Multi-source collaborative optimization scheduling method and device for industrial park

Abstract

The invention discloses an industrial park multi-source collaborative optimization scheduling method and device, and belongs to the field of power system scheduling operation. The method comprises the following steps: respectively establishing a first operation model of a fusion type carbon capture power plant and a second operation model of electrolytic aluminum equipment according to comprehensive operation data of an industrial park; according to the first operation model and the second operation model, the carbon emission quota and the actual carbon emission amount of the electrolytic aluminum equipment are obtained through calculation; according to a preset carbon reduction amount, a carbon emission quota and an actual carbon emission amount of the industrial park, calculating a carbon transaction cost including a carbon reduction cost and a carbon emission cost of the industrial park; and establishing an optimal scheduling model of the industrial park, and performing source load uncertainty optimization processing on the optimal scheduling model by taking the minimum comprehensive cost including the carbon transaction cost as an objective function and the park equipment operation constraint as a constraint condition to obtain an optimal scheduling scheme of the industrial park meeting a preset requirement. According to the invention, low-carbon, economical and safe operation of the high-energy-consumption park can be realized.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching and operation technology, and in particular to a multi-source collaborative optimization dispatching method and device for industrial parks. Background Technology

[0002] Industrial parks, as the smallest units of the energy system, provide a large amount of energy production activities and basic service facilities. High-energy-consuming industrial parks, as specific regional economic organizations dominated by high-energy-consuming industries, are particularly significant sources of carbon emissions. Their daily production requires a large amount of electricity, which currently still relies on thermal power units, resulting in significant greenhouse gas emissions. On the other hand, large-scale grid connection of renewable energy also depends on the regulation capabilities of thermal power units to avoid frequent wind and solar curtailment. Therefore, low-carbon retrofitting of thermal power units and promoting a high proportion of wind and solar energy consumption are important tasks for the clean development of high-energy-consuming industrial parks.

[0003] In related technologies, due to the volatility of wind and solar power generation and load in industrial parks, despite the expansion of the grid connection scale of renewable energy sources such as wind and solar, the phenomenon of wind and solar curtailment occurs frequently. The regulation capacity of thermal power units is limited and their carbon emissions are high, failing to effectively promote the consumption of renewable energy. In addition, industrial parks typically only use a single carbon trading mechanism or green certificate trading, resulting in excessively high operating costs for traditional industrial parks. It is evident that traditional dispatch models are difficult to balance the economy, safety, and low-carbon characteristics of high-energy-consuming industrial parks.

[0004] Therefore, there is an urgent need for a multi-source collaborative optimization scheduling method and device for industrial parks to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides a multi-source collaborative optimization scheduling method and device for industrial parks, which can achieve low-carbon, economical, and safe operation of high-energy-consuming parks. The technical solution is as follows: On the one hand, a multi-source collaborative optimization scheduling method for industrial parks is provided, the method comprising: Based on the comprehensive operation data of the industrial park, a first operation model was established to characterize the adjustment of net output of integrated carbon capture power plants with the fluctuation of renewable energy, and a second operation model was established to characterize the load demand of electrolytic aluminum equipment. Based on the first operating model and the second operating model, the carbon emission quota and actual carbon emissions of the electrolytic aluminum equipment are calculated. Based on the carbon reduction target set by the industrial park, the carbon emission quota, and the actual carbon emission, calculate the carbon trading cost of the industrial park, which includes the carbon reduction cost and the carbon emission cost. An optimal scheduling model for the industrial park is established. The model is subjected to source-load uncertainty optimization with the objective function of minimizing the comprehensive cost including the carbon trading cost and the constraints of park equipment operation as the constraint condition, so as to obtain the optimal scheduling scheme of the industrial park that meets the preset requirements.

[0006] On the other hand, a multi-source collaborative optimization scheduling device for industrial parks is provided, the device comprising: The modeling module is used to establish a first operating model to characterize the net output adjustment of integrated carbon capture power plants with renewable energy fluctuations and a second operating model to characterize the load demand of electrolytic aluminum equipment, based on the comprehensive operating data of the industrial park. The first calculation module is used to calculate the carbon emission quota and actual carbon emission of the electrolytic aluminum equipment based on the first operating model and the second operating model. The second calculation module is used to calculate the carbon trading cost of the industrial park, which includes the carbon reduction cost and the carbon emission cost, based on the preset carbon reduction amount of the industrial park, the carbon emission quota and the actual carbon emission amount. The optimization module is used to establish an optimal scheduling model for the industrial park. With the goal of minimizing the overall cost including the carbon trading cost and the constraints of park equipment operation, the optimization scheduling model is subjected to source-load uncertainty optimization to obtain the optimal scheduling scheme for the industrial park that meets the preset requirements.

[0007] On the other hand, a computer device is provided, the computer device including a memory and a processor, the memory for storing computer programs, and the processor for executing the computer programs stored in the memory to implement the steps of the above-described industrial park multi-source collaborative optimization scheduling method.

[0008] On the other hand, a computer-readable storage medium is provided, wherein a computer program is stored in the storage medium, and when the computer program is executed by a processor, it implements the steps of the above-described multi-source collaborative optimization scheduling method for industrial parks.

[0009] On the other hand, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the above-described industrial park multi-source collaborative optimization scheduling method.

[0010] The technical solution provided by this invention can bring at least the following beneficial effects: By building a model of an integrated carbon capture power plant including a flue gas bypass and a storage tank, it is possible to balance peak shaving and valley filling effects with carbon emission reduction requirements, allowing the net output of the carbon capture power plant to be flexibly adjusted according to the output of renewable energy generation; next, a carbon emission model of the entire electrolytic aluminum production process is built to determine the carbon emission quota and actual carbon emission of the electrolytic aluminum equipment; then, the carbon trading cost is determined through the carbon reduction cost and carbon emission cost of the industrial park; finally, by using preset constraints to minimize the comprehensive cost including carbon trading costs, the optimal scheduling scheme for the industrial park is calculated. This method can reduce the carbon emissions and total cost of electrolytic aluminum equipment, improve the renewable energy absorption capacity, and has extremely high application value. Attached Figure Description

[0011] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart of a multi-source collaborative optimization scheduling method for industrial parks provided by an embodiment of the present invention; Figure 2 This is a structural diagram of the operational framework of a high-energy-consuming industrial park provided in an embodiment of the present invention; Figure 3 This is an energy flow diagram of a carbon capture power plant provided in an embodiment of the present invention; Figure 4 This is a flowchart of an electrolytic aluminum production process provided in an embodiment of the present invention; Figure 5 This is a power prediction curve diagram of conventional load, photovoltaic and wind turbine provided in an embodiment of the present invention; Figure 6 This is a bar chart of electricity prices provided in an embodiment of the present invention; Figure 7 This is a graph showing the power generation curves of thermal power generating units and renewable energy generation under scenarios 1, 2 and 3 provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of the power composition of a carbon capture device provided in an embodiment of the present invention; Figure 9 This is a schematic diagram of the operating curves of the absorption tower, regeneration tower, and storage tank provided in an embodiment of the present invention; Figure 10 This is a power schematic diagram of the energy storage device under scenarios 1, 2 and 3 provided in an embodiment of the present invention; Figure 11This is a schematic diagram illustrating the impact of market price changes on the total cost of high-energy-consuming industrial parks, provided by an embodiment of the present invention. Figure 12 This is a sensitivity analysis chart of total cost to confidence level and variance provided in an embodiment of the present invention; Figure 13 This is a structural diagram of a multi-source collaborative optimization scheduling device for industrial parks provided in an embodiment of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] As mentioned earlier, traditional high-energy-consuming industrial parks rely on thermal power units for power supply, which not only results in high emissions and insufficient renewable energy consumption, but also increases the park's operating costs due to the single carbon trading method.

[0015] Based on this, the concept of this invention is to improve the absorption capacity of renewable resources and reduce the operating costs of the industrial park by constructing an industrial park that integrates carbon capture power plants and electrolytic aluminum equipment. The following describes the specific implementation of the above concept.

[0016] Please refer to Figure 1 The present invention provides a multi-source collaborative optimization scheduling method for industrial parks, the method comprising: Step 100: Based on the comprehensive operation data of the industrial park, establish a first operation model to characterize the adjustment of net output of integrated carbon capture power plants with renewable energy fluctuations and a second operation model to characterize the load demand of electrolytic aluminum equipment. Step 102: Calculate the carbon emission quota and actual carbon emissions of the electrolytic aluminum equipment based on the first operating model and the second operating model. Step 104: Calculate the carbon trading cost of the industrial park, which includes carbon reduction costs and carbon emission costs, based on the preset carbon reduction amount of the industrial park, the carbon emission quota, and the actual carbon emissions. Step 106: Establish an optimal scheduling model for the industrial park, and perform source-load uncertainty optimization on the optimal scheduling model with the objective function of minimizing the comprehensive cost including the carbon trading cost and the constraints of park equipment operation as the constraint condition, to obtain the optimal scheduling scheme of the industrial park that meets the preset requirements.

[0017] In this embodiment of the invention, by constructing a model of an integrated carbon capture power plant including a flue gas bypass and a liquid storage tank, both peak shaving and valley filling effects and carbon emission reduction requirements can be considered, allowing the net output of the carbon capture power plant to be flexibly adjusted according to the output of renewable energy generation. Next, a carbon emission model of the entire electrolytic aluminum production process is constructed to determine the carbon emission quotas and actual carbon emissions of the electrolytic aluminum equipment. Subsequently, carbon trading costs are determined through the carbon reduction costs and carbon emission costs of the industrial park. Finally, by using preset constraints and aiming to minimize the overall cost including carbon trading costs, the optimal scheduling scheme for the industrial park is calculated. This method can reduce the carbon emissions and total cost of electrolytic aluminum equipment, improve the renewable energy absorption capacity, and has extremely high application value.

[0018] The following description Figure 1 The execution method for each step is shown.

[0019] First, for step 100, based on the comprehensive operation data of the industrial park, a first operation model is established to characterize the adjustment of net output of the integrated carbon capture power plant with the fluctuation of renewable energy, and a second operation model is established to characterize the load demand of electrolytic aluminum equipment.

[0020] In this embodiment of the invention, the design is as follows: Figure 2 The diagram illustrates the operational architecture of a High Energy Industrial Park (HEIP), which includes wind power, solar power, energy storage, a combined carbon capture power plant, electrolytic aluminum load, and conventional load. Through refined energy management, the park can reduce carbon emissions and enhance the absorption capacity of renewable energy while ensuring power supply.

[0021] Specifically, carbon capture power plants, which reduce carbon emissions from thermal power units by installing carbon capture equipment, are called carbon capture power plants and are a key part of the low-carbon transformation of high-energy-consuming industrial parks. Compared with storage-type and split-type carbon capture power plants, integrated carbon capture power plants, which combine the two methods, have significant advantages. Compared with storage-type carbon capture power plants, the integrated carbon capture power plant proposed in this embodiment adds a flue gas bypass, which can flexibly adjust the carbon capture capacity. Compared with split-type carbon capture power plants, the integrated carbon capture power plant installs a storage tank between the absorption tower and the regeneration tower, which can transfer carbon capture energy consumption as needed to solve the contradiction between carbon capture demand and power supply demand during peak load periods. The energy flow of the integrated carbon capture power plant is as follows: Figure 3 As shown.

[0022] Furthermore, the first operating model for integrated carbon capture power plants to adjust net output in response to renewable energy fluctuations is established using the following formula: The operating energy consumption of the carbon capture equipment is determined based on the preset carbon supply amount of the storage tank in the integrated carbon capture power plant: In the formula, Let t be the operating energy consumption of the carbon capture equipment in the power plant. The energy consumption coefficient for the carbon capture equipment is taken as 0.269 MW / ton; Carbon capture amount; The amount of CO2 supplied to the liquid storage tanks in the power plant; The carbon emission intensity of the thermal power units in the power plant is taken as 0.9 ton / MW; The maximum operating condition factor for the regeneration tower and compressor in the power plant is taken as 1.05; , These are the flue gas split ratio and the efficiency of the carbon capture equipment, respectively. CO2 emissions from thermal power units; Let t be the power generation capacity of the thermal power unit at time t; is the electrocarbon conversion coefficient.

[0023] The operational relationship between the carbon capture equipment and the power generation of thermal power units and photovoltaic and wind power equipment in industrial parks is determined based on the aforementioned operating energy consumption: In the formula, To supply power to the carbon capture equipment of thermal power units; This refers to the net power of the thermal power unit. Power of carbon capture equipment; The fixed energy consumption of the carbon capture equipment is estimated at 5MW. , The power supplied to the carbon capture equipment is provided by wind power and photovoltaic power respectively.

[0024] Based on the first operating model, the range of net power of thermal power units can be obtained as shown in the following formula: Meanwhile, as a key component in regulating carbon capture energy consumption, the liquid storage tank can temporarily store CO2 during peak load periods and release it during off-peak periods. At this time, the regeneration tower's operating power increases to reduce the net power output of thermal power units, thereby enhancing the capacity for renewable energy absorption. Therefore, installing a liquid storage tank can balance carbon capture demand and load demand through carbon capture energy consumption transfer. The range of CO2 it can provide is shown in the following formula: CO2 is stored in the form of a compound in the alkanolamine solution in the storage tank. Therefore, the mass of CO2 stored and released in the storage tank can be quantified by the volume of the alkanolamine solution, as shown in the following formula: In the formula, The volume of solution corresponding to the mass of CO2 supplied by the storage tank at time t; The molar mass of the alkanolamine is taken as 61.08 g / mol; The CO2 removal rate of the regeneration tower is taken as 0.3; , The concentrations and densities of the MEA solution were 0.3 and 1.01 g / ml, respectively. The molar mass of CO2 is taken as 44 g / mol.

[0025] The electrolytic aluminum production process is as follows: Figure 4 As shown, under the premise of ensuring the safety and stability of aluminum production, electrolytic aluminum equipment can make an important contribution to the low-carbon economic operation of the industrial park by effectively regulating the load power.

[0026] Considering the thermal conductivity characteristics of the electrolytic cell, the demand response of the electrolytic aluminum load can be achieved by controlling the temperature of the electrolytic cell to cope with the uncertainty of renewable energy. Reasonable temperature control can not only ensure the thermal stability of the electrolytic cell, but also ensure the safety of the electrolytic aluminum load when providing demand response.

[0027] Specifically, the relationship between temperature and electrolytic cell current is first determined based on the temperature adjustment cycle of the electrolytic cell: In the formula, Let be the temperature of electrolytic cell i at time t; , , , , K All are constant coefficients; T is the scheduling period; and These are the upper and lower limits of the electrolytic cell temperature, respectively set to 970 °C and 950 °C; Furthermore, based on the linear relationship between electrolytic cell energy consumption and aluminum production, a second operating model for the aluminum electrolysis equipment is established: In the formula, The electrolytic cell at time t i The power; Electrolytic cell i The voltage; Electrolytic cell i The current; and These are the upper and lower power limits for electrolytic cell i, respectively; , These are the upper and lower limits of the current in electrolytic cell i, respectively; Let be the aluminum production of electrolytic cell i at time t; It is the electricity consumption per ton of aluminum produced; and These represent the upper and lower limits of aluminum production from the electrolytic aluminum equipment at time t, respectively. The total number of electrolytic cells is 150 in this section.

[0028] Then, for step 102, the carbon emission quota and actual carbon emissions of the electrolytic aluminum equipment are calculated based on the first operating model and the second operating model.

[0029] Since the carbon trading market incentivizes participants to reduce their carbon emissions through economic means, the carbon emission quota for electrolytic aluminum equipment using the baseline method is calculated based on the aluminum production of the electrolytic aluminum equipment in the first operating model. In the formula: Carbon emission quotas for electrolytic aluminum equipment; The carbon emission allowance for the conventional load within the electrolytic aluminum equipment at time t; Let i be the carbon emission quota for electrolyzer i at time t; The load inside the electrolytic aluminum equipment at time t is the normal load. The carbon emission allowance for electricity is set at 0.6 ton / MWh; For time intervals.

[0030] This embodiment only considers the electrolysis process of aluminum electrolysis and does not involve the fuel combustion part. Therefore, the actual carbon emissions of the aluminum electrolysis equipment are calculated by the following formula: In the formula: This refers to the actual carbon emissions from aluminum electrolysis equipment. The first carbon emissions generated when energy is used as a raw material; This is the second source of carbon emissions from industrial production processes; This refers to the third carbon emissions generated when industrial parks purchase electricity from carbon capture power plants and the power grid.

[0031] Specifically, carbon emissions generated when energy is used as a raw material refer to the carbon dioxide released as the carbon anode is continuously consumed as a reducing agent in the chemical reaction. Therefore, the first carbon emission is calculated based on the amount of anode consumed in the chemical reaction of the aluminum electrolysis equipment: In the formula, Let t be the first carbon emission generated by the consumption of carbon anodes in electrolytic cell i at time t; The carbon dioxide emission factor consumed by the carbon anode; The carbon anode consumption per ton of aluminum produced is taken as 0.42 ton; The average ash content of the carbon anode is taken as 0.4%. The average sulfur content of the carbon anode is 2%.

[0032] Furthermore, during the electrolytic aluminum production process, factors such as excessively low electrolyte levels or delayed processing times may trigger an anode effect. This not only leads to a sharp increase in the electrolytic cell voltage but also results in the emission of CF4 and C2F6. Therefore, based on the anode effect of the electrolytic aluminum equipment in the chemical reaction, the second carbon emission from greenhouse gases can be calculated: In the formula: , These are the GWP value of CF4 and the carbon emission factor of CF4 with an anode effect, respectively. , These are the GWP value of C2F6 and the carbon emission factor of C2F6 with an anode effect, respectively.

[0033] Finally, to ensure normal production of electrolytic aluminum and stable operation of the park's conventional load, electrolytic aluminum equipment typically purchases fuel from thermal power units or electricity from the grid. Therefore, based on the operating power consumption of the integrated carbon capture power plant in the first operating model, the third carbon emission generated by the electrolytic aluminum equipment from the electricity purchased from the integrated carbon capture power plant and the grid is determined as follows: In the formula: The carbon emission factor for electricity purchase is taken as 0.7 ton / MWh, referencing the grid emission factor. This refers to the purchased power for the electrolytic aluminum equipment.

[0034] For step 104, the carbon trading cost of the industrial park, which includes carbon reduction costs and carbon emission costs, is calculated based on the preset carbon reduction amount of the industrial park, the carbon emission quota, and the actual carbon emission amount.

[0035] Green certificates are certifications issued by governments and regulatory agencies for the electricity generated from renewable energy sources and fed into the grid. The main participants in the green certificate trading market include renewable energy power generation companies, electricity retailers, and electricity users. To meet the renewable energy quota system requirements, the number of green certificates needed for electrolytic aluminum equipment is calculated based on the aluminum production capacity of the equipment. In the formula, The theoretical number of green certificates required for electrolytic aluminum equipment; This refers to the green certificate quota coefficient. Furthermore, based on the wind power and photovoltaic power generation capacity of the industrial park, the actual number of green certificates for the industrial park is calculated: In the formula, The actual number of green certificates for electrolytic aluminum equipment; , These represent the wind power and photovoltaic power generation at time t, respectively. To increase carbon reduction quotas and reduce the costs associated with carbon emissions, and since the carbon reduction amount of each green certificate is fixed, the industrial park needs to purchase more green certificates to increase the total carbon reduction. Therefore, based on the theoretical and actual number of green certificates, the carbon reduction costs incurred by the industrial park when purchasing green certificates can be calculated. : In the formula, The price for a green certificate is $6.94 per certificate.

[0036] Because green certificates contain complete information about renewable energy generation, including the carbon emission reductions they generate, holders can deduct these reductions when calculating carbon emission rights based on the carbon emission reductions caused by renewable energy supply compared to conventional energy supply. This allows them to indirectly participate in the carbon trading market. This linkage mechanism enables green certificates to participate in both carbon emission trading and green certificate trading simultaneously, with the two markets interacting based on their respective demand for carbon emission rights and green certificates, as well as trading prices.

[0037] Specifically, the carbon reduction amount corresponding to each green certificate is first determined based on the difference between the carbon emissions from thermal power units and the carbon emissions from renewable energy power generation in the industrial park: In the formula, The amount of carbon reduction corresponding to each green certificate; Carbon emissions generated by thermal power units; Carbon emissions generated from renewable energy generation.

[0038] If the industrial park purchases additional green certificates, its total carbon reduction will increase. Then, based on the carbon emission allowances calculated in the steps above and the park's actual carbon emissions, the tradable carbon emission allowances for the industrial park can be calculated. Finally, the tradable carbon emission allowances are substituted into a pre-set tiered carbon trading incentive model to calculate the carbon emission cost of the industrial park: In the formula, The carbon emission cost under the carbon emission-green certificate interaction mechanism The benchmark price for carbon trading is set at $20.81 per ton. The percentage increase in carbon trading prices is set at 0.25. The interval length is set to 1000 tons. Carbon allowances are tradable for aluminum electrolysis equipment.

[0039] Finally, by summing the carbon reduction costs and carbon emission costs, the carbon trading costs of the industrial park can be calculated.

[0040] It is worth noting that under the carbon emission rights and green certificate linkage mechanism proposed in this embodiment, electrolytic aluminum enterprises actively participate in the electricity-carbon market, trading electricity and carbon emission rights. Enterprises also indirectly participate in the green certificate trading market by investing in and constructing renewable energy units. Government regulatory departments are responsible for verifying carbon emission quotas and renewable energy consumption responsibility quotas, which are then distributed to the carbon trading market and the green certificate market, respectively. Under the carbon emission rights and green certificate linkage mechanism, in addition to purchasing carbon emission rights, electrolytic aluminum enterprises can also use green certificates to offset a portion of their carbon emissions to meet carbon emission assessment requirements.

[0041] For step 106, an optimal scheduling model for the industrial park is established. The model is then subjected to source-load uncertainty optimization with the objective function of minimizing the overall cost including the carbon trading cost and the constraints of park equipment operation as the constraint condition, to obtain the optimal scheduling scheme for the industrial park that meets the preset requirements.

[0042] Specifically, the industrial park optimization scheduling model aims to minimize the total system cost: In the formula: The cost of equipment operation and maintenance, including wind turbines, photovoltaics, energy storage and carbon capture equipment; For energy purchase costs; For carbon capture power plants, this refers to equipment depreciation costs. To incur penalties; For production income.

[0043] Equipment maintenance costs are calculated using the following formula: In the formula, , , , These are the operation and maintenance costs of wind turbines, photovoltaic systems, energy storage, and carbon capture equipment, respectively. , , , The operation and maintenance cost coefficients for wind turbines, photovoltaics, energy storage, and carbon capture equipment are taken as 7.63 $ / MWh, 3.47 $ / MWh, 2.5 $ / MWh, and 55.48 $ / MWh, respectively. , These represent the energy storage charging and discharging power, taken as 0.95; , The solution loss cost coefficient and operating loss coefficient for the storage tank are respectively taken as $162.28 / ton and 1.5 kg / ton; For time intervals.

[0044] Energy purchase cost includes the operating cost of thermal power units and the cost of purchasing electricity, and is calculated using the following formula: In the formula: , , The coal consumption cost coefficient for each thermal power unit is taken as 6.66 × 10. -5 $ / MW 2 h, 2.25 $ / MWh, 138.7 $; , These are the electricity sales price from the power grid and the electricity purchase price; , These represent the electricity purchased from the power grid and the electricity sold to the park, respectively.

[0045] The depreciation cost of carbon capture equipment is calculated using the following formula: In the formula: , The total investment costs for carbon capture and storage tanks are respectively taken as 1700 × 10. 3 $ and 3600×10 3 $; , The depreciation periods for carbon capture and liquid storage tanks are 15 and 5 years, respectively. The discount rate is set to 8%.

[0046] The cost of punishment is calculated using the following formula: In the formula, The penalty cost coefficient is set at $41.61 / MWh; , These represent the predicted wind power and solar power output at time t, respectively.

[0047] Production revenue includes the revenue from the production of electrolytic aluminum equipment and participation in demand response, and is calculated using the following formula: In the formula, Revenue generated from aluminum production; Revenue generated from the participation of electrolytic aluminum loads in demand response; The revenue coefficient for aluminum products has already taken into account factors such as raw material costs and operating costs; Let $t be the compensation price obtained by the electrolytic aluminum load participating in demand response at time t, which is taken as $138.7 / MWh.

[0048] Furthermore, the constraints include operational constraints on the storage tanks of the integrated carbon capture power plant, operational constraints on the power supply equipment, and system power balance constraints.

[0049] The operating constraints of the liquid storage tank are calculated using the following formula: In the formula: , Let t represent the volumes of the liquid-rich and liquid-poor solutions in the storage tank at time t. The upper limit of the storage tank capacity is set at 60,000 m³. 3 The price coefficient is 0.06 $ / m 3 ; , Let T represent the initial volume of the liquid-rich solution in the storage tank and the volume at time T, respectively, taken as 3000 m³. 3 ; , Let T be the initial volume of the lean liquid in the storage tank and the volume at time T, respectively, and take 3000 m³. 3 .

[0050] The constraints on the operation of energy supply equipment include wind power and photovoltaic power constraints; thermal power generation capacity, ramp rate and spinning reserve constraints; energy storage operation constraints; and power exchange constraints between the park and the grid.

[0051] The power constraints for wind and solar power are calculated using the following formula: In the formula: , These are the maximum power generation capacities of wind power and solar power, respectively.

[0052] The generating capacity, grade gradient, and spinning reserve constraints of thermal power units are calculated using the following formulas: In the formula: , These are the upper and lower limits of the generating capacity of thermal power units, respectively set at 455 MW and 150 MW; , The upward and downward ramp rates of the thermal power unit are respectively taken as 150 MW / h; , These represent the upward and downward rotational reserve capacities required by the park at time t.

[0053] Energy storage operation constraints are calculated using the following formula: In the formula: Let be the remaining energy storage capacity at time t; , These are the upper and lower limits of the remaining energy storage capacity, respectively set at 500MW and 50MW; The upper limit of energy storage charging and discharging power is set at 100 MW.

[0054] The power exchange constraint between the industrial park and the power grid is calculated using the following formula: In the formula: The upper limit of the power exchange between the park and the power grid is set at 500MW.

[0055] The system power balance constraint is calculated using the following formula: .

[0056] After determining all the constraints, the source-load uncertainty optimization process can be performed on the optimized scheduling model to obtain the optimal scheduling scheme that meets the preset requirements of the industrial park.

[0057] Since the proportion of renewable energy generation connected to high-energy-consuming industrial parks is relatively high, studying system operation issues that take into account source-load uncertainty is of great significance for improving the economic efficiency and safety of park operation.

[0058] Specifically, the net load is first calculated based on the conventional load of the electrolytic aluminum equipment and the power output of wind turbines and photovoltaic power generation. Then, a fuzzy set of the probability distribution of this net load is established to describe the fluctuation range of the data. The definition of net load and its mean and variance are shown in the following formulas: In the formula, Net electrical load; It is the probability distribution function; This is the set of probability distributions of net electrical load. This represents the set of all distributions of net electrical load after considering uncertainties. , These are the mean and standard deviation of the parameter, respectively. , These are the upper and lower limits of the parameter mean, respectively; , These are the upper and lower limits of the parameter standard deviation, respectively.

[0059] The mean and variance of fuzzy sets at different times can reflect the correlation between wind power, photovoltaics, and load to a certain extent.

[0060] Because considering source-load uncertainty, the power balance constraint would lead to excessively high operating costs for the park. Therefore, opportunity-constrained programming can be used to relax the system's power balance constraint based on a preset confidence level, resulting in opportunity constraints. Considering the risk maximization scenario, the power balance constraint can be rewritten into a general form of opportunity constraints: Furthermore, the CVaR theory is used to measure the risk of the industrial park, and the risk is quantified for opportunity constraints: In the formula, This is the sum of other powers affected by source load uncertainty; This is the net electrical load power factor; It is an auxiliary variable.

[0061] Furthermore, the quantization results are subjected to dual transformation and fuzzy set boundary processing to obtain solvable second-order cone constraint conditions.

[0062] Specifically, the split-bar optimization, after considering uncertainty, will operate under the scenario of maximizing risk. (Setting variables) Based on the mean and variance variables defined in the net load fuzzy set equation, it can be known that... The corresponding mean and variance are respectively , Therefore, the internal maximization problem can be equivalently represented by the integral form shown in the following formula: Based on duality theory, dual variables are introduced. , , , : The inequality constraints in the formula are equivalently transformed into: Next, we introduce variables. , Based on duality theory, the above equation can be rewritten as: Combining the above formulas, we finally obtain the solvable second-order cone constraint conditions: This formula can be solved using the YALMIP toolkit in the MATLAB software environment and by calling the GUROBI solver. The final result is the optimal scheduling scheme and risk value of the industrial park, which can be used to assess the cost risk under uncertain conditions of renewable resources.

[0063] The feasibility of the above method is verified by an example below.

[0064] In this embodiment, the industrial park consists of a carbon capture power plant, energy storage, renewable energy power generation, electrolytic aluminum, and conventional loads. The installed capacities of wind turbines and photovoltaics are 120 MW and 100 MW, respectively, and the electrolytic cell capacity is 500 MW. The forecast curves for wind power, photovoltaics, and conventional loads are shown below. Figure 5 As shown. Field research revealed that the aluminum electrolysis equipment operates on a 24-hour production schedule. To avoid excessively long demand response times affecting product quality, it is assumed that the maximum upward adjustment capacity of the electrolytic cell is 15% of the rated power for a duration not exceeding 3 hours, and the maximum downward adjustment capacity is 10% of the rated power for a duration not exceeding 2 hours. This section adopts the following... Figure 6 The time-of-use electricity pricing shown is intended to guide the adjustment of electricity consumption by electrolytic aluminum equipment. The scheduling cycle is 24 hours, with a 1-hour time interval. The parameters of each piece of equipment in the park have been introduced earlier and will not be repeated here.

[0065] Furthermore, six scenarios were set up for comparison to illustrate the effectiveness of the proposed method. Information for each scenario is shown in Table 1. Scenarios 1-3 illustrate the necessity of installing carbon capture equipment and storage tanks. Scenarios 3-5 illustrate the necessity of CET-GCT interaction. Scenario 3 only considers the carbon trading market; Scenario 4 adds a green certificate trading market to Scenario 3, but does not consider the carbon emission offsetting effect of green certificates; Scenario 5 adopts the CET-GCT interaction decision-making method proposed in this invention. Scenario 6 considers source-load uncertainty. The profit and loss situation for each scenario is shown in Table 2.

[0066] Table 1 Scene Information Description Table 2. Total Cost Composition in Different Scenarios The superiority of the carbon capture equipment model proposed in this invention is illustrated by comparing scenarios 1, 2, and 3. Table 1 shows that scenarios 2 and 3, by introducing carbon capture equipment to capture carbon emissions from thermal power units, demonstrate significant economic efficiency and low carbon emissions. Compared to scenario 1, scenario 2 reduces total cost by 39.39% and carbon emissions by 31.59%; scenario 3 reduces total cost by 41.19% and carbon emissions by 33.98%. Regarding renewable energy consumption, scenarios 2 and 3 are both superior to scenario 1, and the optimal performance of scenario 3 underscores the necessity of installing a storage tank. The relevant costs of the carbon capture equipment for the three scenarios are shown in Table 3. Scenario 3 uses a storage tank to store captured CO2, reducing carbon capture energy consumption during peak load periods, thus lowering the operating cost of the carbon capture equipment. However, the storage tank also results in significantly higher depreciation costs for the carbon capture equipment in scenario 3 compared to scenario 2. As shown in Table 1, compared to Scenario 1 and Scenario 2, Scenario 3 has the lowest wind and solar curtailment rate, and the proportion of renewable energy power generation in Scenario 3 increases by 10.67% and 5.19%, respectively. This indicates that the scheduling strategy proposed in this invention helps to improve the renewable energy absorption capacity and reduce the carbon emissions of the park by increasing the proportion of renewable energy power generation.

[0067] Table 3. Costs of carbon capture equipment in scenarios 1, 2, and 3. The net power of thermal power units, wind turbine output, and photovoltaic output in scenarios 1 through 3 are as follows: Figure 7 As shown in the figure, during peak wind and solar power output periods, the net power output of the thermal power units in the three scenarios decreases sequentially, while the power output of renewable energy increases sequentially. During other periods, the net power output of units in scenarios 2 and 3 is higher than that in scenario 1. The carbon capture energy consumption in scenarios 2 and 3 is shown in the figure. Figure 8 As shown. The carbon flow process in scenario 3 is as follows. Figure 9 As shown in the diagram, Scenario 2's carbon capture energy consumption trend follows the conventional load, achieving carbon emission reduction but with poor peak shaving and valley filling effects. Compared to Scenario 2, Scenario 3 maintains only the basic energy consumption of the carbon capture equipment during peak load periods, and the captured CO2 is stored in a storage tank. During off-peak load periods, the storage tank releases CO2, increasing the power of the regeneration tower and compressor, thus increasing carbon capture energy consumption, thereby achieving carbon emission reduction and peak shaving and valley filling. In this case, carbon capture energy consumption is mainly provided by wind turbines and photovoltaics, which not only improves the renewable energy absorption capacity but also reduces the carbon emissions of thermal power units. This verifies the low-carbon nature and flexibility of the strategy proposed in this invention.

[0068] Table 4 shows partial scheduling results for scenarios 1-3 without energy storage. Comparing Table 2 and Table 4, it can be seen that the wind and solar curtailment rates for scenarios 1-3 all increase without energy storage. Compared to the total cost including energy storage, the total cost for scenarios 1-3 increases by 6.48%, 4.13%, and 0.16% respectively without energy storage. This indicates that energy storage has a greater impact on scenarios 1 and 2, while having a smaller impact on scenario 3.

[0069] Table 4 Total cost e for scenarios 1, 2 and 3 without energy storage devices The energy storage charging and discharging conditions in scenarios 1 to 3, including energy storage, are as follows: Figure 10 As shown, the frequency of energy storage charging and discharging decreases sequentially across the three scenarios. In Scenario 1, the energy storage device charges during peak renewable energy generation periods and discharges during peak load periods to reduce wind and solar curtailment rates. Scenario 2 incorporates carbon capture equipment, which absorbs some renewable energy, further reducing the frequency of energy storage charging and discharging. Scenario 3 utilizes a liquid storage tank to transfer CO2 processing processes on demand, indicating that the liquid storage tank, similar to the energy storage system, has an energy time-shifting function. Therefore, the frequency of energy storage charging and discharging in Scenario 3 is further reduced, consistent with the conclusion in Table 4 that energy storage has the least impact on Scenario 3.

[0070] Compared to Scenario 3, the total cost of the park in Scenario 4 decreased by 14.01%. This is because the electrolytic aluminum equipment participated in the green certificate trading market, selling its excess green certificate quotas and earning profits. Compared to Scenario 3 and Scenario 4, Scenario 5 had the lowest total cost, decreasing by 37.44% and 27.25%, respectively. Compared to the parallel operation of the CET and GCT markets in Scenario 4, Scenario 5 significantly reduced carbon emission costs. The main reason is that green certificates can offset a portion of the carbon quotas that need to be purchased, thereby reducing the carbon emission costs of the park and fully demonstrating the economic benefits of the CET and GCT interactive strategy proposed in this invention.

[0071] Prices in the electricity market, carbon trading market, and green certificate trading market will fluctuate due to economic volatility, policy adjustments, and market uncertainties. Based on scenario 5, when electrolytic aluminum equipment participates simultaneously in the electricity-carbon-green certificate market, it is affected by external market conditions, such as… Figure 11 As shown.

[0072] Compared to fluctuations in carbon trading prices and green certificate trading prices, the total cost of the industrial park is more sensitive to changes in electricity prices. This is mainly because electrolytic aluminum production consumes a large amount of electricity; even slight fluctuations in electricity prices can significantly impact the park's energy purchase costs. Furthermore, the high carbon emissions from electrolytic aluminum production are primarily due to its high electricity consumption. When electricity prices remain constant, slight fluctuations in carbon trading prices will also cause slight fluctuations in the price at which the park purchases carbon allowances, but these will have little impact on the park's total cost. The impact of green certificate trading prices on the park's total cost is mainly reflected in the revenue from green certificate sales. As green certificate trading prices increase, green certificate sales revenue also increases, thus gradually reducing the park's total cost.

[0073] Scenario 6 considers the uncertainty of the park's source load, with the upper and lower limits of the average fluctuation of parameters at each time point set to ±5% of the predicted value. Since the uncertainty is approximated using the maximum risk scenario, the park's load increases and renewable energy output decreases, thus requiring adjustments to the operating strategy. As shown in Table 2, to ensure safe operation, compared to Scenario 5, Scenario 6, considering source load uncertainty, increases the park's energy purchase cost by 13.1% and carbon emissions by 0.42%. Due to the high penalty costs of tiered carbon trading, the park reduces the number of sellable green certificates to offset carbon emissions.

[0074] By adjusting the confidence level and variance, the changes in the total cost of the park are as follows: Figure 12 As shown, when the confidence level is 0.95, the power balance requirement for the park is most stringent, resulting in the highest total cost for ensuring park safety. As the confidence level decreases, the power balance gradually relaxes, allowing for a wider range of power supply exceeding demand, thus reducing the total cost of the park. Conversely, as the variance increases, the range of source-load fluctuations in the park gradually increases, leading to a gradual deterioration in the total cost. At a confidence level of 0.95, the economic deterioration of the park with increasing variance is most severe. The results verify that the model proposed in this invention exhibits superior sensitivity to confidence level and variance, effectively addressing the impact of source-load uncertainty on the safe operation of the park.

[0075] Please refer to Figure 13 This invention provides a multi-source collaborative optimization scheduling device for industrial parks, the device comprising: Modeling module A1 is used to establish a first operating model to characterize the net output adjustment of integrated carbon capture power plants with renewable energy fluctuations and a second operating model to characterize the load demand of electrolytic aluminum equipment, based on the comprehensive operating data of the industrial park. The first calculation module A2 is used to calculate the carbon emission quota and actual carbon emission of the electrolytic aluminum equipment based on the first operating model and the second operating model. The second calculation module A3 is used to calculate the carbon trading cost of the industrial park, which includes the carbon reduction cost and the carbon emission cost, based on the preset carbon reduction amount of the industrial park, the carbon emission quota and the actual carbon emission amount. The optimization module A4 is used to establish an optimal scheduling model for the industrial park. With the goal of minimizing the comprehensive cost including the carbon trading cost and the constraints of park equipment operation, the optimal scheduling model is subjected to source-load uncertainty optimization to obtain the optimal scheduling scheme for the industrial park that meets the preset requirements.

[0076] In this embodiment of the invention, establishing a first operating model for an integrated carbon capture power plant includes: The operating energy consumption of the carbon capture equipment is determined based on the preset carbon supply amount of the storage tank in the integrated carbon capture power plant: In the formula, Let t be the operating energy consumption of the carbon capture equipment in the power plant. Energy consumption coefficient of carbon capture equipment; Carbon capture amount; The amount of CO2 supplied to the liquid storage tanks in the power plant; Carbon emission intensity of thermal power units in power plants; This represents the maximum operating condition coefficient for the regeneration tower and compressor in the power plant. , These are the flue gas split ratio and the efficiency of the carbon capture equipment, respectively. CO2 emissions from thermal power units; Let t be the power generation capacity of the thermal power unit at time t; The electrocarbon conversion coefficient; The operational relationship between the carbon capture equipment and the power generation of thermal power units and photovoltaic and wind power equipment in industrial parks is determined based on the aforementioned operating energy consumption: In the formula, To supply power to the carbon capture equipment of thermal power units; This refers to the net power of the thermal power unit. Power of carbon capture equipment; Fixed energy consumption for carbon capture equipment; , The power supplied to the carbon capture equipment is provided for wind power and solar power, respectively; The establishment of the second operating model for the electrolytic aluminum equipment includes: Based on the temperature adjustment cycle of the electrolytic cell, determine the operating relationship between temperature and electrolytic cell current: In the formula, Let be the temperature of electrolytic cell i at time t; , , , , K All are constant coefficients; T is the scheduling period; and These are the upper and lower limits of the electrolytic cell temperature, respectively. Based on the linear relationship between electrolytic cell energy consumption and aluminum production, a second operating model for the aluminum electrolysis equipment is established: In the formula, The electrolytic cell at time t iThe power; Electrolytic cell i The voltage; Electrolytic cell i The current; Let be the aluminum production of electrolytic cell i at time t; It is the electricity consumption per ton of aluminum produced.

[0077] In this embodiment of the invention, when the first calculation module A2 calculates the carbon emission quota and actual carbon emissions of the electrolytic aluminum equipment based on the first operating model and the second operating model, it specifically performs the following operations: Calculates the carbon emission quota of the electrolytic aluminum equipment based on the aluminum production of the electrolytic aluminum equipment in the first operating model; calculates the first carbon emission based on the anode consumption of the electrolytic aluminum equipment in the chemical reaction; calculates the second carbon emission generated by greenhouse gas emissions based on the anode effect of the electrolytic aluminum equipment in the chemical reaction; determines the third carbon emission generated by the electrolytic aluminum equipment from electricity purchased from the integrated carbon capture power plant and the power grid based on the operating power consumption of the integrated carbon capture power plant in the first operating model; and sums the first carbon emission, the second carbon emission, and the third carbon emission to obtain the actual carbon emissions of the electrolytic aluminum equipment.

[0078] In this embodiment of the invention, when the second calculation module A3 calculates the carbon trading cost of the industrial park, which includes carbon reduction costs and carbon emission costs, based on the preset carbon reduction amount, the carbon emission quota, and the actual carbon emission amount, it specifically performs the following operations: Calculate the theoretical number of green certificates required for the electrolytic aluminum equipment based on the aluminum production of the electrolytic aluminum equipment; calculate the actual number of green certificates for the industrial park based on the wind power and photovoltaic power of the industrial park; calculate the carbon reduction cost incurred by the industrial park when purchasing green certificates based on the difference between the theoretical number of green certificates and the actual number of green certificates; determine the carbon reduction amount corresponding to each green certificate based on the difference between the carbon emissions of thermal power units and the carbon emissions generated by renewable energy power generation in the industrial park; calculate the tradable carbon emission quota for the industrial park based on the carbon emission quota, the actual carbon emission amount, and the total carbon reduction; substitute the tradable carbon emission quota into a preset tiered carbon trading incentive model to calculate the carbon emission cost of the industrial park; and sum the green certificate trading cost and the carbon emission cost to calculate the carbon trading cost of the industrial park.

[0079] In this embodiment of the invention, the constraints include the operation constraints of the storage tanks of the integrated carbon capture power plant, the operation constraints of the power supply equipment, and the system power balance constraints.

[0080] In this embodiment of the invention, when the optimization module A4 performs source-load uncertainty optimization processing on the optimization scheduling model with the objective function of minimizing the comprehensive cost including the carbon trading cost and the constraint condition of park equipment operation, to obtain the optimal scheduling scheme of the industrial park that meets the preset requirements, it specifically performs the following operations: Calculate the net load based on the conventional load of the electrolytic aluminum equipment and the power of wind turbine and photovoltaic power generation, and establish a fuzzy set of the probability distribution of the net load to describe the fluctuation range of the data; perform opportunity constraint programming to relax the system power balance constraint according to the preset confidence level to obtain opportunity constraints; quantify the risk of the opportunity constraints, and perform dual transformation and fuzzy set boundary processing on the quantification results to obtain solvable second-order cone constraint conditions; solve the second-order cone constraint conditions to obtain the optimal scheduling scheme and risk value of the industrial park, so as to assess the cost risk under uncertain renewable resource conditions.

[0081] The industrial park multi-source collaborative optimization scheduling device and the industrial park multi-source collaborative optimization scheduling method provided in the above embodiments belong to the same concept. For details of its specific implementation process, please refer to the method embodiments, which will not be repeated here.

[0082] Finally, it should be noted that in this document, relational terms such as first, second, third, and fourth are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0083] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A multi-source collaborative optimization scheduling method for industrial parks, characterized in that, The method includes: Based on the comprehensive operation data of the industrial park, a first operation model was established to characterize the adjustment of net output of integrated carbon capture power plants with the fluctuation of renewable energy, and a second operation model was established to characterize the load demand of electrolytic aluminum equipment. Based on the first operating model and the second operating model, the carbon emission quota and actual carbon emissions of the electrolytic aluminum equipment are calculated. Based on the carbon reduction target set by the industrial park, the carbon emission quota, and the actual carbon emission, calculate the carbon trading cost of the industrial park, which includes the carbon reduction cost and the carbon emission cost. An optimal scheduling model for the industrial park is established. The model is subjected to source-load uncertainty optimization with the objective function of minimizing the comprehensive cost including the carbon trading cost and the constraints of park equipment operation as the constraint condition, so as to obtain the optimal scheduling scheme of the industrial park that meets the preset requirements.

2. The method as described in claim 1, characterized in that, The first operating model for establishing an integrated carbon capture power plant includes: The operating energy consumption of the carbon capture equipment is determined based on the preset carbon supply amount of the storage tank in the integrated carbon capture power plant: In the formula, Let t be the operating energy consumption of the carbon capture equipment in the power plant. Energy consumption coefficient of carbon capture equipment; Carbon capture amount; The amount of CO2 supplied to the liquid storage tanks in the power plant; Carbon emission intensity of thermal power units in power plants; This represents the maximum operating condition coefficient for the regeneration tower and compressor in the power plant. , These are the flue gas split ratio and the efficiency of the carbon capture equipment, respectively. CO2 emissions from thermal power units; Let t be the power generation capacity of the thermal power unit at time t; The electro-carbon conversion coefficient; The operational relationship between the carbon capture equipment and the power generation of thermal power units and photovoltaic and wind power equipment in industrial parks is determined based on the aforementioned operating energy consumption: In the formula, To supply power to the carbon capture equipment of thermal power units; This refers to the net power of the thermal power unit. Power of carbon capture equipment; Fixed energy consumption for carbon capture equipment; , The power supplied to the carbon capture equipment is provided for wind power and solar power, respectively; The establishment of the second operating model for the electrolytic aluminum equipment includes: Based on the temperature adjustment cycle of the electrolytic cell, determine the operating relationship between temperature and electrolytic cell current: In the formula, Let be the temperature of electrolytic cell i at time t; , , , , K All are constant coefficients; T is the scheduling period; and These are the upper and lower limits of the electrolytic cell temperature, respectively. Based on the linear relationship between electrolytic cell energy consumption and aluminum production, a second operating model for the aluminum electrolysis equipment is established: In the formula, The electrolytic cell at time t i The power; Electrolytic cell i The voltage; Electrolytic cell i The current; Let be the aluminum production of electrolytic cell i at time t; It is the electricity consumption per ton of aluminum produced.

3. The method as described in claim 2, characterized in that, The calculation of carbon emission quotas and actual carbon emissions for electrolytic aluminum equipment based on the first operating model and the second operating model includes: The carbon emission quota for the electrolytic aluminum equipment is calculated based on the aluminum production of the electrolytic aluminum equipment in the first operating model. The first carbon emission is calculated based on the amount of anodes consumed in the chemical reaction of the aluminum electrolysis equipment. The second carbon emissions from greenhouse gas emissions are calculated based on the anode effect of the electrolytic aluminum equipment in the chemical reaction. Based on the operating power consumption of the integrated carbon capture power plant in the first operating model, the third carbon emissions generated by the electrolytic aluminum equipment from the electricity purchased from the integrated carbon capture power plant and the power grid are determined. The actual carbon emissions of the electrolytic aluminum equipment are obtained by summing the first carbon emissions, the second carbon emissions, and the third carbon emissions.

4. The method as described in claim 2, characterized in that, Based on the industrial park's pre-set carbon reduction target, the carbon emission allowance, and the actual carbon emissions, calculate the industrial park's carbon trading cost, which includes both carbon reduction and carbon emission costs, including: The theoretical number of green certificates required for the electrolytic aluminum equipment is calculated based on the aluminum production capacity of the equipment. The actual number of green certificates for the industrial park is calculated based on the wind power and photovoltaic power generation of the industrial park. The carbon reduction cost incurred by the industrial park when purchasing green certificates is calculated based on the difference between the theoretical number of green certificates and the actual number of green certificates. The carbon reduction amount corresponding to each green certificate is determined based on the difference between the carbon emissions of thermal power units and the carbon emissions generated by renewable energy power generation in the industrial park. The tradable carbon emission allowances for the industrial park are calculated based on the carbon emission allowances, the actual carbon emissions, and the total carbon reduction. By substituting the tradable carbon emission allowances into a pre-defined tiered carbon trading incentive model, the carbon emission cost of the industrial park can be calculated. The carbon trading cost of the industrial park is calculated by summing the green certificate trading cost and the carbon emission cost.

5. The method as described in claim 1, characterized in that, The constraints include operational constraints on the storage tanks of the integrated carbon capture power plant, operational constraints on the power supply equipment, and system power balance constraints.

6. The method as described in claim 5, characterized in that, The optimization scheduling model is subjected to source-load uncertainty optimization processing with the objective function of minimizing the comprehensive cost including the carbon trading cost and the constraints of park equipment operation as the condition, to obtain the optimal scheduling scheme of the industrial park that meets the preset requirements, including: The net load is calculated based on the conventional load of the electrolytic aluminum equipment and the power output of wind turbines and photovoltaic power generation. A fuzzy set of the probability distribution of the net load is then established to describe the fluctuation range of the data. Based on a preset confidence level, the system power balance constraint is relaxed using chance constraint programming to obtain the chance constraint. The opportunity constraints are risk-quantified, and the quantification results are subjected to dual transformation and fuzzy set boundary processing to obtain solvable second-order cone constraints. Solving the second-order cone constraint yields the optimal scheduling scheme and risk value for the industrial park, in order to assess the cost risk under uncertain conditions of renewable resources.

7. A multi-source collaborative optimization scheduling device for industrial parks, characterized in that, The device includes: The modeling module is used to establish a first operating model to characterize the net output adjustment of integrated carbon capture power plants with renewable energy fluctuations and a second operating model to characterize the load demand of electrolytic aluminum equipment, based on the comprehensive operating data of the industrial park. The first calculation module is used to calculate the carbon emission quota and actual carbon emission of the electrolytic aluminum equipment based on the first operating model and the second operating model. The second calculation module is used to calculate the carbon trading cost of the industrial park, which includes the carbon reduction cost and the carbon emission cost, based on the preset carbon reduction amount of the industrial park, the carbon emission quota and the actual carbon emission amount. The optimization module is used to establish an optimal scheduling model for the industrial park. With the goal of minimizing the overall cost including the carbon trading cost and the constraints of park equipment operation, the optimization scheduling model is subjected to source-load uncertainty optimization to obtain the optimal scheduling scheme for the industrial park that meets the preset requirements.

8. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to execute the computer programs stored in the memory to implement the steps of the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the steps of the method according to any one of claims 1-6.

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