Green electricity transaction data processing device and method

Abstract

The invention relates to the technical field of energy transaction, and discloses a green electricity transaction data processing device and method. According to the invention, by introducing dynamic monitoring and analysis of the market supply and demand state, accurate control of the green electricity trading market supply and demand relationship is realized, and the market strategy can be rapidly adjusted according to the real-time market supply and demand deviation degree; the geographical area matching and cross-market distribution strategy of the order data effectively improves the order allocation efficiency of the green electricity market, reduces the power gap of the market with insufficient supply, and ensures the dynamic adjustment capability of the market supply and demand balance. In the performance analysis process, the performance stability of the transaction order is deeply analyzed based on the contract performance condition, the transaction success rate, the default record and other multi-dimensional data, and potential performance risks can be found in time. For the risk of power generation fluctuation, the market power supply stability challenge caused by power generation fluctuation can be effectively handled by calculating the power generation uncertainty index and combining the remaining available orders of the adjacent market.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy trading, and in particular relates to a device and method for processing green electricity trading data. Background Art

[0002] The field of energy trading technology includes core contents such as market-based trading of energy resources, power dispatching and distribution, transaction data management and settlement. This technical field involves energy flows and trading mechanisms between energy suppliers, power grid operators, market trading platforms and end users. The core of energy trading is to build a stable, efficient and fair trading system to ensure the balance of energy supply and demand and improve energy utilization efficiency. In practical applications, this technology relies on power market mechanisms, energy big data analysis, intelligent dispatching and settlement systems to optimize trading strategies.

[0003] Among them, the processing system of green electricity transaction data refers to the technical solution for data collection, collation, storage and matching involved in the green electricity transaction process. The system covers the collection and traceability of green electricity transaction data, obtains transaction data on the green electricity generation side, transmission and distribution side and user side by accessing power metering devices, energy management systems, etc., and integrates and archives data based on timestamps, power generation types and other information. The system also includes the processing of transaction matching and settlement data. By constructing a transaction data index structure, data is screened and matched based on green electricity transaction contracts, market quotations, transaction intentions of buyers and sellers, etc., to achieve accurate correspondence of transaction data between green electricity supply and demand parties. The system further involves data storage and distributed management, using a database to store transaction records, and combining hash verification and other methods to ensure data consistency and integrity to support subsequent settlement calculations and regulatory needs.

[0004] In the process of processing green electricity trading data, the existing technology usually relies on static data collection and simple supply and demand matching rules, which makes it difficult to cope with the challenges brought by the dynamic changes in market supply and demand. Due to the lack of real-time analysis of the market supply and demand status, it is often difficult to take effective regulatory measures in time when the market supply is insufficient or excessive, which easily leads to the problem of uneven distribution of market power resources, resulting in power waste in some areas, while other areas are facing the dilemma of power supply shortage. In addition, the order matching mechanism in the existing technology is mostly processed on a single market basis, which fails to make full use of the power resources of adjacent markets, lacks linkage and coordination between markets, and cannot achieve supply and demand balance through cross-regional order adjustment. In terms of performance analysis and risk assessment, the existing technology mainly relies on simple historical data statistics, which makes it difficult to deeply analyze the potential problems in the order performance process, which easily leads to the underestimation of the performance risk of market players and affects the overall stability of the market. In response to the risk of power generation fluctuations, it mostly relies on post-event remedial measures, lacks pre-risk warning and active regulatory measures, resulting in the market encountering power generation uncertainty. The phenomenon of power outages or increased market fluctuations may occur, which in turn affects the healthy development of the green electricity trading market and the trust of market players. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings of the above-mentioned prior art and to provide a device and method for processing green electricity transaction data.

[0006] In order to achieve the above object, the present invention adopts the following technical solution.

[0007] In a first aspect, the present invention provides a device for processing green electricity transaction data, comprising: Market supply and demand monitoring module: obtains the total order volume data of the green electricity trading market, determines the market supply and demand status according to the preset supply and demand balance benchmark range, and obtains the market supply and demand analysis results; Order matching adjustment module: extracts the power sales order gap of the undersupplied market from the market supply and demand analysis results, allocates orders to the undersupplied market according to the geographical area information of all markets, and obtains cross-market order adjustment results; Transaction performance evaluation module: based on the green power transaction contract data of the corresponding market in the cross-market order adjustment result, analyze the performance of each transaction order and generate a transaction performance analysis result; Cross-regional compensation analysis module: based on the green power generation data of the corresponding market in the cross-market order adjustment result, screen the power generation fluctuation risk market, call the remaining available power sales orders in the adjacent market of the power generation fluctuation risk market, calculate the standby transaction activation amount, and obtain the compensation transaction activation amount analysis result; Transaction strategy optimization module: based on the transaction performance analysis results and the compensation transaction activation volume analysis results, screen the optimal market supply and demand matching combination and obtain the green electricity transaction data processing results.

[0008] As a further solution of the present invention, the market supply and demand analysis results include the supply and demand ratio of transaction electricity, the number of completed electricity purchase orders, the number of completed electricity sales orders, the number of uncompleted electricity purchase orders, the number of uncompleted electricity sales orders, the market supply and demand status and the degree of supply and demand deviation; the cross-market order adjustment results include the electricity sales order gap in the undersupplied market, the number of electricity sales orders in the adjacent target market, the supply and demand ratio of the adjacent target market, the feasibility of market matching, the adjustable order transaction data, the order matching adjustment priority and the orders allocated to the undersupplied market; the transaction performance analysis results include the transaction order performance stability, the stability threshold comparison results and the performance analysis results; the compensation transaction activation volume analysis results include the power generation uncertainty index, the power generation fluctuation risk market, the power supply stability compensation demand, the standby transaction activation volume and the compensation transaction activation volume; the green electricity transaction data processing results include the default category of the performance stability orders below the stability threshold, the continuous performance problem transaction subject, the performance failure reason, the compensation transaction coverage rate, the impact of the compensation transaction on the market stability and the optimal market supply and demand matching combination.

[0009] As a further solution of the present invention, the market supply and demand monitoring module includes: The submodule for calculating the supply-demand ratio of electricity purchase and sales is used to obtain the total order data of the green electricity trading market, including the total electricity of electricity purchase orders and the total electricity of electricity sales orders, using the formula: , Calculate the total electricity supply-demand ratio of electricity purchase orders Supply-demand ratio of total electricity sales orders , generate market purchase and sales power supply and demand ratio data; in, Represents the total amount of electricity purchased. Represents the total amount of electricity sold in the order. Representative The amount of electricity purchased by the power purchase order, Representative The amount of electricity sold in a power sales order, Represents the quantity of electricity purchase orders, Represents the quantity of electricity sales orders; Supply and demand deviation calculation submodule: call the market purchase and sale power supply and demand ratio data, extract the number of completed and uncompleted power purchase orders and the number of power sales orders, and use the formula: Calculate the market supply and demand deviation ; in, represents the amount of electricity for the kth electricity purchase order, represents the amount of electricity sold in the kth electricity sales order, Representative The amount of electricity purchased by the market has been completed. Representative The electricity sales orders completed in the market, K represents the total number of electricity purchase orders, represents the number of markets; Market status determination submodule: determines the supply and demand status of each market according to the degree of market supply and demand deviation in the market supply and demand deviation data, and obtains market supply and demand analysis results.

[0010] As a further solution of the present invention, the order matching adjustment module includes: Target market screening submodule: extract the power sales order gap of the undersupplied market from the market supply and demand analysis results, screen the target markets adjacent to the undersupplied market according to the geographical area information of all markets, as well as the grid interconnection relationship and transmission capacity, obtain the power sales order quantity and supply-demand ratio of each adjacent target market, and obtain the target market supply and demand data; Market matching calculation submodule: Based on the target market supply and demand data, the formula is: Calculate the The feasibility of matching in the market , generating inter-market matching data; in, represents the number of electricity sales orders in the adjacent market, Represents the supply-demand ratio of adjacent markets, Represents the electricity demand gap in the undersupplied market, represents the number of adjacent markets that meet the criteria, Represents the quantity of undersupplied markets; Order adjustment allocation submodule: screen the adjustable orders according to the matching feasibility of the inter-market matching data, extract the order transaction data that meets the adjustment conditions, including order quantity, order transaction records, and matching success rate, calculate the order matching adjustment priority, allocate orders to the undersupplied market according to the priority, and obtain the cross-market order adjustment results.

[0011] As a further solution of the present invention, the transaction performance evaluation module includes: Order fulfillment stability calculation submodule: Based on the green power transaction contract data of the corresponding market in the cross-market order adjustment results, including fulfillment time, default records, number of contract changes, and transaction success rate, the formula is used: Calculate the stability of transaction order fulfillment ; in, Represents the order fulfillment time, Represents the order transaction success rate, Represents the cumulative number of default records of the order. Represents the number of order contract changes; Performance analysis submodule: compares the performance stability of each transaction order with the preset stability threshold, analyzes the performance of each transaction order, and generates transaction performance analysis results.

[0012] As a further solution of the present invention, the cross-region compensation analysis module includes: Power generation uncertainty calculation submodule: Based on the green power generation data of the corresponding market in the cross-market order adjustment result, according to the acquired wind speed data, light intensity, and unit availability, the formula is adopted: Calculating the Generation Uncertainty Index ; in, Representative The wind power generation at a given moment, Representative The photovoltaic power generation at a given moment, Representative The expected power generation at a given moment, Represents the number of moments in the statistical period; Power supply stability compensation screening submodule: compares the power generation uncertainty index with a preset power generation threshold, screens the power generation fluctuation risk market, obtains the power supply stability compensation demand of the power generation fluctuation risk market, and obtains compensation demand data of the power generation fluctuation risk market; Compensation transaction activation analysis submodule: calls the remaining available electricity sales orders in the adjacent market to analyze the activation volume of standby transactions and obtains the analysis results of the activation volume of compensation transactions.

[0013] As a further solution of the present invention, the trading strategy optimization module includes: Performance problem screening submodule: based on the transaction performance analysis results and the compensation transaction activation volume analysis results, extract the default categories of orders below the stable threshold, screen the trading entities with continuous performance problems, analyze the reasons for performance failure, and obtain the data of the trading entities with performance problems; Compensation transaction evaluation submodule: Based on the transaction subject data of the performance issue, extract the performance status of the executed compensation transactions, compare the activation volume of compensation transactions with the total volume of performance stability orders corresponding to orders below the stability threshold, analyze the impact of compensation transactions on market stability, and screen the optimal market supply and demand matching combination based on the matching and adjusted order transaction records to obtain the green electricity transaction data processing results.

[0014] In a second aspect, the present invention provides a method for processing green electricity transaction data, the method for processing green electricity transaction data being executed based on the above-mentioned green electricity transaction data processing device, comprising the following steps: S1: Obtain the total order volume data of the green electricity trading market, determine the market supply and demand status according to the preset supply and demand balance benchmark range, and obtain the market supply and demand analysis results; S2: extracting the electricity sales order gap of the undersupplied market from the market supply and demand analysis results, allocating orders to the undersupplied market according to the geographical area information of all markets, and obtaining cross-market order adjustment results; S3: Based on the green power transaction contract data of the corresponding market in the cross-market order adjustment result, analyze the performance of each transaction order and generate a transaction performance analysis result; S4: Based on the green power generation data of the corresponding market in the cross-market order adjustment result, the power generation fluctuation risk market is screened, the remaining available power sales orders in the adjacent market of the power generation fluctuation risk market are called, the standby transaction activation amount is calculated, and the compensation transaction activation amount analysis result is obtained; S5: Based on the transaction performance analysis results and the compensation transaction activation volume analysis results, the optimal market supply and demand matching combination is screened to obtain the green electricity transaction data processing results.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: The present invention not only improves the success rate of green electricity transactions, but also effectively ensures the stable operation of the market, minimizes the impact of market fluctuations on trading entities, and significantly improves the intelligence, flexibility and market stability of the green electricity trading system. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart for processing by the device of the present invention; Figure 2 This is a flow chart of the market supply and demand monitoring module of the present invention; Figure 3 This is a flow chart of the order matching adjustment module of the present invention; Figure 4 It is a flow chart of the transaction performance evaluation module of the present invention; Figure 5 This is a flow chart of the cross-region compensation analysis module of the present invention; Figure 6 It is a flow chart of the trading strategy optimization module of the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] See also Figure 1 This embodiment provides a green electricity transaction data processing device, including a market supply and demand monitoring module, an order matching adjustment module, a transaction performance evaluation module, a cross-regional compensation analysis module and a transaction strategy optimization module.

[0019] Market supply and demand monitoring module: obtains the total order volume data of the green electricity trading market, determines the market supply and demand status according to the preset supply and demand balance benchmark range, and obtains the market supply and demand analysis results.

[0020] Order matching and adjustment module: extracts the electricity sales order gap of the undersupplied market from the market supply and demand analysis results, allocates orders to the undersupplied market based on the geographical area information of all markets, and obtains cross-market order adjustment results.

[0021] Transaction performance evaluation module: Based on the green electricity transaction contract data of the corresponding market in the cross-market order adjustment results, analyze the performance of each transaction order and generate transaction performance analysis results.

[0022] Cross-regional compensation analysis module: Based on the green electricity generation data of the corresponding market in the cross-market order adjustment results, the power generation fluctuation risk market is screened, the remaining available electricity sales orders in the adjacent markets of the power generation fluctuation risk market are called, the standby transaction activation volume is calculated, and the compensation transaction activation volume analysis results are obtained.

[0023] Trading strategy optimization module: Based on the transaction performance analysis results and compensation transaction activation volume analysis results, screen the optimal market supply and demand matching combination and obtain the green electricity transaction data processing results.

[0024] The market supply and demand analysis results include the supply-demand ratio of transaction electricity, the number of completed electricity purchase orders, the number of completed electricity sales orders, the number of uncompleted electricity purchase orders, the number of uncompleted electricity sales orders, the market supply and demand status, and the degree of supply and demand deviation. The cross-market order adjustment results include the electricity sales order gap in the undersupplied market, the number of electricity sales orders in the adjacent target market, the supply-demand ratio of the adjacent target market, the feasibility of market matching, the adjustable order transaction data, the order matching adjustment priority, and the orders allocated to the undersupplied market. The transaction performance analysis results include the transaction order performance stability, the stability threshold comparison results, and the performance analysis results. The compensation transaction activation analysis results include the power generation uncertainty index, the power generation fluctuation risk market, the power supply stability compensation demand, the standby transaction activation volume, and the compensation transaction activation volume. The green electricity transaction data processing results include the default category of orders with low performance stability, the trading subject with continuous performance problems, the reasons for performance failure, the compensation transaction coverage rate, the impact of compensation transactions on market stability, and the optimal market supply and demand matching combination.

[0025] See also Figure 2The market supply and demand monitoring module includes a purchase and sale power supply and demand ratio calculation submodule, a supply and demand deviation calculation submodule and a market status determination submodule.

[0026] The submodule for calculating the supply-demand ratio of electricity purchase and sales is used to obtain the total order data of the green electricity trading market, including the total electricity of electricity purchase orders and the total electricity of electricity sales orders, using the formula: , Calculate the total electricity supply-demand ratio of the electricity purchase order Supply-demand ratio of total electricity sales orders , generate market purchase and sales power supply and demand ratio data; in, Represents the total electricity volume of the electricity purchase order (MWh), which is obtained by summing up the electricity volume values ​​of all electricity purchase orders in the market transaction data; Represents the total electricity volume of the electricity sales order (MWh), which is obtained by summing up the electricity volume values ​​of all electricity sales orders in the market transaction data; Representative The electricity quantity (MWh) of each electricity purchase order is extracted from the transaction database and used as an important parameter for calculating the total market electricity quantity of the electricity purchaser; Representative The electricity quantity (MWh) of each electricity sales order is extracted from the transaction database and used as an important parameter for calculating the total market electricity quantity of the electricity seller; Represents the number of electricity purchase orders, and counts the number of valid electricity purchase orders from the market database; Represents the number of electricity sales orders, and counts the number of valid electricity sales orders from the market database.

[0027] First, the system extracts the electricity data of all power purchase orders and power sales orders from the market transaction database. Each order contains the order number, electricity value (MWh) and transaction status. Then the electricity of all power purchase orders and power sales orders is added up to obtain the total power purchase amount. Total electricity sold For example, if there are four power purchase orders in the market, and their power amounts are 50MWh, 100MWh, 150MWh and 200MWh respectively, the total power purchase amount is 500MWh. Similarly, if there are four power sales orders in the market, and their power amounts are 80MWh, 120MWh, 100MWh and 150MWh respectively, the total power sales amount is 450MWh. Next, calculate the total power supply and demand ratio of power purchase orders and the total power supply and demand ratio of power sales orders. The power purchase order supply and demand ratio measures the stability of the total power purchase order relative to the market transaction volume, while the power sales order supply and demand ratio is used to analyze the supply capacity of power sales orders in the market. Substitute the data: ; ; The final calculation results show the total power supply and demand ratio of the power purchase order. Supply-demand ratio of total electricity sales orders , generate market electricity purchase and sales supply and demand ratio data.

[0028] Supply and demand deviation calculation submodule: call the market purchase and sale power supply and demand ratio data, extract the number of completed and uncompleted power purchase orders and power sales orders, and use the formula: Calculate the market supply and demand deviation D; in, Representative The amount of electricity (MWh) of each electricity purchase order is used to calculate the volatility of electricity purchase orders in the market. The data source is the transaction database; Representative The amount of electricity sold in MWh is used to calculate the volatility of electricity sold in the market. The data source is the transaction database. Representative The amount of electricity purchased by the market (MWh) is calculated by summing up the completed electricity purchase order data; Representative The electricity volume (MWh) of the completed electricity sales orders in each market is obtained by summing up the completed electricity sales orders; Represents the total number of electricity purchase orders, and counts the number of all valid electricity purchase orders from the market database; Represents the number of markets and obtains transaction data of different markets from market data.

[0029] Filter out completed power purchase orders and power sales orders from the transaction records, remove unfinished or cancelled orders, and only keep orders that have actually completed transactions. The system sums up the electricity of completed orders to obtain the power purchase and sales order data completed in the market. Assuming that the completed power purchase orders are 10MWh, 15MWh and 20MWh respectively, and the completed power sales orders are 12MWh, 14MWh and 18MWh respectively, the total transaction power purchase and sales are: , Then, the supply-demand deviation of each market is calculated. This value reflects whether the market supply and demand match, as well as the gap between electricity purchase and electricity sales. Substitute the data into: ; The final calculation shows that the market supply and demand deviation D=0.0102, generating market supply and demand deviation data.

[0030] Market status determination submodule: Determine the supply and demand status of each market according to the degree of supply and demand deviation in the market supply and demand deviation data to obtain the market supply and demand analysis results.

[0031] The market supply and demand deviation data D=0.0102 is called. The system judges the current market status according to the set supply and demand status classification standard. The specific operations include: setting the benchmark range T of supply and demand balance, which can usually be set to T≤0.02 to represent the basic balance of the market, 0.02<T≤0.05 to represent a slight imbalance of supply and demand in the market, and T>0.05 to represent a serious imbalance of supply and demand in the market. Judgment criteria: If D≤0.02, the market supply and demand status is in a balanced state; if 0.02<D≤0.05, the market supply and demand status is slightly imbalanced; if D>0.05, the market supply and demand status is seriously imbalanced. The currently calculated supply and demand deviation D=0.0102 is less than the set supply and demand balance benchmark value of 0.02, so the market is in a supply and demand equilibrium state. The system records the market status as supply and demand equilibrium and stores the analysis results for subsequent trading strategy optimization and market adjustment. Finally, the market supply and demand analysis results are obtained, and the current market status is judged to be a supply and demand equilibrium state.

[0032] See also Figure 3 ,The order matching adjustment module includes a target market screening submodule, a market matching ,computation submodule and an order adjustment allocation submodule.

[0033] Target market screening submodule: extract the power sales order gap of the undersupplied market from the market supply and demand analysis results, screen the target markets adjacent to the undersupplied market based on the geographical area information of all markets, as well as the grid interconnection relationship and transmission capacity, obtain the power sales order quantity and supply-demand ratio of each adjacent target market, and obtain the target market supply and demand data.

[0034] First, the supply and demand analysis results of all markets are obtained from the transaction database, and the markets with power supply lower than the set benchmark value are screened out. The benchmark value is set to 0.8, that is, the market with a supply-demand ratio less than 0.8 is regarded as a market with insufficient supply. This threshold is determined through market research, and the standard deviation of the supply-demand ratio is calculated based on historical transaction data, and one times the standard deviation is taken as the boundary. After screening the market with insufficient supply, all electricity sales orders are extracted from its order data, and the total gap is calculated. Assuming that the total electricity sales order volume in a market is 300MWh and the total electricity purchase demand is 450MWh, the electricity sales gap in this market is calculated as follows: 450-300=150MWh. Then, the geographic area information, grid interconnection and transmission capacity data of all markets are called to determine which markets can be used as target markets, calculate the markets adjacent to the undersupplied markets, and verify whether they have sufficient transmission capacity. Markets with transmission capacity greater than 100MWh are selected to ensure that these markets can technically meet the cross-market transaction needs. After obtaining qualified markets, the number of electricity sales orders and the supply-demand ratio are calculated. Assuming that the electricity sales order volume of a target market is 400MWh and the electricity purchase order volume is 320MWh, the supply-demand ratio of the market is: 400 / 320=1.25. Finally, the screened and calculated data form the target market supply and demand data.

[0035] Market matching calculation submodule: Based on the target market supply and demand data, the formula is: Calculate the The feasibility of matching in the market , generating inter-market matching data; in, Represents the number of electricity sales orders in adjacent markets, which is counted through market transaction data; Represents the supply-demand ratio of the adjacent market, which is calculated by the electricity sales order volume and the electricity purchase order volume; The electricity demand gap representing the undersupplied market is calculated by subtracting the electricity sales orders from the electricity purchase demand; represents the number of adjacent markets that meet the criteria; Represents the quantity of undersupplied markets.

[0036] There are three adjacent target markets, with total electricity sales orders of 400MWh, 350MWh and 380MWh, and corresponding supply-demand ratios of 1.25, 1.10 and 1.30, respectively. The total demand of the undersupplied market is 150MWh, and the matching feasibility is calculated as follows: ; The matching feasibility finally calculated is used for subsequent order adjustments to form inter-market matching data.

[0037] Order adjustment allocation submodule: Filter the adjustable orders according to the matching feasibility of the matching data between markets, extract the order transaction data that meets the adjustment conditions, including order power, order transaction history, and matching success rate, calculate the order matching adjustment priority, and allocate orders to the undersupplied market according to the priority to obtain the cross-market order adjustment results.

[0038] Adjustable orders are screened according to the matching feasibility. During the screening process, markets with matching feasibility greater than 5 are given priority to ensure that the selected orders can match the undersupplied market more stably. Order transaction data that meet the adjustment conditions are extracted, including order power, order transaction history and matching success rate. The order matching success rate is calculated from the historical transaction data, and the transaction rate of orders in this market in the past 12 months is used as the matching success rate. If an order has been successfully traded 9 times in the past 12 months, and the total number of matching attempts is 12, the matching success rate of the order is calculated as follows: 9 / 12=75%. All orders that meet the matching conditions are adjusted according to the transaction history and matching success rate. The priority calculation method is: ,in: Represents the adjustment priority of the order; Represents the order quantity; Represents the order matching success rate; Indicates the order creation time (month). If the power of an order is 50MWh, the matching success rate is 75%, and the creation time is 3 months, the priority calculation is as follows: , orders with high priority will be given priority for adjustment, and the finalized orders will be matched across markets to obtain the cross-market order adjustment results.

[0039] See also Figure 4 ,The transaction fulfillment evaluation module includes the order fulfillment stability calculation sub-module and the fulfillment situation analysis sub-module.

[0040] Order fulfillment stability calculation submodule: Based on the green power transaction contract data of the corresponding market in the cross-market order adjustment results, including fulfillment time, default records, number of contract changes, and transaction success rate, the formula is used: Calculate the stability of transaction order fulfillment ; in, The higher the value, the stronger the stability of performance, which is calculated by the formula; Represents the order fulfillment time (days), which is extracted from the contract fulfillment data and calculated by subtracting the signing time from the contract termination time; Represents the order transaction success rate (%), obtained from the order database, calculated by dividing the number of historical successful fulfillments by the total number of order transactions; Represents the cumulative number of default records of the order, which is counted by the transaction record database. Default records refer to the number of times the delivery is not made as specified in the contract. Represents the number of order contract changes, which is extracted from the contract change history data. Contract changes include but are not limited to changes in delivery time and delivered electricity. Calculate the impact of breaches and changes on performance stability. The greater the number of breaches and changes, the lower the performance stability value.

[0041] First, extract the green power transaction contract data from the transaction database, filter out all completed contracts, remove the contract records that are still in the performance period, and obtain the contract performance time The way to obtain the performance time is the difference between the contract signing time and the contract termination time. For example, if a contract is signed on January 1, 2023 and terminated on December 31, 2023, the performance time is 365 days. Next, count the default records of the order during the performance period. , a breach record refers to a situation where the electricity volume is not delivered as specified in the contract. For example, if a contract has three non-delivery events during the performance period, the number of breach records is , and then extract the number of changes to the contract Contract changes refer to the modification of the agreement due to market fluctuations, transaction adjustments and other factors during the performance of the contract. For example, if a contract adjusts the delivered electricity twice due to market price fluctuations, the number of contract changes is Finally, get the transaction success rate of the order , the transaction success rate is the number of completed transactions divided by the planned number of transactions. For example, if an order is planned to be traded 10 times and the actual number of successful transactions is 8, then: . Substitute the data: Finally, the transaction order fulfillment stability is calculated and the order fulfillment stability data is obtained.

[0042] Performance analysis submodule: compares the performance stability of each transaction order with the preset stability threshold, analyzes the performance of each transaction order, and generates transaction performance analysis results.

[0043] Call the order performance stability data and compare the stability of each transaction order with the preset stability threshold. The stability threshold is set to 90% of the industry average stability level. The industry average stability level is obtained by calculating the average of all contract performance stability. For example, the performance stability data of a historical contract in a market is , calculate the average stability: , set the stability threshold: Compare the calculated performance stability Is it below the threshold? ,like The order fulfillment is judged to be at risk, otherwise it is judged to be normal. In this example, 84.3>76.3, so the order fulfillment is normal. If the order stability is lower than the threshold, the risk level is marked according to the difference, for example: low risk: 0-10% below the threshold, medium risk: 10-20% below the threshold, high risk: If the transaction is lower than the threshold by more than 20%, the fulfillment of all orders will be analyzed and the transaction fulfillment analysis results will be obtained.

[0044] See also Figure 5 ,The cross-regional compensation analysis module includes a power generation uncertainty calculation submodule, a power supply stability compensation screening submodule, and a compensation transaction activation analysis submodule.

[0045] Power generation uncertainty calculation submodule: Based on the green power generation data of the corresponding market in the cross-market order adjustment results, according to the acquired wind speed data, light intensity, and unit availability, the formula is used: Calculating the Generation Uncertainty Index ; in, represents the wind power generation (MWh) at the tth moment, which is obtained by calculating the wind turbine installed capacity, wind turbine efficiency and wind speed; Represents the photovoltaic power generation (MWh) at the tth moment, which is calculated by the photovoltaic unit capacity, photovoltaic unit efficiency and light intensity; represents the expected power generation (MWh) at the tth moment, which is obtained through the market's historical power generation data and load forecast data; T represents the number of moments in the statistical period, and the calculation period is determined from the historical power generation data; Represents the cumulative total amount of power generation error during the statistical period; Represents the square root of the sum of squares of all expected power generation within the statistical period and is used to calculate the normalized uncertainty index.

[0046] First, wind power and photovoltaic power generation data of each market are obtained from the wind power generation and photovoltaic power generation databases, and the extracted parameters include wind speed, light intensity, installed capacity of units, unit availability, etc. Obtained through the wind monitoring system, assuming that the wind speed measured in a certain market at a certain time is 10m / s, the total installed capacity of wind turbines in this market The efficiency of a 200MW wind turbine is is 0.45, then the wind power generation at that moment is obtained: . Light intensity Obtained through the solar radiation monitoring system, assuming that the light intensity in a market is 800W / m 2 , total installed capacity of photovoltaic units The efficiency of the photovoltaic unit is 150MW. is 0.18, then the photovoltaic power generation at that moment is obtained: Next, get the total power generation at that moment: Then, call the historical data to obtain the expected power generation at that moment , assuming that the expected power generation at this moment is 950MWh, the power generation error is obtained: The power generation uncertainty index is obtained by accumulating errors within the statistical period. Assuming that the statistical period is T=5, the power generation data observed at each moment are as follows: At the first moment, wind power generation was 900MWh, photovoltaic power generation was 21.6MWh, total power generation was 921.6MWh, expected power generation was 950MWh, and the error was 28.4MWh.

[0047] At the second moment, wind power generation was 850MWh, photovoltaic power generation was 19.8MWh, total power generation was 869.8MWh, expected power generation was 890MWh, and the error was 20.2MWh.

[0048] At the third moment, wind power generation was 920MWh, photovoltaic power generation was 22.5MWh, total power generation was 942.5MWh, expected power generation was 965MWh, with an error of 22.5MWh.

[0049] At the fourth moment, wind power generation was 880MWh, photovoltaic power generation was 21.0MWh, total power generation was 901.0MWh, expected power generation was 940MWh, with an error of 39.0MWh.

[0050] At the fifth moment, wind power generation was 910MWh, photovoltaic power generation was 23.0MWh, total power generation was 933.0MWh, expected power generation was 960MWh, with an error of 27.0MWh.

[0051] Calculate the generation uncertainty index: .

[0052] Power supply stability compensation screening submodule: compares the power generation uncertainty index with the preset power generation threshold, screens the power generation fluctuation risk market, obtains the power supply stability compensation demand of the power generation fluctuation risk market, and obtains the compensation demand data of the power generation fluctuation risk market.

[0053] Call the market power generation uncertainty data, obtain the power generation uncertainty index of each market, and compare it with the set power generation threshold. The threshold is set to 95% of the industry's average power generation stability level. The industry's average stability level is obtained by statistically averaging the market's historical power generation fluctuation levels: ,in, Represents the generation uncertainty threshold, which is used to judge the stability of market generation; The average value of the historical power generation fluctuation level of the market is obtained by averaging the historical uncertainty index data. The historical power generation fluctuation level data of a market is 8.5, 7.8, 9.2, 8.8, and 8.1. Get its average value: , set the power generation uncertainty threshold: If the market uncertainty index If the threshold is exceeded, the market is marked as a power generation fluctuation risk market, and its power supply stability compensation demand is obtained. Assuming that the uncertainty index of a market is calculated as , then the market exceeds the threshold: 8.9>8.06, and the market is determined to be a power generation fluctuation risk market, and the compensation demand data of the power generation fluctuation risk market is obtained.

[0054] Compensation transaction activation analysis submodule: calls the remaining available electricity sales orders in the adjacent market to analyze the activation volume of standby transactions and obtains the analysis results of the activation volume of compensation transactions.

[0055] Call the compensation demand data of the power generation fluctuation risk market to obtain the power gap of the market, and query the remaining available power sales orders in the adjacent market to obtain the activation volume of standby transactions: ,in, Represents the amount of compensation transaction activation, that is, the amount of transaction electricity that can actually be used to adjust market supply and demand; Represents the total amount of available electricity sales orders in adjacent markets, obtained from the market transaction database; The power gap representing the power generation fluctuation risk market is obtained by calculating the market power demand through the uncertainty index. If the available power sales order quantity of adjacent markets A, B, and C is as follows: the available power sales order quantity of market A is 150MWh, the available power sales order quantity of market B is 200MWh, and the available power sales order quantity of market C is 180MWh. Get the dispatchable compensation quantity of the market: , if the gap in a power generation fluctuation risk market is 400MWh, then obtain its matching transaction activation volume: , screen the markets that meet the compensation conditions, determine the compensation transaction volume that each market can provide, and finally obtain the compensation transaction activation volume analysis results.

[0056] See also Figure 6 ,The transaction strategy optimization module includes the performance issue screening submodule and the ,compensation transaction evaluation submodule.

[0057] Performance problem screening submodule: Based on the transaction performance analysis results and the compensation transaction activation volume analysis results, extract the default categories of orders below the stable threshold, screen the trading entities with continuous performance problems, analyze the reasons for performance failure, and obtain the data of trading entities with performance problems.

[0058] Based on the results of transaction performance analysis and compensation transaction activation analysis, all historical transaction orders are first extracted from the green power transaction contract database to obtain the performance of each order, including order transaction time, contract amount, performance cycle, default record, compensation record and other detailed information. Then, the orders with performance stability lower than the set threshold are screened, and the performance stability threshold is set as the 95% quantile of the historical performance data to ensure the accuracy of the screening. Specifically, the distribution of performance stability is calculated from the historical performance data as follows: The number of orders with a performance stability range of 90%-100% is 150, accounting for 30% in total. The number of orders with a performance stability range of 80%-90% is 120, accounting for 54% in total. The number of orders with a performance stability range of 70%-80% is 100, accounting for 74% in total. The number of orders with a performance stability range of 60%-70% is 80, accounting for 90% in total. There are 50 orders with a performance stability range of 50%-60%, accounting for 100% in total. According to the above data, the performance stability corresponding to the 95% quantile is 70%, so the performance stability threshold is set to 70%, that is, to filter out orders with a performance stability lower than 70%. Subsequently, the order transaction history is called to filter out trading entities with more than 2 performance failures within the set period (such as the past 12 months), and the performance of each trading entity is counted. Assume that in a certain market, a total of 50 trading entities with continuous performance failures are screened out, and the number of defaults of these entities in the past 12 months is distributed as follows: The number of trading entities with 3 defaults is 20, accounting for 40%. The number of trading entities with 4 defaults is 15, accounting for 30%. The number of trading entities with 5 defaults is 10, accounting for 20%. The number of trading entities with more than 5 defaults is 5, accounting for 10%. According to the above statistical results, the proportion of trading entities with three or more defaults reached 60%, so these trading entities were marked as key focus objects. Next, analyze the reasons for performance failure, obtain the specific transaction conditions of all default orders, and classify the default types, including overdue performance, contract changes, and performance failures, and count the proportion of each default type. For example, in a certain trading market, assuming that the total number of default orders is 100, of which 45 are overdue, 30 are contract changes, and 25 are performance failures, then the default categories account for 45%, 30%, and 25% respectively. Finally, summarize all screening and analysis results, build a list of performance issues, and obtain data on trading entities with performance issues.

[0059] Compensation transaction evaluation submodule: Based on the data of the transaction subject with performance issues, the performance status of the executed compensation transactions is extracted, the activation volume of compensation transactions is compared with the total volume of performance stability orders corresponding to orders below the stability threshold, the impact of compensation transactions on market stability is analyzed, and the optimal market supply and demand matching combination is screened according to the matching and adjusted order transaction records to obtain the green electricity transaction data processing results.

[0060] Call the data of the transaction subject with performance problems, screen the performance orders involving compensation transactions, and count the coverage of compensation transactions. Extract all orders for executed compensation transactions, including the order volume of compensation transactions, the volume of covered performance failure orders, and the performance of compensation transactions. Calculate the compensation transaction coverage rate to evaluate the impact of compensation transactions on performance failure orders. For example, assuming that the total number of compensation transaction orders is 120 and the total number of orders with low performance stability is 150, the compensation transaction coverage rate is calculated as follows: 120 / 150×100%=80%, indicating that compensation transactions cover 80% of performance failure orders. Subsequently, compare the activation volume of compensation transactions with the total number of orders with low performance stability to determine whether compensation transactions can meet market demand. For example, assuming that in a certain trading market, the activation volume of compensation transactions is 100MWh, and the total power demand of orders with low performance stability is 120MWh, then the activation volume of compensation transactions is insufficient; if the activation volume is 130MWh, then compensation transactions can meet market demand. If the coverage rate is less than 80%, the activation volume of compensation transactions is insufficient, otherwise the compensation transactions can meet the market stability requirements. Finally, according to the order transaction records of matching adjustment, the optimal market supply and demand matching combination is screened. For example, in a green electricity market, if the supply of market A is sufficient and the supply of market B is insufficient, the power supply from market A to market B is adjusted first to achieve the optimal matching effect. Finally, the processing results of green electricity transaction data are obtained.

[0061] This embodiment further provides a method for processing green electricity transaction data. The method for processing green electricity transaction data is executed based on the above-mentioned device for processing green electricity transaction data, and includes the following steps: S1: Obtain the total order volume data of the green electricity trading market, determine the market supply and demand status according to the preset supply and demand balance benchmark range, and obtain the market supply and demand analysis results; S2: Extract the electricity sales order gap of the undersupplied market from the market supply and demand analysis results, allocate orders to the undersupplied market based on the geographical area information of all markets, and obtain the cross-market order adjustment results; S3: Based on the green power transaction contract data of the corresponding market in the cross-market order adjustment results, analyze the performance of each transaction order and generate transaction performance analysis results; S4: Based on the green power generation data of the corresponding market in the cross-market order adjustment results, the power generation fluctuation risk market is screened, the remaining available power sales orders in the adjacent markets of the power generation fluctuation risk market are called, the standby transaction activation volume is calculated, and the compensation transaction activation volume analysis results are obtained; S5: Based on the transaction performance analysis results and the compensation transaction activation volume analysis results, screen the optimal market supply and demand matching combination and obtain the green electricity transaction data processing results.

[0062] In the present invention, by introducing dynamic monitoring and analysis of the market supply and demand status, accurate control of the supply and demand relationship in the green electricity trading market is achieved, and the market strategy can be quickly adjusted according to the degree of deviation between real-time market supply and demand. The geographical area matching and cross-market allocation strategy of order data effectively improves the order allocation efficiency of the green electricity market, reduces the power gap in the undersupplied market, and ensures the dynamic adjustment ability of the market supply and demand balance. In the performance analysis process, based on multi-dimensional data such as contract performance, transaction success rate, and default records, in-depth analysis of the performance stability of transaction orders is carried out, which helps to timely discover potential performance risks and reduce the incidence of market transaction defaults. In response to the risk of power generation fluctuations, a backup transaction activation mechanism is proposed by calculating the power generation uncertainty index and combining the remaining available orders in adjacent markets, which effectively responds to the challenges of market power supply stability brought about by power generation fluctuations and improves the emergency response capability of the overall market. Finally, through the screening of supply and demand matching combinations and the optimization of trading strategies, the optimal allocation of market resources is achieved. The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A device for processing green electricity transaction data, characterized in that: The processing device comprises: Market supply and demand monitoring module: obtains the total order volume data of the green electricity trading market, determines the market supply and demand status according to the preset supply and demand balance benchmark range, and obtains the market supply and demand analysis results; Order matching adjustment module: extracts the power sales order gap of the undersupplied market from the market supply and demand analysis results, allocates orders to the undersupplied market according to the geographical area information of all markets, and obtains cross-market order adjustment results; Transaction performance evaluation module: based on the green power transaction contract data of the corresponding market in the cross-market order adjustment result, analyze the performance of each transaction order and generate a transaction performance analysis result; Cross-regional compensation analysis module: based on the green power generation data of the corresponding market in the cross-market order adjustment result, screen the power generation fluctuation risk market, call the remaining available power sales orders in the adjacent market of the power generation fluctuation risk market, calculate the standby transaction activation amount, and obtain the compensation transaction activation amount analysis result; Transaction strategy optimization module: based on the transaction performance analysis results and the compensation transaction activation volume analysis results, screen the optimal market supply and demand matching combination and obtain the green electricity transaction data processing results.

2. The green electricity transaction data processing device according to claim 1, characterized in that: The market supply and demand analysis results include the transaction power supply and demand ratio, the number of completed power purchase orders, the number of completed power sales orders, the number of uncompleted power purchase orders, the number of uncompleted power sales orders, the market supply and demand status, and the degree of supply and demand deviation; The cross-market order adjustment results include the electricity sales order gap in the undersupplied market, the number of electricity sales orders in the adjacent target market, the supply-demand ratio of the adjacent target market, the feasibility of inter-market matching, adjustable order transaction data, order matching adjustment priority and orders allocated to the undersupplied market.

3. The green electricity transaction data processing device according to claim 1, characterized in that: The transaction performance analysis results include transaction order performance stability, stability threshold comparison results and performance analysis results; the compensation transaction activation quantity analysis results include power generation uncertainty index, power generation fluctuation risk market, power supply stability compensation demand, standby transaction activation quantity and compensation transaction activation quantity.

4. The green electricity transaction data processing device according to claim 1, characterized in that: The green electricity transaction data processing results include the default categories of performance stability orders below the stability threshold, the trading entities with continuous performance problems, the reasons for performance failure, the compensation transaction coverage rate, the impact of compensation transactions on market stability and the optimal market supply and demand matching combination.

5. The green electricity transaction data processing device according to claim 1, characterized in that: The market supply and demand monitoring module includes: The submodule for calculating the supply-demand ratio of electricity purchase and sales is used to obtain the total order data of the green electricity trading market, including the total electricity of electricity purchase orders and the total electricity of electricity sales orders, using the formula: , Calculate the total electricity supply-demand ratio of the electricity purchase order Supply-demand ratio of total electricity sales orders , generate market purchase and sales power supply and demand ratio data; in, Represents the total amount of electricity purchased. Represents the total amount of electricity sold in the order. Representative The amount of electricity purchased by the power purchase order, Representative The amount of electricity sold in a power sales order, Represents the quantity of electricity purchase orders, Represents the quantity of electricity sales orders; Supply and demand deviation calculation submodule: call the market purchase and sale power supply and demand ratio data, extract the number of completed and uncompleted power purchase orders and the number of power sales orders, and use the formula: Calculate the market supply and demand deviation ; in, Representative The amount of electricity purchased by the power purchase order, Representative The amount of electricity sold in a power sales order, Representative The amount of electricity purchased by the market has been completed. Representative The amount of electricity sold in the market has been completed. Represents the total number of electricity purchase orders, represents the number of markets; Market status determination submodule: determines the supply and demand status of each market according to the degree of market supply and demand deviation in the market supply and demand deviation data, and obtains market supply and demand analysis results.

6. The green electricity transaction data processing device according to claim 5, characterized in that: The order matching adjustment module includes: Target market screening submodule: extract the power sales order gap of the undersupplied market from the market supply and demand analysis results, screen the target markets adjacent to the undersupplied market according to the geographical area information of all markets, as well as the grid interconnection relationship and transmission capacity, obtain the power sales order quantity and supply-demand ratio of each adjacent target market, and obtain the target market supply and demand data; Market matching calculation submodule: Based on the target market supply and demand data, the formula is: Calculate the The feasibility of matching in the market , generating inter-market matching data; in, represents the number of electricity sales orders in the adjacent market, Represents the supply-demand ratio of adjacent markets, Represents the electricity demand gap in the undersupplied market, represents the number of adjacent markets that meet the criteria, Represents the quantity of undersupplied markets; Order adjustment allocation submodule: screen the adjustable orders according to the matching feasibility of the inter-market matching data, extract the order transaction data that meets the adjustment conditions, including order quantity, order transaction records, and matching success rate, calculate the order matching adjustment priority, allocate orders to the undersupplied market according to the priority, and obtain the cross-market order adjustment results.

7. The green electricity transaction data processing device according to claim 6, characterized in that: The transaction performance evaluation module includes: Order fulfillment stability calculation submodule: Based on the green power transaction contract data of the corresponding market in the cross-market order adjustment results, including fulfillment time, default records, number of contract changes and transaction success rate, the formula is used: Calculate the stability of transaction order fulfillment ; in, Represents the order fulfillment time, Represents the order transaction success rate, Represents the cumulative number of default records of the order. Represents the number of order contract changes; Performance analysis submodule: compares the performance stability of each transaction order with the preset stability threshold, analyzes the performance of each transaction order, and generates transaction performance analysis results.

8. The device for processing green electricity transaction data according to claim 6, characterized in that: The cross-region compensation analysis module includes: Power generation uncertainty calculation submodule: Based on the green power generation data of the corresponding market in the cross-market order adjustment result, according to the acquired wind speed data, light intensity and unit availability, the formula is adopted: Calculating the Generation Uncertainty Index ; in, Representative The wind power generation at a given moment, Representative The photovoltaic power generation at a given moment, Representative The expected power generation at a given moment, Represents the number of moments in the statistical period; Power supply stability compensation screening submodule: compares the power generation uncertainty index with a preset power generation threshold, screens the power generation fluctuation risk market, obtains the power supply stability compensation demand of the power generation fluctuation risk market, and obtains compensation demand data of the power generation fluctuation risk market; Compensation transaction activation analysis submodule: calls the remaining available electricity sales orders in the adjacent market to analyze the activation volume of standby transactions and obtain the analysis results of the activation volume of compensation transactions.

9. The green electricity transaction data processing device according to claim 8, characterized in that: The trading strategy optimization module includes: Performance problem screening submodule: based on the transaction performance analysis results and the compensation transaction activation volume analysis results, extract the default categories of orders below the stable threshold, screen the trading entities with continuous performance problems, analyze the reasons for performance failure, and obtain the data of the trading entities with performance problems; Compensation transaction evaluation submodule: Based on the transaction subject data of the performance issue, extract the performance status of the executed compensation transactions, compare the activation volume of compensation transactions with the total volume of performance stability orders corresponding to orders below the stability threshold, analyze the impact of compensation transactions on market stability, and screen the optimal market supply and demand matching combination based on the matching and adjusted order transaction records to obtain the green electricity transaction data processing results.

10. A method for processing green electricity transaction data, characterized in that: The green electricity transaction data processing device according to any one of claims 1 to 9 is executed, comprising the following steps: S1, obtaining the total order volume data of the green electricity trading market, judging the market supply and demand status according to the preset supply and demand balance benchmark range, and obtaining the market supply and demand analysis results; S2, extracting the electricity sales order gap of the undersupplied market from the market supply and demand analysis results, allocating orders to the undersupplied market according to the geographical area information of all markets, and obtaining a cross-market order adjustment result; S3, based on the green power transaction contract data of the corresponding market in the cross-market order adjustment result, analyzing the performance of each transaction order and generating a transaction performance analysis result; S4, based on the green power generation data of the corresponding market in the cross-market order adjustment result, screen the power generation fluctuation risk market, call the remaining available power sales orders in the adjacent market of the power generation fluctuation risk market, calculate the standby transaction activation amount, and obtain the compensation transaction activation amount analysis result; S5, based on the transaction performance analysis results and the compensation transaction activation volume analysis results, screen the optimal market supply and demand matching combination to obtain the green electricity transaction data processing results.

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