Full-time-period multi-scene clearing and pricing method suitable for inter-province multi-channel centralized bidding

Abstract

The invention provides a full-time multi-scene clearing and pricing method suitable for inter-provincial multi-channel centralized bidding. The method comprises the following steps: dividing seller market subjects participating in a centralized bidding transaction into new energy seller market subjects and traditional energy seller market subjects; based on historical prediction data, a transaction period is divided into a new energy large-power generation period set, a traditional energy large-power generation period set and a common output period set, and differential clearing strategies are respectively implemented: clearing and transaction path marginal pricing are only performed on corresponding types of energy in the new energy or traditional energy large-power generation period; and introducing a green power excitation item to the target function in the common output time period, and improving the new energy consumption level by adjusting an excitation factor. According to the method, the scientificity and pertinence of market design are improved, the cross-provincial and cross-regional resource optimization configuration efficiency is optimized, and the low-carbon and economical targets of system operation are cooperatively achieved.

Description

Technical Field

[0001] This invention relates to the field of electricity market technology, and in particular to a clearing and pricing method applicable to inter-provincial multi-channel centralized bidding, covering all time periods and multiple scenarios. Background Technology

[0002] Inter-provincial medium- and long-term centralized bidding transactions in China are a trading mechanism implemented by the Beijing Power Exchange Center Co., Ltd. within the operating scope of the State Grid Corporation of China, relying on inter-provincial and inter-regional interconnection lines. Inter-provincial medium- and long-term centralized bidding transactions employ a multi-channel centralized optimization and clearing mechanism that takes into account channel security constraints. Specifically, both buyers and sellers submit time-of-use electricity and price information at the landing and grid connection points, respectively. The buyer's declared price is then converted to the seller's price according to all feasible trading paths, taking into account the transmission price and network loss rate on the interconnection line, and the price difference between the buyer and seller is calculated. Buyer-seller combinations with a price difference less than zero do not participate in centralized optimization and clearing. With the goal of maximizing social welfare, multi-channel centralized optimization is performed based on parameters such as the available transmission capacity and directional coefficient of the interconnection line.

[0003] Current clearing models fail to differentiate between periods of peak renewable and traditional energy generation. Instead, they require simultaneous submission of time-of-use electricity prices during the application phase, followed by unified clearing and pricing. This approach ignores the fundamental difference between the inherent uncertainty of renewable energy generation and the dispatchability of traditional energy, hindering the normal absorption of renewable energy. Since renewable energy output is highly dependent on weather conditions, scenarios of "peak renewable energy generation," "peak traditional energy generation," or "joint generation" may occur during the trading period. Therefore, designing and modeling mechanisms to ensure the normal absorption of renewable energy unaffected by traditional energy sources is a pressing issue. Summary of the Invention

[0004] In view of this, the present invention provides a clearing and pricing method applicable to inter-provincial multi-channel centralized bidding across all time periods and scenarios, in order to solve the above problems.

[0005] This invention provides a clearing and pricing method applicable to inter-provincial multi-channel centralized bidding across all time periods and scenarios, comprising: dividing the market participants in the centralized bidding transaction into new energy market participants and traditional energy market participants; based on historical forecast data, dividing the trading period into a set of new energy peak generation periods, a set of traditional energy peak generation periods, and a set of shared power generation periods; during the new energy peak generation periods, clearing is performed for the new energy market participants and buyers using a multi-channel centralized optimization clearing model that takes into account channel safety constraints, and pricing is performed using a trading path marginal pricing mechanism; during the traditional energy peak generation periods, clearing is performed for the traditional energy market participants and buyers using the multi-channel centralized optimization clearing model, and pricing is performed using the trading path marginal pricing mechanism; during the shared power generation periods, collaborative clearing is performed for all market participants and buyers using an optimization clearing model that introduces a green electricity incentive term into the objective function, and pricing is performed using the trading path marginal pricing mechanism.

[0006] In another implementation of the present invention, the step of dividing the trading period into a new energy peak generation period, a traditional energy peak generation period, and a shared power generation period based on historical forecast data includes: based on historical forecast data, when the traditional energy power generation in a certain period is lower than a first predetermined threshold of the total power generation of all participating power generators, the period is included in the new energy peak generation period set; when the new energy power generation in a certain period is lower than a second predetermined threshold of the total power generation of all participating power generators, the period is included in the traditional energy peak generation period set; the remaining periods are included in the shared power generation period set; wherein, the first predetermined threshold is calculated using the following formula:

[0007] The formula for calculating the second predetermined threshold is:

[0008] in, and These are the average and standard deviation of low-output periods for new energy sources, calculated based on historical data. and The average and standard deviation of traditional energy low-output periods calculated based on historical data. and These are adjustment factors and risk factors, respectively.

[0009] In another implementation of the present invention, the multi-channel centralized optimization clearing model takes maximizing social welfare as its objective function; the constraints include transaction volume constraints, line power loss conversion constraints, transmission channel capacity constraints, and transmission channel ramping constraints; the optimization clearing model used during the common power output period has an objective function that adds a green electricity incentive term to the social welfare maximization objective, which is expressed as:

[0010] in, For new energy vehicle sellers market entities s During the period t Clearing out electricity; c It is an adjustable green electricity incentive factor used to improve the level of new energy consumption.

[0011] In another implementation of the present invention, the marginal pricing mechanism for the trading path includes: after optimizing and clearing to determine the trading path and trading volume, for each cleared trading path, converting all winning bidder bid prices on the path to the sending end, and retaining all winning seller bid prices at the sending end; taking the highest selling bid price and the lowest converted purchase price on the path; determining the sending end marginal price of the trading path based on the highest selling bid price and the lowest converted purchase price; and calculating the purchasing end price of the trading path by adding the transmission fees and network loss discounts of the transmission channels traversed on the sending end marginal price.

[0012] In another aspect, the present invention provides a clearing and pricing system applicable to inter-provincial multi-channel centralized bidding, covering all time periods and multiple scenarios, comprising: a market participant segmentation module: dividing the seller market participants in centralized bidding into new energy seller market participants and traditional energy seller market participants; a trading period segmentation module: based on historical forecast data, dividing the trading period into a set of new energy peak periods, a set of traditional energy peak periods, and a set of shared power output periods; a clearing and pricing output module: during the new energy peak periods, clearing the new energy seller market participants and buyer market participants using a multi-channel centralized optimization clearing model that takes into account channel safety constraints, and pricing using a trading path marginal pricing mechanism; during the traditional energy peak periods, clearing the traditional energy seller market participants and buyer market participants using the multi-channel centralized optimization clearing model, and pricing using the trading path marginal pricing mechanism; during the shared power output periods, collaborative clearing of all seller market participants and buyer market participants using an optimization clearing model that introduces a green electricity incentive term into the objective function, and pricing using the trading path marginal pricing mechanism.

[0013] In another implementation of the present invention, the step of dividing the trading period into a new energy peak generation period, a traditional energy peak generation period, and a shared power generation period based on historical forecast data includes: based on historical forecast data, when the traditional energy power generation in a certain period is lower than a first predetermined threshold of the total power generation of all participating power generators, the period is included in the new energy peak generation period set; when the new energy power generation in a certain period is lower than a second predetermined threshold of the total power generation of all participating power generators, the period is included in the traditional energy peak generation period set; and the remaining periods are included in the shared power generation period set. The formula for calculating the first predetermined threshold is as follows:

[0014] The formula for calculating the second predetermined threshold is:

[0015] in, and These are the average and standard deviation of low-output periods for new energy sources, calculated based on historical data. and The average and standard deviation of traditional energy low-output periods calculated based on historical data. and These are adjustment factors and risk factors, respectively.

[0016] In another implementation of the present invention, the multi-channel centralized optimization clearing model takes maximizing social welfare as its objective function; the constraints include transaction volume constraints, line power loss conversion constraints, transmission channel capacity constraints, and transmission channel ramping constraints; the optimization clearing model used during the common power output period has an objective function that adds a green electricity incentive term to the social welfare maximization objective, which is expressed as:

[0017] in, c It is an adjustable green electricity incentive factor used to improve the level of new energy consumption.

[0018] In another implementation of the present invention, the marginal pricing mechanism for the trading path includes: after optimizing and clearing to determine the trading path and trading volume, for each cleared trading path, converting all winning bidder bid prices on the path to the sending end, and retaining all winning seller bid prices at the sending end; taking the highest selling bid price and the lowest converted purchase price on the path; determining the sending end marginal price of the trading path based on the highest selling bid price and the lowest converted purchase price; and calculating the purchasing end price of the trading path by adding the transmission fees and network loss discounts of the transmission channels traversed on the sending end marginal price.

[0019] In another aspect, the present invention provides an electronic device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of a full-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding as described in any of the preceding claims. In another aspect, the present invention provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of a full-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding as described in any of the preceding claims.

[0020] This invention presents a full-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding. Based on the inherent attributes of power generators on the sales side, it categorizes them into two main types: renewable energy generators and traditional energy generators. Furthermore, based on the output characteristics reflected in historical forecast data, the entire trading cycle is dynamically divided into renewable energy peak generation periods, traditional energy peak generation periods, and periods of combined output, thus enhancing the scientific rigor and relevance of market design. Based on these period divisions, an inter-provincial multi-channel centralized optimized clearing model is constructed, and differentiated clearing strategies are implemented. During periods when renewable energy dominates, the model specifically targets renewable energy units for centralized clearing. The system prioritizes the elimination of renewable energy sources, ensuring their priority consumption. During periods when traditional energy dominates, the focus is on eliminating traditional generating units to guarantee a stable supply of baseload power. By constructing a differentiated elimination strategy that closely matches the characteristics of different time periods, the efficiency of resource allocation across provinces and regions is optimized. A "green energy incentive term" is introduced into the optimization objective function. This design creates clear price signals and economic incentives. By adjusting the incentive factors, the system's preference for renewable energy consumption can be effectively adjusted, thereby promoting the consumption of renewable energy in a market-oriented manner, avoiding the waste of clean energy, and synergistically achieving the goals of low-carbon and economical system operation. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. By reading the detailed description of the embodiments below, the advantages and benefits of the solutions will become clear to those skilled in the art. The accompanying drawings are only for illustrating preferred embodiments and are not intended to limit the present invention. In the accompanying drawings: Figure 1 This is a schematic diagram of the overall process of a clearing and pricing method applicable to inter-provincial multi-channel centralized bidding across all time periods and scenarios, as an embodiment of the present invention.

[0022] Figure 2This is a detailed flowchart illustrating a method for clearing and pricing across multiple time periods and scenarios applicable to inter-provincial multi-channel centralized bidding, as an embodiment of the present invention.

[0023] Figure 3 This is a graph showing the transaction results of thermal power and new energy power in one embodiment of the present invention. Detailed Implementation

[0024] To enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and thoroughly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0025] Figure 1 This invention provides a schematic flowchart of a full-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding, as shown in the embodiment of the invention. Figure 1 As shown, this embodiment mainly includes: S101. Market entities that will participate in centralized bidding transactions. S Classified as market entities selling new energy vehicles ( S RES ) and traditional energy sellers ( S TE ).

[0026] S102. Based on historical forecast data, the trading period is divided into a set of new energy major release periods ( T RES Traditional energy peak season collection ( T TE ) and the set of periods of joint effort ( T CO ).

[0027] S103. During periods of high demand for new energy, a multi-channel centralized optimization clearing model that takes into account channel security constraints is used to clear the market for both new energy sellers and buyers, and a transaction path marginal pricing mechanism is used for pricing.

[0028] For example, S104. During periods of high demand for traditional energy, the multi-channel centralized optimization clearing model is used to clear out the traditional energy sellers and buyers, and the marginal pricing mechanism of the transaction path is used for pricing.

[0029] S105. During the joint contribution period, for all seller market participants and buyer market participants, an optimized clearing model that incorporates a green electricity incentive term into the objective function is used for collaborative clearing, and the marginal pricing mechanism of the transaction path is used for pricing.

[0030] For example, both the buyer and seller declare time-of-use electricity volume and price information at the landing and grid connection points, respectively. The buyer's declared price is then converted to the seller's price according to all feasible transaction paths, and the price difference between the buyer and seller is calculated. Buyer-seller combinations with a price difference less than zero do not participate in centralized optimization clearing. With the goal of maximizing social welfare, multi-channel centralized optimization is performed based on parameters such as available transmission capacity and directional coefficient of tie lines. After the optimization clearing determines the transaction paths and transaction volumes, the electricity price is determined using a transaction path marginal pricing mechanism for each cleared transaction path.

[0031] This invention presents a full-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding. Based on the inherent attributes of power generators on the sales side, it categorizes them into two main types: renewable energy generators and traditional energy generators. Furthermore, based on the output characteristics reflected in historical forecast data, the entire trading cycle is dynamically divided into renewable energy peak generation periods, traditional energy peak generation periods, and periods of combined output, thus enhancing the scientific rigor and relevance of market design. Based on these period divisions, an inter-provincial multi-channel centralized optimized clearing model is constructed, and differentiated clearing strategies are implemented. During periods when renewable energy dominates, the model specifically targets renewable energy units for centralized clearing. The system prioritizes the elimination of renewable energy sources, ensuring their priority consumption. During periods when traditional energy dominates, the focus is on eliminating traditional generating units to guarantee a stable supply of baseload power. By constructing a differentiated elimination strategy that closely matches the characteristics of different time periods, the efficiency of resource allocation across provinces and regions is optimized. A "green energy incentive term" is introduced into the optimization objective function. This design creates clear price signals and economic incentives. By adjusting the incentive factors, the system's preference for renewable energy consumption can be effectively adjusted, thereby promoting the consumption of renewable energy in a market-oriented manner, avoiding the waste of clean energy, and synergistically achieving the goals of low-carbon and economical system operation.

[0032] In another implementation of the present invention, such as Figure 2 As shown, the division of trading periods into new energy peak generation periods, traditional energy peak generation periods, and shared power generation periods based on historical forecast data includes: based on historical forecast data, when the traditional energy power generation in a certain period is lower than a first predetermined threshold of the total power generation of all participating power generators, that period is included in the new energy peak generation period set; when the new energy power generation in a certain period is lower than a second predetermined threshold of the total power generation of all participating power generators, that period is included in the traditional energy peak generation period set; the remaining periods are included in the shared power generation period set; wherein, the first predetermined threshold is calculated using the following formula:

[0033] The formula for calculating the second predetermined threshold is:

[0034] in, and These are the average and standard deviation of low-output periods for new energy sources, calculated based on historical data. and The average and standard deviation of traditional energy low-output periods calculated based on historical data. and These are adjustment factors and risk factors, respectively.

[0035] In another implementation of the present invention, the multi-channel centralized optimization clearing model takes maximizing social welfare as its objective function; the constraints include transaction volume constraints, line power loss conversion constraints, transmission channel capacity constraints, and transmission channel ramping constraints; the optimization clearing model used during the common power output period has an objective function that adds a green electricity incentive term to the social welfare maximization objective, which is expressed as:

[0036] in, For new energy vehicle sellers market entities s During the period t Clearing out electricity; c This is an adjustable green electricity incentive factor, expressed in yuan / MW, used to improve the level of renewable energy consumption.

[0037] For example, the constraints include one or all of the following: The transaction volume constraint is used to limit the clearing volume of market participants to no more than the volume they have declared.

[0038] Line power loss conversion constraint is used to characterize the inequality relationship between the transaction power at the sending end and the receiving end of the transaction path due to network loss.

[0039] Transmission channel capacity constraints are used to limit the net transmission capacity of each transmission channel to no more than its available transmission capacity (ATC).

[0040] The power transmission channel ramping constraint is used to limit the rate of change of power flow on the power transmission channel in adjacent time periods.

[0041] Specifically, for renewable energy periods, with the goal of maximizing social welfare, the available transmission capacity (ATC) of the transmission channel is taken into account, as well as the transmission price and network loss coefficient of the channel. Based on the information declared by the purchasers and sellers, the inter-provincial multi-channel centralized optimization and clearing is carried out.

[0042] First, the buyer's declared price must be converted to the seller's price according to all feasible transaction paths. Since the transmission fee and losses on the transmission channel are borne by the buyer, the network loss coefficient must be taken into account during the conversion process, and the transmission price of the transmission channel must be deducted. The formula for converting the buyer's electricity price is as follows: (1) In the formula, For the buyer g During the period t Time along the transaction path i Discounted to the seller s Electricity price; l g,i,t For time period t Transaction path i The buyer at the receiving end g Declare electricity price; s l For power transmission channels l The unit price of power transmission; d m For power transmission channels m The network loss coefficient, L i To form a transaction path i A collection of power transmission channels; For transaction path i From the sending end to the transmission channel l The set of power transmission channels, including power transmission channels l itself. T RES This is a collection of events during peak periods for new energy production. S RES It is a collection of market entities selling new energy products.

[0043] Calculate the discounted purchase price difference; trading pairs with a difference less than zero will not participate in centralized bidding.

[0044] The optimization clearing process aims to maximize social welfare, and the objective function is: (2) In the formula, U For social welfare; For the seller s Purchaser g via transaction path i During the period t The transaction volume at the sending end; l s,i,t For time period t Transaction path i The seller of the delivery end s Declare electricity price; I A set of transaction paths; G It refers to the collection of market entities that are buyers.

[0045] All constraints must be met: 1) Transaction volume constraints Both sellers and buyers in the market are subject to maximum clearing limits, meaning that the cleared volume of a market participant must be less than the declared volume. The following constraints are established: (3) (4) In the formula, For the seller s Purchaser g via transaction path i During the period t The transaction volume at the receiving end; For time period t Seller market entity s The declared electricity volume; Q g,t For time period t Buyer market entities g The declared electricity volume.

[0046] In addition, the transaction volume at the beginning and end of any transaction path must meet the physical constraint of not being less than zero: (5) (6) 2) Line power loss conversion constraints For each trading path, due to transmission channel losses, the transaction volume at the sending end is not equal to the transaction volume at the receiving end, and the following relationship exists: (7) 3) Transmission channel capacity constraints Each transmission channel has one or more trading paths passing through it. Taking into account the power flow direction of the trading path and the power flow loss before reaching the channel, the net power transmission capacity of the transmission channel is obtained: (8) In the formula, F l,t For power transmission channels l During the period t Net input current; P l For including power transmission channels l The set of transaction paths; H i,l For transaction path i At the cross section l The power flow distribution factor is 1 on the DC channel and 1 on the DC channel. D i,l For transaction path iIn power transmission channels l The direction coefficient is 1 or -1.

[0047] power transmission channels l Due to limitations imposed by ATC's available transmission capacity, the following constraints exist: (9) In the formula, For time period t, the ATC of transmission channel l is .

[0048] 4) Ramp-up constraints of power transmission channels To ensure the stable operation of the power system and prevent the grid from being impacted by drastic changes in power flow, there are ramping constraints on transmission channels: (10) In the formula, and Transmission channels l The restrictions on lowering the gradient rate and raising the gradient rate are as follows.

[0049] Equations (2) to (10) above constitute a centralized optimization clearing model for inter-provincial multi-channel electricity price conversion and power flow direction coefficient.

[0050] During the new energy period T RES Using renewable energy as the seller, the transaction volume is determined through centralized optimization and clearing based on the aforementioned clearing model. Subsequently, marginal pricing is applied to the trading path. This involves setting marginal prices for market participants using the same trading path after the purchase-sale matching clearing. The bid prices of the winning purchase and sale market participants on the path are converted to the sending end of the trading path. The highest selling bid price and the lowest converted purchase price are used to determine the marginal price at the sending end of the trading path. The buying end price is calculated by adding transmission fees and network loss discounts to this price. Specifically, for each trading path with traded electricity, the clearing price for the seller node on that path is... l The lowest buy price λ among all traded pairs along this path. Dmin The highest price declared by the seller. Smax The price difference coefficient K is determined by the following formula: (11) The clearing price of the buyer's node is obtained by adding the transmission cost and network loss discount of the relevant interconnection lines of the transaction path to the seller's node price.

[0051] (2) For traditional energy periods T TE Traditional energy sources are the main participants in the transactions, forming a group of traditional energy sellers in the market.S TE Similarly, the above model is used for clearing, and the marginal pricing mechanism of the transaction path is used for pricing.

[0052] (3) For periods of joint effort T co Introducing a green electricity incentive term, the model is as follows: With the electricity price conversion formula remaining unchanged, the objective function, after introducing a green electricity incentive term, becomes: (12) in, c This is the green electricity incentive factor, expressed in yuan / MW. For new energy vehicle sellers market entities s During the period t The cleared electricity, and satisfying: (13) The following constraints must be met: (14) (15) (16) (17) (18) (19) (20) (twenty one) (twenty two) Equation (14) represents the electricity volume constraint for new energy sellers, Equation (15) represents the electricity volume constraint for traditional energy sellers, and Equation (16) represents the electricity volume constraint for all buyers. Equations (17) and (18) represent the physical constraint that the electricity volume at the beginning and end of the transaction path should not be less than zero, and Equation (19) represents the line power loss conversion constraint. Equations (20) to (22) represent the transmission channel capacity constraint and ramping constraint.

[0053] The proposed all-time, multi-scenario, inter-provincial, multi-channel centralized bidding mechanism design and modeling involves clearing and pricing new energy and traditional energy separately during peak energy generation periods, and introducing green electricity incentives for clearing and pricing during common periods. By adjusting green electricity incentive factors, the level of new energy consumption is improved, and the economic efficiency and cleanliness of system operation are optimized.

[0054] In another implementation of the present invention, the marginal pricing mechanism for the trading path includes: after optimizing and clearing to determine the trading path and trading volume, for each cleared trading path, converting all winning bidder bid prices on the path to the sending end, and retaining all winning seller bid prices at the sending end; taking the highest selling bid price and the lowest converted purchase price on the path; determining the sending end marginal price of the trading path based on the highest selling bid price and the lowest converted purchase price; and calculating the purchasing end price of the trading path by adding the transmission fees and network loss discounts of the transmission channels traversed on the sending end marginal price.

[0055] In another implementation of the present invention, before performing the clearing calculation, a buyer price conversion step is included. The buyer's declared price is converted to the seller's node according to all feasible transaction paths, taking into account the network loss coefficient and transmission unit price of each transmission channel traversed by the path. The calculation formula is as follows:

[0056] Calculate the price difference between the buyer and seller after discounting, and remove the buyer-seller combinations with a price difference less than zero, so that they are not included in the subsequent centralized optimization clearing.

[0057] The method of this invention is applied to inter-provincial and inter-regional power transmission networks that include DC channels and AC sections, wherein the power transmission channel is a DC channel or AC section that physically transmits electricity, and the transaction path consists of one or more power transmission channels.

[0058] Example 1 This study uses simulated operational data from the "Three Norths" region of China for a case study analysis. The case study includes four seller market participants and eight buyer market participants, connected by 34 trading paths consisting of 16 DC channels and 4 AC sections. The four seller market participants include two renewable energy units and two thermal power units. Considering 24 time periods, the buyer and seller market participants submit time-of-use electricity prices separately. A multi-channel centralized optimization clearing method considering green electricity incentives is adopted for the full-time, multi-scenario clearing process. During thermal power output periods (1-10), only thermal power clearing is performed; during renewable energy output periods (11-15), only renewable energy clearing is performed; and during shared output periods (16-24), green electricity incentives are introduced for shared clearing, and a green electricity incentive factor is set. c The price is 0.1 yuan / MW. The final transaction amounts for thermal power and new energy power, and the corresponding clearing rates, are as follows: Figure 3 As shown.

[0059] The analysis results show that during the renewable energy period, the clearing rate of renewable energy units reaches 100%; during the thermal power period, the clearing rate of thermal power units reaches 100%; during the common period, due to the introduction of green electricity incentives, the clearing rate of renewable energy units is always higher than that of thermal power units, ensuring the absorption of renewable energy.

[0060] The following table compares the transaction volume of new energy power and thermal power power before and after using this method.

[0061]

[0062] Data shows that after adopting the new method, the transaction volume of new energy power increased from 34,865.47 MW to 47,790.55 MW, an increase of nearly 13,000 MW; meanwhile, the transaction volume of thermal power power decreased from 103,968.3 MW to 94,968.32 MW, a reduction of approximately 9,000 MW. This increase and decrease clearly demonstrates the core effect of this invention: in inter-provincial and inter-regional electricity market transactions, it effectively promotes the priority consumption of new energy power, while reducing the dependence on traditional thermal power, optimizing the power supply structure, and directly serving the goal of clean and low-carbon energy development.

[0063] Overall, considering the time-sharing approach to green electricity incentives, which decouples the market participants of new energy and traditional energy during their respective periods of high activity, the introduction of green electricity incentives during periods when both are thriving is conducive to promoting the consumption of new energy and serving the goal of clean and low-carbon development.

[0064] Another aspect of the present invention provides a full-time, multi-scenario clearing and pricing system suitable for inter-provincial multi-channel centralized bidding, comprising: Market Entity Classification Module: The market entities participating in centralized bidding transactions are divided into new energy market entities and traditional energy market entities.

[0065] Trading session segmentation module: Based on historical forecast data, the trading sessions are divided into a set of periods with high output from new energy sources, a set of periods with high output from traditional energy sources, and a set of periods with combined output.

[0066] Clearing and pricing output module: During periods of high renewable energy generation, a multi-channel centralized optimization clearing model considering channel safety constraints is used to clear the renewable energy sellers and buyers, and a transaction path marginal pricing mechanism is used for pricing. During periods of high traditional energy generation, the multi-channel centralized optimization clearing model is used to clear the traditional energy sellers and buyers, and the transaction path marginal pricing mechanism is used for pricing. During periods of shared power generation, a collaborative clearing model incorporating green electricity incentives into the objective function is used to clear all sellers and buyers, and the transaction path marginal pricing mechanism is used for pricing.

[0067] This invention relates to a full-time, multi-scenario clearing and pricing system applicable to inter-provincial multi-channel centralized bidding. Based on the inherent attributes of power generators on the sales side, it categorizes them into two main types: renewable energy generators and traditional energy generators. Furthermore, based on the output characteristics reflected in historical forecast data, the entire trading cycle is dynamically divided into renewable energy peak periods, traditional energy peak periods, and periods of combined output, thus enhancing the scientific rigor and relevance of market design. Based on these period divisions, an inter-provincial multi-channel centralized optimized clearing model is constructed, and differentiated clearing strategies are implemented. During periods when renewable energy dominates, the model specifically targets renewable energy units for centralized clearing. The system prioritizes the elimination of renewable energy sources, ensuring their priority consumption. During periods when traditional energy dominates, the focus is on eliminating traditional generating units to guarantee a stable supply of baseload power. By constructing a differentiated elimination strategy that closely matches the characteristics of different time periods, the efficiency of resource allocation across provinces and regions is optimized. A "green energy incentive term" is introduced into the optimization objective function. This design creates clear price signals and economic incentives. By adjusting the incentive factors, the system's preference for renewable energy consumption can be effectively adjusted, thereby promoting the consumption of renewable energy in a market-oriented manner, avoiding the waste of clean energy, and synergistically achieving the goals of low-carbon and economical system operation.

[0068] In another implementation of the present invention, the step of dividing the trading period into a new energy peak generation period, a traditional energy peak generation period, and a shared power generation period based on historical forecast data includes: based on historical forecast data, when the traditional energy power generation in a certain period is lower than a first predetermined threshold of the total power generation of all participating power generators, the period is included in the new energy peak generation period set; when the new energy power generation in a certain period is lower than a second predetermined threshold of the total power generation of all participating power generators, the period is included in the traditional energy peak generation period set; and the remaining periods are included in the shared power generation period set. The formula for calculating the first predetermined threshold is as follows:

[0069] The formula for calculating the second predetermined threshold is:

[0070] in, and These are the average and standard deviation of low-output periods for new energy sources, calculated based on historical data. and The average and standard deviation of traditional energy low-output periods calculated based on historical data. and These are adjustment factors and risk factors, respectively.

[0071] In another implementation of the present invention, the multi-channel centralized optimization clearing model takes maximizing social welfare as its objective function; the constraints include transaction volume constraints, line power loss conversion constraints, transmission channel capacity constraints, and transmission channel ramping constraints; the optimization clearing model used during the common power output period has an objective function that adds a green electricity incentive term to the social welfare maximization objective, which is expressed as:

[0072] in, c It is an adjustable green electricity incentive factor used to improve the level of new energy consumption.

[0073] In another implementation of the present invention, the marginal pricing mechanism for the trading path includes: after optimizing and clearing to determine the trading path and trading volume, for each cleared trading path, converting all winning bidder bid prices on the path to the sending end, and retaining all winning seller bid prices at the sending end; taking the highest selling bid price and the lowest converted purchase price on the path; determining the sending end marginal price of the trading path based on the highest selling bid price and the lowest converted purchase price; and calculating the purchasing end price of the trading path by adding the transmission fees and network loss discounts of the transmission channels traversed on the sending end marginal price.

[0074] In another aspect of the present invention, the electronic device includes: a processor, a memory, and a communication bus and a communication interface.

[0075] in: The processor, memory, and communication interface communicate with each other via a communication bus.

[0076] A communication interface is used to communicate with other electronic devices or servers.

[0077] The processor is used to execute programs, specifically, it can execute any of the steps of the above embodiments applicable to the all-time, multi-scenario clearing and pricing method for inter-provincial multi-channel centralized bidding.

[0078] Specifically, the program may include program code, which includes computer operation instructions.

[0079] The processor may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or they may be processors of different types, such as one or more CPUs and one or more ASICs.

[0080] Memory is used to store programs. Memory may include high-speed RAM, and may also include non-volatile memory, such as at least one disk drive.

[0081] Specifically, the program can be used to cause the processor to execute the steps of any of the steps described in the embodiments for the all-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding. The specific implementation of each step in the program can be found in the corresponding descriptions of the steps and units executed in any of the above steps for the all-time, multi-scenario clearing and pricing method applicable to inter-provincial multi-channel centralized bidding, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments.

[0082] An exemplary embodiment of this application also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods of various embodiments of this application.

[0083] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.

[0084] Specific embodiments of the present invention have now been described. Other embodiments are within the scope of the appended claims. In some cases, the actions described in the claims can be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result.

[0085] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship between the components in a certain order (as shown in the figure). If the specific order changes, the directional indication will also change accordingly.

[0086] In the description of this invention, the terms "first" and "second" are used only for convenience in describing different components or names, and should not be construed as indicating or implying a sequential relationship, relative importance, or implicitly specifying the number of technical features indicated. Thus, a feature defined with "first" and "second" may explicitly or implicitly include at least one of that feature.

[0087] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0088] It should be noted that although specific embodiments of the present invention have been described in detail with reference to the accompanying drawings, this should not be construed as limiting the scope of protection of the present invention. Various modifications and variations that can be made by those skilled in the art without inventive effort within the scope described in the claims still fall within the scope of protection of the present invention.

[0089] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention, so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be an improper limitation of the embodiments of the present invention.

[0090] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A full-period multi-scenario clearing and pricing method suitable for inter-regional multi-channel centralized auction, characterized in that, The method comprises the steps of: dividing the seller market subjects participating in centralized bidding transactions into new energy seller market subjects and traditional energy seller market subjects; based on historical prediction data, dividing the transaction period into a new energy large generation period set, a traditional energy large generation period set, and a common output period set; in the new energy large generation period, for the new energy seller market subjects and the buyer market subjects, a multi-channel centralized optimization dispatching model considering channel safety constraints is used for dispatching, and a transaction path marginal pricing mechanism is used for pricing; in the traditional energy large generation period, for the traditional energy seller market subjects and the buyer market subjects, the multi-channel centralized optimization dispatching model is used for dispatching, and the transaction path marginal pricing mechanism is used for pricing; in the common output period, for all seller market subjects and buyer market subjects, an optimization dispatching model with a green electricity incentive term introduced in the objective function is used for collaborative dispatching, and the transaction path marginal pricing mechanism is used for pricing.

2. The method of claim 1, wherein, The step of dividing the transaction period into a new energy large generation period, a traditional energy large generation period, and a common output period based on historical prediction data comprises the steps of: based on historical prediction data, when the traditional energy generation capacity in a certain period is less than the first predetermined threshold of the total generation capacity of all participating transaction power generation companies, the period is divided into the new energy large generation period set; when the new energy generation capacity in a certain period is less than the second predetermined threshold of the total generation capacity of all participating transaction power generation companies, the period is divided into the traditional energy large generation period set; the remaining periods are divided into the common output period set; wherein the first predetermined threshold is calculated by the formula: the second predetermined threshold is calculated by the formula: wherein, and are the average and standard deviation of the low output period of new energy calculated according to historical data, and are the average and standard deviation of the low output period of traditional energy calculated according to historical data, and are the adjustment factor and risk factor, respectively.

3. The method of claim 1, wherein, The multi-channel centralized optimization dispatching model takes the maximization of social welfare as the objective function; the constraint conditions include transaction electricity capacity constraint, line electricity loss conversion constraint, transmission channel capacity constraint, and transmission channel climbing constraint; The optimization dispatching model used in the common output period has a green electricity incentive term in the objective function based on the maximization of social welfare, and the incentive term is represented as: Wherein, is a new energy seller market subject s In the time period t of clearing power; The transaction path marginal pricing mechanism comprises the steps of: is an adjustable green electricity incentive factor for improving new energy consumption level.

4. The method of claim 3, wherein, after the optimization dispatching determines the transaction path and transaction electricity capacity, for each dispatched transaction path, all the winning buyer bid prices on the path are converted to the sending end, and all the winning seller bid prices are kept at the sending end; the highest seller bid price and the lowest buyer converted price on the path are taken; the sending end marginal price of the transaction path is determined according to the highest seller bid price and the lowest buyer converted price; on the basis of the sending end marginal price, the transmission fee of the transmission channel passed and the loss discount are superimposed to calculate the purchase end price of the transaction path. The method comprises the steps of:

5. A full-period multi-scenario clearing and pricing system suitable for inter-regional multi-channel centralized auction, characterized in that, a market subject division module: dividing the seller market subjects participating in centralized bidding transactions into new energy seller market subjects and traditional energy seller market subjects; a transaction period division module: based on historical prediction data, dividing the transaction period into a new energy large generation period set, a traditional energy large generation period set, and a common output period set; ​ The clearing and pricing output module: in the new energy high generation period, the new energy seller market subject and the buyer market subject are cleared by a multi-channel centralized optimization clearing model considering channel safety constraints, and are priced by a transaction path marginal pricing mechanism; in the traditional energy high generation period, the traditional energy seller market subject and the buyer market subject are cleared by the multi-channel centralized optimization clearing model, and are priced by the transaction path marginal pricing mechanism; in the common output period, all seller market subjects and buyer market subjects are cooperatively cleared by an optimization clearing model in which a green electricity incentive term is introduced into a target function, and are priced by the transaction path marginal pricing mechanism.

6. The system of claim 5, wherein, The new energy high generation period, the traditional energy high generation period and the common output period are divided based on historical prediction data, and the method comprises the following steps: When the traditional energy generation amount in a certain period is lower than a first predetermined threshold of the total generation amount of all participating power generation companies, the period is divided into the new energy high generation period set; When the new energy generation amount in a certain period is lower than a second predetermined threshold of the total generation amount of all participating power generation companies, the period is divided into the traditional energy high generation period set; The remaining periods are divided into the common output period set; The first predetermined threshold is calculated by the following formula: The second predetermined threshold is calculated by the following formula: wherein, and are the average and standard deviation of the low output period of new energy calculated according to historical data, and are the average and standard deviation of the low output period of traditional energy calculated according to historical data, and are the adjustment factor and risk factor, respectively.

7. The system of claim 5, wherein, The multi-channel centralized optimization clearing model takes the maximization of social welfare as the objective function; The constraint conditions include transaction electricity amount constraint, line electricity amount loss conversion constraint, transmission channel capacity constraint and transmission channel climbing constraint; The optimization clearing model used in the common output period adds a green electricity incentive term to the social welfare maximization target, and the incentive term is expressed as: wherein, The transaction path marginal pricing mechanism comprises the following steps: is an adjustable green electricity incentive factor for improving new energy consumption level.

8. The system of claim 7, wherein, After the optimization clearing determines the transaction path and the transaction electricity amount, for each cleared transaction path, all the winning buyer bid prices on the path are converted to the sending end, and all the winning seller bid prices are kept at the sending end; The highest seller bid price and the lowest buyer converted price on the path are taken; The sending end marginal price of the transaction path is determined according to the highest seller bid price and the lowest buyer converted price; On the basis of the sending end marginal price, the transmission fee of the transmission channel and the network loss discount are superimposed to calculate the purchase end price of the transaction path. The computer program is stored on the computer storage medium and is executed by the processor to realize the steps of the full-period multi-scenario clearing and pricing method for inter-provincial multi-channel centralized bidding.

9. An electronic device, comprising: The computer program is stored on the computer storage medium and is executed by the processor to realize the steps of the full-period multi-scenario clearing and pricing method for inter-provincial multi-channel centralized bidding. ​ 10. A computer storage medium, characterized in that, ​

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