A method, apparatus, device, medium and product for optimizing carbon emissions

Abstract

This application discloses a method, apparatus, equipment, medium, and product for optimizing carbon emissions, relating to the field of carbon emission optimization technology. The method includes: obtaining a first decision variable to be optimized; constructing a first objective function based on the first decision variable; taking the first decision variable whose function value is minimized as the locally optimal first decision variable; obtaining a second decision variable to be optimized; constructing a second objective function based on the second decision variable; taking the second decision variable whose function value is minimized as the locally optimal second decision variable; constructing a third objective function based on the locally optimal first decision variable; constructing a third constraint condition based on the locally optimal second decision variable; and solving the third objective function under the third constraint condition to obtain the globally optimal first decision variable and the globally optimal second decision variable, thereby optimizing carbon emissions and improving the accuracy of carbon emission optimization.

Description

Technical Field

[0001] This application relates to the field of carbon emission optimization technology, and in particular to a method, apparatus, equipment, medium and product for optimizing carbon emissions. Background Technology

[0002] Traditional carbon emission optimization methods are mostly based on experience-based judgments, using historical carbon emission data for simple calculations to formulate optimization plans. Specifically, they typically calculate total carbon emissions by statistically analyzing basic data such as energy consumption and production output over a specific period, using fixed conversion factors, and then adjusting energy use structures and production process parameters based on past production experience to achieve carbon emission control targets.

[0003] Traditional carbon emission optimization methods suffer from numerous shortcomings, with insufficient accuracy being particularly prominent. On one hand, the limited scope and precision of historical data mean that fixed conversion factors are difficult to adapt to the actual carbon emissions of different enterprises and under different production conditions, leading to discrepancies between calculated and actual carbon emissions. Consequently, optimization schemes developed based on this discrepancy fail to meet actual needs. On the other hand, relying on experience-based judgment for optimization adjustments makes it difficult to comprehensively consider the interrelationships between various stages of the production process and to respond promptly to external factors such as raw material fluctuations and energy price changes. This results in unstable optimization effects and an inability to effectively control carbon emission levels in the long term. Summary of the Invention

[0004] This application provides a method, apparatus, equipment, medium, and product for optimizing carbon emissions, which can improve the accuracy of carbon emission optimization.

[0005] To achieve the above objectives, this application adopts the following technical solution:

[0006] In a first aspect, this application provides a method for optimizing carbon emissions, the method comprising:

[0007] Obtain the first decision variable to be optimized, which includes the active power output of the c-th thermal power unit during time period h, the actual active power output of the d-th wind farm during time period h, and the actual active power output of the e-th photovoltaic power station during time period h; determine the system operation and maintenance cost based on the first decision variable to be optimized; construct a first objective function based on the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost; and take the first decision variable corresponding to the minimum function value of the first objective function as the local optimal first decision variable.

[0008] Obtain the second decision variable to be optimized, which includes the load change in the h-th time period; based on the second decision variable to be optimized, determine the load adjustment amount of each aggregation unit; based on the load adjustment amount of each aggregation unit and the carbon sensitivity index of each aggregation unit, construct a second objective function; take the second decision variable corresponding to the minimum function value of the second objective function as the local optimal second decision variable;

[0009] Based on the locally optimal first decision variable, the total cost of power generation and the total carbon emission cost of the power grid are determined. Based on the total cost of power generation and the total carbon emission cost of the power grid, a third objective function is constructed. Based on the locally optimal second decision variable, a third constraint condition is constructed.

[0010] Under the third constraint, the third objective function is solved to obtain the globally optimal first decision variable and the globally optimal second decision variable;

[0011] Carbon emissions are optimized based on the globally optimal first decision variable and the globally optimal second decision variable.

[0012] In this application, traditional carbon emission optimization relies on empirical judgment and simple calculations. Because it fails to comprehensively consider the impact of each stage of the system and external factors, it suffers from insufficient accuracy and a disconnect between the optimized scheme and actual operating conditions. This application, through step-by-step optimization and global coordination, derives an effect that improves optimization accuracy: First, the active power output of thermal power units, wind farms, and photovoltaic power plants is obtained as the first decision variable. Combined with multiple carbon emission-related costs such as system operation and maintenance, energy purchase and sale, and carbon sequestration, a first objective function is constructed. Solving this function yields the locally optimal first decision variable, ensuring that the generation-side decision fully reflects the economic and environmental factors in actual operation. Second, load changes are obtained as the second decision variable. Combined with the load adjustment of each aggregation unit and carbon sensitivity indicators, a second objective function is constructed. Solving this function yields the locally optimal second decision variable, ensuring that the load-side decision aligns with the core requirements of carbon emission optimization. Third, based on the above two locally optimal decision variables, a third objective function is constructed that integrates the total generation cost and the total carbon emission cost of the power grid. A third constraint condition is set based on the locally optimal second decision variable, and finally, the globally optimal decision variable is solved. This process comprehensively covers key influencing factors on both the power generation and load sides, avoiding the one-sidedness of experience-based judgments. It can respond promptly to changes in external factors, effectively reduce calculation deviations, and ensure that the optimization scheme is highly consistent with actual operating conditions, thereby improving the accuracy of carbon emission optimization.

[0013] In some possible implementations, the first objective function is:

[0014]

[0015] in, This represents the function value of the first objective function. This indicates the system operation and maintenance cost. Indicates the cost of purchasing and selling energy. Indicates the cost of carbon sequestration. This indicates the penalty cost for abandoning wind and solar power. Indicates the system's carbon emission cost;

[0016]

[0017] in, This indicates the system operation and maintenance cost. This represents the maintenance cost of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the maintenance cost of the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the maintenance cost of the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. H represents the total number of photovoltaic power plants, and H represents the total number of time periods.

[0018]

[0019] in, This represents the gas price in time period h. This represents the gas volume purchased during the h-th time period. Indicates the total number of time periods. This represents the unit electricity purchase price for the h-th time period. This represents the power purchased during the h-th time period. This represents the unit electricity price for the h-th time period. ΔT represents the electricity sales power in the h-th time period, and ΔT represents the duration of each dispatch period;

[0020]

[0021] in, This represents the carbon sequestration cost coefficient. This represents the mass of carbon dioxide sealed during the h-th time period;

[0022]

[0023] in, This indicates the penalty cost per unit for curtailing wind and solar power. This represents the predicted output power of the photovoltaic system during time period h. This represents the actual grid-connected power of photovoltaic power in time period h. This represents the predicted output power of wind power in time period h. This represents the actual grid-connected power of wind power in the h-th time period;

[0024]

[0025] in, Indicates the carbon price coefficient. (h) represents the carbon intensity of power supply in region z during the h-th time period.

[0026] In this application, traditional methods use fixed conversion factors to calculate relevant costs, leading to discrepancies between the calculated results and the actual values, thus affecting the accuracy of optimization decisions. This application clarifies the specific composition of the first objective function and the calculation methods for each cost. Its beneficial effects are derived as follows: The calculation of system operation and maintenance costs distinguishes between the unit maintenance costs of thermal power units, wind farms, and photovoltaic power plants and the active power output at different times, achieving accurate accounting of maintenance costs for different power generation equipment; the energy purchase and sale cost combines gas prices, gas purchase volume, electricity prices, and purchased and sold power at different times, dynamically reflecting the energy transaction costs at different times; carbon sequestration costs, wind and solar curtailment penalty costs, and system carbon emission costs all correspond to clear calculation variables and logic, ensuring that all carbon emission-related costs can be quantified based on actual operating data. The refined calculation of each cost avoids the deviation caused by fixed conversion factors, making the function value of the first objective function more accurately reflect the system operating status, thereby making the locally optimal first decision variable obtained more reasonable, providing reliable power generation-side data support for subsequent global optimization, and further improving the accuracy of carbon emission optimization.

[0027] In some possible implementations, the first constraints corresponding to the first objective function include the first system power balance constraint, the first thermal power unit output upper and lower limit constraint, the first thermal power unit ramp rate constraint, and the first total carbon emission constraint.

[0028] The power balance constraint of the first system is:

[0029]

[0030] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h;

[0031] The upper and lower limits of the output of the first thermal power unit are:

[0032]

[0033] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0034] The gradient rate constraint for the first thermal power unit is:

[0035]

[0036] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0037] The first total carbon emission constraint is:

[0038]

[0039] in, This represents the carbon emission factor of the c-th thermal power unit. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0040] In this application, without effective constraints, the decision variables obtained from solving the first objective function may exceed the actual operating capacity of the system, leading to the inability to execute the optimization scheme or violation of carbon emission control targets. The beneficial effects of the four first constraints set in this application are derived as follows: the first system power balance constraint ensures that the total output of the power generation side matches the total load demand and total network loss of the system in each time period, avoiding power imbalance from affecting the stable operation of the system; the first upper and lower limit constraints and ramp rate constraints of the thermal power unit output define the operating boundaries based on the technical characteristics of the thermal power units, preventing the units from operating beyond the safe technical range; the first total carbon emission constraint clarifies the upper limit of carbon emissions within the optimization period, ensuring that the decisions made by the power generation side comply with the overall carbon emission control requirements. These constraints define reasonable boundaries for solving the first objective function, eliminating decision variables that do not conform to technical specifications and carbon emission targets, making the locally optimal first decision variables both feasible and compliant, avoiding optimization failure due to unreasonable decision variables, and ensuring the accuracy of the overall carbon emission optimization.

[0041] In some possible implementations, the second objective function is:

[0042]

[0043] in, This represents the function value of the second objective function. This represents the load adjustment amount of the u-th aggregation unit in the h-th time period, where U represents the total number of aggregation units. This represents the average process-level carbon intensity of the u-th polymerization unit during the h-th time period;

[0044]

[0045] in, This represents the carbon emission rate of electricity consumption at the k-th process in the e-th node during the h-th time period. This represents the set of processes for the u-th aggregation unit. This represents the power consumption of the k-th process.

[0046] In this application, traditional optimization methods struggle to consider carbon emission characteristics at the process level, resulting in a lack of focus in load-side optimization and impacting overall optimization effectiveness. This application clarifies the calculation method for the second objective function and the average process-level carbon intensity, with the following beneficial effects: The second objective function correlates the load adjustment amount of each aggregation unit with the average process-level carbon intensity, allowing load-side optimization to directly focus on the core influencing factors of carbon emissions; the average process-level carbon intensity is calculated as the ratio of process electricity consumption carbon emission rate to process electricity power, achieving the quantification of carbon emission characteristics at the process level and enabling precise identification of carbon-sensitive load units and processes. This technical solution breaks through the extensive mode of traditional load optimization, enabling the allocation of load adjustments based on process-level carbon emission characteristics, avoiding the blindness of load adjustments. The locally optimal second decision variable obtained is more aligned with the core requirements of carbon emission optimization, providing a refined load-side decision-making basis for global optimization and effectively improving the accuracy of carbon emission optimization.

[0047] In some possible implementations, the second constraint condition corresponding to the second objective function is:

[0048]

[0049] in, This represents the load change during the h-th time period;

[0050]

[0051] in, This represents the minimum load adjustment value of the u-th aggregation unit. This represents the maximum load adjustment value of the u-th aggregation unit.

[0052] In this application, if the allocation of load regulation lacks constraints, the sum of regulation amounts from all aggregation units may not match the system load change, or the regulation amount of a single unit may exceed its actual regulation capacity, leading to the failure of the load-side optimization scheme. The beneficial effects of the two second constraints set in this application are derived as follows: the constraint that "the sum of load regulation amounts from all aggregation units equals the load change in time period h" ensures that load-side regulation can accurately respond to the overall system load demand, avoiding under-regulation or over-regulation; the upper and lower limit constraints on the load regulation amounts of each aggregation unit define boundaries based on the actual regulation capacity of the unit, preventing the regulation amount from exceeding the unit's operating limits. These constraints guarantee the rationality and feasibility of load regulation allocation, ensuring that the solution to the second objective function meets both system demand and unit capacity requirements, avoiding optimization failure due to unreasonable load regulation schemes, providing support for the effectiveness of the locally optimal second decision variable, and thus improving the accuracy of overall carbon emission optimization.

[0053] In some possible implementations, the third objective function is:

[0054]

[0055] in, This represents the function value of the third objective function. This represents the total cost of electricity generation. This represents the carbon price conversion factor. This represents the total carbon emissions from the power grid, and H represents the total number of time periods.

[0056]

[0057] in, This represents the cost coefficient of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the fixed cost coefficient of the c-th thermal power unit. This represents the variable cost coefficient for the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the variable cost coefficient for the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. This represents the total number of photovoltaic power plants, and ΔT represents the duration of each scheduling period.

[0058]

[0059] in, (h) represents the carbon intensity of power supply in region z during time period h;

[0060] The third constraint conditions corresponding to the third objective function are: second system power balance constraint, second thermal power unit output upper and lower limit constraint, second thermal power unit ramp rate constraint, second power flow balance constraint, second line transmission capacity constraint, second node voltage constraint, and second total carbon emission constraint.

[0061] The power balance constraint of the second system is:

[0062]

[0063] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h. This represents the load change during the h-th time period;

[0064] The upper and lower limits of the output of the second thermal power unit are:

[0065]

[0066] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0067] The gradient rate constraint for the second thermal power unit is:

[0068]

[0069] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0070] The second power flow equilibrium constraint is:

[0071]

[0072] in, This represents the line power between node i and node j in time period h. This represents the line susceptance between node i and node j; This represents the voltage phase angle at node i in the h-th time period. This represents the voltage phase angle at node j in the h-th time period;

[0073] The second line transmission capacity constraint is:

[0074]

[0075] This represents the maximum line power between node i and node j;

[0076] The voltage constraint at the second node is:

[0077]

[0078] in, This represents the voltage at node i in the h-th time period. This indicates the lower limit of the node voltage allowed by the system. Indicates the upper limit of the node voltage allowed by the system;

[0079] The second total carbon emission constraint is:

[0080]

[0081] in, This represents the total carbon emissions of the power grid. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0082] In this application, traditional optimization schemes often consider only economic efficiency or carbon emissions, neglecting the requirements for safe grid operation, leading to a disconnect between the optimization scheme and actual operating conditions. This application clarifies a third objective function and a third constraint condition, the beneficial effects of which are derived as follows: The third objective function integrates the total generation cost and the total carbon emissions of the grid, and achieves the unification of the two objectives through the carbon price conversion factor, enabling global optimization to simultaneously consider both economic efficiency and low carbon emissions; the third constraint condition, based on the first constraint condition, adds a second power flow balance constraint, a second line transmission capacity constraint, and a second node voltage constraint, comprehensively covering the core requirements for safe grid operation. This technical solution integrates the local optimal decision results of the generation side and the load side, and supplements the constraints of safe grid operation, ensuring that the global optimal decision variables not only meet the cost and carbon emission targets, but also guarantee the stable operation of the grid, avoiding the problem that the optimization scheme cannot be executed due to neglecting grid safety, comprehensively covering the multi-dimensional needs of system operation, and further improving the accuracy and feasibility of carbon emission optimization.

[0083] Secondly, this application provides a carbon emission optimization device, the device comprising:

[0084] The acquisition module is used to acquire the first decision variable to be optimized, which includes the active power output of the c-th thermal power unit in time period h, the actual active power output of the d-th wind farm in time period h, and the actual active power output of the e-th photovoltaic power station in time period h; based on the first decision variable to be optimized, the system operation and maintenance cost is determined; based on the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost, a first objective function is constructed; the first decision variable corresponding to the minimum function value of the first objective function is taken as the locally optimal first decision variable; the second decision variable to be optimized is acquired, which includes the load change in time period h; based on the second decision variable to be optimized, the load adjustment amount of each aggregation unit is determined; based on the load adjustment amount of each aggregation unit and the carbon sensitivity index of each aggregation unit, a second objective function is constructed; the second decision variable corresponding to the minimum function value of the second objective function is taken as the locally optimal second decision variable;

[0085] The processing module is used to determine the total power generation cost and the total carbon emission cost of the power grid based on the locally optimal first decision variable; to construct a third objective function based on the total power generation cost and the total carbon emission cost of the power grid; to construct a third constraint based on the locally optimal second decision variable; and to solve the third objective function under the third constraint to obtain the globally optimal first decision variable and the globally optimal second decision variable.

[0086] The optimization module is used to optimize carbon emissions based on the globally optimal first decision variable and the globally optimal second decision variable.

[0087] In some possible implementations, the first objective function is:

[0088]

[0089] in, This represents the function value of the first objective function. This indicates the system operation and maintenance cost. Indicates the cost of purchasing and selling energy. Indicates the cost of carbon sequestration. This indicates the penalty cost for abandoning wind and solar power. Indicates the system's carbon emission cost;

[0090]

[0091] in, This indicates the system operation and maintenance cost. This represents the maintenance cost of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the maintenance cost of the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the maintenance cost of the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. H represents the total number of photovoltaic power plants, and H represents the total number of time periods.

[0092]

[0093] in, This represents the gas price in time period h. This represents the gas volume purchased during the h-th time period. Total time period This represents the unit electricity purchase price for the h-th time period. This represents the power purchased during the h-th time period. This represents the unit electricity price for the h-th time period. ΔT represents the electricity sales power in the h-th time period, and ΔT represents the duration of each dispatch period;

[0094]

[0095] in, This represents the carbon sequestration cost coefficient. This represents the mass of carbon dioxide sealed during the h-th time period;

[0096]

[0097] in, This indicates the penalty cost per unit for curtailing wind and solar power. This represents the predicted output power of the photovoltaic system during time period h. This represents the actual grid-connected power of photovoltaic power in time period h. This represents the predicted output power of wind power in time period h. This represents the actual grid-connected power of wind power in the h-th time period;

[0098]

[0099] in, Indicates the carbon price coefficient. (h) represents the carbon intensity of power supply in region z during the h-th time period.

[0100] In some possible implementations, the first constraints corresponding to the first objective function include the first system power balance constraint, the first thermal power unit output upper and lower limit constraint, the first thermal power unit ramp rate constraint, and the first total carbon emission constraint.

[0101] The power balance constraint of the first system is:

[0102]

[0103] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h;

[0104] The upper and lower limits of the output of the first thermal power unit are:

[0105]

[0106] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0107] The gradient rate constraint for the first thermal power unit is:

[0108]

[0109] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0110] The first total carbon emission constraint is:

[0111]

[0112] in, This represents the carbon emission factor of the c-th thermal power unit. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0113] In some possible implementations, the second objective function is:

[0114]

[0115] in, This represents the function value of the second objective function. This represents the load adjustment amount of the u-th aggregation unit in the h-th time period, where U represents the total number of aggregation units. This represents the average process-level carbon intensity of the u-th polymerization unit during the h-th time period;

[0116]

[0117] in, This represents the carbon emission rate of electricity consumption at the k-th process in the e-th node during the h-th time period. This represents the set of processes for the u-th aggregation unit. This represents the power consumption of the k-th process.

[0118] In some possible implementations, the second constraint condition corresponding to the second objective function is:

[0119]

[0120] in, This represents the load change during the h-th time period;

[0121]

[0122] in, This represents the minimum load adjustment value of the u-th aggregation unit. This represents the maximum load adjustment value of the u-th aggregation unit.

[0123] In some possible implementations, the third objective function is:

[0124]

[0125] in, This represents the function value of the third objective function. This represents the total cost of electricity generation. This represents the carbon price conversion factor. This represents the total carbon emissions from the power grid, and H represents the total number of time periods.

[0126]

[0127] in, This represents the cost coefficient of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the fixed cost coefficient of the c-th thermal power unit. This represents the variable cost coefficient for the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the variable cost coefficient for the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. This represents the total number of photovoltaic power plants, and ΔT represents the duration of each scheduling period.

[0128]

[0129] in, (h) represents the carbon intensity of power supply in region z during time period h;

[0130] The third constraint conditions corresponding to the third objective function are: second system power balance constraint, second thermal power unit output upper and lower limit constraint, second thermal power unit ramp rate constraint, second power flow balance constraint, second line transmission capacity constraint, second node voltage constraint, and second total carbon emission constraint.

[0131] The power balance constraint of the second system is:

[0132]

[0133] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h;

[0134] The upper and lower limits of the output of the second thermal power unit are:

[0135]

[0136] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0137] The gradient rate constraint for the second thermal power unit is:

[0138]

[0139] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0140] The second power flow equilibrium constraint is:

[0141]

[0142] in, This represents the line power between node i and node j in time period h. This represents the line susceptance between node i and node j; This represents the voltage phase angle at node i in the h-th time period. This represents the voltage phase angle at node j in the h-th time period;

[0143] The second line transmission capacity constraint is:

[0144]

[0145] This represents the maximum line power between node i and node j;

[0146] The voltage constraint at the second node is:

[0147]

[0148] in, This represents the voltage at node i in the h-th time period. This indicates the lower limit of the node voltage allowed by the system. Indicates the upper limit of the node voltage allowed by the system;

[0149] The second total carbon emission constraint is:

[0150]

[0151] in, This represents the total carbon emissions of the power grid. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0152] Thirdly, this application provides a computing device, including a memory and a processor;

[0153] The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.

[0154] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.

[0155] Fifthly, this application provides a computer program product comprising one or more computer instructions, wherein when the computer instructions are loaded and executed on a computing device, the computing device is configured to perform the method as described in any one of the first aspects.

[0156] As can be seen from the above technical solution, this application has at least the following beneficial effects:

[0157] In this application, traditional carbon emission optimization relies on empirical judgment and simple calculations. Because it fails to comprehensively consider the impact of each stage of the system and external factors, it suffers from insufficient accuracy and a disconnect between the optimized scheme and actual operating conditions. This application, through step-by-step optimization and global coordination, derives an effect that improves optimization accuracy: First, the active power output of thermal power units, wind farms, and photovoltaic power plants is obtained as the first decision variable. Combined with multiple carbon emission-related costs such as system operation and maintenance, energy purchase and sale, and carbon sequestration, a first objective function is constructed. Solving this function yields the locally optimal first decision variable, ensuring that the generation-side decision fully reflects the economic and environmental factors in actual operation. Second, load changes are obtained as the second decision variable. Combined with the load adjustment of each aggregation unit and carbon sensitivity indicators, a second objective function is constructed. Solving this function yields the locally optimal second decision variable, ensuring that the load-side decision aligns with the core requirements of carbon emission optimization. Third, based on the above two locally optimal decision variables, a third objective function is constructed that integrates the total generation cost and the total carbon emission cost of the power grid. A third constraint condition is set based on the locally optimal second decision variable, and finally, the globally optimal decision variable is solved. This process comprehensively covers key influencing factors on both the power generation and load sides, avoiding the one-sidedness of experience-based judgments. It can respond promptly to changes in external factors, effectively reduce calculation deviations, and ensure that the optimization scheme is highly consistent with actual operating conditions, thereby improving the accuracy of carbon emission optimization.

[0158] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description

[0159] Figure 1 A flowchart illustrating a carbon emission optimization method provided in this application embodiment;

[0160] Figure 2 A solution architecture diagram provided for an embodiment of this application;

[0161] Figure 3 A schematic diagram of a carbon emission optimization device provided in an embodiment of this application;

[0162] Figure 4 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation

[0163] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.

[0164] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0165] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first:

[0166] The first decision variable is the relevant parameters on the power generation side to be optimized, including the active power output of the c-th thermal power unit in time period h, the actual active power output of the d-th wind farm in time period h, and the actual active power output of the e-th photovoltaic power station in time period h. These are the basic inputs for constructing the first objective function.

[0167] The second decision variable is the load-side related parameter to be optimized, specifically the load change in the h-th time period, which is used to determine the load adjustment of each aggregation unit and construct the second objective function.

[0168] Local optimal first decision variable: The first decision variable that minimizes the value of the first objective function reflects the optimal operating parameters of the power generation side under a single objective.

[0169] Locally optimal second decision variable: The second decision variable that minimizes the value of the second objective function reflects the optimal adjustment parameter on the load side under a single objective.

[0170] Global optimal decision variables: Under the third constraint, the combination of the first and second decision variables that minimizes the value of the third objective function is the final basis for carbon emission optimization.

[0171] The first objective function is a function that aims to minimize the sum of system operation and maintenance costs, energy purchase and sale costs, carbon sequestration costs, wind and solar curtailment penalty costs, and system carbon emission costs. It is used to solve for the local optimal decision variables on the power generation side.

[0172] The second objective function is a function that aims to minimize the sum of the products of the load adjustment amount of each polymerization unit and the corresponding carbon sensitivity index. It is used to solve for the local optimal decision variables on the load side.

[0173] The third objective function is a function that aims to minimize the sum of the total cost of power generation and the total carbon emissions of the power grid (after conversion by the carbon price factor), and is used to achieve coordinated optimization between the power generation side and the load side.

[0174] System operation and maintenance cost: The total maintenance cost incurred in maintaining the operation of thermal power units, wind farms, and photovoltaic power stations, calculated based on the unit maintenance cost of each power generation equipment and the active power output during the corresponding time period.

[0175] Energy purchase and sale cost: The difference between the system's expenditure on purchasing gas and electricity and the revenue from selling electricity at different times, which is related to the gas price, gas volume purchased, electricity price, and power consumption of the time period.

[0176] Carbon sequestration cost: The cost incurred in sequestering carbon dioxide, calculated based on the carbon sequestration cost coefficient and the mass of carbon dioxide sequestered in each time period.

[0177] Curtailment penalty cost: The penalty cost incurred when the actual grid-connected power of photovoltaic and wind power is lower than the predicted output power. It is related to the unit curtailment penalty cost and the power difference.

[0178] System carbon emission cost: The cost corresponding to carbon emissions related to system power generation, calculated based on carbon price coefficient, regional power supply carbon intensity, and active power output of each power generation device.

[0179] Total power generation cost: All costs incurred during the power generation process of the system, including the variable costs and fixed costs of thermal power units, as well as the variable costs of wind farms and photovoltaic power plants.

[0180] Total carbon emissions from the power grid: The total amount of carbon dioxide emissions generated during the operation of the power grid, calculated based on the regional power supply carbon intensity and the active power output of each power generation device.

[0181] First constraint: Constraints that must be satisfied when solving the first objective function, including the first system power balance constraint, the first thermal power unit output upper and lower limit constraint, the first thermal power unit ramp rate constraint, and the first total carbon emission constraint.

[0182] Second constraint: The constraints that must be satisfied when solving the second objective function include the constraint that the sum of the load adjustment of each aggregation unit is consistent with the load change during the time period, and the upper and lower limit constraints of the load adjustment of each aggregation unit.

[0183] The third constraint is the constraint that must be satisfied when solving the third objective function, including the second system power balance constraint, the second thermal power unit output upper and lower limit constraint, the second thermal power unit ramp rate constraint, the second power flow balance constraint, the second line transmission capacity constraint, the second node voltage constraint, and the second total carbon emission constraint.

[0184] System power balance constraint: The constraint that requires the total power output of the generation side to be balanced with the total load demand and total network loss (including load regulation under the third constraint condition) in each time period.

[0185] Thermal power unit ramp rate constraint: Constraints that limit the variation in active power output of thermal power units in adjacent time periods, including upward ramp rate constraint and downward ramp rate constraint.

[0186] Carbon emission limit: A constraint that limits the maximum amount of carbon emissions allowed by the system within the optimization period.

[0187] Carbon price coefficient: A coefficient used to calculate the carbon emission cost of a system, reflecting the economic cost corresponding to a unit of carbon dioxide emission.

[0188] Carbon sequestration cost factor: A factor used to calculate the cost of carbon sequestration, reflecting the economic cost corresponding to the sequestration of a unit mass of carbon dioxide.

[0189] Carbon intensity of electricity supply: The carbon emission intensity index of region z in the h-th period, reflecting the carbon emissions per unit of electricity supplied in the region during the h-th period.

[0190] Carbon sensitivity index: The average process-level carbon intensity of the u-th polymerization unit in the h-th time period is equal to the ratio of the sum of the carbon emission rates of electricity consumption of each process in the polymerization unit to the sum of the electricity consumption of each process. It is used to characterize the degree of carbon emission correlation of the load unit.

[0191] Aggregation unit: A collection of entities with specific electrical characteristics on the load side (such as enterprises or production lines), which is the basic unit for load regulation allocation.

[0192] Scheduling period duration: The time unit for dividing the optimization cycle, used to calculate the relevant costs, emissions and penalty fees for each period.

[0193] Carbon price conversion factor: A factor used to convert the total carbon emissions of the power grid into economic costs, so as to unify the economic and low-carbon objectives of the third objective function.

[0194] In the field of carbon emission optimization, traditional optimization methods rely on experience-based judgments and formulate optimization plans after simple calculations based on historical carbon emission data. Specifically, the traditional method calculates the total carbon emissions by statistically analyzing basic data such as energy consumption and production output within a specific period, using a fixed conversion factor, and then adjusts energy use structure and production process parameters based on past experience to achieve carbon emission control.

[0195] However, traditional methods have several inherent drawbacks. First, the scope and accuracy of historical data are limited, and fixed conversion factors are difficult to adapt to the actual carbon emissions of different enterprises and under different production conditions. This leads to discrepancies between the calculated carbon emissions and the actual values, and optimization schemes based on these discrepancies cannot meet actual needs. Second, traditional methods rely on experience-based judgment for optimization adjustments, making it difficult to comprehensively consider the interactions between various stages of the production process and to respond promptly to external factors such as raw material fluctuations and energy price changes. This results in unstable optimization effects and an inability to effectively control carbon emission levels in the long term.

[0196] From a technical implementation perspective, traditional methods fail to systematically analyze and optimize key influencing parameters on both the generation and load sides. The active power output of thermal power units, wind farms, and photovoltaic power plants on the generation side, as well as load changes and load regulation of various aggregation units on the load side, are all core parameters affecting carbon emission levels. However, traditional methods do not treat these parameters as specific optimization targets. Furthermore, traditional methods lack a multi-dimensional objective function encompassing system operation and maintenance costs, energy purchase and sale costs, carbon sequestration costs, wind and solar curtailment penalties, and system carbon emission costs. They also fail to set scientifically reasonable constraints, resulting in a lack of systematic and comprehensive optimization, further exacerbating the disconnect between the optimized solution and actual operating conditions.

[0197] To address the aforementioned technical problems, this application proposes a carbon emission optimization method that achieves precise optimization and synergistic coordination between the power generation and load sides, ultimately achieving the goal of carbon emission optimization. Specifically:

[0198] First, addressing the optimization needs on the power generation side, the scope of the first decision variable is defined, and a first objective function is constructed. The first decision variable encompasses the active power output of the c-th thermal power unit during time period h, the actual active power output of the d-th wind farm during time period h, and the actual active power output of the e-th photovoltaic power station during time period h. These variables directly determine the energy consumption and carbon emission levels on the power generation side. Based on this first decision variable, the system operation and maintenance cost is first determined, and then the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost are integrated to construct the first objective function. By solving this objective function, the first decision variable corresponding to the minimum function value is taken as the locally optimal first decision variable. The purpose of this design is to incorporate key parameters affecting carbon emissions on the power generation side into the optimization system, while comprehensively considering various economic costs related to power generation and carbon emission-related costs, avoiding the calculation bias caused by relying solely on single data or empirical judgments in traditional methods, and ensuring that the optimization decisions on the power generation side are data-supported and comprehensive.

[0199] Secondly, to address the load-side optimization requirements, the scope of the second decision variable is defined, and a second objective function is constructed. The second decision variable is the load change in time period h, which directly affects the energy consumption intensity and carbon emission distribution on the load side. Based on this second decision variable, the load adjustment amount of each aggregation unit is determined. Then, combining the load adjustment amount of each aggregation unit with its carbon sensitivity index, the second objective function is constructed. The second decision variable corresponding to the minimum function value is taken as the locally optimal second decision variable. The core objective of this design is to focus on the key adjustment parameters on the load side, combined with the carbon sensitivity characteristics of the aggregation units, to achieve targeted optimization of load adjustment, avoiding the coarse processing of load-side optimization in traditional methods, and enabling load-side decisions to accurately match carbon emission optimization requirements.

[0200] Furthermore, a global collaborative optimization system is constructed to achieve the organic integration of the generation and load sides. Based on the aforementioned locally optimal first decision variables, the total generation cost and the total carbon emission cost of the power grid are determined. A third objective function is then constructed based on this, taking into account both the economic and low-carbon requirements of the generation side. Simultaneously, a third constraint condition is constructed based on the locally optimal second decision variables to ensure that the optimization results of the load side are respected and continued during the global optimization process. Under the constraints of the third constraint condition, the third objective function is solved to obtain the globally optimal first and second decision variables. The purpose of this design is to avoid decision conflicts caused by the independent optimization of the generation and load sides. Through the synergy of the global objective function and the constraint conditions, a comprehensive consideration of both is achieved, ensuring that the optimized scheme not only conforms to the respective operating characteristics of the generation and load sides but also meets the overall carbon emission control requirements of the system.

[0201] Finally, carbon emission optimization is performed based on the globally optimal decision variables. The globally optimal first and second decision variables are applied to the actual carbon emission control process, enabling coordinated action between active power output on the generation side and load regulation on the load side, ultimately achieving carbon emission optimization. The purpose of this design is to transform the results of hierarchical optimization into actionable optimization steps, ensuring a closed loop in the entire optimization process, avoiding a disconnect between optimization decisions and actual implementation, and solving the problem of traditional optimization methods being difficult to implement.

[0202] This application, focusing on key parameters on both the power generation and load sides, constructs objective functions hierarchically, solves for local optima, and then integrates these local results through global collaborative optimization, forming a complete technical path encompassing local optimization, global collaboration, and practical implementation. This application comprehensively considers multiple dimensions affecting carbon emissions, solving the problem of data measurement bias in traditional methods and overcoming the lack of systematic and collaborative optimization decision-making, ultimately achieving scientific management and optimization of carbon emissions.

[0203] To make the technical solution of this application clearer and easier to understand, the technical solution of this application will be described below with reference to the accompanying drawings, such as... Figure 1 As shown, this figure is a flowchart of a carbon emission optimization method provided in an embodiment of this application.

[0204] This method can be executed by a processing device, which can be a terminal or a server. Terminals include, but are not limited to, smartphones, tablets, laptops, personal digital assistants, or smart wearable devices. Servers can be cloud servers, such as central servers in a central cloud computing cluster or edge servers in an edge cloud computing cluster. Alternatively, servers can be located in a local data center. A local data center refers to a data center directly controlled by the user.

[0205] Specifically, the method includes:

[0206] S101. Obtain the first decision variable to be optimized. The first decision variable includes the active power output of the c-th thermal power unit in time period h, the actual active power output of the d-th wind farm in time period h, and the actual active power output of the e-th photovoltaic power station in time period h. Based on the first decision variable to be optimized, determine the system operation and maintenance cost. Based on the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost, construct a first objective function. The first decision variable corresponding to the minimum function value of the first objective function is taken as the local optimal first decision variable.

[0207] The first objective function is:

[0208]

[0209] in, This represents the function value of the first objective function. This indicates the system operation and maintenance cost. Indicates the cost of purchasing and selling energy. Indicates the cost of carbon sequestration. This indicates the penalty cost for abandoning wind and solar power. Indicates the system's carbon emission cost;

[0210]

[0211] in, This indicates the system operation and maintenance cost. This represents the maintenance cost of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the maintenance cost of the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the maintenance cost of the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. H represents the total number of photovoltaic power stations, H represents the total number of time periods, and the h-th time period is the time period from time t-1 to time t.

[0212]

[0213] in, This represents the gas price in time period h. This represents the gas volume purchased during the h-th time period. Indicates the total number of time periods. This represents the unit electricity purchase price for the h-th time period. This represents the power purchased during the h-th time period. This represents the unit electricity price for the h-th time period. ΔT represents the electricity sales power in the h-th time period, and ΔT represents the duration of each dispatch period;

[0214]

[0215] in, This represents the carbon sequestration cost coefficient. This represents the mass of carbon dioxide sealed during the h-th time period;

[0216]

[0217] in, This indicates the penalty cost per unit for curtailing wind and solar power. This represents the predicted output power of the photovoltaic system during time period h. This represents the actual grid-connected power of photovoltaic power in time period h. This represents the predicted output power of wind power in time period h. This represents the actual grid-connected power of wind power in the h-th time period;

[0218]

[0219] in, Indicates the carbon price coefficient. (h) represents the carbon intensity of power supply in region z during the h-th time period.

[0220] The first constraints corresponding to the first objective function include the first system power balance constraint, the first thermal power unit output upper and lower limit constraint, the first thermal power unit ramp rate constraint, and the first total carbon emission constraint.

[0221] The power balance constraint of the first system is:

[0222]

[0223] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h;

[0224] The upper and lower limits of the output of the first thermal power unit are:

[0225]

[0226] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0227] The gradient rate constraint for the first thermal power unit is:

[0228]

[0229] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0230] The first total carbon emission constraint is:

[0231]

[0232] in, This represents the carbon emission factor of the c-th thermal power unit. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0233] in, It can be determined using the following formula:

[0234]

[0235] in, For coefficients less than 1, Indicates the proportion of carbon emissions from electricity. This is expressed as the model's predicted carbon emission rate for the kx-th sector in region z at time t. This indicates the amount of power exchange adjustment between regions.

[0236] in, It can be determined using the following formula:

[0237]

[0238] in, This represents the baseline carbon emission rate of the model at time t for the kx-th sector in region z. The residual term represents the systematic bias caused by differences in statistical caliber, behavioral elasticity, changes in equipment efficiency, spatial heterogeneity, and unmodeled factors. KX represents the set of sectors whose carbon emissions need to be calculated. This application employs a multi-task network structure with a shared temporal encoder and a sector-specific head, learning only the residual term, thereby improving the model's ability to characterize complex spatiotemporal nonlinearities while maintaining physical consistency. It can be represented as Each department Corresponding to a set of parameters ; For the corresponding node at time t carbon concentration; Real-time power at each metering point; This is a real-time electricity price signal; This refers to the amount of heat energy consumed. As an indicator of traffic flow; The term "heating day" or "cooling day" measures the degree of coldness or heat.

[0239] in, It can be determined using the following formula:

[0240]

[0241]

[0242]

[0243]

[0244] in, This represents the carbon emissions from electricity generated in region z at time t. For the region Subregions within; sub-region At any moment Electricity consumption; For at any time Enterprise access point corresponding node The carbon concentration in electricity;

[0245] This represents the amount of carbon emissions from natural gas in region z at time t. sub-region At any moment Gas consumption; This is the net calorific value of the gas; The emission factor of natural gas;

[0246] This represents the carbon emissions from heating in region z at time t; sub-region At any moment Heating supply; Emission factors for heating.

[0247] in, The corresponding loss function is expressed as follows:

[0248]

[0249] KX represents the set of sectors that need to have their carbon emissions calculated. For at any time area No. Measured values ​​of carbon emission rates for each sector The loss function is used to measure the difference between predicted and actual values. A collection of time periods for supervised data;

[0250] The first penalty coefficient, For the region The projected total carbon emission rate for the internal power generation side. Indicates flow into the area through other boundaries. carbon flow rate, Indicates the area flowing out through other boundaries. carbon flow rate, This represents the carbon loss rate within the z-region system.

[0251] This is the second penalty coefficient. For the first Each department in the year Total carbon emissions;

[0252] The third penalty coefficient, Indicates when the predicted value When the value is negative, this term is greater than zero, resulting in a penalty; when the value is positive, this term is zero.

[0253] This is the fourth penalty coefficient. The net emission rate of region z at time t. Represents a collection of power sectors;

[0254] The first, second, third, and fourth penalty coefficients are used to adjust the relative importance of each constraint in the total loss function.

[0255] make sure The bias is <1%. If the model predicts a carbon imbalance, increase... If the annual total deviation is large, increase If a negative prediction occurs, increase If the total of the power sector is equal to... Large deviation, increase After training is complete, save the model parameters. A fixed LSTM residual learning model is obtained. .

[0256] The following is an introduction How to obtain:

[0257]

[0258] In the formula, Let z be the emission rate of the power supply caliber in region z at time t; Indicates at time The total carbon flow rate entering region z from outside the region; Indicates at time The total carbon flow rate from inside region z to outside region z; This indicator represents the net carbon outflow from the regional power system at its physical boundary. It is independent of the choice of accounting caliber and provides a unified and verifiable benchmark for total electricity carbon emissions at the regional level.

[0259] in, and Determined by the following formula:

[0260]

[0261] For at any time From node To the node Carbon flow rate on the line; For at any time From node To the node The carbon flow rate on the line.

[0262]

[0263] For at any time From node To the node Carbon flow rate on the line; For at any time From node To the node The active power on the line; For at any time node The carbon concentration.

[0264] in, It can be obtained by solving the following method:

[0265] like Figure 2 As shown, a directed graph of the power grid and a discrete time axis are defined, and a sparse linear system of equations for the carbon concentration at nodes is established and solved based on the principle of proportional allocation.

[0266] Sparse linear equations:

[0267]

[0268]

[0269] Where i represents the current central node of the calculation; m represents the node. The downstream node that supplies power to it; j indicates the node to which the power is supplied. The upstream node of the power supply; t represents a specific moment on the discrete time axis; Represents the set of power generation nodes in the entire power grid; For at any time From node Flow to Node The active power; For at any time node The active power of the load; For at any time node The active power of network loss; For at any time node carbon concentration; ; ; For at any time node Source-side carbon injection; At any moment node The active power output of the generator unit; For at any time node Marginal carbon factor of the generator set; For at any time Average carbon concentration in terms of carbon loss; It is an M-matrix type sparse coefficient matrix, which is constructed in real time by the current power grid topology, load and network loss, and its elements are derived from the collected power data; The vector on the right is a constant vector; .

[0270] By solving the system of equations, the carbon concentration at the output node is calculated, serving as a unified benchmark data source for the entire carbon monitoring system.

[0271] Due to marginal consistency, the carbon concentration of a power generation node is equal to its marginal carbon factor:

[0272]

[0273] The sparse linear equation system The solution is obtained by iteratively solving a sparse matrix solver based on the preconditional conjugate gradient method to obtain the carbon concentration at each node. The solution process is set to a convergence tolerance of [value missing]. Convergence is determined when the relative norm difference between two consecutive iterations is less than the tolerance. This is due to the coefficient matrix formed under power flow constraints. With the diagonally dominant M-matrix property, this iterative algorithm can guarantee numerical stability and fast convergence.

[0274] The following describes the process of solving the first objective function to obtain the locally optimal first decision variable.

[0275] Parameter initialization phase:

[0276] Particle population size: set at 100-200 particles, each particle corresponding to a feasible solution for the first decision variable, and the particle dimension is the same as the number of the first decision variables (i.e., the number of thermal power units N). c +Number of wind farms N d +Number of photovoltaic power plants N e ).

[0277] Particle position and velocity initialization: Particle positions (corresponding to the active power output of each unit) are randomly generated within specified constraint boundaries. The initial output value of the thermal power unit is... The initial output values ​​of wind farms and photovoltaic power stations are respectively in [0, P]. w,d,max ]、[0,P pv,e,max Uniformly distributed within the range, (P) w,d,max P pv,e,max (To correspond to the maximum technical output of the main body); the initial value of the particle velocity is set to [-V max V max ], where V max The maximum speed threshold is set to 10%-20% of the range of the corresponding decision variable.

[0278] The core parameters of the algorithm are: the initial value of the inertia weight is set to 0.9, and during the iteration process, it decreases linearly according to the formula w(t1)=w max -(w max-w min ) t1 / T max Adjust (w(t1), w max , w min , T max are algorithm-specific parameters, where t1 represents the current iteration number), w min takes the value of 0.4; acceleration coefficients , are both set to 2.0; the maximum number of iterations T max is set to 200 - 300 times, adjusted according to the problem complexity, for example, it can be 260 times in this application. External archive set initialization: Create an external archive set with a capacity of 100 - 150 to store non-dominated solutions, and the initial state of the archive set is empty.

[0279] Non-dominated sorting stage:

[0280] Calculate the first objective function value F1 corresponding to each particle, and check one by one whether the particle satisfies all the first constraint conditions, and eliminate the infeasible particles that do not meet the constraint conditions.

[0281] Perform non-dominated sorting on the feasible particles: Divide the front rank according to the Pareto domination criterion. If the objective function value F1(A) of particle A ≤ F1(B), and A satisfies all the constraint conditions, B does not satisfy or F1(A) < F1(B), then A dominates B; the first rank is the set of optimal particles that are not dominated by other particles, the second rank is the set of particles that are only dominated by the particles in the first rank, and so on.

[0282] Calculate the crowding distance of the particles: Based on the distribution of F1 values of the particles within the same front rank, the formula is , where M = 1 because there is only one objective function F1, are the F1 values of the adjacent particles of particle ix respectively, is the maximum and minimum values of F1 of this front rank, which is used to measure the degree of dispersion of the particles in the solution set.

[0283] Individual and population learning stage:

[0284] Particle velocity update: Update according to the formula, and the formula is:

[0285]

[0286] Among them, represents the velocity vector of the updated particle, represents the velocity vector of the particle before update, and are random numbers in the interval [0, 1], This represents the position corresponding to the best F1 value of particle ix in the history of its best solution. The global optimal solution is the position corresponding to the non-dominated solution with the largest distance from the crowding density of the external archives. This represents the position vector of particle ix at the current iteration number t1, and the acceleration coefficient. , All are set to 2.0.

[0287] The feasible solutions in the updated particles are compared with the existing solutions in the external archive set in terms of dominance. If the new solution is not dominated by any solution in the archive set, it is added to the archive set; if the new solution dominates some solutions in the archive set, the dominated solutions are deleted and the new solution is added. When the size of the archive set exceeds the set capacity (100-150), the solutions are sorted in descending order of crowding distance, and the top N solutions (N is the set capacity) are retained to ensure the diversity and optimality of the archive set.

[0288] Iteration termination condition: When the iteration number t1 reaches the maximum iteration number T max (e.g., 260 times), or the change in the F1 value of the global optimal solution gbest(t1) is less than 1×10 in 20 consecutive iterations. -6 When the iteration stops, the iteration stops. After the iteration terminates, the position vector corresponding to the non-dominated solution with the smallest F1 value in the external archive set is the local optimal first decision variable, including: local optimal thermal power unit output, local optimal wind farm output, and local optimal photovoltaic power station output.

[0289] S102. Obtain the second decision variable to be optimized, which includes the load change in the h-th time period; based on the second decision variable to be optimized, determine the load adjustment amount of each aggregation unit; based on the load adjustment amount of each aggregation unit and the carbon sensitivity index of each aggregation unit, construct a second objective function; take the second decision variable corresponding to the minimum function value of the second objective function as the local optimal second decision variable.

[0290]

[0291] in, This represents the function value of the second objective function. This represents the load adjustment amount of the u-th aggregation unit in the h-th time period, where U represents the total number of aggregation units. This represents the average process-level carbon intensity of the u-th polymerization unit during the h-th time period;

[0292]

[0293] in, This represents the carbon emission rate of electricity consumption at the k-th process in the e-th node during the h-th time period. This represents the set of processes for the u-th aggregation unit. This represents the power consumption of the k-th process in the h-th time period.

[0294] in, It can be determined using the following formula:

[0295]

[0296] in, Indicates the first The set of all sampling times within a given time period. The value estimated at time t is obtained by nonnegative least squares method. No. The power consumption of each process For at any time Enterprise access point corresponding node The carbon concentration.

[0297] in, It can be determined using the following formula:

[0298]

[0299] The second constraint condition corresponding to the second objective function is:

[0300]

[0301] in, This represents the load change during the h-th time period;

[0302]

[0303] in, This represents the minimum load adjustment value of the u-th aggregation unit. This represents the maximum load adjustment value of the u-th aggregation unit.

[0304] in, It can be determined using the following formula:

[0305]

[0306] In the formula, For the first Changes in electricity consumption over a period of time; For the first Changes in the overall electricity price over a given period; For the first The initial comprehensive electricity price for the time period; For the first Initial electricity consumption for the time period.

[0307] The price elasticity of demand matrix E is represented as follows:

[0308]

[0309] Represents the price elasticity of demand, indicating the... The impact of time-of-use electricity price changes on the first The impact of electricity consumption during different time periods, diagonal elements The self-elasticity coefficient indicates that an increase in electricity price during this period will lead to a decrease in electricity consumption during this period; off-diagonal elements. The mutual elasticity coefficient indicates that an increase in electricity prices during the current period will cause users to shift their electricity consumption to other periods.

[0310] The initial consolidated electricity price consists of a base time-of-use electricity price and a carbon internalization electricity price component:

[0311]

[0312] In the formula, This represents the initial comprehensive electricity price for the h-th time period; The base time-of-use electricity price for the h-th time period; This represents the carbon intensity of power supply in region z during time period h; .

[0313] in, It can be determined using the following formula:

[0314]

[0315] In the formula, Indicates the first The set of all sampling times within a given time period; gather The number of sampling times; The region where the company is located The carbon intensity of the power supply at time t.

[0316]

[0317] In the formula, Indicates the area under study; Indicates the region The collection of all internal power transmission lines; For the region In the The carbon intensity of the power supply at any given moment; This represents the active power output of generator node i at time t; For power generation nodes The marginal carbon factor at time t; Let be the active power loss of line (i,j) at time t; Let be the average carbon concentration at time t due to network loss; It represents the set of power generation nodes in the entire power grid.

[0318] Then based on Electricity price adjustment Subsequently, the actual comprehensive electricity price implemented was:

[0319]

[0320] In the formula, The actual comprehensive electricity price for the h-th time period; For the first The adjustment amount of the comprehensive electricity price during a given period can be positive (price increase) or negative (price decrease). Based on the aforementioned comprehensive electricity price, the demand-price elasticity matrix model E is constructed to quantify the guiding effect of electricity price changes on load demand.

[0321] By combining right The specific adjustment instructions for each aggregation unit are obtained by decomposition. The specific method is as follows: An optimization problem is established with the objective of minimizing the change in carbon emissions caused by load adjustment, i.e., the second objective function mentioned above, while satisfying the second constraint condition. By solving this optimization problem, the optimal load adjustment amount for each aggregation unit in time period h can be obtained. The optimization problem aims to minimize the change in carbon emissions caused by load regulation, ensuring that, while meeting the overall system regulation requirements, priority is given to regulating the carbon-sensitive polymer units, thereby achieving the best carbon reduction effect.

[0322] Based on the adjustment amount of each polymerization unit obtained from the decomposition A virtual power plant (VPP) aggregation scheme is constructed. As a unified participant in the electricity market, the virtual power plant's aggregation and regulation capabilities are as follows:

[0323]

[0324] In the formula, The aggregated regulation power of the virtual power plant during time period h; This is the set of load aggregation units contained in a virtual power plant.

[0325] A load feature vector incorporating carbon attributes is constructed and aggregated using a Dynamic Graph Convolutional Network (DGCN). To achieve intelligent aggregation of massive, heterogeneous load resources, this step involves each load node... Construct a multidimensional feature vector , as input to the DGCN model.

[0326]

[0327] In the formula, The power of the node in the h-th time period is calculated by aggregating the real-time power data at each sampling time within that time period; A carbon sensitivity index for nodes; For the node's geographical location; The node response type can be specified, such as interruptible, translatable, or adjustable capacity and speed.

[0328] Load resource model considering spatiotemporal characteristics:

[0329]

[0330] In the formula, Represents the DGCN model function; These are model parameters; The feature matrix of all nodes; Aggregation unit Load adjustment amount during time period h; This represents the prediction error.

[0331] The DGCN model, through learning, is able to identify entities with high carbon sensitivity. Furthermore, geographically proximate and coordinated load groups can be prioritized for adjustment as a unified high-carbon package when the system's carbon potential is high, thereby achieving carbon-oriented precision load management.

[0332] The process of solving the second objective function is described below:

[0333] In this embodiment, a local optimum can be found using linear programming. The solution logic is: within the feasible region satisfying all constraints, the minimum value of the objective function appears at a vertex of the feasible region. Since the coefficients of the objective function represent carbon sensitivity... The optimal solution will prioritize allocating load adjustment to... The largest aggregation unit (i.e., the high-carbon-sensitive unit) is allocated to the next highest-carbon-sensitive units until it reaches its upper limit of regulation capacity, ultimately minimizing the change in total carbon emissions. Output result: The solution obtained. , ,..., The optimal load adjustment for each aggregation unit corresponds to the system-level load change. This is the locally optimal second decision variable.

[0334] S103. Based on the locally optimal first decision variable, determine the total power generation cost and the total carbon emission cost of the power grid; based on the total power generation cost and the total carbon emission cost of the power grid, construct a third objective function; based on the locally optimal second decision variable, construct a third constraint condition.

[0335] The third objective function is:

[0336]

[0337] in, This represents the function value of the third objective function. This represents the total cost of electricity generation. This represents the carbon price conversion factor. This represents the total carbon emissions from the power grid, and H represents the total number of time periods.

[0338]

[0339] in, This represents the cost coefficient of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the fixed cost coefficient of the c-th thermal power unit. This represents the variable cost coefficient for the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the variable cost coefficient for the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. This represents the total number of photovoltaic power plants, and ΔT represents the duration of each scheduling period.

[0340]

[0341] in, (h) represents the carbon intensity of power supply in region z during time period h;

[0342] The third constraint conditions corresponding to the third objective function are: second system power balance constraint, second thermal power unit output upper and lower limit constraint, second thermal power unit ramp rate constraint, second power flow balance constraint, second line transmission capacity constraint, second node voltage constraint, and second total carbon emission constraint.

[0343] The power balance constraint of the second system is:

[0344]

[0345] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h;

[0346]

[0347] in, No. Changes in total system load over a period of time; This represents the set of all virtual power plants (VPPs) in the system. Indicates the first A virtual power plant during the time period The net regulating power of aggregate (positive value indicates increased load, negative value indicates decreased load); This represents the set of all independent load aggregation units that have not been aggregated by any VPP; Indicates the first Individual aggregation units in time period The direct load regulation amount.

[0348] The upper and lower limits of the output of the second thermal power unit are:

[0349]

[0350] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0351] The gradient rate constraint for the second thermal power unit is:

[0352]

[0353] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0354] The second power flow equilibrium constraint is:

[0355]

[0356] in, This represents the line power between node i and node j in time period h. This represents the line susceptance between node i and node j; This represents the voltage phase angle at node i in the h-th time period. This represents the voltage phase angle at node j in the h-th time period;

[0357] The second line transmission capacity constraint is:

[0358]

[0359] This represents the maximum line power between node i and node j;

[0360] The voltage constraint at the second node is:

[0361]

[0362] in, This represents the voltage at node i in the h-th time period. This indicates the lower limit of the node voltage allowed by the system. Indicates the upper limit of the node voltage allowed by the system;

[0363] The second total carbon emission constraint is:

[0364]

[0365] in, This represents the total carbon emissions of the power grid. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0366] S104. Under the third constraint, solve the third objective function to obtain the globally optimal first decision variable and the globally optimal second decision variable.

[0367] In this embodiment, under the third constraint, the process of solving the third objective function is similar to that of solving the first objective function, and will not be repeated here. The difference lies in the initialization phase, where the first and second decision variables used for global optimization need to be initialized near the locally optimal first and second decision variables, respectively. For example, 95% of the original scheme is adopted, and 5% is randomly adjusted; another example is 90% of the original scheme is adopted, and 10% is randomly adjusted.

[0368] S105. Optimize carbon emissions based on the global optimal first decision variable and the global optimal second decision variable.

[0369] After obtaining the globally optimal first decision variable and the globally optimal second decision variable, carbon emissions can be optimized based on these two decision variables.

[0370] In some embodiments, carbon emissions before and after optimization can also be compared. Specifically, the carbon intensity before optimization is calculated. Then, the optimized instructions are executed, and the optimized carbon strength is calculated. Then calculate the change in carbon intensity. ,when At that time, it indicates that the company The carbon intensity per unit product decreases, while carbon efficiency increases.

[0371] in, The calculation method is as follows:

[0372]

[0373] In the formula, For the time period enterprise Carbon intensity per unit product; This represents the set of all sampling times within the h-th time interval; For at any time enterprise Instantaneous carbon emission rate; For at any time enterprise Production.

[0374] At any moment enterprise The instantaneous carbon emission rate is calculated using the following formula:

[0375]

[0376] in, This is a collection of electricity metering points for enterprise e; The set of fossil fuel types used by firm e; This refers to a collection of self-owned generator sets for enterprises. Let mx be the power purchased at time t; The region where the company is located The carbon intensity of the power supply at time t; fuel At any moment Consumption amount; The lower heating value of fuel f; The emission factor for fuel f; For the company's self-provided generator u at any time contribution; The marginal carbon factor of unit u; Instantaneous power generation of rooftop photovoltaic systems for enterprises; Renewable energy equivalent factor; This is an energy storage correction term used to prevent calculation omissions.

[0377]

[0378]

[0379] in, For energy storage correction items; For energy storage at any time The charging and discharging power; For at any time Enterprise access point corresponding node carbon concentration; The weighted average carbon intensity of electricity over historical charging periods; For a set of historical charging time periods; The loop efficiency is (<1); ; The charging power of energy storage.

[0380] In some embodiments, the total carbon emission rate can also be calculated. By discretizing and summing the city's total carbon emission rate within the optimization period H, the total city carbon emissions are obtained. Then, calculate the total urban carbon emissions before optimization. and optimized total urban carbon emissions Then calculate the change in total urban carbon emissions. ,when This indicates a reduction in the city's total carbon emissions.

[0381] In some embodiments, the power supply carbon intensity of region z before optimization in time period h can also be calculated separately. And the power supply carbon intensity of the optimized region z in the h-th time period Then, the change in average carbon intensity is calculated. ,when When this occurs, it indicates a decrease in the carbon intensity of the system.

[0382] Based on the above description, traditional carbon emission optimization relies on empirical judgment and simple calculations. Because it fails to comprehensively consider the impact of each stage of the system and external factors, it suffers from insufficient accuracy and a disconnect between the optimized scheme and actual operating conditions. This application, through step-by-step optimization and global coordination, derives an effect that improves optimization accuracy: First, the active power output of thermal power units, wind farms, and photovoltaic power plants is obtained as the first decision variable. Combined with multiple carbon emission-related costs such as system operation and maintenance, energy purchase and sale, and carbon sequestration, a first objective function is constructed. Solving for this first objective function yields locally optimal first decision variables, ensuring that the generation-side decision fully reflects the economic and environmental factors in actual operation. Second, load changes are obtained as the second decision variable. Combined with the load adjustment of each aggregation unit and carbon sensitivity indicators, a second objective function is constructed. Solving for this second objective function yields locally optimal second decision variables, ensuring that load-side decisions align with the core requirements of carbon emission optimization. Third, based on the above two locally optimal decision variables, a third objective function is constructed that integrates the total generation cost and the total carbon emission cost of the power grid. A third constraint condition is set based on the locally optimal second decision variables, and finally, the globally optimal decision variable is solved. This process comprehensively covers key influencing factors on both the power generation and load sides, avoiding the one-sidedness of experience-based judgments. It can respond promptly to changes in external factors, effectively reduce calculation deviations, and ensure that the optimization scheme is highly consistent with actual operating conditions, thereby improving the accuracy of carbon emission optimization.

[0383] The above text combined Figures 1 to 2The carbon emission optimization method provided in the embodiments of this application has been described in detail. The apparatus and equipment provided in the embodiments of this application will be described below with reference to the accompanying drawings.

[0384] like Figure 3 As shown in the figure, this is a schematic diagram of a carbon emission optimization device provided in an embodiment of this application. The device includes:

[0385] The acquisition module 301 is used to acquire the first decision variable to be optimized, which includes the active power output of the c-th thermal power unit in time period h, the actual active power output of the d-th wind farm in time period h, and the actual active power output of the e-th photovoltaic power station in time period h; based on the first decision variable to be optimized, the system operation and maintenance cost is determined; based on the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost, a first objective function is constructed; the first decision variable corresponding to the minimum function value of the first objective function is taken as the locally optimal first decision variable; the second decision variable to be optimized is acquired, which includes the load change in time period h; based on the second decision variable to be optimized, the load adjustment amount of each aggregation unit is determined; based on the load adjustment amount of each aggregation unit and the carbon sensitivity index of each aggregation unit, a second objective function is constructed; the second decision variable corresponding to the minimum function value of the second objective function is taken as the locally optimal second decision variable;

[0386] Processing module 302 is used to determine the total power generation cost and the total carbon emission cost of the power grid based on the locally optimal first decision variable; construct a third objective function based on the total power generation cost and the total carbon emission cost of the power grid; construct a third constraint based on the locally optimal second decision variable; and solve the third objective function under the third constraint to obtain the globally optimal first decision variable and the globally optimal second decision variable.

[0387] The optimization module 303 is used to optimize carbon emissions based on the global optimal first decision variable and the global optimal second decision variable.

[0388] In some possible implementations, the first objective function is:

[0389]

[0390] in, This represents the function value of the first objective function. This indicates the system operation and maintenance cost. Indicates the cost of purchasing and selling energy. Indicates the cost of carbon sequestration. This indicates the penalty cost for abandoning wind and solar power. Indicates the system's carbon emission cost;

[0391]

[0392] in, This indicates the system operation and maintenance cost. This represents the maintenance cost of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the maintenance cost of the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the maintenance cost of the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. H represents the total number of photovoltaic power plants, and H represents the total number of time periods.

[0393]

[0394] in, This represents the gas price in time period h. This represents the gas volume purchased during the h-th time period. Indicates the total number of time periods. This represents the unit electricity purchase price for the h-th time period. This represents the power purchased during the h-th time period. This represents the unit electricity price for the h-th time period. ΔT represents the electricity sales power in the h-th time period, and ΔT represents the duration of each dispatch period;

[0395]

[0396] in, This represents the carbon sequestration cost coefficient. This represents the mass of carbon dioxide sealed during the h-th time period;

[0397]

[0398] in, This indicates the penalty cost per unit for curtailing wind and solar power. This represents the predicted output power of the photovoltaic system during time period h. This represents the actual grid-connected power of photovoltaic power in time period h. This represents the predicted output power of wind power in time period h. This represents the actual grid-connected power of wind power in the h-th time period;

[0399]

[0400] in, Indicates the carbon price coefficient. (h) represents the carbon intensity of power supply in region z during the h-th time period.

[0401] In some possible implementations, the first constraints corresponding to the first objective function include the first system power balance constraint, the first thermal power unit output upper and lower limit constraint, the first thermal power unit ramp rate constraint, and the first total carbon emission constraint.

[0402] The power balance constraint of the first system is:

[0403]

[0404] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h;

[0405] The upper and lower limits of the output of the first thermal power unit are:

[0406]

[0407] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0408] The gradient rate constraint for the first thermal power unit is:

[0409]

[0410] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0411] The first total carbon emission constraint is:

[0412]

[0413] in, This represents the carbon emission factor of the c-th thermal power unit. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0414] In some possible implementations, the second objective function is:

[0415]

[0416] in, This represents the function value of the second objective function. This represents the load adjustment amount of the u-th aggregation unit in the h-th time period, where U represents the total number of aggregation units. This represents the average process-level carbon intensity of the u-th polymerization unit during the h-th time period;

[0417]

[0418] in, This represents the carbon emission rate of electricity consumption at the k-th process in the e-th node during the h-th time period. This represents the set of processes for the u-th aggregation unit. This represents the power consumption of the k-th process.

[0419] In some possible implementations, the second constraint condition corresponding to the second objective function is:

[0420]

[0421] in, This represents the load change during the h-th time period;

[0422]

[0423] in, This represents the minimum load adjustment value of the u-th aggregation unit. This represents the maximum load adjustment value of the u-th aggregation unit.

[0424] In some possible implementations, the third objective function is:

[0425]

[0426] in, This represents the function value of the third objective function. This represents the total cost of electricity generation. This represents the carbon price conversion factor. This represents the total carbon emissions from the power grid, and H represents the total number of time periods.

[0427]

[0428] in, This represents the cost coefficient of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the fixed cost coefficient of the c-th thermal power unit. This represents the variable cost coefficient for the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the variable cost coefficient for the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. This represents the total number of photovoltaic power plants, and ΔT represents the duration of each scheduling period.

[0429]

[0430] in, (h) represents the carbon intensity of power supply in region z during time period h;

[0431] The third constraint conditions corresponding to the third objective function are: second system power balance constraint, second thermal power unit output upper and lower limit constraint, second thermal power unit ramp rate constraint, second power flow balance constraint, second line transmission capacity constraint, second node voltage constraint, and second total carbon emission constraint.

[0432] The power balance constraint of the second system is:

[0433]

[0434] in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h. This represents the load change during the h-th time period;

[0435] The upper and lower limits of the output of the second thermal power unit are:

[0436]

[0437] in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit;

[0438] The gradient rate constraint for the second thermal power unit is:

[0439]

[0440] This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit;

[0441] The second power flow equilibrium constraint is:

[0442]

[0443] in, This represents the line power between node i and node j in time period h. This represents the line susceptance between node i and node j; This represents the voltage phase angle at node i in the h-th time period. This represents the voltage phase angle at node j in the h-th time period;

[0444] The second line transmission capacity constraint is:

[0445]

[0446] This represents the maximum line power between node i and node j;

[0447] The voltage constraint at the second node is:

[0448]

[0449] in, This represents the voltage at node i in the h-th time period. This indicates the lower limit of the node voltage allowed by the system. Indicates the upper limit of the node voltage allowed by the system;

[0450] The second total carbon emission constraint is:

[0451]

[0452] in, This represents the total carbon emissions of the power grid. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

[0453] The carbon emission optimization apparatus according to the embodiments of this application can correspond to the execution of the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the carbon emission optimization apparatus are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.

[0454] This application also provides a computing device. For example... Figure 4 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.

[0455] The 701 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0456] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).

[0457] The communication interface 703 is used for communication with external devices.

[0458] Memory 704 may include volatile memory, such as random access memory (RAM). Memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0459] The memory 704 stores executable code, which the processor 702 executes to perform the aforementioned carbon emission optimization method.

[0460] Specifically, in achieving Figure 3 In the case of the illustrated embodiment, and Figure 3 When the modules or units of the carbon emission optimization device described in the embodiments are implemented by software, the following steps are performed: Figure 3 The software or program code required for the functions of each module / unit can be partially or entirely stored in memory 704. Processor 702 executes the program code corresponding to each unit stored in memory 704 to perform the aforementioned carbon emission optimization method.

[0461] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive). The computer-readable storage medium includes instructions that instruct the computing device to perform the aforementioned carbon emission optimization method.

[0462] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.

[0463] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.

[0464] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for optimizing carbon emissions. The computer program product can be a software installation package; when any of the aforementioned methods for optimizing carbon emissions is required, the computer program product can be downloaded and executed on the computer.

[0465] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.

[0466] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for optimizing carbon emissions, characterized in that, The method includes: Obtain the first decision variable to be optimized, which includes the active power output of the c-th thermal power unit during time period h, the actual active power output of the d-th wind farm during time period h, and the actual active power output of the e-th photovoltaic power station during time period h; determine the system operation and maintenance cost based on the first decision variable to be optimized; construct a first objective function based on the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost; and take the first decision variable corresponding to the minimum function value of the first objective function as the local optimal first decision variable. Obtain the second decision variable to be optimized, which includes the load change on the load side during time period h; based on the second decision variable to be optimized, determine the load adjustment amount of each aggregation unit; based on the load adjustment amount of each aggregation unit and the carbon sensitivity index of each aggregation unit, construct a second objective function; take the second decision variable corresponding to the minimum function value of the second objective function as the local optimal second decision variable; the aggregation unit refers to a collection of loads with specific electricity consumption characteristics; the second objective function is: in, This represents the function value of the second objective function. This represents the load adjustment amount of the u-th aggregation unit in the h-th time period, where U represents the total number of aggregation units. This represents the average process-level carbon intensity of the u-th polymerization unit during the h-th time period; in, This represents the carbon emission rate of electricity consumption at the k-th process in the e-th node during the h-th time period. This represents the set of processes for the u-th aggregation unit. This represents the power consumption of the k-th process. Based on the locally optimal first decision variable, the total power generation cost and the total carbon emission cost of the power grid are determined. Based on the total power generation cost and the total carbon emission cost of the power grid, a third objective function is constructed. Based on the locally optimal second decision variable, a third constraint condition is constructed. The third objective function is: in, This represents the function value of the third objective function. This represents the total cost of electricity generation. This represents the carbon price conversion factor. This represents the total carbon emissions of the power grid; in, H represents the cost coefficient of the c-th thermal power unit, and H represents the total number of time periods. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the fixed cost coefficient of the c-th thermal power unit. This represents the variable cost coefficient for the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the variable cost coefficient for the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. This represents the total number of photovoltaic power plants, and ΔT represents the duration of each scheduling period. in, (h) represents the carbon intensity of power supply in region z during time period h; The third constraint conditions corresponding to the third objective function are: second system power balance constraint, second thermal power unit output upper and lower limit constraint, second thermal power unit ramp rate constraint, second power flow balance constraint, second line transmission capacity constraint, second node voltage constraint, and second total carbon emission constraint. The power balance constraint of the second system is: in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h. No. Changes in total system load over a period of time; Under the third constraint, the third objective function is solved to obtain the globally optimal first decision variable and the globally optimal second decision variable; Carbon emissions are optimized based on the globally optimal first decision variable and the globally optimal second decision variable.

2. The method according to claim 1, characterized in that, The first objective function is: in, This represents the function value of the first objective function. This indicates the system operation and maintenance cost. Indicates the cost of purchasing and selling energy. Indicates the cost of carbon sequestration. This indicates the penalty cost for abandoning wind and solar power. Indicates the system's carbon emission cost; in, This indicates the system operation and maintenance cost. This represents the maintenance cost of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the maintenance cost of the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the maintenance cost of the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. H represents the total number of photovoltaic power plants, and H represents the total number of time periods. in, This represents the gas price in time period h. This represents the gas volume purchased during the h-th time period. Indicates the total number of time periods. This represents the unit electricity purchase price for the h-th time period. This represents the power purchased during the h-th time period. This represents the unit electricity price for the h-th time period. ΔT represents the electricity sales power in the h-th time period, and ΔT represents the duration of each dispatch period; in, This represents the carbon sequestration cost coefficient. This represents the mass of carbon dioxide sealed during the h-th time period; in, This indicates the penalty cost per unit for curtailing wind and solar power. This represents the predicted output power of the photovoltaic system during time period h. This represents the actual grid-connected power of photovoltaic power in time period h. This represents the predicted output power of wind power in time period h. This represents the actual grid-connected power of wind power in the h-th time period; in, Indicates the carbon price coefficient. (h) represents the carbon intensity of power supply in region z during the h-th time period.

3. The method according to claim 2, characterized in that, The first constraints corresponding to the first objective function include the first system power balance constraint, the first thermal power unit output upper and lower limit constraint, the first thermal power unit ramp rate constraint, and the first total carbon emission constraint. The power balance constraint of the first system is: in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h; The upper and lower limits of the output of the first thermal power unit are constrained as follows: in, This represents the minimum active power output of the c-th thermal power unit. This represents the maximum active power output of the c-th thermal power unit; The gradient rate constraint for the first thermal power unit is: This represents the downward ramp rate of the c-th thermal power unit. This represents the active power output of the c-th thermal power unit during the time period h-1. This represents the upward gradient rate of the c-th thermal power unit; The first total carbon emission constraint is: in, This represents the carbon emission factor of the c-th thermal power unit. This indicates the upper limit of the total carbon emissions allowed by the system during the optimization period.

4. The method according to claim 1, characterized in that, The second constraint condition corresponding to the second objective function is: in, This represents the load change during the h-th time period; in, This represents the minimum load adjustment value of the u-th aggregation unit. This represents the maximum load adjustment value of the u-th aggregation unit.

5. A carbon emission optimization device, characterized in that, The device includes: The acquisition module is used to acquire the first decision variable to be optimized, which includes the active power output of the c-th thermal power unit in time period h, the actual active power output of the d-th wind farm in time period h, and the actual active power output of the e-th photovoltaic power station in time period h; based on the first decision variable to be optimized, the system operation and maintenance cost is determined; based on the system operation and maintenance cost, energy purchase and sale cost, carbon sequestration cost, wind and solar curtailment penalty cost, and system carbon emission cost, a first objective function is constructed; the first decision variable corresponding to the minimum function value of the first objective function is taken as the locally optimal first decision variable; the second decision variable to be optimized is acquired, which includes the load change on the load side in time period h; based on the second decision variable to be optimized, the load adjustment amount of each aggregation unit is determined; based on the load adjustment amount of each aggregation unit and the carbon sensitivity index of each aggregation unit, a second objective function is constructed; the second decision variable corresponding to the minimum function value of the second objective function is taken as the locally optimal second decision variable; the aggregation unit refers to a collection of load-side units with specific electricity consumption characteristics; the second objective function is: in, This represents the function value of the second objective function. This represents the load adjustment amount of the u-th aggregation unit in the h-th time period, where U represents the total number of aggregation units. This represents the average process-level carbon intensity of the u-th polymerization unit during the h-th time period; in, This represents the carbon emission rate of electricity consumption at the k-th process in the e-th node during the h-th time period. This represents the set of processes for the u-th aggregation unit. This represents the power consumption of the k-th process. The processing module is used to determine the total power generation cost and the total carbon emission cost of the power grid based on the locally optimal first decision variable; to construct a third objective function based on the total power generation cost and the total carbon emission cost of the power grid; and to construct a third constraint condition based on the locally optimal second decision variable. The third objective function is: in, This represents the function value of the third objective function. This represents the total cost of electricity generation. This represents the carbon price conversion factor. This represents the total carbon emissions of the power grid; in, H represents the cost coefficient of the c-th thermal power unit, and H represents the total number of time periods. This represents the active power output of the c-th thermal power unit during time period h. This indicates the total number of thermal power units. This represents the fixed cost coefficient of the c-th thermal power unit. This represents the variable cost coefficient for the d-th wind farm. This represents the actual active power output of the d-th wind farm during the time period h. This indicates the total number of wind farms. This represents the variable cost coefficient for the e-th photovoltaic power station. This represents the actual active power output of the e-th photovoltaic power station during time period h. This represents the total number of photovoltaic power plants, and ΔT represents the duration of each scheduling period. in, (h) represents the carbon intensity of power supply in region z during time period h; The third constraint conditions corresponding to the third objective function are: second system power balance constraint, second thermal power unit output upper and lower limit constraint, second thermal power unit ramp rate constraint, second power flow balance constraint, second line transmission capacity constraint, second node voltage constraint, and second total carbon emission constraint. The power balance constraint of the second system is: in, This represents the total load demand of the system in time period h. This represents the total network loss of the system in time period h. No. Changes in total system load over a period of time; Under the third constraint, the third objective function is solved to obtain the globally optimal first decision variable and the globally optimal second decision variable; The optimization module is used to optimize carbon emissions based on the globally optimal first decision variable and the globally optimal second decision variable.

6. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 4.

8. A computer program product, characterized in that, The computer program product includes one or more computer instructions, which, when loaded and executed on a computing device, enable the computing device to perform the method as described in any one of claims 1 to 4.

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