Zero-carbon park electricity-carbon joint distributed transaction method based on carbon-green certificate market coupling

Abstract

The invention relates to the technical field of power systems and carbon trading, in particular to a zero-carbon park electricity-carbon joint distributed trading method based on carbon-green certificate market coupling. The method comprises the steps of constructing an objective function of a zero-carbon park electricity-carbon joint distributed transaction model based on carbon-green certificate market coupling, and constructing a carbon and green certificate market transaction model and a carbon-green certificate market coupling method of the zero-carbon park. The method comprises the following steps: constructing gas turbine, energy storage equipment, central air conditioning equipment, electric power balance and carbon emission balance constraints of a zero-carbon park electricity-carbon joint distributed transaction model based on carbon-green certificate market coupling, and carrying out distributed solution on the multi-park electricity-carbon joint transaction model by adopting an adaptive step length alternating direction multiplier algorithm; and obtaining an operation decision of the zero-carbon park system, and scheduling the zero-carbon park system. According to the zero-carbon park electricity-carbon joint distributed transaction method based on carbon-green certificate market coupling provided by the invention, collaborative optimization of electricity transaction and carbon emission right and green certificate matching is realized, the utilization efficiency of renewable energy sources in the park is remarkably improved, and the overall carbon emission level of the zero-carbon park is reduced.

Description

Technical Field

[0001] This invention relates to the fields of power system and carbon trading technology, specifically to a distributed trading method for zero-carbon industrial park electricity and carbon based on carbon-green certificate market coupling. Background Technology

[0002] Currently, with the large-scale deployment of renewable energy sources such as offshore wind power and onshore photovoltaics, the proportion of new energy output in the power system has significantly increased. On the one hand, this high proportion of renewable energy has greatly reduced the carbon intensity at the power generation end, promoting the green transformation of the energy structure. On the other hand, the consumption and market trading of renewable energy have brought new challenges. Especially in zero-carbon scenarios at the park level, the grid connection of a large number of distributed wind and solar power generation and offshore wind power has made the power supply and demand and carbon emission characteristics within the park more complex. The traditional operating mechanism based on centralized dispatch and single market tools is no longer able to fully realize the value of distributed resources.

[0003] Currently, the carbon market and green certificate market, as important policy tools for regulating carbon emissions and encouraging the development of renewable energy, play a crucial role in achieving carbon peaking and carbon neutrality goals. However, the carbon-green certificate market coupling mechanism is still imperfect, with problems such as weak institutional coordination, inconsistent accounting standards, and fragmented trading paths. Against this backdrop, if a robust carbon-green certificate market coupling mechanism suitable for high integration of marine, onshore, wind, and solar power can be combined with a distributed electricity-carbon joint trading method suitable for multiple industrial parks, forming a zero-carbon industrial park distributed electricity-carbon joint trading method based on carbon-green certificate market coupling, it would be possible to optimize power output and carbon / green certificate value in real time at the industrial park level. Furthermore, considering scheduling based on this model would effectively improve renewable energy absorption capacity, reduce system operating costs, and enhance the overall low-carbon nature and market-oriented operational efficiency of zero-carbon industrial parks. Summary of the Invention

[0004] Therefore, it is necessary to provide a distributed trading method for electricity carbon in zero-carbon industrial parks based on the coupling of the carbon-green certificate market, which includes the following steps:

[0005] Step 1: Construct the objective function of a zero-carbon industrial park electricity-carbon joint distributed trading model based on the coupling of the carbon-green certificate market;

[0006] Step 2: Construct a trading model for zero-carbon industrial parks participating in the carbon and green certificate market and a carbon-green certificate market coupling method;

[0007] Step 3: Construct a distributed trading model for electricity and carbon emissions in zero-carbon industrial parks based on the coupling of the carbon-green certificate market, including constraints on gas turbines, energy storage equipment, central air conditioning equipment, power balance, and carbon emission balance.

[0008] Step 4: Use the adaptive step-size alternating direction multiplier algorithm to solve the multi-park joint electricity and carbon trading model in a distributed manner, obtain the operation decision of the zero-carbon park system, and schedule the zero-carbon park system.

[0009] Furthermore, in step (1), the objective function of the zero-carbon park electricity carbon joint distributed trading model based on carbon-green certificate market coupling is constructed as follows:

[0010]

[0011] In the formula: i represents the zero-carbon park, N i Let be the total number of zero-carbon industrial parks, s be the renewable energy output scenarios (hereinafter referred to as scenario s), S be the total number of renewable energy output scenarios, t be the operating time period, T be the total number of operating time periods, and ρ be the total number of operating time periods. s Let be the probability of the occurrence of renewable energy scenario s. Let $t$ be the cost of a transaction between the $i$-th park and the upper-level power grid during the $t$-th time period in scenario $s$. Let $\frac{i}{t}$ be the energy storage usage cost in the $i$-th park during the $t$-th time period under scenario $s$. Let $t$ be the cost of user dissatisfaction in the $i$-th park during the $t$-th time period in scenario $s$. Let $\frac{i}{t}$ be the operating cost of the gas turbine in the $i$-th park during the $t$-th time period under scenario $s$. C represents the operation and maintenance cost of renewable energy units in the i-th park during the t-th time period under scenario s. i,s,t The cost of peer-to-peer trading in the i-th park during the t-th time period under scenario s includes the cost of peer-to-peer trading of electricity and the cost of peer-to-peer trading of carbon allowances. Let be the operating cost of the central air conditioning system in the i-th park during the t-th time period under scenario s. Let $\frac{i}{t}$ be the transaction cost with the carbon market for the $i$-th park in scenario $s$ during time period $t$. The cost of participating in the green certificate market in the i-th park during the t-th time period under scenario s. Let be the profit generated by users in the i-th park during the t-th time period through energy consumption.

[0012] The following is a detailed explanation of the various cost items involved in formula (1):

[0013] ①Profits generated by users within the park through production activities involving the consumption of electricity

[0014]

[0015] In the formula: k i,t For the i-th park, the combination of user weight coefficients and preference parameters for the t-th time period reflects the energy preference weights of park users. The amount of electricity consumed by users in the i-th park during the t-th time period.

[0016] ② Costs of participating in electricity market transactions

[0017]

[0018] In the formula: Let be the electricity purchase price for time period t. Let be the electricity price for time period t. Let t be the electricity purchased in the i-th park during the t-th time period under scenario s. Let t be the electricity sales volume of the i-th park in scenario s during the t-th time period.

[0019] ③ Energy storage charging and discharging costs

[0020]

[0021] In the formula: Let the charging power and discharging power of the energy stored in the i-th park during the t-th time period under scenario s be given. These are the charging cost coefficient and discharging cost coefficient for the i-th energy storage park, respectively.

[0022] ④ Gas turbine operating costs

[0023]

[0024] In the formula: a MT b MT c MT To determine the correlation coefficient with gas turbine operating costs, Let be the power generation capacity of the gas turbine in the i-th park during the t-th time period under scenario s.

[0025] ⑤ Costs of user dissatisfaction within the park

[0026]

[0027] Where: β i Let β be the priority coefficient of the i-th park, and β i >0, with a larger β i The fact that these are park users means they place a greater emphasis on comfort. t It is the weighting factor for the cost of dissatisfaction in time period t. Let be the amount of power available for consumption in the i-th park during the t-th time period under scenario s.

[0028] ⑥ Output cost of new energy units within the park

[0029]

[0030] In the formula: To provide power to the new energy generating units in the i-th park during the t-th time period under scenario s, The maintenance cost per unit power generation of new energy generating units within the park.

[0031] ⑦ Costs of participating in carbon market trading in the industrial park

[0032]

[0033] In the formula: Let be the purchase price of the carbon allowance in time period t. Let be the selling price of the carbon allowance in time period t. These represent the amount of carbon allowances purchased and sold in the carbon market in the i-th park during the t-th time period under scenario s.

[0034] ⑧ The cost of participating in peer-to-peer transactions in the park C i,s,t

[0035]

[0036] In the formula: c ij t ij P represents the electricity and carbon quota trading cost coefficients between the i-th and j-th parks, respectively. i,j,s,t T i,j,s,t Let be the point-to-point trading volume of electricity and carbon allowances between the i-th and j-th parks during the t-th time period in scenario s.

[0037] ⑨ Central air conditioning operating costs

[0038]

[0039] In the formula: m is the cost coefficient. Let T be the indoor temperature of the i-th park in scenario s during the t-th time period. i ref Let be the reference temperature for the i-th park.

[0040] ⑩ The cost of the system participating in the green certificate market for:

[0041]

[0042] In the formula: α represents the cost of participating in the green certificate market in the i-th park during the t-th time period under scenario s. GCT The price per unit of green certificate. For the renewable energy output unit in the i-th park during the t-th time period under scenario s, the green certificates required to meet the quota system are... Let represent the number of green certificates obtained by the new energy power output unit in the i-th park during the t-th time period under scenario s.

[0043] Furthermore, the specific process of step (2) is as follows:

[0044] (201) Establish a model for the park to participate in carbon market trading:

[0045] Carbon capture systems installed on gas turbines generate energy consumption, which is divided into baseline energy consumption and operating energy consumption. Since the baseline energy consumption is very small and negligible, only operational losses are considered. In addition, to handle CO2 more flexibly, the carbon capture unit is equipped with carbon storage equipment, and the CO2 stored in the carbon storage equipment comes solely from the carbon capture unit.

[0046] The carbon emission model for a gas turbine equipped with a carbon capture system is as follows:

[0047]

[0048] In the formula: Let be the amount of CO2 produced by the gas turbine in the i-th park during the t-th time period in scenario s. Let be the carbon emission intensity coefficient of the gas turbine in the i-th park. Let α be the amount of CO2 to be processed by the carbon capture system of the gas turbine in the i-th park during the t-th time period in scenario s. c This represents the adjustment coefficient of the flue gas bypass system of the gas turbine. Let β be the amount of CO2 captured by the carbon capture system of the gas turbine in the i-th park during the t-th time period under scenario s. c For carbon capture efficiency, γ c The power consumed by a carbon capture system to capture one unit of CO2. The energy consumption for CO2 capture by the carbon capture system of the gas turbine in the i-th park during the t-th time period in scenario s.

[0049] The carbon dioxide balance model for carbon storage equipment is as follows:

[0050]

[0051] In the formula: t-1 is the previous runtime segment of the current runtime segment. Let's define the amount of CO2 stored by the carbon storage device in the i-th park during the t-th time period under scenario s. Let CO2 be the amount stored by the carbon storage device in the i-th park during the (t-1)-th time period under scenario s. These represent the amounts of CO2 injected and extracted by the carbon storage device in the i-th park during the t-th time period, respectively, under scenario s. These represent the maximum and minimum carbon storage capacities of the carbon storage equipment, respectively. These represent the maximum values ​​for CO2 injection and extraction from the carbon storage device, respectively. The amount of CO2 transferred and stored by the carbon storage device in the i-th park during the t-th time period in scenario s.

[0052] To explore carbon market and electricity market trading, the initial carbon quota for each gas turbine must first be determined. Currently, there are two main methods: allocation based on historical carbon emissions and allocation based on baselines. This paper adopts the latter.

[0053] The carbon trading model for gas turbines is as follows:

[0054]

[0055] In the formula: Let α be the carbon trading cost of the gas turbine in the carbon capture system of the i-th park during the t-th time period under scenario s. CET Let α be the unit carbon trading price in the carbon market. sto The price of CO2 stored per unit of transport. Let μ be the carbon emission baseline allowance for the gas turbine of the carbon capture system in the i-th park during the t-th time period under scenario s. baseline This serves as the baseline emission factor.

[0056] (204) Establish a model for the park to participate in the green certificate market transaction:

[0057] The formula for calculating the quota of new energy sources within the park is as follows:

[0058]

[0059] In the formula: Let ω be the renewable energy quota power of the i-th park in scenario s during time period t; ω be the renewable energy quota coefficient; ξ be the conversion coefficient for the quota completion status of the previous assessment period; ε be the weighting coefficient for the quota completion status of the previous assessment period; δ be the renewable energy quota completion rate of the previous assessment period; δ m This represents the average quota completion rate during the previous assessment period.

[0060] Assuming one green certificate is equivalent to generating 1 MWh of electricity, the number of green certificates required to meet the quota is:

[0061]

[0062] In the formula: Δt is a runtime segment.

[0063] Incentive measures will be adopted to appropriately increase the issuance of green certificates during periods of high accuracy in new energy output forecasting. The calculation method for green certificates in the park is as follows:

[0064]

[0065] In the formula: ψ is the parameter affecting the prediction of new energy power output; ζ is the weight of the influence of new energy prediction; τ is the accuracy of the new energy power output prediction in the previous assessment period; τ m This is the standard value for the accuracy of new energy output prediction in the previous assessment cycle.

[0066] (205) Establish a carbon-green certificate market coupling model:

[0067] Carbon emission reductions behind green certificates:

[0068]

[0069] In the formula: This represents the carbon emission reduction per unit green certificate for the i-th park in scenario s during the t-th time period. These represent the CO2 output per unit power generated by traditional fossil fuel units and new energy units in the i-th park under scenario s during the t-th time period.

[0070] Carbon-Green Certificate Market Interaction Model:

[0071]

[0072] In the formula: To consider the carbon emission trading costs when the carbon market and green certificate market interact in the i-th park during the t-th time period under scenario s; Let be the carbon emissions offset by green certificates under the quota system for the i-th park in scenario s during the t-th time period.

[0073] Furthermore, the specific process of step (3) is as follows:

[0074] (301) Establish constraints for gas turbines equipped with carbon capture systems:

[0075]

[0076] In the formula: This represents the net output electrical power of the gas turbine in the i-th park during the t-th time period under scenario s. P represents the maximum downward and upward ramp rates of the i-th gas turbine, respectively. i MT,min and P i MT,max Let represent the minimum and maximum output electrical power of the i-th gas turbine, respectively.

[0077] (302) Establish constraints for energy storage devices:

[0078]

[0079] In the formula: This represents the energy storage capacity level of the i-th park during the t-th time period in scenario s. P represents the minimum and maximum energy storage capacity levels for the i-th park, respectively. i c,max P i d,max These represent the maximum charging and maximum discharging power of the energy storage in the i-th park, respectively.

[0080] (305) Establish constraints on central air conditioning equipment:

[0081]

[0082] In the formula: R i C i Let represent the equivalent thermal resistance and equivalent heat capacity of the i-th park, respectively. Let be the outdoor temperature of the i-th park at time t in scenario s. This represents the power consumption of the central air conditioning equipment in the i-th park during the t-th time period in scenario s. η represents the cooling capacity of the central air conditioning system in the i-th park during the t-th time period under scenario s. i This represents the cooling efficiency of the central air conditioning system in the i-th park. Let represent the minimum and maximum indoor temperatures allowed in the i-th park during the t-th time period under scenario s, respectively.

[0083] (304) Establish power balance constraints:

[0084]

[0085] In the formula: This represents the amount of wind and solar power curtailed in the i-th park during the t-th time period under scenario s.

[0086] This represents the maximum allowed point-to-point electricity transaction between the i-th and j-th parks during time period t in scenario s.

[0087] (306) Carbon emission balance constraints under the context of carbon-green certificate market coupling:

[0088]

[0089] Furthermore, the specific process of step (4) is as follows:

[0090] (401) Establish the augmented Lagrangian function for the i-th park:

[0091]

[0092] In the formula: Let be the augmented Lagrange function value of the i-th park. The operating cost for the i-th park to participate in the electricity carbon sharing trading is... ξ represents the dual variables related to electricity consumption and carbon quotas between the i-th and j-th parks during the k-th iteration in scenario s at time t; k ω k P represents the penalty factors related to electricity and carbon quota at the k-th iteration. j,i,s,t T j,i,s,t Let be the point-to-point trading volume of electricity and carbon allowances between the j-th park and the i-th park during the t-th time period in scenario s.

[0093] At this point, the optimization objective of the multi-park alliance, namely minimizing the total alliance cost, is rewritten as follows:

[0094]

[0095] Each park updates its own electricity-carbon trading strategy. The parks only need to share the electricity and carbon emission rights to be traded, without involving internal equipment or other related information, which can effectively protect the privacy and security of each park.

[0096] (406) Iteratively update the value of the dual variable:

[0097]

[0098] In the formula: Let be the dual variables related to electricity consumption and carbon quotas between the i-th and j-th parks during the (k+1)-th iteration in scenario s at time t.

[0099] (407) Calculate the original and dual residuals of electricity and carbon quota:

[0100]

[0101] In the formula: P i,j,s,t (k), P j,i,s,t (k), T i,j,s,t (k), T j,i,s,t (k) represents P at the kth iteration. i,j,s,t P j,i,s,t T i,j,s,t T j,i,s,t The value of P i,j,s,t (k-1), T i,j,s,t (k-1) represent P at iteration k-1. i,j,s,t T i,j,s,t The value of r k r c,kThese are the original residual values ​​of electricity and carbon allowances in the distributed trading after the k-th iteration, s. k s c,k These are the dual residuals of electricity and carbon quotas in the distributed transaction after the k-th iteration.

[0102] Set the convergence conditions for the original and dual residuals after k iterations:

[0103]

[0104] In the formula: K max ε is the maximum number of iterations. pri ε dual These are the maximum allowable values ​​for the original and dual residuals, respectively. If the original and dual residuals do not satisfy the convergence condition and K+1≤K, then... max At that time, the penalty factor is updated according to the ADMM algorithm with adaptive step size proposed in (404).

[0105] (408) Update the penalty factor using the ADMM algorithm based on adaptive step size:

[0106]

[0107] In the formula: ξ k+1 ω k+1 These are the penalty factors for electrical energy and carbon quotas, respectively, at the (k+1)th iteration. The value of the penalty factor is dynamically adjusted based on the relationship of the residuals to ensure the efficiency of the iteration.

[0108] (409) Specific steps for solving the distributed transaction model:

[0109] Step 1: Input raw data, including electricity prices in a certain region of China, carbon prices in the Chinese carbon market, and parameters of the constituent units within each park, such as gas turbines, central air conditioning systems, energy storage systems, loads, and output of new energy units.

[0110] Step 2: Based on the mathematical models of each unit in the park, establish a multi-park alliance operation cost minimization model. Then, based on the multi-market multi-VPP joint electricity carbon trading model established in (11)-(30), decouple the model proposed in Step 2, establish a distributed trading model, and set the initial values ​​of variables and parameters in the ADMM algorithm for solving.

[0111] Step 3: Iterate P from formula (27) i,j,s,t T i,j,s,t Update the corresponding dual variables

[0112] Step 4: Determine whether the original residual and the dual residual satisfy the convergence condition according to equation (29). If equation (29) is satisfied, the algorithm converges, and the trading strategy for each VPP is output. If equation (29) is not satisfied, the algorithm does not converge, and the corresponding penalty factor ξ is updated according to equation (30). k+1 ω k+1 Then proceed to the next iteration. When the number of iterations exceeds the set upper limit or the convergence condition mentioned in (29) is met, stop the iteration.

[0113] The beneficial effects of adopting the above technical solution are as follows:

[0114] The aforementioned distributed trading method for electricity and carbon in zero-carbon parks based on carbon-green certificate market coupling considers the participation of zero-carbon parks in carbon and green certificate market trading and carbon-green certificate market coupling. It constructs a complete mathematical model for the distributed trading method for electricity and carbon in zero-carbon parks based on carbon-green certificate market coupling, and uses an adaptive step-size alternating direction multiplier algorithm to solve the multi-park electricity and carbon joint trading model in a distributed manner, obtaining the operational decisions of each zero-carbon park. By scheduling the systems of each park, the utilization efficiency of renewable energy in the park is significantly improved, and the overall carbon emission level of the zero-carbon park is reduced. Attached Figure Description

[0115] Figure 1 This is a flowchart illustrating a distributed trading method for electricity and carbon emissions in zero-carbon industrial parks based on the coupling of the carbon-green certificate market.

[0116] Figure 2 For the basic load diagram of users in the park,

[0117] Figure 3 A scene depicting the power generation of new energy units. Detailed Implementation

[0118] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0119] In one embodiment, such as Figure 1 As shown, this invention provides a method for joint distributed trading of electricity and carbon in zero-carbon industrial parks based on carbon-green certificate market coupling. Taking the application of this method to a terminal as an example, the method includes the following steps:

[0120] Step 1: Construct the objective function of a zero-carbon industrial park electricity-carbon joint distributed trading model based on the coupling of the carbon-green certificate market;

[0121] Step 2: Construct a trading model for zero-carbon industrial parks participating in the carbon and green certificate market and a carbon-green certificate market coupling method;

[0122] Step 3: Construct a distributed trading model for electricity and carbon emissions in zero-carbon industrial parks based on the coupling of the carbon-green certificate market, including constraints on gas turbines, energy storage equipment, central air conditioning equipment, power balance, and carbon emission balance.

[0123] Step 4: Use the adaptive step-size alternating direction multiplier algorithm to solve the multi-park joint electricity and carbon trading model in a distributed manner, obtain the operation decision of the zero-carbon park system, and schedule the zero-carbon park system.

[0124] In this embodiment, a zero-carbon park alliance system consisting of three zero-carbon parks is used for testing. The resources aggregated within the three zero-carbon parks all include new energy generating units, energy storage, central air conditioning, user loads, and gas turbines. Among them, the gas turbine in park 1 contains a carbon capture system. The user base loads of each park are as follows: Figure 2 As shown. To compare the operation of the zero-carbon park alliance under different conditions, six different new energy power output scenarios were set up for comparative analysis. The new energy power output scenarios are as follows: Figure 3 As shown in Table 1, scenarios 1 to 4 represent point-to-point transactions between multiple parks, scenario 5 represents centralized transactions between multiple parks, and scenario 6 represents park-wide transactions. The park operation information for each scenario is shown in Table 1. The IPOPT solver on the GAMS platform is used to solve the zero-carbon park electricity-carbon joint distributed trading model based on the carbon-green certificate market coupling, and the operation strategy of the zero-carbon park system is obtained.

[0125] Table 1. Case Study Scenario Settings

[0126]

[0127] Table 2 shows the costs and benefits for each scenario. As shown in Table 2, Scenario 1 has the highest total system operating benefit, increasing by RMB 377.33, 707.29, 764.04, and 57.76 respectively compared to Scenario 2-5. Compared to Scenario 2, which does not consider the coupling of the carbon-green certificate market, the carbon trading cost in Scenario 1 decreases by RMB 48.83. This is because the clean energy and low-carbon emission reduction effect embodied in green certificates reduces the carbon trading cost in the park, reflecting the economic viability of the carbon market and green certificate market interaction strategy. Scenario 1 and Scenario 2 show a high proportion of green electricity usage, with Scenario 1 having the highest proportion, 1.49% higher than Scenario 2. Furthermore, after using the carbon capture system, the carbon capture unit can use some green electricity, further reducing the curtailment rate of solar power and reducing some of the operating costs of the gas turbine. Furthermore, Table 2 shows that Scenario 5 differs from Scenario 1 only in the transaction method, yet its revenue is lower than that of peer-to-peer transactions, reflecting the greater economic efficiency of peer-to-peer transactions. Moreover, comparing Scenario 6 with the previous five scenarios reveals that single-park participation yields the lowest profit. Calculating user satisfaction costs, Scenario 6 is 1142.64 and 1097.63 yuan higher than Scenario 1 and Scenario 5, respectively, indicating that when optimizing with a single park, the load has high comfort requirements but the output cannot match them, resulting in high dissatisfaction costs. However, in multi-park transactions, power sources and loads are divided into smaller aggregates, making the allocation of load and power output more reasonable, thereby reducing user costs. In summary, considering the coupling of the carbon-green certificate market, the total revenue of multi-park transactions is higher than that of the current parallel carbon and green certificate market, and revenue can be obtained in the carbon market, demonstrating the rationality of the proposed zero-carbon park electricity-carbon joint distributed trading method.

[0128] Table 2 Optimization results for each scenario

[0129]

[0130] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for joint distributed trading of electricity and carbon in zero-carbon industrial parks based on carbon-green certificate market coupling, the method comprising: Step 1: Construct the objective function of a zero-carbon industrial park electricity-carbon joint distributed trading model based on the coupling of the carbon-green certificate market; Step 2: Construct a trading model for zero-carbon industrial parks participating in the carbon and green certificate market and a carbon-green certificate market coupling method; Step 3: Construct a distributed trading model for electricity and carbon emissions in zero-carbon industrial parks based on the coupling of the carbon-green certificate market, including constraints on gas turbines, energy storage equipment, central air conditioning equipment, power balance, and carbon emission balance. Step 4: Use the adaptive step-size alternating direction multiplier algorithm to solve the multi-park joint electricity and carbon trading model in a distributed manner, obtain the operation decision of the zero-carbon park system, and schedule the zero-carbon park system.

2. The method for joint distributed trading of electricity and carbon in zero-carbon industrial parks based on carbon-green certificate market coupling as described in claim 1, characterized in that, In step (1), the objective function of the zero-carbon park electricity carbon joint distributed trading model based on carbon-green certificate market coupling is constructed as follows: In the formula: i represents the zero-carbon park, N i Let be the total number of zero-carbon industrial parks, s be the renewable energy output scenarios (hereinafter referred to as scenario s), S be the total number of renewable energy output scenarios, t be the operating time period, T be the total number of operating time periods, and ρ be the total number of operating time periods. s Let be the probability of the occurrence of renewable energy scenario s. Let $t$ be the cost of a transaction between the $i$-th park and the upper-level power grid during the $t$-th time period in scenario $s$. Let $\frac{i}{t}$ be the energy storage usage cost in the $i$-th park during the $t$-th time period under scenario $s$. Let $t$ be the cost of user dissatisfaction in the $i$-th park during the $t$-th time period in scenario $s$. Let $\frac{i}{t}$ be the operating cost of the gas turbine in the $i$-th park during the $t$-th time period in scenario $s$. C represents the operation and maintenance cost of renewable energy units in the i-th park during the t-th time period under scenario s. i,s,t The cost of peer-to-peer trading in the i-th park during the t-th time period under scenario s includes the cost of peer-to-peer trading of electricity and the cost of peer-to-peer trading of carbon allowances. Let be the operating cost of the central air conditioning system in the i-th park during the t-th time period under scenario s. Let $\frac{i}{t}$ be the transaction cost with the carbon market for the $i$-th park in scenario $s$ during time period $t$. The cost of participating in the green certificate market in the i-th park during the t-th time period under scenario s. Let be the profit generated by users in the i-th park during the t-th time period through energy consumption. The following is a detailed explanation of the various cost items involved in formula (1): ①Profits generated by users within the park through production activities involving the consumption of electricity In the formula: k i,t For the i-th park, the combination of user weight coefficients and preference parameters for the t-th time period reflects the park users' preference weights for energy. The amount of electricity consumed by users in the i-th park during the t-th time period. ② Costs of participating in electricity market transactions In the formula: Let be the electricity purchase price for time period t. Let be the electricity price for time period t. Let t be the electricity purchased in the i-th park during the t-th time period under scenario s. Let t represent the electricity sales in the i-th park during the t-th time period under scenario s. ③ Energy storage charging and discharging costs In the formula: Let the charging power and discharging power of the energy stored in the i-th park during the t-th time period under scenario s be given. These are the charging cost coefficient and discharging cost coefficient for the i-th energy storage park, respectively. ④ Gas turbine operating costs In the formula: a MT b MT c MT To determine the correlation coefficient with gas turbine operating costs, Let be the power generation capacity of the gas turbine in the i-th park during the t-th time period under scenario s. ⑤ Costs of user dissatisfaction within the park Where: β i Let β be the priority coefficient of the i-th park, and β i >0, with a larger β i The fact that these are park users means they place a greater emphasis on comfort. t It is the weighting factor for the cost of dissatisfaction in time period t. Let be the amount of power available for consumption in the i-th park during the t-th time period under scenario s. ⑥ Output cost of new energy units within the park In the formula: To provide power output for the new energy generating units in the i-th park during the t-th time period under scenario s, The maintenance cost per unit power generation of new energy generating units within the park. ⑦ Costs of participating in carbon market trading in the industrial park In the formula: Let be the purchase price of the carbon allowance in time period t. Let be the selling price of the carbon allowance in time period t. These represent the amount of carbon allowances purchased and sold in the carbon market in the i-th park during the t-th time period under scenario s. ⑧ The cost of participating in peer-to-peer transactions in the park C i,s,t In the formula: c ij t ij P represents the electricity and carbon quota trading cost coefficients between the i-th and j-th parks, respectively. i,j,s,t T i,j,s,t Let be the point-to-point trading volume of electricity and carbon allowances between the i-th and j-th parks during the t-th time period in scenario s. ⑨ Central air conditioning operating costs In the formula: m is the cost coefficient. Let T be the indoor temperature of the i-th park in scenario s during the t-th time period. i ref Let be the reference temperature for the i-th park. ⑩ The cost of the system participating in the green certificate market for: In the formula: α represents the cost of participating in the green certificate market in the i-th park during the t-th time period under scenario s. GCT The price per unit of green certificate. For the renewable energy output unit in the i-th park during the t-th time period under scenario s, the green certificates required to meet the quota system are... Let represent the number of green certificates obtained by the new energy power output unit in the i-th park during the t-th time period under scenario s.

3. The method for joint distributed trading of electricity and carbon in zero-carbon industrial parks based on carbon-green certificate market coupling as described in claim 1, characterized in that, The specific process of step (2) is as follows: (201) Establish a model for the park to participate in carbon market trading: Carbon capture systems installed on gas turbines generate energy consumption, which is divided into baseline energy consumption and operating energy consumption. Since the baseline energy consumption is very small and negligible, only operational losses are considered. In addition, to handle CO2 more flexibly, the carbon capture unit is equipped with carbon storage equipment, and the CO2 stored in the carbon storage equipment comes solely from the carbon capture unit. The carbon emission model for a gas turbine equipped with a carbon capture system is as follows: In the formula: Let be the amount of CO2 produced by the gas turbine in the i-th park during the t-th time period in scenario s. Let be the carbon emission intensity coefficient of the gas turbine in the i-th park. Let α be the amount of CO2 to be processed by the carbon capture system of the gas turbine in the i-th park during the t-th time period in scenario s. c This represents the adjustment coefficient of the flue gas bypass system of the gas turbine. Let β be the amount of CO2 captured by the carbon capture system of the gas turbine in the i-th park during the t-th time period under scenario s. c For carbon capture efficiency, γ c The power consumed by a carbon capture system to capture one unit of CO2. The energy consumption for CO2 capture by the carbon capture system of the gas turbine in the i-th park during the t-th time period in scenario s. The carbon dioxide balance model for carbon storage equipment is as follows: In the formula: t-1 is the previous runtime segment of the current runtime segment. Let's define the amount of CO2 stored by the carbon storage device in the i-th park during the t-th time period under scenario s. Let CO2 be the amount stored by the carbon storage device in the i-th park during the (t-1)-th time period under scenario s. These represent the amounts of CO2 injected and extracted by the carbon storage device in the i-th park during the t-th time period, respectively, under scenario s. These represent the maximum and minimum carbon storage capacities of the carbon storage equipment, respectively. These represent the maximum values ​​for CO2 injection and extraction from the carbon storage device, respectively. The amount of CO2 transferred and stored by the carbon storage device in the i-th park during the t-th time period in scenario s. To explore carbon market and electricity market trading, the initial carbon quota for each gas turbine must first be determined. Currently, there are two main methods: allocation based on historical carbon emissions and allocation based on baselines. This paper adopts the latter. The carbon trading model for gas turbines is as follows: In the formula: Let α be the carbon trading cost of the gas turbine in the carbon capture system of the i-th park during the t-th time period under scenario s. CET Let α be the unit carbon trading price in the carbon market. sto The price of CO2 stored per unit of transport. Let μ be the carbon emission baseline allowance for the gas turbine of the carbon capture system in the i-th park during the t-th time period under scenario s. baseline This serves as the baseline emission factor. (202) Establish a model for parks to participate in the green certificate market transaction: The formula for calculating the quota of new energy sources within the park is as follows: In the formula: Let ω be the renewable energy quota power of the i-th park in scenario s during time period t; ω be the renewable energy quota coefficient; ξ be the conversion coefficient for the quota completion status of the previous assessment period; ε be the weighting coefficient for the quota completion status of the previous assessment period; δ be the renewable energy quota completion rate of the previous assessment period; δ m This represents the average quota completion rate during the previous assessment period. Assuming one green certificate is equivalent to generating 1 MWh of electricity, the number of green certificates required to meet the quota is: In the formula: Δt is a runtime segment. Incentive measures will be adopted to appropriately increase the issuance of green certificates during periods of high accuracy in new energy output forecasting. The calculation method for green certificates in the park is as follows: In the formula: ψ is the parameter affecting the prediction of new energy power output; ζ is the weight of the influence of new energy prediction; τ is the accuracy of the new energy power output prediction in the previous assessment period; τ m This is the standard value for the accuracy of new energy output prediction in the previous assessment cycle. (203) Establish a carbon-green certificate market coupling model: Carbon emission reductions behind green certificates: In the formula: This represents the carbon emission reduction per unit green certificate for the i-th park in scenario s during the t-th time period. These represent the CO2 output per unit power generated by traditional fossil fuel units and new energy units in the i-th park under scenario s during the t-th time period. Carbon-Green Certificate Market Interaction Model: In the formula: To consider the carbon emission trading costs when the carbon market and green certificate market interact in the i-th park during the t-th time period under scenario s; Let be the carbon emissions offset by green certificates under the quota system for the i-th park in scenario s during the t-th time period.

4. The method for joint distributed trading of electricity and carbon in zero-carbon industrial parks based on carbon-green certificate market coupling as described in claim 1, characterized in that, The specific process of step (3) is as follows: (301) Establish constraints for gas turbines equipped with carbon capture systems: In the formula: This represents the net output electrical power of the gas turbine in the i-th park during the t-th time period under scenario s. P represents the maximum downward and upward ramp rates of the i-th gas turbine, respectively. i MT,min and P i MT,max Let represent the minimum and maximum output electrical power of the i-th gas turbine, respectively. (302) Establish constraints for energy storage devices: In the formula: This represents the energy storage capacity level of the i-th park during the t-th time period in scenario s. P represents the minimum and maximum energy storage capacity levels for the i-th park, respectively. i c,max P i d,max These represent the maximum charging and maximum discharging power of the energy storage in the i-th park, respectively. (303) Establish constraints on central air conditioning equipment: In the formula: R i C i Let represent the equivalent thermal resistance and equivalent heat capacity of the i-th park, respectively. Let be the outdoor temperature of the i-th park at time t in scenario s. This represents the power consumption of the central air conditioning equipment in the i-th park during the t-th time period in scenario s. η represents the cooling capacity of the central air conditioning system in the i-th park during the t-th time period under scenario s. i This represents the cooling efficiency of the central air conditioning system in the i-th park. Let represent the minimum and maximum indoor temperatures allowed in the i-th park during the t-th time period under scenario s, respectively. (304) Establish power balance constraints: In the formula: This represents the amount of wind and solar power curtailed in the i-th park during the t-th time period under scenario s. This represents the maximum allowed point-to-point electricity transaction between the i-th and j-th parks during time period t in scenario s. (304) Carbon emission balance constraints under the context of carbon-green certificate market coupling:

5. The method for joint distributed trading of electricity and carbon in zero-carbon industrial parks based on carbon-green certificate market coupling as described in claim 1, characterized in that, The specific process of step (4) is as follows: (401) Establish the augmented Lagrangian function for the i-th park: In the formula: Let be the augmented Lagrange function value of the i-th park. The operating cost for the i-th park to participate in the electricity carbon sharing trading is... ξ represents the dual variables related to electricity consumption and carbon quotas between the i-th and j-th parks during the k-th iteration in scenario s at time t; k ω k P represents the penalty factors related to electricity and carbon quota at the k-th iteration. j,i,s,t T j,i,s,t Let be the point-to-point trading volume of electricity and carbon allowances between the j-th park and the i-th park during the t-th time period in scenario s. At this point, the optimization objective of the multi-park alliance, namely minimizing the total alliance cost, is rewritten as follows: Each park updates its own electricity-carbon trading strategy. The parks only need to share the electricity and carbon emission rights to be traded, without involving internal equipment or other related information, which can effectively protect the privacy and security of each park. (402) Iteratively update the value of the dual variable: In the formula: Let be the dual variables related to electricity consumption and carbon quotas between the i-th and j-th parks during the (k+1)-th iteration in scenario s at time t. (403) Calculate the original and dual residuals of electricity and carbon quota: In the formula: P i,j,s,t (k), P j,i,s,t (k), T i,j,s,t (k), T j,i,s,t (k) represents P at the kth iteration. i,j,s,t P j,i,s,t T i,j,s,t T j,i,s,t The value of P i,j,s,t (k-1), T i,j,s,t (k-1) represent P at iteration k-1. i,j,s,t T i,j,s,t The value of r k r c,k These are the original residual values ​​of electricity and carbon allowances in the distributed trading after the k-th iteration, s. k s c,k These are the dual residuals of electricity and carbon quotas in the distributed transaction after the k-th iteration. Set the convergence conditions for the original and dual residuals after k iterations: In the formula: K max ε is the maximum number of iterations. pri ε dual These are the maximum allowable values ​​for the original and dual residuals, respectively. If the original and dual residuals do not satisfy the convergence condition and K+1≤K, then... max At that time, the penalty factor is updated according to the ADMM algorithm with adaptive step size proposed in (404). (404) Update the penalty factor using the ADMM algorithm based on adaptive step size: In the formula: ξ k+1 ω k+1 These are the penalty factors for electrical energy and carbon quota, respectively, in the (k+1)th iteration. The value of the penalty factor is dynamically adjusted based on the relationship of the residuals, thereby ensuring the efficiency of the iteration. (405) Specific steps for solving the distributed transaction model: Step 1: Input raw data, including electricity prices in a certain region of China, carbon prices in the Chinese carbon market, and parameters of the constituent units within each park, such as gas turbines, central air conditioning systems, energy storage systems, loads, and output of new energy units. Step 2: Based on the mathematical models of each unit in the park, establish a multi-park alliance operation cost minimization model. Then, based on the multi-market multi-VPP joint electricity carbon trading model established in (11)-(30), decouple the model proposed in Step 2, establish a distributed trading model, and set the initial values ​​of variables and parameters in the ADMM algorithm for solving. Step 3: Iterate P from formula (27) i,j,s,t T i,j,s,t Update the corresponding dual variables Step 4: Determine whether the original residual and the dual residual satisfy the convergence condition according to equation (29). If equation (29) is satisfied, the algorithm converges, and the trading strategy for each VPP is output. If equation (29) is not satisfied, the algorithm does not converge, and the corresponding penalty factor ξ is updated according to equation (30). k+1 ω k+1 Then proceed to the next iteration. When the number of iterations exceeds the set upper limit or the convergence condition mentioned in (29) is met, stop the iteration.

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