Source-load two-way interactive transaction method and system based on electricity-carbon real-time coupling

Abstract

The invention discloses a source-load two-way interactive transaction method and system based on electricity-carbon real-time coupling, and belongs to the field of data processing and energy management. The method comprises the steps of real-time carbon flow accounting and account management, source-load matching, electricity-carbon coupling bargaining, dynamic contract generation, performance accounting and real-time settlement adjustment. The method comprises the following steps: firstly, acquiring real-time marginal carbon emission factors and electric energy metering data, and calculating and updating real-time carbon flow values and accounts of participants; and constructing an electricity-carbon coupling bargaining model for the source-load transaction pair, negotiating to generate an electricity-carbon coupling transaction price, and generating a dynamic contract comprising a price-response function and a carbon flow-penalty reward function according to the electricity-carbon coupling transaction price. And after the contract is executed, settlement is carried out according to the actual carbon flow deviation. According to the method, a dual Nash bargaining mechanism and a dynamic interoperation contract are adopted, real-time carbon flow contribution is taken as core excitation, multi-subject interest conflict and transaction uncertainty can be effectively solved, and low carbon, efficiency and performance robustness of source-load interactive transaction are remarkably improved.

Description

Technical Field

[0001] This invention relates to the fields of data processing and energy management, and in particular to a source-load bidirectional interactive trading method and system based on real-time coupling of electricity and carbon. Background Technology

[0002] With the advancement of global "dual carbon" goals, the power system, as the core of energy transition, is undergoing profound changes in its market-based trading mechanisms. Two-way interactive power trading, as an important component of the new power system, aims to guide flexible interaction between the generation and consumption sides through market mechanisms, thereby improving system operating efficiency and the capacity for renewable energy absorption. Against this backdrop, how to effectively combine electricity trading with carbon emission management, and how to utilize information technology and data processing to construct a trading system that reflects the environmental attributes of electricity as a commodity, has become a key issue urgently needing research in the power market sector.

[0003] In existing technologies, electricity market transactions typically focus on electrical energy itself. The matching of transactions and price formation mechanisms are mainly based on physical and economic factors such as supply and demand, generation costs, and line congestion. Market participants submit their electricity volume and prices through bidding, and the trading center clears transactions based on the principle of economic optimization, generating the trading results. Carbon emission management is usually separated from the electricity market. For example, it may be achieved through an independent national carbon emission trading market, which manages and trades quotas for emission-controlled enterprises, or indirectly reflects the environmental value of electricity through green electricity certificate trading.

[0004] However, the aforementioned existing technical solutions have significant shortcomings. First, the electricity trading and carbon emission management systems are disconnected, resulting in price signals in the electricity market failing to accurately reflect the real environmental costs of electricity production and consumption in real time. Market participants lack an effective way to directly engage in low-carbon activities and obtain immediate economic returns through electricity trading. Second, existing carbon market management is largely based on relatively macro-level and static methods such as annual quotas, making it difficult to conduct refined carbon emission accounting and incentives for dynamic energy consumption behavior on both the source and load sides at different times. Furthermore, existing trading mechanisms rarely consider the historical carbon contribution performance of market participants, failing to form a long-term incentive loop that continuously guides market players towards low-carbon development. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a source-load bidirectional interactive trading method and system based on real-time electricity-carbon coupling. This method employs a business approach that involves constructing real-time carbon flow accounts, establishing an electricity-carbon coupling negotiation model to negotiate transaction prices, and generating dynamic contracts with carbon flow reward and penalty mechanisms for performance management. This approach can endogenize the carbon emission attributes of electricity into the transaction price, thereby precisely incentivizing and guiding both source and load parties to engage in low-carbon interaction, and enhancing the overall environmental and economic benefits of the electricity market.

[0006] The above objectives can be achieved through the following approach: A source-load bidirectional interactive trading method based on real-time electricity-carbon coupling includes: acquiring the real-time marginal carbon emission factor of the power grid and the electricity metering data of each market participant; calculating and generating the real-time carbon flow value of each market participant based on the real-time marginal carbon emission factor and electricity metering data; updating the corresponding real-time carbon flow account according to the real-time carbon flow value to generate real-time carbon flow account data; receiving electricity trading requests from the source and load sides, including information on the electricity to be traded and the intended carbon flow contribution target, and matching them according to the information on the electricity to be traded to generate potential trading pairs; constructing an electricity-carbon coupling negotiation model for the potential trading pairs with the comprehensive benefits of electricity and carbon flow for both parties as the objective, and using the proposed electricity-carbon coupling negotiation model as the objective of the two parties to trade ... Using electricity information, intended carbon flow contribution targets, and real-time carbon flow account data as inputs, an electricity-carbon coupling pricing model is used to negotiate and generate an electricity-carbon coupling transaction price. Based on the electricity-carbon coupling transaction price, the electricity information to be traded, and the intended carbon flow contribution targets, a dynamic contract containing a price-response curve and a carbon flow-penalty-reward function is generated. After the execution cycle of the dynamic contract ends, actual electricity data is acquired, and actual carbon flow data is calculated. The actual carbon flow data is compared with the carbon flow targets defined in the dynamic contract to obtain carbon flow deviation data. Based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract, settlement adjustments are made to the real-time carbon flow accounts of both parties to the transaction.

[0007] Optionally, updating the corresponding real-time carbon flow account based on the real-time carbon flow value to generate real-time carbon flow account data includes: acquiring power generation and consumption behavior data of market participants; distinguishing the carbon sink and carbon emission attributes corresponding to the behavior data based on the behavior data and the real-time marginal carbon emission factor, and calculating and generating a real-time carbon flow value; and accumulating the real-time carbon flow value into the real-time carbon flow account to form real-time carbon flow account data.

[0008] Optionally, receiving electricity trading requests from the source and load sides, which include information on the electricity to be traded and the intended carbon flow contribution target, and matching them based on the information on the electricity to be traded to generate potential trading pairs, includes: extracting the historical carbon contribution performance of each market participant from the real-time carbon flow account data to generate a carbon flow benefit index; performing a weighted matching calculation based on the information on the electricity to be traded and the carbon flow benefit index to generate a comprehensive matching score; and selecting the source-load combination with the highest comprehensive matching score to generate a potential trading pair that prioritizes carbon flow benefit.

[0009] Optionally, the electricity-carbon coupling bargaining model aimed at maximizing the comprehensive benefits of electricity and carbon flow for both parties in the transaction includes: constructing an inner-layer optimization model for a single transaction, which generates an electricity-carbon coupling transaction price by maximizing the comprehensive benefits of a single transaction for both the supply and demand sides; and constructing an outer-layer optimization model for multiple transaction cycles, which, after multiple transaction cycles, distributes secondary revenue in a public carbon revenue pool based on the actual carbon flow benefits accumulated from all transactions.

[0010] Optionally, the construction of the inner-layer optimization model for a single transaction includes: obtaining the utility functions of both the supply and demand sides and the carbon flow contribution factor converted from real-time carbon flow account data; using the utility functions and carbon flow contribution factor as inputs, solving for the optimal solution that maximizes the product of the utility increments of both sides through the Nash bargaining model, and using the optimal solution as the electricity-carbon coupling transaction price.

[0011] Optionally, the construction of the outer optimization model for multiple trading cycles includes: after the end of a settlement cycle, calculating the cumulative carbon flow benefits of each market participant during the cycle to form contribution data; obtaining total revenue funds from the carbon revenue pool consisting of high-carbon behavior fees and external incentives; and, based on the contribution data, distributing the total revenue funds to market participants who have made positive carbon contributions using an algorithm that allocates according to contribution ratio, and updating their accounts.

[0012] Optionally, generating a dynamic contract comprising a price-response curve and a carbon flow-penalty reward function based on the electricity-carbon coupling transaction price, the information on the electricity to be traded, and the intended carbon flow contribution target includes: constructing a price-response curve based on the electricity-carbon coupling transaction price to define dynamic price adjustment rules when the response electricity deviates; constructing a carbon flow-penalty reward function based on the information on the electricity to be traded and the intended carbon flow contribution target to define the reward and penalty amounts when the actual carbon flow deviates; and combining the price-response curve and the carbon flow-penalty reward function to generate the dynamic contract.

[0013] Optionally, the settlement adjustment of the real-time carbon flow accounts of both parties based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract includes: determining whether the carbon flow deviation data meets the reward and penalty conditions defined in the carbon flow-penalty-reward function; if the reward conditions are met, calculating the reward amount based on the carbon flow-penalty-reward function and adjusting the gain of the responding party's real-time carbon flow account; if the penalty conditions are met, calculating the penalty amount based on the carbon flow-penalty-reward function and adjusting the loss of the non-compliant party's real-time carbon flow account.

[0014] Based on the same inventive concept, this invention also provides a source-load bidirectional interactive trading method and system based on real-time electricity-carbon coupling. The system includes: a real-time carbon flow calculation module, used to acquire the real-time marginal carbon emission factor of the power grid and the electricity metering data of each market participant; calculate and generate the real-time carbon flow value for each market participant based on the real-time marginal carbon emission factor and electricity metering data; update the corresponding real-time carbon flow account according to the real-time carbon flow value; generate real-time carbon flow account data; a transaction matching module, used to receive electricity trading requests from the source and load sides, containing information on the electricity to be traded and the intended carbon flow contribution target; and match requests based on the information on the electricity to be traded to generate potential trading pairs; and a price negotiation module, used to construct a price negotiation mechanism for the potential trading pairs, with the goal of achieving comprehensive benefits of electricity and carbon flow for both parties in the transaction. A carbon-coupled pricing model is used, taking the proposed traded electricity information, intended carbon flow contribution targets, and real-time carbon flow account data as inputs, to negotiate and generate an electricity-carbon coupled trading price through the electricity-carbon coupled pricing model; a contract generation module is used to generate a dynamic contract containing a price-response curve and a carbon flow-penalty reward function based on the electricity-carbon coupled trading price, the proposed traded electricity information, and the intended carbon flow contribution targets; a contract execution module is used to acquire actual electricity data after the execution period of the dynamic contract, calculate and generate actual carbon flow data, compare the actual carbon flow data with the carbon flow targets defined in the dynamic contract, and obtain carbon flow deviation data; a performance adjustment module is used to perform settlement adjustments on the real-time carbon flow accounts of both parties to the transaction based on the carbon flow deviation data and the carbon flow-penalty reward function in the dynamic contract.

[0015] Compared with the prior art, the present invention has the following advantages: 1. This invention expands electricity trading from a single dimension of electrical energy value to a dual dimension of the environmental value of both electrical energy and carbon flow by constructing an electricity-carbon coupled pricing model. This method enables transaction prices to reflect the environmental impact of trading activities in real time and dynamically, internalizing external environmental costs, providing clear and direct economic incentive signals for market participants' low-carbon behavior, and innovating the value discovery and pricing mechanism of the electricity market.

[0016] 2. This invention establishes a real-time carbon flow account to quantify and track the carbon contribution or carbon footprint of each market participant in real time and dynamically. This mechanism not only provides an objective and traceable data foundation for transaction matching, price negotiation, and performance settlement, but also enhances the transparency and fairness of the entire transaction process, constructs a credit evaluation and incentive system based on actual contributions, and effectively promotes the healthy development of the market.

[0017] 3. By generating dynamic contracts that include price-response curves and carbon flow-penalty-reward functions, this invention enhances the flexibility and automated execution capabilities of trading contracts. Dynamic contracts can adaptively adjust settlement terms based on actual performance, effectively managing risks arising from electricity fluctuations and carbon flow deviations, ensuring clear rights and responsibilities for both parties and precise implementation of incentive constraints, thereby improving market efficiency and reliability.

[0018] 4. This invention not only optimizes the matching and pricing of single transactions, but also achieves macro-level guidance of overall market behavior by introducing a matching mechanism that considers historical carbon contributions and a secondary revenue distribution model based on periodic settlement. This mechanism can prioritize incentivizing market participants with good carbon performance and reward sustained green contributions over a longer timescale, thereby systematically guiding market resources towards a cleaner and lower-carbon direction and improving the overall social and environmental benefits of the entire power system.

[0019] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon according to an embodiment of the present invention.

[0022] Figure 2 This is a graph showing the optimal price solution curve of the Nash bargaining model in an embodiment of the present invention.

[0023] Figure 3 This is an example diagram of the dynamic contract price-response function in an embodiment of the present invention.

[0024] Figure 4 This is a schematic diagram of the structure of a source-load bidirectional interactive trading system based on real-time coupling of electricity and carbon according to an embodiment of the present invention. Detailed Implementation

[0025] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Reference Figure 1 One embodiment of the present invention proposes a source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon. It adopts a combination of real-time carbon flow accounting and a dual Nash negotiation mechanism, with the goal of comprehensive benefits of electricity and carbon flow. It negotiates and generates an electricity-carbon coupled trading price, and generates a dynamic interoperable contract containing a price-response function and a carbon flow-penalty-reward function based on the price and the proposed trading information. This method can solve the problems of conflict of interests among multiple parties and trading uncertainty, realize the deep coupling of electricity trading and carbon emission reduction incentives, and improve the decarbonization, efficiency and compliance robustness of source-load interactive trading.

[0027] The method described in this embodiment specifically includes: The system acquires the real-time marginal carbon emission factor of the power grid and the electricity metering data of each market participant. Based on the real-time marginal carbon emission factor and electricity metering data, it calculates and generates the real-time carbon flow value of each market participant. Update the corresponding real-time carbon flow account based on the real-time carbon flow value to generate real-time carbon flow account data; It receives electricity trading requests from the source and load sides, which include information on the electricity to be traded and the intended carbon flow contribution target, and matches them according to the information on the electricity to be traded to generate potential trading pairs. For the potential trading pair, an electricity-carbon coupling negotiation model is constructed with the goal of the comprehensive benefits of electricity and carbon flow for both parties. The electricity information to be traded, the intended carbon flow contribution target, and the real-time carbon flow account data are used as inputs. Through negotiation using the electricity-carbon coupling negotiation model, an electricity-carbon coupling trading price is generated. Based on the electricity-carbon coupling trading price, the information on the electricity to be traded, and the intended carbon flow contribution target, a dynamic contract containing a price-response curve and a carbon flow-penalty-reward function is generated. After the execution cycle of the dynamic contract ends, the actual electrical energy data is obtained, and the actual carbon flow data is calculated and generated. The actual carbon flow data is compared with the carbon flow target defined in the dynamic contract to obtain the carbon flow deviation data. Based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract, the real-time carbon flow accounts of both parties to the transaction are adjusted for settlement.

[0028] Specifically, by introducing a real-time marginal carbon emission factor from the power grid, the electricity behavior of market participants in the physical world is quantified in real time into measurable and accumulative virtual carbon flows, and dedicated real-time carbon flow accounts are established, thereby transforming abstract environmental impacts into tangible digital assets. Based on this, carbon flow contribution is taken as a core element and fully integrated into the entire process of transaction matching, price negotiation, contract formulation, and settlement. By constructing an electricity-carbon coupled pricing model, the transaction price no longer only reflects the supply and demand value of electricity, but also endogenously includes value compensation for the environmental benefits of the transaction behavior, generating an electricity-carbon coupled transaction price with both economic and environmental attributes. Finally, through dynamic contract design including price-response curves and carbon flow-penalty reward functions, and account adjustments based on actual carbon flow deviations after contract expiration, a complete market-signal-based incentive and constraint closed loop is formed, decomposing and implementing macro-level carbon reduction targets into every micro-level market transaction.

[0029] Optionally, updating the corresponding real-time carbon flow account based on the real-time carbon flow value to generate real-time carbon flow account data includes: Obtain data on the power generation and consumption behavior of market participants; Specifically, it is necessary to acquire real-time data on the power generation and consumption behavior of each market participant. Market participants include power generation units on the source side, such as photovoltaic power plants and wind farms, which are renewable energy power generation entities, and power consumption units on the load side, such as industrial users, commercial buildings, and electric vehicle charging stations. This behavioral data is usually collected through smart meters, energy management systems, or power monitoring systems, and is expressed as the amount of electricity fed into or removed from the grid within a specific time interval, which is an electrical energy metering value accurate to a specific time segment.

[0030] Based on the behavioral data and the real-time marginal carbon emission factor, the carbon sink and carbon emission attributes corresponding to the behavioral data are distinguished, and a real-time carbon flow value is calculated and generated. Specifically, based on acquired behavioral data and the grid's real-time marginal carbon emission factor, real-time carbon flow values ​​for each market participant are calculated. The real-time marginal carbon emission factor is a key dynamic parameter representing the carbon emission intensity generated by marginal generating units that the grid needs to utilize to meet the next unit of additional load demand at the current moment. The unit is typically kilograms of carbon dioxide per kilowatt-hour. The real-time marginal carbon emission factor is calculated and published by the grid dispatch center based on information such as the real-time operating unit combination, load level, and generation costs. When calculating real-time carbon flow values, it is necessary to distinguish between carbon sink and carbon emission attributes corresponding to the behavioral data. For load-side users, electricity consumption consumes grid energy, indirectly leading to carbon emissions from marginal generating units; therefore, this is defined as a carbon emission attribute, with a positive real-time carbon flow value indicating carbon emissions into the environment. For source-side renewable energy generation entities, their generation injects zero-carbon or low-carbon energy into the grid, effectively replacing the generation of marginal high-carbon emission units, thereby reducing overall carbon emissions; therefore, this is defined as a carbon sink attribute, with a negative real-time carbon flow value indicating a contribution to carbon emission reduction for the environment. The formula for calculating real-time carbon flow value is: ; in, Representing market participants exist Real-time carbon flow value at any given moment; For market participants, the attribute coefficients are those for load-side electricity users. The value is 1, for source-side renewable energy power generation entities. The value is -1; For market participants exist The electrical energy metering data at any given time, i.e., its electricity consumption or power generation; For the power grid Real-time marginal carbon emission factor at any given moment.

[0031] The real-time carbon flow values ​​are accumulated into the real-time carbon flow account to form real-time carbon flow account data.

[0032] Specifically, the calculated real-time carbon flow value is accumulated into the corresponding market participant's real-time carbon flow account. The real-time carbon flow account is a dynamically accumulated numerical record, similar to a bank account. Each market participant has an independent real-time carbon flow account. When a new real-time carbon flow value is generated, the balance of the real-time carbon flow account is updated, forming the latest real-time carbon flow account data. For example, a user's initial account value is... The real-time carbon flow value generated in the new time interval is Then the updated account data for: ; This process is ongoing, thus dynamically reflecting the cumulative carbon contribution or carbon footprint of each market participant at historical and current moments.

[0033] Optionally, receiving electricity trading requests from the source and load sides, which include information on the electricity to be traded and the intended carbon flow contribution target, and matching them based on the information on the electricity to be traded to generate potential trading pairs, includes: Extract the historical carbon contribution performance of each market participant from the real-time carbon flow account data to generate carbon flow benefit indicators; Specifically, historical carbon contribution performance is extracted from the real-time carbon flow account data of each market participant. Real-time carbon flow account data dynamically records the cumulative carbon sinks or emissions generated by market participants through power generation or consumption. For quantitative comparison, this raw account data is processed to generate standardized carbon flow benefit indicators. First, the average or cumulative net carbon flow value of each participant over a certain historical period is calculated. Then, through normalization, the carbon contribution performance of different participants is transformed into a dimensionless evaluation value. For example, for power generation entities on the source side, a larger negative carbon flow value indicates a more significant green contribution and a higher carbon flow benefit indicator; for power consumption entities on the load side, a smaller positive carbon flow value, or even a negative value, indicates a smaller carbon footprint of electricity consumption behavior and a higher carbon flow benefit indicator.

[0034] A weighted matching calculation is performed based on the proposed electricity trading information and carbon flow benefit indicators to generate a comprehensive matching score; Specifically, based on the proposed traded electricity information and carbon flow benefit indicators submitted by market participants, a weighted matching calculation is performed to generate a comprehensive matching score. The proposed traded electricity information includes key parameters such as the traded volume and trading period. First, the matching degree of potential source-load combinations in terms of electricity information is assessed, resulting in an electricity matching degree score. Then, the electricity matching degree score is weighted and summed with the carbon flow benefit indicators of both parties to calculate the comprehensive matching score of the potential trading pair. The calculation process is as follows: ; in, Source-side participants and the participants on the side of the load The overall matching score of the potential trading pairs; and These are weighting coefficients, representing the market's emphasis on power matching and carbon flow benefits, respectively. These weights can be dynamically adjusted by market operators based on overall carbon reduction targets; It is a dimensionless value calculated based on the similarity of the electricity information to be traded between the source and the load. and They are the source-side participants and the participants on the side of the load The dimensionless carbon flow efficiency index has been normalized. A formula ensures that evaluation indicators of different properties can be reasonably integrated.

[0035] The source-load combination with the highest comprehensive matching score is selected to generate potential trading pairs that prioritize carbon flow benefits.

[0036] Specifically, a comprehensive matching score is calculated for all possible source-load combinations, and these combinations are then ranked. The source-load combination with the highest comprehensive matching score is selected to generate one or more potential trading pairs that prioritize carbon flow benefits. These selected potential trading pairs will then proceed to the next stage of the electricity-carbon coupling trading price negotiation process.

[0037] Optionally, the electricity-carbon coupling bargaining model aimed at achieving the combined benefits of electricity and carbon flows for both trading parties includes: An inner-layer optimization model is constructed for a single transaction. The inner-layer optimization model aims to maximize the comprehensive benefits of a single transaction for both supply and demand sides, and generates an electricity-carbon coupled transaction price. Specifically, the inner-layer optimization model focuses on the negotiation process of a single transaction. Once a potential trading pair is formed, the goal of the inner-layer optimization model is to generate an electricity-carbon coupled trading price that maximizes the overall benefits for both supply and demand in this specific electricity transaction. These overall benefits include not only the economic utility from traditional electricity trading but also the environmental value generated by the carbon flow contribution accompanying this transaction. The optimization objective of the inner-layer optimization model is to maximize the sum of the incremental overall benefits gained by both parties in this transaction. The overall benefit function consists of two parts: an economic utility function calculated based on the electricity transaction price and the amount of electricity, and a carbon flow value function quantified based on the intended carbon flow contribution target and real-time carbon flow account data. By solving the optimization problem, a unique electricity-carbon coupled trading price is output, which not only reflects the spatiotemporal value of electricity but also includes incentives or constraints for the trading parties to achieve their intended carbon flow contribution targets.

[0038] An outer optimization model is constructed for multiple trading cycles. After multiple trading cycles, the outer optimization model distributes secondary revenue in a public carbon revenue pool based on the actual carbon flow benefits generated by all transactions.

[0039] Specifically, the outer optimization model operates on a longer timescale, such as a settlement cycle. It does not directly affect the price of a single transaction, but rather is responsible for macro-controlling and secondary revenue distribution of the cumulative actual carbon flow benefits generated by all market participants at the end of the cycle. The outer optimization model first establishes a public carbon revenue pool. The funds in the pool mainly come from fees collected from market participants who generate high carbon emissions during the cycle, as well as possible external policy incentives or subsidies. At the end of a settlement cycle, the outer optimization model calculates and tracks the cumulative actual carbon flow benefits generated by all market participants across all transactions during the cycle, forming their respective net carbon contribution values. Subsequently, based on these contribution values, the outer optimization model uses a fair allocation algorithm to distribute secondary revenue within the public carbon revenue pool. Specifically, the allocation process can adopt the principle of proportional allocation based on contribution, with the following formula: ; in, Market participants The amount of secondary profit distribution obtained; It represents the total funds in the carbon revenue pool; Market participants Cumulative net carbon contribution during the settlement period; It represents the sum of the cumulative net carbon contributions from all market participants who make positive carbon contributions. Through the outer optimization model, market participants who make significant carbon emission reduction contributions will receive additional economic rewards from the pool.

[0040] Optionally, the construction of the inner optimization model for a single transaction includes: Obtain the utility functions of both supply and demand sides and the carbon flow contribution factor converted from real-time carbon flow account data; Specifically, the inner-layer optimization model requires the utility functions of both the supply and demand sides, and the carbon flow contribution factor derived from real-time carbon flow account data, as key inputs. The utility functions of both sides are mathematical descriptions of the economic benefits of participating in this transaction. For the source-side electricity seller, the utility function is typically expressed as electricity sales revenue minus generation costs; for the load-side electricity buyer, the utility function is expressed as the value gained from using electricity or the losses avoided minus the purchase expenditure. The carbon flow contribution factor quantifies the value of the intended carbon flow contribution target committed in this transaction. It is obtained by combining historical carbon performance reflected in real-time carbon flow account data with the intended carbon flow contribution target of this transaction, and multiplying it by a market-recognized carbon value coefficient, representing the monetized environmental value of this transaction.

[0041] Using the utility function and carbon flow contribution factor as input, the optimal solution that maximizes the product of the utility increments of both parties is obtained through the Nash bargaining model, and the optimal solution is used as the electricity-carbon coupling transaction price.

[0042] Specifically, the utility function and carbon flow contribution factor are integrated to construct a comprehensive benefit function for both parties in the transaction. Based on this, the Nash bargaining model is built. The goal of the Nash bargaining model is to find the optimal solution that maximizes the product of the utility increments for both parties. The utility increment refers to the additional comprehensive benefit gained by both parties after reaching a transaction compared to not engaging in the transaction. The mathematical expression of the Nash bargaining model is as follows: ; in, The electricity-carbon coupling trading price is the solution to be found. It refers to the electrical energy to be traded; The electricity seller on the source side is at a price of , power consumption The comprehensive benefit function at that time, which not only includes The electricity revenue is added to the environmental value represented by the carbon flow contribution factor obtained as a green energy supplier. It is the comprehensive benefit function for the load-side electricity purchaser under the same conditions. This function includes the value of electricity consumption minus The cost of electricity, plus the environmental value represented by the carbon flow contribution factor obtained from consuming green electricity; and These represent the retained utility of the source and load parties when they do not participate in the transaction, and are typically set to zero. By solving this optimization problem, a unique electricity price can be found. This price maximizes the product of the utility increments for both parties. The solution to this optimization problem is taken as the electricity-carbon coupling transaction price for this transaction. This price not only reflects the supply and demand value of electricity itself, but more importantly, it intrinsically includes value compensation for the carbon flow contribution accompanying the transaction. Figure 2 As shown, the horizontal axis represents the price of the electricity-carbon coupling transaction, and the vertical axis represents the utility increment of both parties in the transaction. The curve clearly shows that the utility increment on the source side increases with the price, while the utility increment on the load side decreases with the price. The product of the two reaches its maximum value at the optimal coupling price, which is the optimal solution that maximizes the overall benefits for both parties in the transaction.

[0043] Optionally, the construction of the outer optimization model for multiple trading cycles includes: At the end of a settlement cycle, the cumulative carbon flow benefits of each market participant during the cycle are tallied to form contribution data; Specifically, after a pre-set settlement period ends, a statistical procedure will be initiated to comprehensively calculate the cumulative carbon flow benefits of all transactions completed by each market participant during that period. This is done by aggregating the actual carbon flow data generated by each participant in every transaction during the period and calculating the net value. This final net value is the participant's cumulative carbon flow benefit; a positive value represents net carbon emissions, and a negative value represents net carbon sinks. The collection of cumulative carbon flow benefits from all participants constitutes the contribution data for this settlement period.

[0044] The total revenue is obtained from a carbon revenue pool consisting of fees for high-carbon behaviors and external incentives; Specifically, the total revenue funds for distribution are obtained from a dedicated carbon revenue pool. This carbon revenue pool is funded from two sources: firstly, through market self-regulation, i.e., fees or penalties levied on market participants exhibiting high-carbon behavior during the settlement period; and secondly, through external incentives, such as special subsidies or green funds injected to promote low-carbon transition. These two sources together constitute the total amount available for distribution.

[0045] Based on the contribution data, an algorithm that allocates funds proportionally to contributions is used to distribute the total revenue to market participants who make positive carbon contributions and update their accounts.

[0046] Specifically, based on the calculated contribution data, an algorithm that allocates funds proportionally to contributions precisely distributes the total revenue in the carbon revenue pool to market participants who have made positive carbon contributions. Positive carbon contributions specifically refer to market participants who have accumulated negative carbon flow benefits during the settlement period, achieving net carbon emission reductions. The allocation algorithm ensures that the greater the contribution, the greater the reward. Based on the calculated reward amount, the corresponding market participant's account is updated, typically by increasing the balance of their financial account or revenue account by the corresponding amount.

[0047] Optionally, based on the electricity-carbon coupling trading price, the information on the electricity to be traded, and the intended carbon flow contribution target, generating a dynamic contract that includes a price-response curve and a carbon flow-penalty-reward function includes: Based on the electricity-carbon coupling trading price, a price-response curve is constructed to define the dynamic price adjustment rules when the response electricity volume deviates; Specifically, a price-response curve is constructed based on the negotiated electricity-carbon coupled trading price. This curve is not a fixed price point, but rather a functional relationship that clearly defines how the trading price will dynamically adjust when the actual electricity response deviates from the agreed-upon electricity quantity in the contract execution information. For example, a piecewise function can be set so that the price remains unchanged when the actual response electricity quantity fluctuates within a certain tolerance range of the agreed-upon electricity quantity; if it exceeds this range, the price will be adjusted upwards or downwards in a step-like or continuous manner based on the sign and magnitude of the deviation. The purpose of this curve is to incentivize electricity buyers to consume electricity precisely according to the agreement, while providing clear, market-based economic settlement rules for situations where full compliance is not possible, increasing the flexibility and certainty of contract execution. Figure 3 As shown, this illustrates the piecewise function relationship between the actual transaction settlement price and the percentage deviation from the actual electricity response. Within a preset tolerance range, the price remains unchanged; when the deviation exceeds this range, the settlement price will be adjusted in a tiered manner according to preset reward and penalty zone rules, thereby incentivizing the trading parties to fulfill their obligations accurately.

[0048] Based on the proposed electricity trading information and the intended carbon flow contribution target, a carbon flow-penalty-reward function is constructed to define the reward and penalty amounts when the actual carbon flow deviates. Specifically, a carbon flow-penalty-reward function is constructed based on the information of the electricity to be traded and the intended carbon flow contribution target. This function is one of the core elements of this dynamic contract, transforming the carbon emission target of the transaction into a specific and quantifiable incentive and constraint mechanism. The function defines the rules for calculating the amount of reward or penalty for both parties after the contract is executed, based on the deviation between the actual carbon flow generated and the intended carbon flow contribution target in the contract. For example, a threshold is set: when the actual carbon emission reduction effect is better than the target, a certain amount of economic reward will be given to the respondent party for each unit of carbon emission reduction exceeding the target; conversely, if the actual carbon emission exceeds the target, a corresponding economic penalty will be imposed on the non-compliant party. The construction of this function concretizes the abstract environmental commitment into tangible economic gains and losses, directly guiding both parties to focus not only on electricity volume but also on the actual carbon flow benefits when executing the contract.

[0049] The price-response curve and the carbon flow-penalty-reward function are combined to generate a dynamic contract.

[0050] Specifically, the price-response curve and the carbon flow-penalty-reward function are combined to form a complete dynamic contract. This contract records in detail the basic information of both parties, the information of the electricity to be traded, the electricity-carbon coupling transaction price, and includes the two core dynamic adjustment clauses mentioned above. It is not a static, fixed agreement, but an intelligent contract that can adapt to actual implementation and self-regulate and incentivize through economic levers.

[0051] Optionally, the settlement adjustment of the real-time carbon flow accounts of both parties to the transaction based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract includes: Determine whether the carbon flow deviation data satisfies the reward and penalty conditions defined in the carbon flow-penalty-reward function; Specifically, the carbon flow compliance status is assessed. This assessment is based on the carbon flow deviation data calculated after contract execution, and the predefined carbon flow-penalty-reward function in the dynamic contract. The actual carbon flow deviation data is substituted into this function, which internally sets clear reward and penalty conditions. For example, it defines the minimum excess emission reduction threshold to trigger a reward and the maximum permissible carbon emission excess threshold to trigger a penalty. Through logical judgment, it is determined whether the carbon flow compliance result of this transaction meets the reward conditions, meets the penalty conditions, or falls within an acceptable no-reward / penalty range.

[0052] If the reward conditions are met, the reward amount is calculated based on the carbon flow-penalty reward function, and the real-time carbon flow account of the responder is adjusted accordingly. Specifically, if the assessment result meets the reward conditions—that is, the actual carbon reduction effect is better than the carbon flow target defined in the dynamic contract—the reward procedure will be initiated. Based on the reward calculation rules defined in the carbon flow-penalty reward function, the reward amount will be calculated according to the excess carbon reduction. This reward amount can be direct cash, or equivalent carbon credits or points. Subsequently, a gain adjustment will be made to the respondent's real-time carbon flow account. This adjustment means improving their account status; for example, if the account records carbon emissions, the corresponding value will be deducted to make the carbon footprint record better.

[0053] If the penalty conditions are met, the penalty amount is calculated based on the carbon flow-penalty-reward function, and the real-time carbon flow account of the non-compliant party is adjusted for loss reduction.

[0054] Specifically, if the assessment determines that the penalty conditions are met—that is, the actual carbon flow performance fails to meet the contractually agreed carbon flow target—the penalty procedure is initiated. Similarly, according to the penalty calculation rules defined in the carbon flow-penalty-reward function, the corresponding penalty amount is calculated based on the failed carbon emission reductions or the excessive carbon emissions. After calculating the penalty amount, a reduction adjustment is applied to the non-compliant party's real-time carbon flow account. This reduction adjustment means worsening the account status; for example, adding punitive carbon emissions to its existing carbon emission record, thus making the accumulated carbon footprint worse, which may affect its future trading reputation and eligibility.

[0055] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a source-load bidirectional interactive trading system based on real-time coupling of electricity and carbon, the system comprising: The real-time carbon flow accounting module is used to obtain the real-time marginal carbon emission factor of the power grid and the electricity metering data of each market participant. Based on the real-time marginal carbon emission factor and electricity metering data, it calculates and generates the real-time carbon flow value of each market participant, and updates the corresponding real-time carbon flow account according to the real-time carbon flow value to generate real-time carbon flow account data. The transaction matching module is used to receive electricity transaction requests from the source side and the load side, which include information on the electricity to be traded and the intended carbon flow contribution target, and to match them according to the information on the electricity to be traded to generate potential transaction pairs. The price negotiation module is used to construct an electricity-carbon coupling negotiation model for the potential trading pair, with the goal of the comprehensive benefits of electricity and carbon flow for both parties. The model takes the proposed electricity information, the intended carbon flow contribution target, and the real-time carbon flow account data as inputs, and generates an electricity-carbon coupling trading price through negotiation via the electricity-carbon coupling negotiation model. The contract generation module is used to generate a dynamic contract containing a price-response curve and a carbon flow-penalty-reward function based on the electricity-carbon coupling trading price, the information on the electricity to be traded, and the intended carbon flow contribution target. The contract execution module is used to acquire actual electrical energy data after the execution cycle of the dynamic contract ends, calculate and generate actual carbon flow data, compare the actual carbon flow data with the carbon flow target defined in the dynamic contract, and obtain carbon flow deviation data. The performance adjustment module is used to adjust the settlement of the real-time carbon flow accounts of both parties to the transaction based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract.

[0056] To verify the feasibility of this invention in practice, it was applied to the source-load interaction trading management of a certain industrial park. This park includes distributed photovoltaic power generation units as the source side, as well as various electricity consumers such as manufacturing plants and electric vehicle charging station clusters as the load side. The park management aims to incentivize low-carbon energy use through market mechanisms, reduce the overall carbon footprint of the park, and improve energy efficiency.

[0057] Currently, electricity trading in the park is mainly based on time-of-use pricing, which fails to effectively reflect the environmental value of clean energy and lacks real-time incentives for users' low-carbon behavior. In this embodiment, the park deploys the source-load two-way interactive trading system based on real-time electricity-carbon coupling proposed in this invention to manage the electricity trading of various market participants within the park.

[0058] In this embodiment, real-time carbon flow accounting is first used to continuously acquire real-time marginal carbon emission factors released by the power grid and smart meter data from various participants within the industrial park, such as photovoltaic power station S1, manufacturing plant L1, and charging station L2, to establish and dynamically update a real-time carbon flow account for each participant. When a transaction occurs, the transaction matching system receives transaction requests from all parties and performs weighted matching based on the proposed electricity information and historical carbon contribution performance. Price negotiation generates an electricity-carbon coupled transaction price for successfully matched transaction pairs using an electricity-carbon coupling bargaining model. Subsequently, a dynamic contract containing a price-response curve and a carbon flow-penalty reward function is generated. After contract execution, the real-time carbon flow accounts of both parties are settled and adjusted according to the deviation between the actual carbon flow and the target.

[0059] To verify the beneficial effects of the present invention, transaction data from a certain day was selected for analysis. At 10:00 AM on that day, the real-time marginal carbon emission factor of the power grid was at a relatively high level (0.7 kg CO2 / kWh). At this time, photovoltaic power station S1 issued a request to sell electricity, intending to sell 1000 kWh of electrical energy; manufacturing plant L1 and charging station L2 both issued requests to purchase electricity, intending to buy 1000 kWh of electrical energy.

[0060] First, transaction matching was conducted. By retrieving real-time carbon flow account data, it was found that factory L1 actively participated in demand response over the past month, with a carbon flow benefit index of 0.85, while charging station L2's historical carbon performance was generally poor, with a carbon flow benefit index of 0.60. Although both parties had similar electricity demand matching degrees, based on the comprehensive matching score formula, an electricity weight was assigned. Carbon flow weight The S1-L1 trading pair was found to have a higher score, so S1 was prioritized for matching with L1 to generate potential trading pairs.

[0061] Subsequently, S1 and L1 entered into a price negotiation phase. The utility functions and carbon flow contribution factors of both parties were input into the Nash bargaining model. After solving the model, an electricity-carbon coupling transaction price of 0.75 yuan / kWh was generated. This price was higher than the then-current pure electricity market price of 0.70 yuan / kWh because it included environmental value compensation for S1 providing clean electricity and L1 consuming green electricity.

[0062] A dynamic contract was generated accordingly, stipulating that if L1's actual electricity consumption deviates from 1000kWh by more than 5%, the price will be adjusted according to the price-response curve. Simultaneously, the contract sets the intended carbon flow contribution target for this transaction at 700kgCO2 and defines a carbon flow-penalty-reward function. If the actual carbon emission reduction exceeds the target by 10%, a reward adjustment will be given to L1's carbon flow account.

[0063] After the contract execution period ended, the actual electricity consumption of L1 was 990 kWh, and the actual carbon flow data generated was... Carbon flow deviation data shows that the actual carbon emission reductions are better than the target. The reward conditions are met, and the reward amount is calculated based on the carbon flow-penalty reward function. An adjustment is made to the real-time carbon flow account of L1, reducing the accumulated carbon emissions in the account.

[0064] At the end of a settlement cycle, the outer optimization model is activated. Statistics show that due to their continuous participation in green trading, photovoltaic power plant S1 and factory L1 have cumulative net carbon contributions of -15,000 kg CO2 and -2,500 kg CO2, respectively, during the cycle. From the carbon revenue pool consisting of fees collected from high-carbon emitters, S1 and L1 are allocated secondary revenue according to their contribution ratio, further incentivizing their low-carbon behavior.

[0065] Compared to traditional trading models, the method of this invention achieves significantly better results. By prioritizing users with better carbon performance, it promotes the efficient use of green electricity. The electricity-carbon coupled pricing quantifies and realizes the environmental value of clean energy, increasing the returns for source-side participants. Dynamic contracts and performance adjustment mechanisms ensure the achievement of carbon reduction targets and provide continuous guidance for low-carbon behavior through rewards and penalties.

[0066] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0067] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon, characterized in that, The method includes: The system acquires the real-time marginal carbon emission factor of the power grid and the electricity metering data of each market participant. Based on the real-time marginal carbon emission factor and electricity metering data, it calculates and generates the real-time carbon flow value of each market participant. Update the corresponding real-time carbon flow account based on the real-time carbon flow value to generate real-time carbon flow account data; It receives electricity trading requests from the source and load sides, which include information on the electricity to be traded and the intended carbon flow contribution target, and matches them according to the information on the electricity to be traded to generate potential trading pairs. For the potential trading pair, an electricity-carbon coupling negotiation model is constructed with the goal of the comprehensive benefits of electricity and carbon flow for both parties. The electricity information to be traded, the intended carbon flow contribution target, and the real-time carbon flow account data are used as inputs. Through negotiation using the electricity-carbon coupling negotiation model, an electricity-carbon coupling trading price is generated. Based on the electricity-carbon coupling trading price, the information on the electricity to be traded, and the intended carbon flow contribution target, a dynamic contract containing a price-response curve and a carbon flow-penalty-reward function is generated. After the execution cycle of the dynamic contract ends, the actual electrical energy data is obtained, and the actual carbon flow data is calculated and generated. The actual carbon flow data is compared with the carbon flow target defined in the dynamic contract to obtain the carbon flow deviation data. Based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract, the real-time carbon flow accounts of both parties to the transaction are adjusted for settlement.

2. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 1, characterized in that, The step of updating the corresponding real-time carbon flow account based on the real-time carbon flow value and generating real-time carbon flow account data includes: Obtain data on the power generation and consumption behavior of market participants; Based on the behavioral data and the real-time marginal carbon emission factor, the carbon sink and carbon emission attributes corresponding to the behavioral data are distinguished, and a real-time carbon flow value is calculated and generated. The real-time carbon flow values ​​are accumulated into the real-time carbon flow account to form real-time carbon flow account data.

3. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 1, characterized in that, The receiving side receives electricity trading requests from both the source and load sides, which include information on the electricity to be traded and the intended carbon flow contribution target. Based on the information on the electricity to be traded, potential trading pairs are generated through matching. Extract the historical carbon contribution performance of each market participant from the real-time carbon flow account data to generate carbon flow benefit indicators; A weighted matching calculation is performed based on the proposed electricity trading information and carbon flow benefit indicators to generate a comprehensive matching score; The source-load combination with the highest comprehensive matching score is selected to generate potential trading pairs that prioritize carbon flow benefits.

4. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 1, characterized in that, The proposed electricity-carbon coupling bargaining model, which aims to achieve comprehensive benefits for both parties in the transaction, includes: An inner-layer optimization model is constructed for a single transaction. The inner-layer optimization model aims to maximize the comprehensive benefits of a single transaction for both supply and demand sides, and generates an electricity-carbon coupled transaction price. An outer optimization model is constructed for multiple trading cycles. After multiple trading cycles, the outer optimization model distributes secondary revenue in a public carbon revenue pool based on the actual carbon flow benefits generated by all transactions.

5. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 4, characterized in that, The construction of the inner-layer optimization model for a single transaction includes: Obtain the utility functions of both supply and demand sides and the carbon flow contribution factor converted from real-time carbon flow account data; Using the utility function and carbon flow contribution factor as input, the optimal solution that maximizes the product of the utility increments of both parties is obtained through the Nash bargaining model, and the optimal solution is used as the electricity-carbon coupling transaction price.

6. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 4, characterized in that, The construction of the outer optimization model for multiple trading cycles includes: At the end of a settlement cycle, the cumulative carbon flow benefits of each market participant during the cycle are tallied to form contribution data; The total revenue is obtained from a carbon revenue pool consisting of fees for high-carbon behaviors and external incentives; Based on the contribution data, an algorithm that allocates funds proportionally to contributions is used to distribute the total revenue to market participants who make positive carbon contributions and update their accounts.

7. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 1, characterized in that, Based on the aforementioned electricity-carbon coupling trading price, the information on the electricity to be traded, and the intended carbon flow contribution target, a dynamic contract is generated that includes a price-response curve and a carbon flow-penalty-reward function, comprising: Based on the electricity-carbon coupling trading price, a price-response curve is constructed to define the dynamic price adjustment rules when responding to deviations in electricity consumption. Based on the proposed electricity trading information and the intended carbon flow contribution target, a carbon flow-penalty-reward function is constructed to define the reward and penalty amounts when the actual carbon flow deviates. The price-response curve and carbon flow-penalty-reward function are combined to generate a dynamic contract.

8. The source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in claim 1, characterized in that, The settlement adjustment of the real-time carbon flow accounts of both parties to the transaction based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract includes: Determine whether the carbon flow deviation data satisfies the reward and penalty conditions defined in the carbon flow-penalty-reward function; If the reward conditions are met, the reward amount is calculated based on the carbon flow-penalty reward function, and the real-time carbon flow account of the responder is adjusted accordingly. If the penalty conditions are met, the penalty amount is calculated based on the carbon flow-penalty-reward function, and the real-time carbon flow account of the non-compliant party is adjusted for loss reduction.

9. A source-load bidirectional interactive trading system based on real-time coupling of electricity and carbon, characterized in that, The system is used in a source-load bidirectional interactive trading method based on real-time coupling of electricity and carbon as described in any one of claims 1-8, the system comprising: The real-time carbon flow accounting module is used to obtain the real-time marginal carbon emission factor of the power grid and the electricity metering data of each market participant. Based on the real-time marginal carbon emission factor and electricity metering data, it calculates and generates the real-time carbon flow value of each market participant, and updates the corresponding real-time carbon flow account according to the real-time carbon flow value to generate real-time carbon flow account data. The transaction matching module is used to receive electricity transaction requests from the source side and the load side, which include information on the electricity to be traded and the intended carbon flow contribution target, and to match them according to the information on the electricity to be traded to generate potential transaction pairs. The price negotiation module is used to construct an electricity-carbon coupling negotiation model for the potential trading pair, with the goal of the comprehensive benefits of electricity and carbon flow for both parties. The model takes the proposed electricity information, the intended carbon flow contribution target, and the real-time carbon flow account data as inputs, and generates an electricity-carbon coupling trading price through negotiation via the electricity-carbon coupling negotiation model. The contract generation module is used to generate a dynamic contract containing a price-response curve and a carbon flow-penalty-reward function based on the electricity-carbon coupling trading price, the information on the electricity to be traded, and the intended carbon flow contribution target. The contract execution module is used to acquire actual electrical energy data after the execution cycle of the dynamic contract ends, calculate and generate actual carbon flow data, compare the actual carbon flow data with the carbon flow target defined in the dynamic contract, and obtain carbon flow deviation data. The performance adjustment module is used to adjust the settlement of the real-time carbon flow accounts of both parties to the transaction based on the carbon flow deviation data and the carbon flow-penalty-reward function in the dynamic contract.

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