Carbon emission right allocation and value quantification method and system based on double-layer allocation mechanism

Abstract

The application discloses a kind of carbon emission right distribution and value quantification method and system based on double-layer distribution mechanism, method includes: first, the historical cost input of power generation resource, comprehensive output and carbon emission benchmark are acquired, and the upper limit of system carbon emission total amount is set;Then construct double-layer distribution model, the first layer uses improved data envelopment analysis to solve the front calibration share under the output growth maximization of unit carbon emission reduction contribution, the second layer is based on proportion rule and tamde rule and is distributed by unanimous consent election adjustment share, obtains comprehensive carbon emission right;Then establish power and electricity balance optimization model, with the sum of annual fuel cost and carbon trading cost under step carbon price minimum as target, solve the actual operating state of each resource;Finally, effective carbon price and net value are calculated, and the quantification result is output.The application considers the efficiency and fairness of carbon emission right distribution, realizes carbon value accurate quantification, and provides decision support for low-carbon operation and carbon asset management of power system.

Description

Technical Field

[0001] This invention belongs to the field of carbon emission rights allocation and power system operation optimization technology, and particularly relates to a method and system for carbon emission rights allocation and value quantification based on a two-level allocation mechanism. Background Technology

[0002] With the deepening implementation of the "dual carbon" strategic goals, the power industry, as a key sector for carbon emissions, shoulders the primary task of carbon reduction. Carbon emission rights allocation, as a crucial means of constraining carbon emission reduction in thermal power plants, is of great significance for the power generation industry to achieve low-carbon development. However, existing carbon emission rights allocation methods mainly suffer from the following problems: First, single efficiency-oriented allocation methods (such as the baseline method and data envelopment analysis) over-concentrate carbon emission rights on high-efficiency units, while low-efficiency units receive too few carbon emission rights, which may affect their basic production and operation, and even endanger the overall power supply capacity of the power system. Second, existing methods mostly focus on the allocation of carbon emission rights, failing to integrate the allocation results with the actual operation and optimization of the power system, and lacking a quantitative assessment of the economic value of carbon emission rights. Traditional mechanisms typically calculate carbon costs using fixed carbon prices or simple tiered pricing, failing to fully consider the dynamic relationship between carbon emission excess levels and carbon prices, and thus failing to accurately reflect the scarcity value of carbon emission rights. Furthermore, existing research rarely considers the impact of carbon emission rights constraints on unit dispatching, and insufficiently addresses the uncertainties brought about by the high proportion of renewable energy grid integration, making it difficult to achieve synergistic optimization of carbon emission reduction targets with system economic operation and secure power supply.

[0003] To address the aforementioned issues, this invention proposes a method for carbon emission rights allocation and carbon value quantification based on a two-tier allocation mechanism. By constructing a two-tier carbon emission rights allocation mechanism of "frontier calibration + adjustment allocation" and combining it with power balance optimization and a tiered carbon price model, the method achieves synergy between carbon emission rights allocation and system operation optimization, providing scientific decision support for new power systems under the multiple objectives of "ensuring supply, reducing carbon emissions, and increasing efficiency". Summary of the Invention

[0004] To address the problems mentioned in the background art, this invention discloses a method and system for carbon emission rights allocation and value quantification based on a two-tier allocation mechanism. This method enables the rational allocation of carbon emission rights among power generation resources while ensuring a secure power supply, balancing emission reduction efficiency and allocation fairness. Furthermore, it quantifies and assesses the trading value of carbon emission rights, providing a decision-making basis for the management of carbon assets in power generation resources. To achieve the above objectives, the technical solution adopted by this invention is as follows: A method for allocating and quantifying the value of carbon emission rights based on a two-tier allocation mechanism, characterized by the following steps: S1, acquire historical cost input data, comprehensive output indicators and carbon emission benchmarks of the power generation resource set, and set the upper limit for total carbon emission control of the system; the historical cost input data includes fuel consumption costs, fixed capital input and labor costs; S2 employs an improved Data Envelopment Analysis (DEA) method, aiming to maximize output growth per unit carbon emission reduction contribution. It constructs an objective function that includes the system's overall output amplification coefficient and carbon ratio factor, and sets production feasibility constraints, system total constraints, individual boundary constraints, and output-capital linkage constraints to solve for the first-layer frontier carbon emission rights share. S3. For the remaining amount to be adjusted after the first layer of allocation, a multi-criteria allocation mechanism based on proportional rules and Talmud rules is adopted. Each power generation resource selects the rule most favorable to itself from the allocation rule set to generate a preferred allocation scheme. Through consensus election rules and iterative updates, all the remaining amount to be adjusted is allocated to obtain the second layer of adjustable carbon emission rights. S4, add the first layer of front-end carbon emission rights share to the second layer of regulatory carbon emission rights to obtain the comprehensive carbon emission rights of each power generation resource; S5. Based on typical daily aggregated data, an electricity balance optimization model is constructed with the goal of minimizing the sum of annual power generation costs and carbon trading costs. The carbon trading cost adopts a tiered carbon pricing mechanism, and the tiered carbon price is set according to the degree of carbon emission excess. The actual operating status and carbon emission status of each power generation resource are obtained by solving the model. S6. Based on the actual operating status obtained in step S5, calculate the effective carbon price and net value of each resource, and output the carbon emission rights allocation and value quantification results.

[0005] Furthermore, the relevant formula for the objective function in step S2 is:

[0006] in, It is the system's overall output amplification factor. It is the system carbon ratio factor.

[0007] Furthermore, the output-capital linkage constraint in step S2 is as follows: when the target output of resources is higher than the historical level, the asset occupancy cost needs to be increased by a certain proportion; when the target output is not higher than the historical level, the adjustment amount approaches zero, and no increase in investment is required.

[0008] Furthermore, in step S3, the proportional rule allocates the total amount to be allocated according to the proportion of the remaining demand benchmark of each resource; the Talmud rule allocates according to the consistent benefit when the total amount to be allocated is less than half of the total remaining demand benchmark, otherwise it allocates according to the consistent loss; each resource selects the rule that makes it obtain more carbon emission rights to generate a preferred allocation scheme; the consensus election rule takes the minimum value of the carbon emission rights actually obtained by each power generation resource as the allocation result of this round.

[0009] Furthermore, the tiered carbon pricing mechanism in step S5 specifically includes: The carbon gap is defined as the difference between actual carbon emissions and total carbon emission rights. When there is a carbon surplus, carbon emission rights are sold at a benchmark carbon price. When there is a carbon excess, additional carbon emission rights are purchased at a tiered carbon price. The tiered carbon price is determined by the benchmark carbon price, the tiered growth coefficient, and the tiered threshold ratio coefficient.

[0010] Furthermore, the power balance optimization model in step S5 includes the following constraints: power supply and demand balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, renewable energy consumption constraints, total carbon emission constraints, carbon gap decomposition constraints, and tiered carbon price decomposition constraints.

[0011] Furthermore, the typical daily aggregated data in step S5 is generated as follows: Collect 8760 hours of load data, wind power output data, and photovoltaic power output data for the target area, and generate typical time periods of 12 months × 24 hours on a monthly average basis. Each time period includes the total electricity and average power of that time period.

[0012] Furthermore, step S6, calculating the effective carbon price of each resource, specifically includes: For excess carbon resources, the effective carbon price is the ratio of net carbon trading costs to excess carbon resources; for surplus carbon resources, the effective carbon price is the benchmark carbon price; the net value is the sum of power generation revenue and heating revenue minus carbon trading costs.

[0013] Furthermore, the method also includes, after calculating the comprehensive carbon emission rights in step S4, calculating a satisfaction index reflecting the degree of satisfaction with the demands, with the relevant formula being: .

[0014] This invention also discloses a carbon emission rights allocation and value quantification system based on a two-tier allocation mechanism, characterized in that it includes: The data acquisition module is used to acquire historical cost input data, comprehensive output indicators, and carbon emission benchmarks of the power generation resource set, and to set the upper limit for the total carbon emission control of the system; the historical cost input data includes fuel consumption costs, fixed capital input, and labor costs. The first allocation module is used to construct an objective function that includes the system's overall output amplification factor and carbon ratio factor, with the goal of maximizing output growth per unit carbon emission reduction contribution, using an improved data envelopment analysis (DEA) method. It also sets production feasibility constraints, system total constraints, individual boundary constraints, and output-capital linkage constraints to solve for the first-layer frontier carbon emission rights share. The second allocation module is used to allocate the remaining amount to be adjusted after the first layer of allocation using a multi-criteria allocation mechanism based on proportional rules and Talmud rules. Each power generation resource selects the rule most favorable to itself from the allocation rule set to generate a preferred allocation scheme. Through consensus election rules and iterative updates, all the remaining amount to be adjusted is allocated to obtain the second layer of adjustable carbon emission rights. The integrated carbon emission rights calculation module is used to add the first-layer frontier calibrated carbon emission rights share to the second-layer regulating carbon emission rights to obtain the integrated carbon emission rights of each power generation resource. The power balance optimization module is used to construct a power balance optimization model based on typical daily aggregated data, with the goal of minimizing the sum of annual power generation costs and carbon trading costs. The carbon trading cost adopts a tiered carbon pricing mechanism, and the tiered carbon price is set according to the degree of carbon emission excess. The actual operating status and carbon emission status of each power generation resource are obtained by solving the model. The carbon value quantification module is used to calculate the effective carbon price and net value of each resource based on the actual operating state obtained from the power balance optimization module, and output the carbon emission rights allocation and value quantification results.

[0015] The present invention has the following beneficial effects: (1) This invention adopts a two-layer structure of “frontier calibration + adjustment allocation”. The first layer uses an improved DEA method to achieve synergistic optimization of carbon emission reduction and output growth, avoiding excessive concentration of carbon emission rights in high-efficiency units. The second layer adopts a multi-criteria allocation mechanism that combines proportional rules and Talmud rules, and achieves consensus allocation through unanimous election. The proposed carbon emission rights method takes into account both production efficiency and the interests of the main body.

[0016] (2) This invention incorporates the carbon emission rights allocation results into the power balance optimization model, dynamically links carbon trading costs with the degree of carbon emission excess through a tiered carbon pricing mechanism, and truly reflects the carbon cost structure; it uses typical daily aggregated data to calculate the total annual cost, reducing model complexity while ensuring accuracy, and achieving synergistic optimization of carbon emission reduction and system economic operation.

[0017] (3) Based on operational optimization, this invention calculates multi-dimensional indicators such as effective carbon price and net value. The effective carbon price reflects the average cost of obtaining excess carbon emission rights for resources, providing a quantitative decision-making basis for the management and trading strategies of power generation resource carbon assets.

[0018] (4) In the allocation of carbon emission rights, the present invention ensures the basic operating space of each unit through individual boundary constraints, and ensures the minimum output demand of the system through upper and lower limit constraints in operation optimization. It simultaneously achieves multiple objectives of safe supply, efficient consumption, low-carbon operation and economic optimization, and has good engineering application prospects. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating the carbon emission rights allocation and value quantification method based on a two-tier allocation mechanism, as described in a specific embodiment.

[0020] Figure 2 This is a diagram showing the integrated carbon emission rights result obtained from the integrated two-tier carbon emission rights allocation described in a specific embodiment.

[0021] Figure 3 This is a schematic diagram of the step carbon price mechanism described in a specific embodiment.

[0022] Figure 4 This is a comparison chart of actual emissions and comprehensive carbon emission rights as described in a specific embodiment.

[0023] Figure 5 This is a schematic diagram illustrating the operating costs and effective carbon prices of different thermal power units as described in a specific embodiment. Detailed Implementation

[0024] To facilitate understanding by those skilled in the art, the present invention will be further described below in conjunction with embodiments and accompanying drawings.

[0025] This embodiment provides a method for allocating and quantifying the value of carbon emission rights based on a two-tier allocation mechanism. (See attached document.) Figure 1 The method includes the following steps: Step S1, obtaining supply guarantee resource pool data and total carbon emission constraints, specifically includes: The system studied in this invention is a power system mainly composed of thermal power units. Since the carbon emissions of renewable energy units are extremely small, their carbon emissions and carbon emission rights requirements can be ignored. Therefore, the allocation of carbon emission rights and the measurement of carbon value primarily focus on traditional thermal power units. Based on their operating characteristics, thermal power units can be divided into two categories: generator-only units and combined heat and power (CHP) units, whose outputs are electrical energy and thermal energy, respectively.

[0026] To achieve a reasonable allocation and value assessment of carbon emissions and carbon emission rights, it is first necessary to obtain a set of thermal power plants from which carbon emission rights are to be allocated, collect input and output data for each thermal power plant, clarify their economic input and output levels, and determine the total carbon emissions and carbon emission rights control limits for the planned emission reduction year. This will provide a data foundation for subsequent allocation and assessment. Specifically, this involves collecting data on the set of power generation resources from which carbon emission rights are to be allocated. For each power generation resource Collect its historical operational data and construct the following basic dataset: S11, cost input vector, related formula:

[0027] in, For the first The fuel consumption cost of a power generation resource reflects the main variable costs of resource operation; For the first Fixed capital investment in resources, including equipment depreciation, infrastructure investment, etc.; No. The labor cost of a resource reflects the level of human resource input.

[0028] S12, Comprehensive Output Index. To facilitate the calculation of output levels, an equivalent conversion method is used to uniformly convert thermal energy output into electrical energy output, thus constructing a comprehensive output index. The relevant formula for the comprehensive output index is as follows:

[0029] in, For the first The annual power generation of each resource, expressed in 100 million kWh. For the first The annual heat supply of each resource, in units of 10,000 GJ; The thermoelectric conversion coefficient is determined based on the actual operating efficiency of the combined heat and power (CHP) unit, typically ranging from 0.3 to 0.5 billion kWh / 10,000 GJ. For generator units only, The comprehensive output index can be simplified to .

[0030] S13, carbon emission benchmark; taking the first The carbon dioxide emissions of a resource in the previous year are used as the benchmark for carbon emission compensation for that resource. This benchmark reflects the actual emission levels of resources in historical years and provides a reference for determining carbon emission rights.

[0031] The annual emission reduction targets of the regional power system determine the upper limit of the total carbon emissions control for the system. The relevant formula is as follows:

[0032] in, This is the emission reduction ratio coefficient, the specific value of which can be determined according to the carbon emission policy. This coefficient is typically set at [value missing]. This means that the overall carbon emissions of the system must decrease compared to the historical baseline year. .

[0033] Step S2, based on improved data envelopment analysis, includes frontier calibration share allocation, specifically: Data Envelopment Analysis (DEA) is an efficiency evaluation method based on input-output data. In this invention, DEA is used to construct a production frontier and identify the efficiency reference relationships of various power generation resources, serving as the basis for the first-level carbon emission rights allocation.

[0034] S21, to achieve the allocation of first-tier carbon emission rights, define the relevant decision variables: Define the system's overall output amplification factor The system's overall output amplification factor reflects the growth rate of the system's overall output relative to historical levels. Relevant formulas:

[0035] in, For the first The comprehensive output indicators for each resource target year; For the first Historical values ​​of comprehensive resource output indicators.

[0036] Define the system carbon ratio factor The system carbon ratio factor reflects the reduction ratio of the overall carbon emission rights of the system relative to the carbon emission benchmark. Relevant formulas:

[0037] in, For the first The first tier of carbon emission rights obtained from each resource.

[0038] Define intensity variables Used to establish efficiency reference relationships between resources, representing the first... The resource for the first The reference strength of each resource.

[0039] S22, Construct the objective function; with the objective of maximizing output growth per unit of carbon emission reduction contribution, the relevant formulas for the objective function are as follows:

[0040] The physical meaning of this objective function is: under the constraint that the total input and output of the system remain basically unchanged, maximize the output growth that can be brought about by the reduction of carbon emissions per unit, so as to achieve the optimal carbon emission reduction efficiency.

[0041] S23, Constraints, specifically include: S231, Production Feasibility Constraints

[0042] For each power generation resource The input-output combination for the target year must lie within the production possibility set constructed from all historical resource data, by introducing an intensity variable. accomplish:

[0043]

[0044]

[0045]

[0046] in: This indicates that the investment in the target year should not be less than the weighted investment of the reference resources; For the first The resource's first Historical values ​​for this type of input; For the first The first resource target year Type of input; This indicates that the target year's output is no higher than the weighted output of the reference resources; For the first Historical values ​​of a resource comprehensive output indicator; This indicates that the carbon emission rights for the target year shall not exceed the weighted carbon emissions of the reference resources; For the first A baseline value for carbon emissions from each resource.

[0047] S232, Systemic Total Constraints; For systems involving carbon emission rights, the expansion of various inputs at the system level must be limited. The relevant formula for systemic total constraints is as follows:

[0048] in, For the first The system adjustment coefficient for a type of input can reflect the flexibility of the overall resource allocation of the system and is determined according to the industry's technical and economic laws.

[0049] Define the total system output constraint and the total system carbon emission limit as follows:

[0050]

[0051] in, The growth target for the system's overall output has been defined. A proportional relationship between carbon emission rights and carbon emission benchmarks has been established.

[0052] At the same time, the system's carbon emissions meet the upper limit constraint:

[0053] in, This is the upper limit for controlling the total carbon emissions of the system.

[0054] S233, Individual Boundary Constraints; Individual boundaries are mainly divided into carbon emission rights constraints, output boundaries, and input boundaries, with the relevant formulas as follows:

[0055]

[0056]

[0057] Wherein, formula Ensure basic operating space for each unit and prevent excessive concentration of carbon emission rights; formula Limit the range of output variation of each unit relative to historical levels; Formula Limit the range of variation in various inputs of each unit relative to historical levels; among them... , The first The lower and upper limits of carbon emission rights for each resource; , The first one is the first one. The lower and upper limits of the comprehensive output of each resource; , The first The resource's first The lower and upper limits of the investment categories; S234, Output-Capital Linkage Constraint; Considering that increasing output often requires additional capital investment (such as equipment upgrades and technological transformation), a linkage relationship between output and capital investment is constructed. The relevant formula for the output-capital linkage constraint is as follows:

[0058] in, For the first The asset occupancy cost of each resource; This is the output-capital linkage coefficient, which can be set to 1.2. This is the indicator function. The physical meaning of this constraint is: when the target output of resources is higher than the historical level, the asset occupancy cost needs to be increased by a certain proportion, reflecting the techno-economic law that "increased production requires increased investment"; when the target output is not higher than the historical level, this adjustment amount approaches zero, and there is no mandatory requirement to increase investment. This mechanism enables the model to more realistically reflect the actual cost of output improvement.

[0059] S24, Model transformation; The optimization model constructed in step S2 is a fractional linear programming model, which is transformed into an equivalent linear programming model for solution using the Charnes-Cooper transformation.

[0060] make Define the transformed variables:

[0061] Substituting into the original model, the objective function becomes:

[0062] All constraints are transformed into linear form, and standard linear programming models can be solved using mature commercial solvers.

[0063] Step S3, the allocation of regulated carbon emission rights based on iterative election, specifically includes: After the first-level allocation in step S2, there is still a margin for adjustment in the total carbon emissions of the system. and the remaining demand benchmarks for each resource. Step S3 employs a multi-criteria allocation rule, combined with election and iteration mechanisms, to redistribute the adjustment surplus.

[0064] S31, Initialize iteration variables; Set the total amount to be allocated in the current iteration round. The initial value is the adjustment margin:

[0065] The remaining demands benchmark for the current iteration round The initial value is set as the baseline for the remaining claims after the first level of allocation:

[0066] Setting Regulated Carbon Emission Rights The initial value is zero:

[0067] The remaining baseline total amount of demands in the current iteration round is:

[0068] S32, Generate preference allocation scheme

[0069] For each power generation resource The algorithm selects the most advantageous rule from the set of regulatory allocation rules and calculates its preferred allocation scheme. Specifically, the allocation scheme includes proportional rules and Talmud rules, where the proportional rules allocate the total amount to be allocated according to the proportion of each resource surplus claim benchmark. ,

[0070] in, For the first under the proportionality rule Carbon emission rights obtained from resources.

[0071] The core principle of the Talmud is to view the claims of both parties in the distribution as "rights of action," achieving balance through phased allocation. The rules fall into two categories:

[0072] When the total amount to be allocated is less than half of the remaining base amount of claims:

[0073] In the formula: For consistent return coefficients, satisfying .

[0074] When the total amount to be allocated is greater than or equal to half of the remaining base amount of claims:

[0075] In the formula: For a consistent loss coefficient, satisfying .

[0076] Comparison of resources Compare the carbon emission rights you gain under the proportionality rule and the Talmud rule, and choose the rule that gives you more carbon emission rights:

[0077] In the formula: The allocation vector is the proportional rule. The assignment vector for the Talmud rules.

[0078] S33, Construct the allocation matrix; arrange all resource preference allocation schemes in order to form the allocation matrix. :

[0079] in, Representing resources In the preference scheme, resources are allocated carbon emission rights

[0080] S34, Unanimous Consensus Election Rule: This rule stipulates that an allocation result is adopted only when all resources agree upon it. For resources... The actual carbon emission rights obtained are the minimum among all preferred options:

[0081] This rule ensures that the carbon emission rights actually obtained by each resource do not exceed the allocation expected of any other resource to it, thereby achieving unanimous agreement.

[0082] S45, Update the regulatory carbon emission rights; add the carbon emission rights obtained in the current round to the regulatory carbon emission rights:

[0083] S46, Iterative update; calculate the difference between the allocated total and the remaining amount to be adjusted:

[0084] like ( If the preset tolerance threshold is used (which can be 100,000-60,000 tons), then the allocation is complete, proceed to step S47; if the allocation is not complete, update the iteration variables:

[0085] Update the total amount to be allocated:

[0086] Update the remaining claims baseline:

[0087] Update the baseline total of remaining demands:

[0088] like and If the remaining amount is not allocated proportionally to the resources, return to step S42 for the next iteration; otherwise, end the process by distributing the remaining amount proportionally to the resources.

[0089] Step S4, comprehensive carbon emission rights calculation; specifically including: The combined carbon emission rights for each resource are obtained by adding the first-tier frontier quota to the second-tier adjustment carbon emission rights:

[0090] Satisfaction indicators can be reflected through the calculation of demand satisfaction level:

[0091] This indicator reflects the proportion of carbon emission rights obtained by each resource relative to its carbon emission baseline; a higher value indicates that the resource is more accepting of the allocation results.

[0092] Step S5, Power Balance Operation Optimization; specifically includes: Load data, wind power output data, and photovoltaic power output data for the target area were collected over 8760 hours. To reduce computational complexity, typical time periods of 12 months × 24 hours were generated on a monthly average basis, with each time period including the total electricity generation and average power output for that period.

[0093] S51, Set up a tiered carbon pricing model; set resources The actual carbon emissions are Comprehensive carbon emission rights are The carbon gap is then defined as:

[0094] The tiered carbon price is defined as:

[0095] in, Benchmark carbon price; This is the step growth coefficient, which can be 0.25. This is the step threshold ratio coefficient, which can take values ​​of 0.05, 0.10, and 0.15.

[0096] Considering that carbon emission rights can be flexibly sold in the market, when there is a carbon surplus in resources ( (can be based on benchmark price) Revenue is generated from selling surplus carbon emission rights; when resource carbon is in excess ( If so, additional carbon emission rights must be purchased at tiered prices.

[0097] S52, Objective function; the objective is to minimize the sum of annual power generation costs and carbon trading costs:

[0098] in, For coal-fired power units On a typical day Time period The power; , , For coal-fired power units The coal combustion coefficient; For typical days Corresponding number of days; For the first The carbon surplus of each resource; For the first The resource in the first Excess emissions in tiered emission standards; For the first Tiered carbon prices.

[0099] S53, Model Constraints: When optimizing power balance, it is necessary to consider constraints such as power balance constraints, output of thermal power units, and output of new energy units, which can be expressed as the following constraint form.

[0100] S531, Electricity Supply and Demand Balance Constraints

[0101] In the formula: For typical days Time period The actual wind power absorption capacity; For typical days Time period The actual photovoltaic power absorption capacity.

[0102] S532, Output Constraints of Thermal Power Units

[0103] in, For the first Minimum technical output of each resource; For the first The rated capacity of each resource.

[0104] S533, Climbing Constraints for Thermal Power Units:

[0105] in, , thermal power units The uphill gradient coefficient and the downhill gradient coefficient; S534, Renewable Energy Consumption Constraints:

[0106]

[0107] in, For typical days Time period The theoretical power generation capacity of wind power; For typical days Time period The theoretical power generation capacity of photovoltaics.

[0108] S535, total carbon emission constraints; the actual carbon emissions of each resource are calculated based on annual power generation and carbon emission intensity.

[0109] in, For the first Carbon emission intensity of each resource; The unit of time is the length of time.

[0110] S536, Carbon Gap Decomposition Constraints; decomposing the carbon gap into excess and surplus components:

[0111]

[0112] in, For the unit The excess carbon; For the unit The carbon surplus portion.

[0113] S537, tiered carbon pricing decomposition constraints; decomposing excess emissions into different tiers:

[0114]

[0115]

[0116]

[0117]

[0118] S54, Model Solving; The constructed power balance optimization model is a quadratic programming model (mainly involving fuel costs as a quadratic function), which can be solved directly using commercial solvers (such as Gurobi and CPLEX). After the solution is completed, the output saves the optimization results with relevant variables.

[0119] Step S6, carbon value quantification and result output; specifically including: Based on the optimization results of step S5, the carbon value of each resource is quantitatively assessed, and the following additional indicators are calculated for evaluation.

[0120] S61, Effective carbon price calculation; for cases with excess carbon ( Calculate the effective carbon price of the resources:

[0121] in, For resources The effective carbon price reflects the average price paid for resources to obtain excess carbon emission rights. For resources with carbon surplus, the effective carbon price serves as the benchmark price. .

[0122] S62, Net Value Calculation; Calculate the net value of each resource, which is the sum of power generation revenue and heating revenue minus carbon trading costs. Related formula:

[0123] in, The electricity price; No. Annual heat supply of each resource; This is the price for hot items.

[0124] In this embodiment of the invention, a regional power system is taken as the research object. This system includes 15 thermal power units, of which 8 are combined heat and power (CHP) units and 7 are pure condensing units. The system's annual carbon emission baseline is 59.555 million tons, and the annual carbon emission control limit is set at 90% of the baseline. The boundaries of each parameter are set according to industry standards: the upper and lower limits of carbon emission rights are 100% and 80%, respectively; the upper and lower limits of output are 115% and 100%, respectively; and the upper and lower limits of input are 110% to 90%.

[0125] The simulation results of the embodiments of the present invention will be described below.

[0126] See Figure 2 , Figure 2 The carbon share allocation results for the first and second tiers of each unit are determined by... Figure 2It is known that Units 1, 6, and 11 have the highest emission reduction efficiency and obtained full carbon emission rights (compensation rights ratio of 100%); Units 2, 4, 5, 7, 9, 10, 12, 13, and 15 have lower emission reduction efficiency and obtained 80% of the baseline carbon emission rights; Units 3, 8, and 14 obtained carbon emission rights ratios of 83.30%, 81.75%, and 93.67%, respectively. This allocation result reflects the efficiency-oriented allocation principle, that is, the higher the emission reduction efficiency of the units, the more carbon emission rights they receive, which is conducive to incentivizing the units to actively improve their emission reduction capabilities. After the first tier allocation, the remaining adjustment margin is 3.0281 million tons, and the remaining baseline demand of all units is 8.9836 million tons. After nine rounds of iterative elections, the allocation of the second tier of regulatory carbon emission rights was completed, mainly allocated to the units with lower carbon emission rights in the first tier allocation, reflecting the role of fair regulation. Units 1, 6, and 11, having already obtained full carbon emission rights in the first tier, had zero remaining demand baseline and therefore did not receive carbon emission rights in the second tier. This allocation mechanism resulted in a more balanced satisfaction rate for each unit's demands. The total carbon emission rights for each unit were the sum of those in the first and second tiers, with demand satisfaction rates ranging from 85.60% to 100.00%. Units 1, 6, and 11 had the highest satisfaction rate (100%), while Unit 9 had the lowest (85.60%), with an average demand satisfaction rate of 90.28%. Compared to a purely efficiency-based allocation scheme, this invention increased the satisfaction rate of inefficient units by approximately 5 percentage points, while maintaining 100% satisfaction for high-efficiency units, verifying that the proposed two-tier allocation mechanism can effectively achieve a balance between production efficiency and demand satisfaction.

[0127] See Figure 3 , Figure 3 This is a schematic diagram of the tiered carbon pricing mechanism. The tiered carbon pricing is set as follows: a base carbon price of 80 yuan / ton, and a tiered growth coefficient. =0.25, threshold ratios d1=0.05, d2=0.10, d3=0.15. When the carbon excess exceeds the carbon emission rights by 5%, the carbon price rises to 100 yuan / ton; when it exceeds 10%, it rises to 120 yuan / ton; and when it exceeds 15%, it rises to 140 yuan / ton. This tiered carbon price mechanism effectively incentivizes generating units to actively control carbon emissions. When facing high excess carbon prices, units will proactively adjust their operating strategies to reduce carbon emissions, thereby lowering carbon trading costs. Figure 4 It is known that there is a certain discrepancy between the actual carbon emissions of each unit and its comprehensive carbon emission rights. Among them, the actual emissions of high-efficiency units are lower than their carbon emission rights, resulting in a carbon surplus, with the surplus ranging from 180,000 to 570,000 tons; the actual emissions of low-efficiency units are higher than their carbon emission rights, resulting in a carbon deficit, which requires carbon emission rights to be traded in the carbon trading market according to tiered carbon pricing. Figure 5 This is a diagram illustrating the operating costs and effective carbon prices of different thermal power units. Figure 5It is known that fuel costs account for 97%-99% of total costs, making them the main component of system operating costs; carbon trading costs account for 1%-3%, with high-efficiency units reducing total costs due to carbon revenue, while low-efficiency units increase total costs due to carbon expenditure. Regarding the effective carbon price, the effective carbon price for units with excess carbon emissions increases as the carbon deficit grows, reaching a maximum of 94.71 yuan / ton, 18.4% higher than the benchmark carbon price, indicating that the tiered carbon pricing mechanism effectively plays its role as a price signal. In summary, the carbon emission rights allocation and carbon value quantification method based on a two-tiered allocation mechanism proposed in this invention can effectively achieve a rational allocation of carbon emission rights among power generation resources, balancing resource optimization and the demands of multiple parties. Furthermore, it achieves accurate quantification of carbon value through power balance optimization and the tiered carbon pricing mechanism, demonstrating practical application value.

[0128] The above embodiments are merely illustrative of the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of this invention.

Claims

1. A method for carbon emission right allocation and value quantification based on a double-layer allocation mechanism, characterized in that, The method includes the following steps: S1, acquire historical cost input data, comprehensive output indicators and carbon emission benchmarks of the power generation resource set, and set the upper limit for total carbon emission control of the system; the historical cost input data includes fuel consumption costs, fixed capital input and labor costs; S2 employs an improved Data Envelopment Analysis (DEA) method, aiming to maximize output growth per unit carbon emission reduction contribution. It constructs an objective function that includes the system's overall output amplification coefficient and carbon ratio factor, and sets production feasibility constraints, system total constraints, individual boundary constraints, and output-capital linkage constraints to solve for the first-layer frontier carbon emission rights share. S3. For the remaining amount to be adjusted after the first layer of allocation, a multi-criteria allocation mechanism based on proportional rules and Talmud rules is adopted. Each power generation resource selects the rule most favorable to itself from the allocation rule set to generate a preferred allocation scheme. Through consensus election rules and iterative updates, all the remaining amount to be adjusted is allocated to obtain the second layer of adjustable carbon emission rights. S4, add the first layer of front-end carbon emission rights share to the second layer of regulatory carbon emission rights to obtain the comprehensive carbon emission rights of each power generation resource; S5. Based on typical daily aggregated data, an electricity balance optimization model is constructed with the goal of minimizing the sum of annual power generation costs and carbon trading costs. The carbon trading cost adopts a tiered carbon pricing mechanism, and the tiered carbon price is set according to the degree of carbon emission excess. The actual operating status and carbon emission status of each power generation resource are obtained by solving the model. S6. Based on the actual operating status obtained in step S5, calculate the effective carbon price and net value of each resource, and output the carbon emission rights allocation and value quantification results.

2. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, The relevant formula for the objective function in step S2 is: ， wherein, is the system integrated output amplification factor, is the system carbon proportionality factor.

3. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, The output-capital linkage constraint in step S2 is as follows: when the target output of resources is higher than the historical level, the asset occupancy cost needs to be increased by a certain proportion; when the target output is not higher than the historical level, the adjustment amount approaches zero, and no increase in investment is required.

4. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, In step S3, the proportional rule allocates the total amount to be allocated according to the proportion of the remaining demand benchmark of each resource; the Talmud rule allocates according to the consistent benefit when the total amount to be allocated is less than half of the remaining demand benchmark total amount, otherwise it allocates according to the consistent loss; each resource selects the rule that makes it obtain more carbon emission rights to generate a preference allocation scheme. The unanimous election rule takes the minimum carbon emission rights actually obtained by each power generation resource among all preferred options as the allocation result for that round.

5. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, The step-price carbon pricing mechanism in step S5 specifically includes: The carbon gap is defined as the difference between actual carbon emissions and total carbon emission rights. When there is a carbon surplus, carbon emission rights are sold at a benchmark carbon price. When there is a carbon excess, additional carbon emission rights are purchased at a tiered carbon price. The tiered carbon price is determined by the benchmark carbon price, the tiered growth coefficient, and the tiered threshold ratio coefficient.

6. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, The power balance optimization model in step S5 includes the following constraints: power supply and demand balance constraints, upper and lower limits of thermal power unit output constraints, thermal power unit ramping constraints, renewable energy consumption constraints, total carbon emission constraints, carbon gap decomposition constraints, and tiered carbon price decomposition constraints.

7. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, The typical daily aggregated data in step S5 is generated as follows: Collect 8760 hours of load data, wind power output data, and photovoltaic power output data for the target area, and generate typical time periods of 12 months × 24 hours on a monthly average basis. Each time period includes the total electricity and average power of that time period.

8. The method for carbon emission right allocation and value quantification based on double-layer allocation mechanism according to claim 1, characterized in that, Step S6, calculating the effective carbon price of each resource, specifically includes: For excess carbon resources, the effective carbon price is the ratio of net carbon trading costs to excess carbon resources; for surplus carbon resources, the effective carbon price is the benchmark carbon price; the net value is the sum of power generation revenue and heating revenue minus carbon trading costs. 9.The method of allocating and quantifying carbon emission rights based on a double-layer distribution mechanism according to claim 1, wherein, The method further includes, after calculating the comprehensive carbon emission rights in step S4, calculating a satisfaction index reflecting the degree of satisfaction with the demands, with the relevant formula being: 。 10. A carbon emission right distribution and value quantification system based on a double-layer distribution mechanism, characterized in that, include: The data acquisition module is used to acquire historical cost input data, comprehensive output indicators, and carbon emission benchmarks of the power generation resource set, and to set the upper limit for the total carbon emission control of the system; the historical cost input data includes fuel consumption costs, fixed capital input, and labor costs. The first allocation module is used to construct an objective function that includes the system's overall output amplification factor and carbon ratio factor, with the goal of maximizing output growth per unit carbon emission reduction contribution, using an improved data envelopment analysis (DEA) method. It also sets production feasibility constraints, system total constraints, individual boundary constraints, and output-capital linkage constraints to solve for the first-layer frontier carbon emission rights share. The second allocation module is used to allocate the remaining amount to be adjusted after the first layer of allocation using a multi-criteria allocation mechanism based on proportional rules and Talmud rules. Each power generation resource selects the rule most favorable to itself from the allocation rule set to generate a preferred allocation scheme. Through consensus election rules and iterative updates, all the remaining amount to be adjusted is allocated to obtain the second layer of adjustable carbon emission rights. The integrated carbon emission rights calculation module is used to add the first-layer frontier calibrated carbon emission rights share to the second-layer regulating carbon emission rights to obtain the integrated carbon emission rights of each power generation resource. The power balance optimization module is used to construct a power balance optimization model based on typical daily aggregated data, with the goal of minimizing the sum of annual power generation costs and carbon trading costs. The carbon trading cost adopts a tiered carbon pricing mechanism, and the tiered carbon price is set according to the degree of carbon emission excess. The actual operating status and carbon emission status of each power generation resource are obtained by solving the model. The carbon value quantification module is used to calculate the effective carbon price and net value of each resource based on the actual operating state obtained from the power balance optimization module, and output the carbon emission rights allocation and value quantification results.

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