Multi-industrial park carbon trading method based on carbon emission streams

Abstract

This invention discloses a multi-industrial park electricity carbon trading method based on carbon emission flows, which includes: acquiring historical electricity purchase data, photovoltaic output forecasts, and fossil fuel carbon emissions data for each industrial park; based on carbon emission flow theory, the power flow distribution is obtained by optimizing the scheduling of the distribution network based on the electricity purchase of each industrial park, and the dynamic carbon emission factor corresponding to each industrial park is obtained by combining the power flow calculation with the carbon emission flow to accurately measure the actual carbon emissions generated by the electricity purchase of each industrial park; based on the operational constraints of the industrial parks, time-of-use pricing and tiered carbon trading mechanisms are added to determine the electricity carbon trading model, which fully considers carbon emission allowances and carbon emissions, especially accurately measuring the carbon emissions from the electricity purchase of each industrial park through carbon emission flows; the electricity purchase and dynamic carbon emission factors are updated alternately through an iterative method, and the multi-industrial park electricity carbon trading model based on carbon emission flows is solved collaboratively in multiple iterative interactions, and the electricity carbon trading volume is determined according to the optimal solution.

Description

Technical Field

[0001] This invention relates to the field of low-carbon economic operation technology in industrial parks, and in particular to a multi-industrial park electricity-carbon trading method based on carbon emission flows. Background Technology

[0002] Industrial parks, as crucial carriers of industrial production, face particularly prominent carbon emission issues. Many industrial parks suffer from high energy consumption and emission intensity, facing stringent carbon emission limits and urgently needing new low-carbon energy solutions. Electricity carbon trading, as a market mechanism, can effectively regulate and optimize carbon emissions from energy-related industries through market mechanisms. Through electricity carbon trading, industrial parks can reduce carbon emissions while meeting their own electricity needs by purchasing or selling carbon emission rights. This mechanism incentivizes enterprises to proactively reduce emissions and promotes the optimal allocation of energy resources, thereby achieving sustainable economic, environmental, and social development. However, in existing multi-industry park electricity carbon trading systems, the carbon emissions from purchasing electricity from external grids are difficult to accurately estimate, posing a significant challenge to the accuracy of electricity carbon trading. Most methods estimate emissions based on coefficients such as carbon emission factors, which may have significant errors compared to actual carbon emissions, hindering the scientific guidance of industrial parks to reduce carbon emissions and failing to contribute to the achievement of "dual carbon" goals (carbon reduction and emission control).

[0003] Yue Ziyi, Li Yonggang. A Multi-Park Low-Carbon Scheduling Method and System Based on Dual Game Theory [P]. Hebei Province: CN116862144A, 2023-10-10. This patent provides a multi-park low-carbon scheduling method and system based on dual game theory. However, this method ignores the difficulty of accurately calculating the actual carbon emissions of parks in carbon trading, directly treating the carbon emissions of parks as known quantities, only considering the offsetting effect of green certificates on carbon emissions, and not considering the carbon emissions generated by the actual production process of the parks. The obtained carbon emission data may have a large error with the actual carbon emissions, and cannot accurately measure the actual carbon emissions of the parks. It also does not fully consider the impact of the carbon trading mechanism. In addition, this method adopts a carbon trading mechanism with a fixed carbon emission price. Compared with the tiered carbon trading mechanism, it fails to effectively promote the parks to reasonably reduce carbon emissions, resulting in the loss of environmental benefits. Summary of the Invention

[0004] This invention provides a multi-industrial park electricity carbon trading method based on carbon emission flows, which addresses the technical problems in the prior art where it is difficult to accurately estimate the carbon emissions from electricity purchases and the carbon accounting is inaccurate, and the impact of the multi-industrial park electricity carbon trading mechanism is not fully considered.

[0005] The objective of this invention is achieved by at least one of the following technical solutions.

[0006] This invention discloses a multi-industrial park electricity carbon trading method based on carbon emission streams, comprising the following steps:

[0007] S1. Obtain historical electricity purchase data, photovoltaic output forecasts, and fossil fuel carbon emissions data for each industrial park through smart meters and carbon emission meters.

[0008] S2. Based on the carbon emission flow theory, the power distribution network operator optimizes the scheduling to obtain the power flow distribution of the entire network based on the electricity purchase data reported by each industrial park. Combined with the power flow calculation, the carbon emission flow is used to obtain the different dynamic carbon emission factors corresponding to multiple industrial parks, so as to accurately measure the actual carbon emissions generated by the electricity purchase of each industrial park.

[0009] S3. Based on the operational constraints of each industrial park, add time-of-use electricity pricing and tiered carbon trading mechanisms to determine a multi-industrial park electricity carbon trading model, fully consider the carbon emission allowances and carbon emissions of each industrial park, and especially accurately measure the carbon emissions from electricity purchases of each industrial park through carbon emission flows.

[0010] S4. The electricity purchase and dynamic carbon emission factors of each industrial park are updated alternately through iterative methods. The multi-industrial park electricity carbon trading model based on carbon emission flow is solved collaboratively through multiple iterative interactions. The electricity carbon trading volume of each industrial park is determined according to the optimal solution.

[0011] Furthermore, the model for optimized scheduling by the distribution network operator is expressed as follows:

[0012]

[0013] Among them, C dist For distribution network operating costs; a i The quadratic cost coefficient of the i-th generator, b i The primary cost coefficient of the i-th generator, c i Let be the constant cost coefficient for the i-th generator; Let be the active power (kW) output by the i-th generator in the t-th time period; Inject active power (kW) into the generator at node i during time period t. If there is a j-th generator connected to node i, then... otherwise Let be the active load (kW) of the i-th node in the t-th time period; Let be the line active power (kW) injected from node j to node i during the t-th time period; Let be the active power (kW) flowing from node i to node k during the t-th time period; Let be the upper limit (kW) of the active power output of the i-th generator. P i gThese are the lower limits (kW) of the active power output of the i-th generator; Let be the upper limit of the active power (kW) of the i-th line. P i l These are the lower limits (kW) of the active power of the i-th line; n p For the number of time periods, n g For the number of generators, n b For the number of nodes, n l This represents the number of lines.

[0014] Furthermore, the primary objective in the carbon emission flow calculation is the calculation of nodal carbon potential, expressed as:

[0015]

[0016] Among them, e i,t Let be the carbon potential (kgCO2 / kWh) of the i-th node in the t-th time period; Inject active power (kW) into the generator at node i during time period t. If there is a j-th generator connected to node i, then... otherwise Let be the carbon emission intensity of the generator at the i-th node during the t-th time period (kgCO2 / kWh); Let be the line active power (kW) injected from node j to node i during the t-th time period; Let be the branch carbon flux density (kgCO2 / kWh) injected from node j to node i during time period t.

[0017] The carbon flux density of all outflowing currents from a node is equal to the carbon potential of that node:

[0018]

[0019] in, Let be the branch carbon flow density (kgCO2 / kWh) from node i to node k during time period t.

[0020] To facilitate the calculation of nodal carbon potentials, the following matrix form can be used:

[0021]

[0022] P N,t =diag(ξP) Z,t )

[0023] P Z,t =(P B,t P G,t ) T

[0024]

[0025] Among them, E N,t n is the nth time period. b 3D node carbon potential column vector; P N,t n is the nth time period. b The active flux diagonal matrix of the order nodes; P B,t n is the nth time period. b The power flow distribution matrix of the branch lines is given by the active power of the lines flowing from the i-th node to the k-th node in the t-th time period. If it is greater than zero, then Otherwise P Bik,t =0; P G,t n is the nth time period. g ×n b Given the injection distribution matrix of the generator units, if the i-th generator is connected to the j-th node, then... Otherwise P Gij,t =0; E G,t n is the nth time period. g Column vector of carbon emission intensity of generator sets; P Z,t For (n b +n g )×n b An auxiliary matrix of order n; ξ is an n-order auxiliary matrix. b +n g A complete row vector; diag(v) is a diagonal matrix of the elements of the constructed vector v along the main diagonal.

[0026] Obtain the nodal carbon potential vector E in the t-th time period. N,t Subsequently, the carbon potential e corresponding to the nodes of each industrial park connected to the power distribution network i,t This refers to the different dynamic carbon emission factors of electricity purchased by various industrial parks.

[0027] Furthermore, the operational constraints of the industrial park are the operational constraints of the cement plant, expressed as:

[0028]

[0029] in, Let t be the cement output (t) of the i-th cement plant in the t-th time period; For the i-th cement plant in n p The target output (t) within a specific time period; Let be the maximum cement output (t) of the i-th cement plant within a time period; Let represent the operating status of the raw material crushing production process in the t-th time period of the i-th cement plant (0-1 variables). Let represent the operating status of the raw material grinding production process in the t-th time period of the i-th cement plant (0-1 variables). Let represent the operating status of the clinker calcination production process in the i-th cement plant during the t-th time period (0-1 variables). Let represent the operating status of the cement grinding production process in the t-th time period of the i-th cement plant (0-1 variables). Let t represent the operating status of the fuel grinding production process in the i-th cement plant during the t-th time period (0-1 variables); Let η be the clinker output (t) of the i-th cement plant in the t-th time period; i,5 Let be the supply ratio of the i-th cement plant in the cement grinding stage (i.e., the mass of raw materials required to produce one unit mass of product); Q represents the standard coal consumption (t / t) required to produce clinker per unit of the i-th cement plant; sce The standard coal calorific value is taken as 2.931 × 10⁻² TJ / t; Q coal The calorific value of ordinary coal is taken as 2.149 × 10⁻² TJ / t; Q gas The calorific value of natural gas is taken as 3.559 × 10⁻⁵ TJ / m³. 3 ; Let t be the amount of coal (t) consumed by the i-th cement plant in the t-th time period. The amount of natural gas consumed by the i-th cement plant in the t-th time period (m 3 ); Let be the maximum coal consumption (t) of the i-th cement plant within a certain time period. Let m be the maximum natural gas consumption of the i-th cement plant within a time period (m 3 ); The power consumption (kW) required for the i-th cement plant to produce in the t-th time period; P i ce,0 P represents the fixed load (kW) of the i-th cement plant. i ce,1 Let P be the load (kW) of the raw material crushing production stage in the i-th cement plant. i ce,2 Let P be the load (kW) of the raw material grinding production stage in the i-th cement plant. i ce,3 Let P be the load (kW) of the clinker calcination production stage in the i-th cement plant. i ce,4 Let P be the load (kW) of the cement grinding production stage in the i-th cement plant. i ce,5 Let be the load (kW) of the fuel grinding production stage in the i-th cement plant; This represents the amount of electricity (kW) purchased by the i-th cement plant from the upstream distribution network during the t-th time period. The actual output of distributed photovoltaic power at the i-th cement plant in the t-th time period. Let be the predicted maximum output (kW) of the distributed photovoltaic system of the i-th cement plant in the t-th time period; Let be the flexible load (kW) of the i-th cement plant in the t-th time period; For the i-th cement plant, the electrical load can be shifted during the t-th time period. Let be the electrical load (kW) of the i-th cement plant after participating in demand response in the t-th time period. A positive value indicates the transfer of movable load, while a negative value indicates the transfer of electrical load (kW); ω is the demand response correlation coefficient.

[0030] Furthermore, the tiered carbon trading mechanism includes determining free carbon emission allowances using a baseline method, calculating carbon emissions based on dynamic carbon emission factors, and trading carbon emission rights allowances in a tiered manner, expressed as:

[0031]

[0032] in, For the carbon emission allowance of the i-th industrial park; r e The carbon emission benchmark for electricity purchase (kgCO2 / kWh); r i ce,4 The carbon emission benchmark (tCO2 / t) for cement clinker production in the i-th industrial park, r i ce,5 The carbon emission benchmark (tCO2 / t) for cement grinding in the i-th industrial park; This represents the amount of electricity (kW) purchased by the i-th industrial park from the upstream distribution network during the t-th time period. Let t be the clinker output (t) of the i-th industrial park during the t-th time period; Let t be the cement production (t) of the i-th industrial park in the t-th time period; e represents the actual carbon emissions of the i-th industrial park. i,t Let be the dynamic carbon emission factor for electricity purchases at time t for the i-th industrial park. Let be the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials in the i-th industrial park at time t. Let T be the carbon emissions (t) released by fuel combustion during the cement raw material calcination process in the i-th industrial park at time t; i total Let be the actual amount of carbon emission rights traded in the carbon trading market for the i-th industrial park; λ represents the tiered carbon trading cost; λ is the base price for carbon trading (yuan / t); α is the price growth rate; and d is the tiered threshold for carbon emission deviation (a fixed value) used to divide different price ranges.

[0033] Furthermore, the overall objective function C of the multi-industrial park carbon trading model is determined by the tiered carbon trading costs of each industrial park. Electricity purchase cost and cement production and operating costs Composition, expressed as:

[0034]

[0035] Among them, Let t be the time-of-use electricity price at time t. c represents the electricity (kW) purchased from the upstream distribution network by the i-th industrial park during the t-th time period; coal The cost coefficient for purchasing coal, c gas c is the cost coefficient for purchasing natural gas. ma The material cost coefficient for cement produced per unit output; Let t be the amount of coal (t) consumed by the i-th industrial park in the t-th time period. The amount of natural gas consumed (m³) in the i-th industrial park during the t-th time period 3 ); Let n be the cement production (t) of the i-th industrial park in the t-th time period; pa For industrial parks.

[0036] Furthermore, the iterative solution method is expressed as follows:

[0037] Step 1: Initialize the power demand of each industrial park based on the historical load data of each park, and set the initial iteration number k=1, indicating that the algorithm starts from the first iteration;

[0038] Step 2: Solve the distribution network optimization model based on electricity demand, calculate carbon emission flow, and update the dynamic carbon emission factor of each industrial park node;

[0039] Step 3: Solve the electricity carbon trading model for each industrial park based on the updated dynamic carbon emission factor, and update the electricity demand of each industrial park.

[0040] Step 4: If oscillation occurs, a heuristic method based on the dichotomy is used to handle it;

[0041] Step 5: If the current electricity demand of each industrial park is very close to the electricity demand of the previous iteration, and the convergence condition is met, then output the solution; otherwise, update the iteration number to k = k + 1, that is, increment the iteration counter by 1, enter the next round of iteration, and return to step 2.

[0042] Beneficial effects:

[0043] This invention provides a multi-industrial park electricity carbon trading method based on carbon emission flows. It accurately calculates the carbon emission factors of different electricity purchases in multiple industrial parks through carbon emission flows, thereby precisely measuring the carbon emissions from electricity purchases in each industrial park. Simultaneously, it combines a tiered carbon trading mechanism to effectively limit the carbon emissions of each industrial park. Therefore, this invention effectively solves the problems of inaccurate estimation of carbon emissions from electricity purchases and inaccurate carbon accounting in existing technologies, as well as the insufficient consideration of the impact of the electricity carbon trading mechanism. It provides a practical solution for the low-carbon economic operation of multiple industrial parks. Step 1 initializes the algorithm based on historical data, providing a starting point for the iterative algorithm. Step 2 solves the power flow problem and calculates the carbon emission flow to obtain the dynamic carbon emission factor, solving the problem of fixed carbon emission factors and inaccurate carbon accounting in existing technologies. Step 3 solves the electricity demand of each industrial park, preparing for the next iteration of power flow calculation. Step 4 considers the special case of oscillations in the dynamic carbon emission factor or electricity demand during the iteration process, using a heuristic method based on bisection to improve the applicability of the iterative algorithm. Step 5 determines whether the iterative algorithm has converged based on changes in electricity demand. Attached Figure Description

[0044] Figure 1 A schematic diagram of the process for a multi-industrial park electricity carbon trading method based on carbon emission streams provided in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of the structure of multiple industrial parks provided in an embodiment of the present invention;

[0046] Figure 3 A graph showing the predicted maximum output of photovoltaic power in multiple industrial parks is provided as an application example of this invention.

[0047] Figure 4 A graph showing the predicted power output of flexible loads in multiple industrial parks is provided as an application example of this invention.

[0048] Figure 5 A graph showing the calculation results of dynamic carbon emission factors for multiple industrial parks is provided as an application example of this invention.

[0049] Figure 6 A graph showing the carbon emissions from electricity purchases in multiple industrial parks, provided as an application example of the present invention. Detailed Implementation

[0050] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0051] Example:

[0052] For ease of understanding, such as Figure 1 As shown, the present invention provides a multi-industrial park electricity carbon trading method based on carbon emission streams, which includes the following steps:

[0053] S1. Obtain historical electricity purchase data, photovoltaic output forecasts, and fossil fuel carbon emissions data for each industrial park through smart meters, carbon emission meters, etc.

[0054] In one embodiment, the structure of the multi-industrial park is as follows: Figure 2 As shown, the relevant data for each industrial park may include, but is not limited to, cement plant output, average load of production processes, carbon emissions, fossil fuel consumption, maximum output forecast curves for distributed photovoltaic systems, and flexible load forecast curves. In addition, other relevant data and information may be obtained based on actual market transaction needs; this is not limited here and is merely an example.

[0055] S2. Based on the carbon emission flow theory, the power distribution network operator optimizes the scheduling to obtain the power flow distribution of the entire network based on the electricity purchase data reported by each industrial park. Combined with the power flow calculation, the carbon emission flow is used to obtain the different dynamic carbon emission factors corresponding to multiple industrial parks, so as to accurately measure the actual carbon emissions generated by the electricity purchase of each industrial park.

[0056] In one embodiment, the model for optimized scheduling by the distribution network operator is expressed as follows:

[0057]

[0058] Among them, C dist For distribution network operating costs; a i Let b be the quadratic cost coefficient of the i-th generator. i Let c be the primary cost coefficient for the i-th generator. i Let be the constant cost coefficient for the i-th generator; Let be the active power (kW) output by the i-th generator in the t-th time period; Inject active power (kW) into the generator at node i during time period t. If there is a j-th generator connected to node i, then... otherwise Let be the active load (kW) of the i-th node in the t-th time period; Let be the line active power (kW) injected from node j to node i during the t-th time period; Let be the active power (kW) flowing from node i to node k during the t-th time period; Let be the upper limit (kW) of the active power output of the i-th generator.P i g This is the lower limit (kW) of the active power output of the i-th generator; Let be the upper limit (kW) of the active power of the i-th line. P i l These are the lower limits (kW) of the active power of the i-th line; n p n is the number of time periods. g Let n be the number of generators. b n is the number of nodes. l This represents the number of lines.

[0059] In one embodiment, the primary objective in calculating the carbon emission flow is the calculation of the nodal carbon potential, expressed as:

[0060]

[0061] Among them, e i,t Let be the carbon potential (kgCO2 / kWh) of the i-th node in the t-th time period; Inject active power (kW) into the generator at node i during time period t. If there is a j-th generator connected to node i, then... otherwise Let be the carbon emission intensity of the generator at the i-th node during the t-th time period (kgCO2 / kWh); Let be the line active power (kW) injected from node j to node i during the t-th time period; Let be the branch carbon flux density (kgCO2 / kWh) injected from node j to node i during time period t.

[0062] The carbon flux density of all outflowing currents from a node is equal to the carbon potential of that node:

[0063]

[0064] in, Let be the branch carbon flow density (kgCO2 / kWh) from node i to node k during time period t.

[0065] To facilitate the calculation of nodal carbon potentials, the following matrix form can be used:

[0066]

[0067] P N,t =diag(ξP) Z,t )

[0068] P Z,t =(P B,t P G,t ) T

[0069]

[0070] Among them, E N,t n is the nth time period. b 3D node carbon potential column vector; P N,t n is the nth time period. b The active flux diagonal matrix of the order nodes; P B,t n is the nth time period. b The power flow distribution matrix of the branch lines is given by the active power of the lines flowing from the i-th node to the k-th node in the t-th time period. If it is greater than zero, then Otherwise P Bik,t =0; P G,t n is the nth time period. g ×n b Given the injection distribution matrix of the generator units, if the i-th generator is connected to the j-th node, then... Otherwise P Gij,t =0; E G,t n is the nth time period. g Column vector of carbon emission intensity of generator sets; P Z,t For (n b +n g )×n b An auxiliary matrix of order n; ξ is an n-order auxiliary matrix. b +n g A complete row vector; diag(v) is a diagonal matrix of the elements of the constructed vector v along the main diagonal.

[0071] Obtain the nodal carbon potential vector E in the t-th time period. N,t Subsequently, the carbon potential e corresponding to the nodes of each industrial park connected to the power distribution network i,t This refers to the different dynamic carbon emission factors of electricity purchased by various industrial parks.

[0072] S3. Based on the operational constraints of each industrial park, add time-of-use electricity pricing and tiered carbon trading mechanisms to determine a multi-industrial park electricity carbon trading model, fully consider the carbon emission allowances and carbon emissions of each industrial park, and especially accurately measure the carbon emissions from electricity purchases of each industrial park through carbon emission flows.

[0073] In one embodiment, the industrial park operation constraints are cement plant operation constraints, expressed as:

[0074]

[0075]

[0076] in, Let t be the cement output (t) of the i-th cement plant in the t-th time period; For the i-th cement plant in n p The target output (t) within a specific time period; Let be the maximum cement output (t) of the i-th cement plant within a time period; Let represent the operating status (0-1 variables) of the raw material crushing production process in the t-th time period of the i-th cement plant. Let represent the operating status (0-1 variables) of the raw material grinding production process in the t-th time period of the i-th cement plant. Let represent the operating status (0-1 variable) of the clinker calcination production process in the t-th time period of the i-th cement plant. Let represent the operating status (0-1 variable) of the cement grinding production process in the t-th time period of the i-th cement plant. Let t represent the operating status of the fuel grinding production process in the i-th cement plant during the t-th time period (0-1 variables); Let η be the clinker output (t) of the i-th cement plant in the t-th time period; i,5 Let be the supply ratio of the i-th cement plant in the cement grinding stage (i.e., the mass of raw materials required to produce one unit mass of product); Q represents the standard coal consumption (t / t) required to produce clinker per unit of the i-th cement plant; sce The standard coal calorific value is taken as 2.931 × 10⁻² TJ / t; Q coal The calorific value of ordinary coal is taken as 2.149 × 10⁻² TJ / t; Q gas The calorific value of natural gas is taken as 3.559 × 10⁻⁵ TJ / m³. 3 ; Let t be the amount of coal (t) consumed by the i-th cement plant in the t-th time period. The amount of natural gas consumed by the i-th cement plant in the t-th time period (m 3 ); Let be the maximum coal consumption (t) of the i-th cement plant within a certain time period. Let m be the maximum natural gas consumption of the i-th cement plant within a time period (m 3 ); The power consumption (kW) required for the i-th cement plant to produce in the t-th time period; P i ce,0 P represents the fixed load (kW) of the i-th cement plant. i ce,1 The load (kW) and P of the raw material crushing production stage of the i-th cement plant i ce,2 Load (kW), P of the raw material grinding production stage of the i-th cement plant i ce,3 The load (kW) and P of the clinker calcination production stage of the i-th cement plant i ce,4The load (kW) and P of the cement grinding production stage in the i-th cement plant i ce,5 Let be the load (kW) of the fuel grinding production stage in the i-th cement plant; This represents the amount of electricity (kW) purchased by the i-th cement plant from the upstream distribution network during the t-th time period. The actual output of distributed photovoltaic power at the i-th cement plant in the t-th time period. Let be the predicted maximum output (kW) of the distributed photovoltaic system of the i-th cement plant in the t-th time period; Let be the flexible load (kW) of the i-th cement plant in the t-th time period; For the i-th cement plant, the electrical load can be shifted during the t-th time period. Let be the electrical load (kW) of the i-th cement plant after participating in demand response in the t-th time period. A positive value indicates the transfer of movable load, while a negative value indicates the transfer of electrical load (kW); ω is the demand response correlation coefficient.

[0077] In one embodiment, the tiered carbon trading mechanism includes determining free carbon emission allowances using a baseline method, calculating carbon emissions based on dynamic carbon emission factors, and trading carbon emission rights allowances in a tiered manner, expressed as:

[0078]

[0079] in, For the carbon emission allowance of the i-th industrial park; r e The carbon emission benchmark for electricity purchase (kgCO2 / kWh); r i ce,4 For the carbon emission benchmark (tCO2 / t) of cement clinker production in the i-th industrial park, r i ce,5 The carbon emission benchmark (tCO2 / t) for cement grinding in the i-th industrial park; This represents the amount of electricity (kW) purchased by the i-th industrial park from the upstream distribution network during the t-th time period. Let t be the clinker output (t) of the i-th industrial park during the t-th time period; Let t be the cement production (t) of the i-th industrial park in the t-th time period; e represents the actual carbon emissions of the i-th industrial park. i,t Let be the dynamic carbon emission factor for electricity purchases at time t for the i-th industrial park. Let be the carbon emissions (t) generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials in the i-th industrial park at time t. Let T be the carbon emissions (t) released by fuel combustion during the cement raw material calcination process in the i-th industrial park at time t; i totalLet be the actual amount of carbon emission rights traded in the carbon trading market for the i-th industrial park; λ represents the tiered carbon trading cost; λ is the base price for carbon trading (yuan / t); α is the price growth rate; and d is the tiered threshold for carbon emission deviation (a fixed value) used to divide different price ranges.

[0080] In one embodiment, the overall objective function C of the multi-industrial park carbon trading model is determined by the tiered carbon trading costs of each industrial park. Electricity purchase cost and cement production and operating costs Composition, expressed as:

[0081]

[0082] Among them, Let t be the time-of-use electricity price at time t. c represents the electricity (kW) purchased from the upstream distribution network by the i-th industrial park during the t-th time period; coal c is the cost coefficient for purchasing coal. gas c is the cost coefficient for purchasing natural gas. ma The material cost coefficient for cement produced per unit output; Let t be the amount of coal (t) consumed by the i-th industrial park in the t-th time period. The amount of natural gas consumed (m³) in the i-th industrial park during the t-th time period 3 ); Let n be the cement production (t) of the i-th industrial park in the t-th time period; pa For industrial parks.

[0083] S4. The electricity purchase and dynamic carbon emission factors of each industrial park are updated alternately through iterative methods. The multi-industrial park electricity carbon trading model based on carbon emission flow is solved collaboratively through multiple iterative interactions. The electricity carbon trading volume of each industrial park is determined according to the optimal solution.

[0084] In one embodiment, the iterative solution method is expressed as:

[0085] Step 1: Initialize the power demand of each industrial park based on the historical load data of each park, and set the initial iteration number k=1, indicating that the algorithm starts from the first iteration;

[0086] Step 2: Solve the distribution network optimization model based on electricity demand, calculate carbon emission flow, and update the dynamic carbon emission factor of each industrial park node;

[0087] Step 3: Solve the electricity carbon trading model for each industrial park based on the updated dynamic carbon emission factor, and update the electricity demand of each industrial park.

[0088] Step 4: If oscillation occurs, a heuristic method based on the dichotomy is used to handle it;

[0089] Step 5: If the current electricity demand of each industrial park is very close to the electricity demand of the previous iteration, and the convergence condition is met, then output the solution; otherwise, update the iteration number to k = k + 1, that is, increment the iteration counter by 1, enter the next round of iteration, and return to step 2.

[0090] For ease of understanding, one embodiment provides an application example of a multi-industrial park electricity-carbon trading method based on carbon emission streams. Relevant operational data for the three cement plant industrial parks are shown in Table 1; electricity purchase and fossil fuel prices are shown in Table 2; carbon trading-related parameters are shown in Table 3; and the predicted maximum output and flexible load predicted power curves for distributed photovoltaic power are shown in Table 4. Figure 3 and Figure 4 As shown.

[0091] Table 1. Relevant operational data for the three cement plant industrial parks.

[0092]

[0093] Table 2 Electricity Purchase and Fossil Energy Prices

[0094]

[0095] Table 3. Parameters Related to Carbon Trading

[0096]

[0097] To verify the effectiveness of the multi-industrial park carbon trading method based on carbon emission streams of the present invention, in one embodiment, the following three schemes are compared and analyzed:

[0098] Option 1: Without considering the carbon trading mechanism, optimize the scheduling of the load of cement plants and distributed photovoltaic power generation in the industrial park;

[0099] Option 2: Consider fixed carbon emission factors and tiered carbon trading mechanisms to optimize the scheduling of cement plant load and distributed photovoltaic power generation in industrial parks;

[0100] Option 3: Using the method provided in the embodiments of the present invention, based on carbon emission flows, considering dynamic carbon emission factors and tiered carbon trading mechanisms, resources within the industrial park are jointly and flexibly regulated to achieve low-carbon economic operation of the industrial park.

[0101] Table 4 compares the optimization results of Schemes 1, 2, and 3. Please refer to the table for the dynamic carbon emission factor and carbon emissions from electricity purchase of the method provided in this invention. Figure 5 and Figure 6A comparison of the optimization results of the three schemes shows that Scheme 2, based on Scheme 1, adds a fixed carbon price trading mechanism. Considering carbon emission costs, the industrial park reduces its coal consumption, thus lowering carbon emissions. Scheme 3, based on Scheme 2, improves the fixed carbon emission factor to a dynamic carbon emission factor based on carbon emission flows. While achieving similar carbon reduction effects as Scheme 2, it reduces the industrial park's operating costs by more accurately calculating carbon emissions from electricity purchases. Compared to Schemes 1 and 2, Scheme 3, through electricity carbon trading, reduces carbon emissions while minimizing operating costs, achieving low-carbon economic operation of the industrial park. In summary, the multi-industrial park electricity carbon trading method based on carbon emission flows proposed in this invention has significant advantages in reducing carbon emissions and lowering operating costs, and the above results fully demonstrate the effectiveness of this method.

[0102] Table 4 Comparison of Optimization Results of Three Schemes

[0103]

[0104] This invention provides a multi-industrial park electricity carbon trading method based on carbon emission flows. It utilizes carbon emission flow theory to calculate different dynamic carbon emission factors corresponding to multiple industrial parks, accurately measuring the actual carbon emissions generated by each industrial park's electricity purchases. By incorporating time-of-use pricing and tiered carbon trading mechanisms into the operational constraints of each industrial park, a multi-industrial park electricity carbon trading model is determined. This model fully considers the carbon emission allowances and carbon emissions of each industrial park, particularly accurately measuring the carbon emissions from electricity purchases through carbon emission flows. An iterative method is used to alternately update the electricity purchase volume and dynamic carbon emission factors of each industrial park. Through multiple iterative interactions, the multi-industrial park electricity carbon trading model based on carbon emission flows is collaboratively solved, and the electricity carbon trading volume of each industrial park is determined based on the optimal solution. This invention calculates the carbon emissions from electricity purchases of multiple industrial parks based on carbon emission flows, constructs an optimized multi-industrial park electricity carbon trading scheme to reduce park operating costs and carbon emissions, and solves the technical problems of inaccurate carbon emission measurement and insufficient consideration of the impact of multi-industrial park electricity carbon trading mechanisms in industrial park operations.

Claims

1. A multi-industrial park electricity carbon trading method based on carbon emission streams, characterized in that, Includes the following steps: S1. Obtain historical electricity purchases, photovoltaic output forecasts, and fossil fuel carbon emissions for each industrial park through smart meters and carbon emission meters. S2. Based on the carbon emission flow theory, the power distribution network operator optimizes the scheduling to obtain the power flow distribution of the entire network based on the electricity purchase data reported by each industrial park. Combined with the power flow calculation, the carbon emission flow is used to obtain the different dynamic carbon emission factors corresponding to multiple industrial parks, so as to accurately measure the actual carbon emissions generated by the electricity purchase of each industrial park. S3. Based on the operational constraints of each industrial park, add time-of-use electricity pricing and tiered carbon trading mechanisms to determine a multi-industrial park electricity carbon trading model, fully consider the carbon emission allowances and carbon emissions of each industrial park, and accurately measure the carbon emissions from electricity purchases of each industrial park through carbon emission flows. S4. The electricity purchase and dynamic carbon emission factors of each industrial park are updated alternately through iterative methods. The multi-industrial park electricity carbon trading model based on carbon emission flow is solved collaboratively through multiple iterative interactions. The electricity carbon trading volume of each industrial park is determined according to the optimal solution. The tiered carbon trading mechanism includes determining free carbon emission allowances using a baseline method, calculating carbon emissions based on dynamic carbon emission factors, and trading carbon emission rights allowances in a tiered manner, expressed as: in, For the carbon emission allowance of the i-th industrial park; r e The carbon emission benchmark unit for purchasing electricity is: kgCO2 / kWh; r i ce,4 As the carbon emission benchmark for cement clinker production in the i-th industrial park, r i ce,5 The carbon emission benchmark for cement grinding in the i-th industrial park is given in tCO2 / t. The unit is the electricity purchased by the i-th industrial park from the upper-level distribution network in the t-th time period, in kW; Let t represent the clinker output of the i-th industrial park during the t-th time period, in tons. Let t represent the cement production of the i-th industrial park during the t-th time period, in tons. e represents the actual carbon emissions of the i-th industrial park. i,t Let be the dynamic carbon emission factor for electricity purchases at time t for the i-th industrial park. Let be the carbon emissions generated by the high-temperature decomposition of carbonates during the calcination of cement raw materials in the i-th industrial park at time t. The carbon emissions released by fuel combustion during the cement raw material calcination process in the i-th industrial park at time t are all in tons. T i total Let be the actual amount of carbon emission rights traded in the carbon trading market for the i-th industrial park; λ represents the tiered carbon trading cost; λ is the base price for carbon trading, in yuan / t; α is the price growth rate; and d is the threshold for carbon emission deviation, used to divide different price ranges.

2. The multi-industrial park carbon trading method based on carbon emission streams according to claim 1, characterized in that, The model for optimal scheduling by the distribution network operator is expressed as follows: Among them, C dist For distribution network operating costs; a i Let b be the quadratic cost coefficient of the i-th generator. i Let c be the primary cost coefficient of the i-th generator. i Let be the constant cost coefficient for the i-th generator; The active power output of the i-th generator in the t-th time period is expressed in kW. Inject active power (in kW) into the generator at node i during time period t. If there is a j-th generator connected to node i, then... otherwise The active load of the i-th node in the t-th time period is expressed in kW. The active power injected from the j-th node to the i-th node during the t-th time period is expressed in kW. The active power of the line flowing from the i-th node to the k-th node in the t-th time period is expressed in kW. The upper limit of the active power output of the i-th generator. P i g This represents the lower limit of the active power output of the i-th generator, with units of kW. Let be the upper limit of the active power of the i-th line. P i l This represents the lower limit of the active power of the i-th line, in kW; n p For the number of time periods, n g For the number of generators, n b For the number of nodes, n l This represents the number of lines.

3. The multi-industrial park carbon trading method based on carbon emission streams according to claim 2, characterized in that, The primary objective in calculating the carbon emission flow is to calculate the nodal carbon potential, expressed as follows: Among them, e i,t Let be the carbon potential of the i-th node at the t-th time period, in kgCO2 / kWh. Inject active power (in kW) into the generator at node i during time period t. If there is a j-th generator connected to node i, then... otherwise The carbon emission intensity of the generator at the i-th node during the t-th time period is expressed in kgCO2 / kWh. The active power injected from the j-th node to the i-th node during the t-th time period is expressed in kW. Let n be the branch carbon flux density injected from node j to node i during time period t, in kgCO2 / kWh; b The number of nodes; The carbon flux density of all outflowing currents from a node is equal to the carbon potential of that node: in, Let be the branch carbon flux density flowing from node i to node k during time period t, in kgCO2 / kWh; To facilitate the calculation of nodal carbon potentials, the following matrix form is adopted: P N,t =diag(ξP Z,t ) P Z,t =(P B,t P G,t ) T Among them, E N,t n is the nth time period. b 3D node carbon potential column vector; P N,t n is the nth time period. b The active flux diagonal matrix of the order nodes; P B,t n is the nth time period. b The power flow distribution matrix of the branch lines is given by the active power flowing from the i-th node to the k-th node in the t-th time period. If it is greater than zero, then Otherwise P Bik,t =0; P G,t n is the nth time period. g ×n b Given the injection distribution matrix of the generator units, if the i-th generator is connected to the j-th node, then... Otherwise P Gij,t =0; E G,t n is the nth time period. g Column vector of carbon emission intensity of generator sets; P Z,t For (n b +n g )×n b An auxiliary matrix of order n; ξ is an n-order auxiliary matrix. b +n g A single-row vector; diag(v) is a diagonal matrix whose elements are constructed along the main diagonal of vector v; Obtain the nodal carbon potential vector E in the t-th time period. N,t Subsequently, the carbon potential e corresponding to the nodes of each industrial park connected to the power distribution network i,t This refers to the different dynamic carbon emission factors of electricity purchased by various industrial parks.

4. The multi-industrial park carbon trading method based on carbon emission streams according to claim 3, characterized in that, The operational constraints of the industrial park are the same as those of the cement plant, expressed as follows: in, Let t represent the cement output of the i-th cement plant in the t-th time period, in tons. For the i-th cement plant in n p The target output for each time period, in tons; Let be the maximum cement output of the i-th cement plant within a time period, expressed in tons (t). The operating status of the raw material crushing production process in the i-th cement plant during the t-th time period. The operating status of the raw material grinding production process in the t-th time period of the i-th cement plant. The operating status of the clinker calcination production process in the i-th cement plant during the t-th time period. The operating status of the cement grinding production process in the t-th time period of the i-th cement plant. Let t represent the operating status of the fuel grinding production process in the i-th cement plant during the t-th time period. All operating statuses are 0-1 variables. To ensure continuous material flow and equipment linkage stability, the start-up and shutdown statuses of the raw material crushing, raw meal grinding, cement grinding, and fuel grinding production processes must be consistent. However, the clinker calcination process can operate independently. Therefore, the model uses binary variables to constrain the consistency of the start-up and shutdown of the aforementioned processes and allows the clinker calcination process to start and stop independently. η represents the clinker output of the i-th cement plant in the t-th time period, in tons. i,5 Let be the supply ratio of the i-th cement plant in the cement grinding stage, that is, the mass of raw materials required to produce a unit mass of product; Q represents the standard coal consumption required for producing clinker per unit of the i-th cement plant, expressed in t / t. sce The standard coal calorific value is taken as 2.931 × 10⁻² TJ / t; Q coal The calorific value of ordinary coal is taken as 2.149 × 10⁻² TJ / t; Q gas The calorific value of natural gas is taken as 3.559 × 10⁻⁵ TJ / m³. 3 ; The unit is the amount of coal consumed by the i-th cement plant in the t-th time period, expressed in tons. The natural gas consumed by the i-th cement plant in the t-th time period is expressed in m³. 3 ; Let be the maximum coal consumption of the i-th cement plant within a time period, in tons; The maximum natural gas consumption of the i-th cement plant within a time period is expressed in m³. 3 ; P represents the electricity consumption required for the i-th cement plant to produce electricity in the t-th time period, in kW. i ce,0 P represents the fixed load of the i-th cement plant, in kW. i ce,1 Let P be the load of the raw material crushing production stage in the i-th cement plant. i ce,2 Let P be the load of the raw material grinding production stage in the i-th cement plant. i ce,3 Let P be the load of the clinker calcination production stage in the i-th cement plant. i ce,4 Let P be the load of the cement grinding production stage in the i-th cement plant. i ce,5 Let represent the load of the fuel grinding production stage in the i-th cement plant, in kW. The unit is the electricity purchased by the i-th cement plant from the upper-level distribution network in the t-th time period, in kW; The actual output of distributed photovoltaic power at the i-th cement plant in the t-th time period. The maximum output of the distributed photovoltaic system at the i-th cement plant in the t-th time period is predicted, and the unit is kW. This represents the flexible load of the i-th cement plant in the t-th time period, in kW. For the i-th cement plant, the electrical load can be shifted during the t-th time period. The electrical load of the i-th cement plant participating in demand response during the t-th time period is denoted by kW. A positive value indicates the transfer of movable load, while a negative value indicates the transfer of electrical load; the unit is kW; ω is the demand response correlation coefficient.

5. The multi-industrial park carbon trading method based on carbon emission streams according to claim 4, characterized in that, The overall objective function C of the multi-industrial park carbon trading model is determined by the tiered carbon trading costs of each industrial park. Electricity purchase cost and cement production and operating costs Composition, expressed as: Among them, Let t be the time-of-use electricity price at time t. c represents the electricity purchased from the upstream distribution network by the i-th industrial park during the t-th time period, in kW; coal c is the cost coefficient for purchasing coal. gas c is the cost coefficient for purchasing natural gas. ma This is the material cost coefficient for cement produced per unit output. The unit is tons (t) of coal consumed by the i-th industrial park during the t-th time period. The natural gas consumed by the i-th industrial park in the t-th time period, in m³. 3 ; Let n be the cement production of the i-th industrial park during the t-th time period, in tons (t). pa For industrial parks.

6. The multi-industrial park carbon trading method based on carbon emission streams according to claim 1, characterized in that, The iterative solution method is expressed as follows: Step 1: Initialize the power demand of each industrial park based on the historical load data of each park, and set the initial iteration number k=1, indicating that the algorithm starts from the first iteration; Step 2: Solve the distribution network optimization model based on electricity demand, calculate carbon emission flow, and update the dynamic carbon emission factor of each industrial park node; Step 3: Solve the electricity carbon trading model for each industrial park based on the updated dynamic carbon emission factor, and update the electricity demand of each industrial park. Step 4: If oscillation occurs, a heuristic method based on the dichotomy is used to handle it; Step 5: If the current electricity demand of each industrial park is very close to the electricity demand of the previous iteration, and the convergence condition is met, then output the solution; otherwise, update the iteration number to k = k + 1, that is, increment the iteration counter by 1, enter the next round of iteration, and return to step 2.

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