emission reduction data and the demand attribute information, the incentive cost data of each candidate green electricity demander is determined, and based on the incentive cost data and the production cost data, the transaction cost data of each candidate green electricity demander is determined. Sixth, based on the transaction cost data, a target green electricity demander is determined from the multiple candidate green electricity demanders, and green electricity data interaction processing is performed based on the target green electricity supplier and the target green electricity demander. This approach first matches potential buyers and sellers, then calculates the optimal transaction cost by obtaining cost data from green electricity suppliers and carbon emission reduction costs from demand buyers. This effectively solves the problem of lack of coordination between green electricity suppliers and demanders, and improves the matching efficiency of buyers and sellers in delivering satisfactory quantities and prices of green electricity during the transaction process.

[0252] Reference Figure 3 The diagram illustrates a flowchart of another method for processing green electricity data according to an embodiment of the present invention, which may specifically include the following steps:

[0253] Step 301: Obtain the bid range of green electricity demanders and the bid price of green electricity suppliers.

[0254] Through a blockchain trading platform for green electricity transactions, it is possible to obtain units in a preset area that produce new energy or renewable energy and can provide new energy or renewable energy to other units, determine the seller's price for the new energy or renewable energy provided by the units, and then obtain green electricity demand users in the preset area who generate carbon emissions and need new energy or renewable energy to achieve carbon emission balance, and determine the buyer's bidding range issued by the green electricity demand users.

[0255] Step 302: Based on the buyer's bidding range and the seller's quotation, determine the target green electricity supplier and multiple candidate green electricity demanders whose prices match the target green electricity supplier.

[0256] Based on the buyer's bidding range and the seller's quotation, when the obtained seller's quotation falls within the buyer's bidding range set by the candidate buyer's willingness to reduce carbon emissions, the seller corresponding to the current seller's quotation can be identified as the target green electricity supplier, while the green electricity demand buyer who sets the bidding range is identified as multiple candidate green electricity demanders.

[0257] Step 303: Obtain the production cost data and carbon emission reduction data of the target green electricity supplier.

[0258] Before obtaining the production cost data and carbon emission reduction data of the target green electricity supplier, a distributed data acquisition terminal can be deployed to collect the amount of green electricity resources generated by the target green electricity supplier within a preset time period. The corresponding production cost data can then be calculated. At the same time, by determining the unit instantaneous output green electricity resource data of the green electricity supplier, the instantaneous carbon emission reduction within a preset time period can be determined based on the instantaneous output green electricity resource data. The instantaneous carbon emission reduction is then determined as the carbon emission reduction data of the target green electricity supplier.

[0259] Step 304: Determine the demander attribute information for each candidate green electricity demander.

[0260] Based on the carbon emission intensity of each candidate green electricity demander, the industry or electricity consumption nature is distinguished, and each candidate green electricity demander is determined to be either an industrial user with major carbon emissions or a commercial user with relatively low carbon emissions. Based on different user types, the demander attribute information of each candidate green electricity demander is determined.

[0261] Step 305: Determine the carbon emission data of the target area where each candidate green electricity demander is located.

[0262] Before determining the carbon emission data of the target area where each candidate green electricity demander is located, a distributed data acquisition terminal can be deployed. The distributed data acquisition terminal includes photovoltaic irradiance and wind speed sensors of new energy power plants and energy consumption monitoring devices of transmission lines to monitor and acquire the electricity supply of the target area where each candidate green electricity demander is located. Based on the certain amount of electricity supplied, the corresponding real-time carbon emission level (real-time carbon emission data) can be determined.

[0263] Step 306: Obtain the green energy consumption of each candidate green energy demander.

[0264] Before obtaining the green energy consumption of each candidate green energy demander, the instantaneous output data of green energy produced by the green energy supplier in the target area where each candidate green energy demander is located can be determined first. The power generation in the next 24 hours can be predicted by a random forest model. The amount of green energy consumed by the candidate green energy demander in a certain preset time period can be determined. Based on the predicted green energy generation and historical green energy consumption, the green energy consumption of the candidate buyer can be predicted.

[0265] Step 307: Determine the target feedback coefficient based on the carbon emission data and the green electricity consumption.

[0266] The target feedback coefficient can be determined by adding the calculated carbon emission data and green electricity consumption.

[0267] Step 308: Determine the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demander attribute information.

[0268] The target feedback coefficient can be multiplied by the carbon emission reduction data and the demand-side attribute information to calculate the incentive cost data for each candidate green electricity demander.

[0269] Step 309: Determine the transaction cost data for each candidate green electricity demander based on the incentive cost data and the production cost data.

[0270] The incentive cost data for each candidate green electricity demander is calculated, and the transaction cost data for each candidate green electricity demander is determined based on the calculated incentive cost data and the production cost data of the green electricity produced by the candidate green electricity suppliers.

[0271] Step 310: Based on the transaction cost data, determine the target green electricity demander from the multiple candidate green electricity demanders, and perform green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander.

[0272] The system can determine the transaction cost data between multiple candidate green energy demanders and multiple target green energy demanders. Based on the minimum transaction cost data, the system can determine the target green energy demander from the multiple candidate green energy demanders. At the same time, the system can perform green energy data interaction processing between the target green energy supplier and the target green energy demander.

[0273] In this embodiment of the invention, the following steps are taken: First, the bid range of green electricity demanders and the seller's price quote of green electricity suppliers are obtained. Second, based on the bid range and the seller's price quote, a target green electricity supplier and multiple candidate green electricity demanders matching the target green electricity supplier's price are determined. Third, the production cost data and carbon emission reduction data of the target green electricity supplier are obtained. Fourth, the demand attribute information of each candidate green electricity demander is determined. Fifth, based on the carbon emission reduction data and the demand attribute information, the incentive cost data of each candidate green electricity demander is determined, and based on the incentive cost data and the production cost data, the transaction cost data of each candidate green electricity demander is determined. Sixth, based on the transaction cost data, a target green electricity demander is determined from the multiple candidate green electricity demanders, and green electricity data interaction processing is performed based on the target green electricity supplier and the target green electricity demander. This approach first matches potential buyers and sellers, then calculates the optimal transaction cost by acquiring cost data from green electricity suppliers and carbon emission reduction costs from demand buyers. It uses both carbon emission reduction and carbon emissions as core factors in transaction pricing and as triggers for responses. This solves the problem of quantifying the low-carbon value of green electricity transactions, effectively addresses the lack of coordination between green electricity suppliers and demanders, and improves the matching efficiency of buyers and sellers in delivering satisfactory quantities and prices of green electricity during the transaction process.

[0274] Reference Figure 4 The diagram illustrates a flowchart of another method for processing green electricity data according to an embodiment of the present invention, which may specifically include the following steps:

[0275] Step 401: Obtain the bid range of green electricity demanders and the bid price of green electricity suppliers.

[0276] Through a blockchain trading platform for green electricity transactions, it is possible to obtain units in a preset area that produce new energy or renewable energy and can provide new energy or renewable energy to other units, determine the seller's price for the new energy or renewable energy provided by the units, and then obtain green electricity demand users in the preset area who generate carbon emissions and need new energy or renewable energy to achieve carbon emission balance, and determine the buyer's bidding range issued by the green electricity demand users.

[0277] Step 402: Based on the buyer's bidding range and the seller's quotation, determine the target green electricity supplier and multiple candidate green electricity demanders whose prices match the target green electricity supplier.

[0278] Based on the buyer's bidding range and the seller's quotation, when the obtained seller's quotation falls within the buyer's bidding range set by the candidate buyer's willingness to reduce carbon emissions, the seller corresponding to the current seller's quotation can be identified as the target green electricity supplier, while the green electricity demand buyer who sets the bidding range is identified as multiple candidate green electricity demanders.

[0279] Step 403: Obtain the production cost data and carbon emission reduction data of the target green electricity supplier.

[0280] By deploying distributed acquisition terminals, the amount of green electricity resources generated by the target green electricity supplier within a preset time period can be collected, thereby calculating the corresponding production cost data. At the same time, by determining the unit instantaneous output green electricity resource data of the green electricity supplier, the instantaneous carbon emission reduction within a preset time period can be determined based on the instantaneous output green electricity resource data, and the instantaneous carbon emission reduction is determined as the carbon emission reduction data of the target green electricity supplier.

[0281] Step 404: Determine the demander attribute information for each candidate green electricity demander.

[0282] Based on the carbon emission intensity of each candidate green electricity demander, the industry or electricity consumption nature is distinguished, and each candidate green electricity demander is determined to be either an industrial user with major carbon emissions or a commercial user with relatively low carbon emissions. Based on different user types, the demander attribute information of each candidate green electricity demander is determined.

[0283] Step 405: Based on the carbon emission reduction data and the demand side attribute information, determine the incentive cost data for each candidate green electricity demander, and based on the incentive cost data and the production cost data, determine the transaction cost data for each candidate green electricity demander.

[0284] Based on the carbon emission reduction data of the region where each candidate green electricity demander is located and the attribute information of each candidate green electricity demander, the incentive cost data of each candidate green electricity demander can be calculated. Based on the calculated incentive cost data and the production cost data of the green electricity produced by the candidate green electricity supplier, the transaction cost data of each candidate green electricity demander can be determined.

[0285] Step 406: Based on the transaction cost data, determine the target green electricity demander from the plurality of candidate green electricity demanders, and perform green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander.

[0286] The system can determine the transaction cost data between multiple candidate green energy demanders and multiple target green energy demanders. Based on the minimum transaction cost data, the system can determine the target green energy demander from the multiple candidate green energy demanders. At the same time, the system can perform green energy data interaction processing between the target green energy supplier and the target green energy demander.

[0287] Step 407: Determine the transmission line from the target green electricity supplier to the target green electricity demander.

[0288] The transaction identifies the target green energy supplier and the target green energy demander. Based on their different locations in the actual area, the transmission line from the target green energy supplier to the target green energy demander is determined.

[0289] Step 408: Obtain the rated capacity of the transmission line.

[0290] In some examples, the rated capacity of the transmission line can be the maximum rated capacity for transmitting electrical energy.

[0291] Step 409: Based on the rated capacity and safety margin coefficient of the transmission line, determine the maximum amount of green electricity that can be transmitted in real time to the target green electricity demander in the target green electricity supply direction.

[0292] The safety margin factor can set the available capacity of the line to a proportion lower than its theoretical maximum carrying capacity. Based on the product of the rated capacity of the transmission line and the safety margin factor, the maximum amount of green electricity that can be transmitted in real time to the target green electricity demander can be determined in the target green electricity supply direction.

[0293] Step 410: Based on the maximum amount of green electricity transmitted, control the maximum upper limit of the amount of real-time green electricity supplied by the target green electricity supply direction to the target green electricity demander.

[0294] Based on the maximum amount of green electricity to be transmitted, when supplying a real-time amount of green electricity to the target green electricity demander from the target green electricity supply direction, the maximum value of this real-time amount of green electricity shall not exceed the maximum amount of green electricity to be transmitted.

[0295] In this embodiment of the invention, the following steps are taken: First, the bid range of green electricity demanders and the seller's price quote of green electricity suppliers are obtained. Second, based on the bid range and the seller's price quote, a target green electricity supplier and multiple candidate green electricity demanders matching the target green electricity supplier's price are determined. Third, the production cost data and carbon emission reduction data of the target green electricity supplier are obtained. Fourth, the demand attribute information of each candidate green electricity demander is determined. Fifth, based on the carbon emission reduction data and the demand attribute information, the incentive cost data of each candidate green electricity demander is determined, and based on the incentive cost data and the production cost data, the transaction cost data of each candidate green electricity demander is determined. Sixth, based on the transaction cost data, a target green electricity demander is determined from the multiple candidate green electricity demanders, and green electricity data interaction processing is performed based on the target green electricity supplier and the target green electricity demander. This approach first matches potential buyers and sellers, then calculates the optimal transaction cost by obtaining cost data from green electricity suppliers and carbon emission reduction costs from demand buyers. This improves the efficiency of matching buyers and sellers in delivering satisfactory quantities and prices of green electricity during the transaction process, and ensures the security of green electricity transmission during the transmission process.

[0296] Reference Figure 5 The diagram shows a structural schematic of a green electricity data processing device according to an embodiment of the present invention, which may specifically include the following modules:

[0297] The 501 module for obtaining quotes and bids is used to obtain the bid range of buyers from green electricity demanders and the bids of sellers from green electricity suppliers.

[0298] The seller and buyer candidate module 502 is used to determine the target green electricity supplier and multiple candidate green electricity demanders whose prices match the target green electricity supplier's price, based on the buyer's bidding range and the seller's quotation.

[0299] The supply information acquisition module 503 is used to acquire the production cost data and carbon emission reduction data of the target green electricity supplier.

[0300] The demand information determination module 504 is used to determine the demand attribute information of each candidate green electricity demander.

[0301] The total transaction cost determination module 505 is used to determine the incentive cost data of each candidate green electricity demander based on the carbon emission reduction data and the demander attribute information, and to determine the transaction cost data of each candidate green electricity demander based on the incentive cost data and the production cost data.

[0302] The green electricity interaction processing module 506 is used to determine the target green electricity demander from the plurality of candidate green electricity demanders based on the transaction cost data, and to perform green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander.

[0303] In some embodiments of the present invention, the quotation and bidding module 501 includes:

[0304] The supplier green electricity determination submodule is used to determine the amount of green electricity generated by the green electricity supplier.

[0305] The carbon emission reduction data determination submodule is used to determine the carbon emission reduction data of the green electricity supplier based on the green electricity generation.

[0306] The seller quotation determination submodule is used to determine the seller quotation of the green electricity supplier based on the carbon emission reduction data, regional benchmark electricity price and premium coefficient.

[0307] In some embodiments of the present invention, the total transaction cost determination module 505 includes:

[0308] The regional carbon emission data determination submodule is used to determine the carbon emission data of the target region where each candidate green electricity demander is located.

[0309] The green energy consumption determination submodule is used to obtain the green energy consumption of each candidate green energy demander;

[0310] The target feedback coefficient determination submodule is used to determine the target feedback coefficient based on the carbon emission data and the green electricity consumption.

[0311] The incentive cost determination submodule is used to determine the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demander attribute information.

[0312] In some embodiments of the present invention, the incentive cost determination submodule includes:

[0313]

[0314] in, Incentive cost data for each candidate green electricity demander, For the carbon emission reduction data, For the aforementioned demand-side attribute information, Let be the target feedback coefficient, j be the j-th candidate green electricity demander, and t be a preset time period.

[0315] In some embodiments of the present invention, the target feedback coefficient determination submodule includes:

[0316] The first feedback coefficient determination unit is used to determine the first feedback coefficient based on the carbon emission data and the regional carbon emission threshold.

[0317] The demand-side green electricity consumption and regional green electricity generation determination unit is used to obtain the green electricity consumption of each candidate green electricity demander and the green electricity generation of the target area where each candidate green electricity demander is located.

[0318] The second feedback system determination unit is used to determine the second feedback coefficient based on the green electricity generation and the green electricity consumption.

[0319] The target feedback coefficient determination unit is used to determine the target feedback coefficient based on the first feedback coefficient and the second feedback coefficient.

[0320] In some embodiments of the present invention, the regional carbon emission data determination submodule includes:

[0321] The regional total power generation acquisition unit is used to acquire the total power generation of the target area where each candidate green electricity demander is located.

[0322] The first carbon emission data determination unit is used to determine the first carbon emission data based on the total power generation.

[0323] The second carbon emission data determination unit is used to determine the second carbon emission data based on the cross-regional power difference when there is cross-regional power transmission in the target area where each candidate green electricity demander is located.

[0324] The carbon emission data determination unit for the target area is used to determine the carbon emission data of the target area where each candidate green electricity demander is located based on the first carbon emission data and the second carbon emission data.

[0325] In some embodiments of the present invention, the apparatus further includes:

[0326] The transmission line determination module is used to determine the transmission line from the target green electricity supplier to the target green electricity demander.

[0327] The line capacity acquisition module is used to acquire the rated capacity of the transmission line.

[0328] The maximum green electricity transmission quantity determination module is used to determine the maximum amount of green electricity to be transmitted in real time to the target green electricity demander in the target green electricity supply direction, based on the rated capacity and safety margin coefficient of the transmission line.

[0329] The transmission limit control module is used to control the maximum limit of the real-time green electricity supply from the target green electricity supply direction to the target green electricity demander, based on the maximum amount of green electricity to be transmitted.

[0330] In some embodiments of the present invention, the apparatus further includes:

[0331] The power shortage rate determination module is used to determine the power shortage rate of the target area where each candidate green energy demander is located.

[0332] The electricity consumption result determination module is used to determine the electricity consumption result of the target area where each candidate green electricity demander is located based on the power shortage rate.

[0333] The bidding range adjustment module is used to adjust the buyer bidding range of the green electricity demander based on the electricity consumption results.

[0334] The terms "first," "second," etc., used in the specification and claims of this invention are used to distinguish similar objects and are not used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0335] As the device embodiment is basically similar to the method embodiment, the description is relatively simple, and relevant parts can be found in the description of the method embodiment.

[0336] Some embodiments of the present invention also provide an electronic device, including a processor, a memory, and a computer program stored in the computer memory and capable of running on the processor, wherein the method described above is determined to be implemented when the computer program is executed by the processor.

[0337] Some embodiments of the present invention also provide a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and wherein the computer program is executed by a processor to implement the method described above.

[0338] Some embodiments of the present invention also provide a computer-readable storage medium for determining that a computer program implements the above-described method when executed by a processor.

[0339] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this invention are all information and data that have been agreed to by the user or have been fully agreed to or accepted by all parties. Furthermore, the collection, use and processing of the relevant data must comply with the relevant laws, regulations and standards of the relevant regions, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0340] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0341] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, apparatus, or computer program products. Therefore, embodiments of the present invention can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of the present invention can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0342] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0343] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0344] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0345] Although preferred embodiments of the present invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present invention.

[0346] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes the aforementioned element.

[0347] The above provides a detailed description of the green electricity data processing method, apparatus, electronic device, storage medium, and program product. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as limiting the present invention.

Claims

1. A method for processing green electricity data, characterized in that, The method includes: Obtain the bid range of green electricity demanders and the bids of green electricity suppliers; Based on the buyer's bidding range and the seller's offer, a target green energy supplier and multiple candidate green energy demanders whose prices match the target green energy supplier are determined; Obtain the production cost data and carbon emission reduction data of the target green electricity supplier; Determine the demander attribute information for each candidate green energy demander; Based on the carbon emission reduction data and the demand-side attribute information, determine the incentive cost data for each candidate green electricity demander, and based on the incentive cost data and the production cost data, determine the transaction cost data for each candidate green electricity demander. The step of determining the incentive cost data for each candidate green electricity demander based on the carbon emission reduction data and the demander attribute information includes: Determine the carbon emission data for the target region where each candidate green energy demander is located; Obtain the green energy consumption of each candidate green energy demander; Based on the carbon emission data and the green electricity consumption, the target feedback coefficient is determined; Based on the target feedback coefficient, the carbon emission reduction data, and the demand-side attribute information, determine the incentive cost data for each candidate green electricity demander; Based on the transaction cost data, a target green electricity demander is determined from the plurality of candidate green electricity demanders, and green electricity data interaction processing is performed based on the target green electricity supplier and the target green electricity demander; By preprocessing the data and generating dynamic carbon footprint labels, the data is tagged and labeled. Blockchain is used to manage the data with the labels throughout its entire lifecycle, and smart contracts are used to complete the issuance of green certificates and the green electricity transactions between buyers and sellers. After the green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander, the process further includes: Determine the transmission lines from the target green energy supplier to the target green energy demander; Obtain the rated capacity of the power transmission line of the transmission line; Based on the rated capacity and safety margin coefficient of the transmission line, determine the maximum amount of green electricity that can be transmitted in real time to the target green electricity demander in the target green electricity supply direction; Based on the maximum amount of green electricity transmitted, control the maximum upper limit of the amount of green electricity supplied in real time to the target green electricity demander from the target green electricity supply direction.

2. The method according to claim 1, characterized in that, The acquisition of the buyer's bidding range for green electricity demanders and the seller's bid for green electricity suppliers includes: Determine the amount of green electricity generated by the green electricity supplier; Based on the green electricity generation, determine the carbon emission reduction data of the green electricity supplier; Based on the carbon emission reduction data, regional benchmark electricity price, and premium factor, the seller's offer price from the green electricity supplier is determined.

3. The method according to claim 1, characterized in that, The step of determining the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demand-side attribute information includes: in, Incentive cost data for each candidate green electricity demander, For the carbon emission reduction data, For the aforementioned demand-side attribute information, Let be the target feedback coefficient, j be the j-th candidate green electricity demander, and t be a preset time period.

4. The method according to claim 1, characterized in that, The determination of the target feedback coefficient based on the carbon emission data and the green electricity consumption includes: Based on the carbon emission data and the regional carbon emission threshold, a first feedback coefficient is determined; Obtain the green electricity consumption of each candidate green electricity demander and the green electricity generation of the target area where each candidate green electricity demander is located; The second feedback coefficient is determined based on the green electricity generation and the green electricity consumption. The target feedback coefficient is determined based on the first feedback coefficient and the second feedback coefficient.

5. The method according to claim 1, characterized in that, The determination of carbon emission data for the region where each candidate green electricity demander is located includes: Obtain the total power generation of the target area where each candidate green energy demander is located; The first carbon emission data is determined based on the total power generation. When there is cross-regional power transmission in the target area where each candidate green electricity demander is located, the second carbon emission data is determined based on the cross-regional power difference. Based on the first carbon emission data and the second carbon emission data, determine the carbon emission data of the target area where each candidate green electricity demander is located.

6. The method according to any one of claims 1-5, characterized in that, After the green electricity transaction processing based on the target green electricity supplier and the target green electricity demander, the process further includes: Determine the power shortage rate of the target area where each candidate green energy demander is located; Based on the power shortage rate, the electricity consumption results of the target area where each candidate green electricity demander is located are determined; Based on the electricity consumption results, the bid range for buyers of green electricity is adjusted.

7. A device for processing green electricity data, characterized in that, The device includes: The quotation and bidding module is used to obtain the bid range of green electricity demanders and the bids of green electricity suppliers. The seller and buyer candidate module is used to determine the target green electricity supplier and multiple candidate green electricity demanders whose prices match the target green electricity supplier's price, based on the buyer's bidding range and the seller's quotation. The supply information acquisition module is used to acquire the production cost data and carbon emission reduction data of the target green electricity supplier. The demand information determination module is used to determine the demand attribute information of each candidate green electricity demander; The total transaction cost determination module is used to determine the incentive cost data of each candidate green electricity demander based on the carbon emission reduction data and the demander attribute information, and to determine the transaction cost data of each candidate green electricity demander based on the incentive cost data and the production cost data. The total transaction cost determination module includes: The regional carbon emission data determination submodule is used to determine the carbon emission data of the target region where each candidate green energy demander is located; The green energy consumption determination submodule is used to obtain the green energy consumption of each candidate green energy demander; The target feedback coefficient determination submodule is used to determine the target feedback coefficient based on the carbon emission data and the green electricity consumption. The incentive cost determination submodule is used to determine the incentive cost data for each candidate green electricity demander based on the target feedback coefficient, the carbon emission reduction data, and the demander attribute information. The green electricity transaction processing module is used to determine the target green electricity demander from the multiple candidate green electricity demanders based on the transaction cost data, and to perform green electricity data interaction processing based on the target green electricity supplier and the target green electricity demander. The device further includes: The transmission line determination module is used to determine the transmission line from the target green electricity supplier to the target green electricity demander; The line capacity acquisition module is used to acquire the rated capacity of the transmission line of the transmission line; The maximum green electricity transmission quantity determination module is used to determine the maximum amount of green electricity to be transmitted in real time to the target green electricity demander in the target green electricity supply direction, based on the rated capacity of the transmission line and the safety margin coefficient. The transmission limit control module is used to control the maximum limit of the real-time green electricity supply from the target green electricity supply direction to the target green electricity demander, based on the maximum amount of green electricity to be transmitted. The device is also used to preprocess the data and generate dynamic carbon footprint labels, label the data, use blockchain to manage the data with the labels throughout its entire lifecycle, and complete the issuance of green certificates and green electricity transactions between buyers and sellers through smart contracts.

8. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and capable of running on the processor, wherein the computer program, when executed by the processor, implements the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium, which, when executed by a processor, implements the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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