Intelligent power plant load scheduling method based on carbon capture and P2G

Abstract

This invention provides a smart power plant load dispatching method based on carbon capture and P2G, belonging to the field of smart energy technology. Specifically, it includes: constructing load forecasting results based on load influencing factors and obtaining a load adjustment target; setting the minimum unit output target as a second stability threshold when the load adjustment target is less than a first stability threshold; setting the minimum unit output target as a first economic threshold when the load adjustment target is less than the second stability threshold, wherein the first economic threshold is greater than the second stability threshold; using the minimum unit output target and power balance as constraints, considering the economic losses generated by carbon capture revenue, hydrogen production revenue, and the impact of load fluctuations on equipment, and taking the highest economic benefit as the objective function, adjusting the thermal power unit, hydrogen production system, gas production system, and hydrogen-to-electricity system to maximize the economic benefits.

Description

Technical Field

[0001] This invention belongs to the field of smart energy technology, and in particular relates to a smart power plant load dispatching method based on carbon capture and P2G. Background Technology

[0002] Statistics show that coal-fired power plants, which primarily consume fossil fuels such as coal, account for as much as 38.76% of the nation's annual carbon emissions, indicating a significant potential for energy conservation and emission reduction. Employing power-to-gas (P2G) technology to transform high-carbon coal-fired power plants into low-carbon ones can effectively reduce CO2 emissions and convert carbon into methane, which can then be converted into natural gas, holding significant practical importance. Furthermore, carbon capture and storage (CCS) technology provides an effective way to address the cost of P2G carbon feedstock, promoting increased wind power utilization while further enhancing the economic efficiency of system operation.

[0003] To achieve optimized scheduling of thermal power plants based on carbon capture and power-to-gas (P2G), author Li Menghan, in her master's thesis "Low-Carbon Economic Scheduling of Electric-Gas Integrated Energy System Based on Carbon Capture and Demand Response," constructed a two-stage IEGS low-carbon economic scheduling model considering demand response and source-load coordinated emission reduction of integrated flexible carbon capture power plants. Numerical examples demonstrate that coupling the operation of P2G equipment with integrated flexible carbon capture power plants on the source side can achieve energy time shifting, while introducing demand response on the load side achieves the goal of peak shaving and valley filling between sources and loads, providing a positive impact on IEGS low-carbon economic operation. However, the following technical problems exist:

[0004] 1. The minimum stable output constraint of the boiler in the thermal power plant was not considered. When the boiler output reaches a certain limit, it is no longer possible to maintain a stable output, which may lead to an unstable combustion state and ultimately cause the collapse of the stable operating state.

[0005] 2. At the same time, different minimum output adjustment targets were not set for different combustion stages. During the oil-assisted combustion stage, the combustion economy and stability of the boiler are relatively poor. If the minimum output adjustment targets are not set for different stages, the final combustion stability and safety will be relatively low.

[0006] To address the aforementioned technical problems, this invention provides a smart power plant load dispatching method based on carbon capture and P2G. Summary of the Invention

[0007] To achieve the objectives of this invention, the following technical solution is adopted:

[0008] According to one aspect of the present invention, a smart power plant load dispatching method based on carbon capture and P2G is provided.

[0009] A smart power plant load dispatching method based on carbon capture and P2G, characterized in that it specifically includes:

[0010] S1 constructs load forecasting results based on load influencing factors, and obtains load adjustment targets based on the load forecasting results;

[0011] S2 determines whether the load adjustment target is less than the first stability threshold. If so, the minimum output target of the unit is set to the second stability threshold, which is greater than the first stability threshold. The first stability threshold is the minimum stable output of the boiler during the oil-assisted combustion stage, and the second stability threshold is the minimum stable combustion load of the boiler when oil-assisted combustion is not required.

[0012] S3 determines whether the load adjustment target is less than the second stability threshold. If so, the minimum output target of the unit is set to the first economic threshold, which is greater than the second stability threshold.

[0013] S4 takes the minimum output target of the unit and the power balance as constraints, considers the economic losses caused by carbon capture revenue, hydrogen production revenue, and the impact of load fluctuations on the equipment, and takes the highest economic benefit as the objective function to adjust the thermal power unit, the hydrogen production system, the gas production system, and the hydrogen production system.

[0014] By setting the first stability threshold, the second stability threshold, and the first economic threshold, the stability, reliability, and economy of the unit operation are further improved, ensuring that the load control target of the unit is highly economical and that the unit operation is more reliable.

[0015] By factoring in the economic losses resulting from the impact of load fluctuations on equipment, we not only consider the benefits generated by a single load fluctuation, but also take into account the equipment losses caused by load fluctuations. This makes the load regulation objectives more comprehensive and avoids the impact of drastic load fluctuations on equipment.

[0016] A further technical solution is that the load influencing factors include at least wind speed, temperature, and humidity.

[0017] A further technical solution involves the following specific steps in constructing the load forecasting results:

[0018] S21 Based on the aforementioned load forecasting influencing factors, construct the load forecasting input set;

[0019] S22 feeds the input set of the load into a prediction model based on a neural network algorithm to obtain the prediction result;

[0020] S23 constructs load prediction results based on the prediction results.

[0021] A further technical solution involves determining the first economic threshold based on the boiler's load efficiency curve and the impact of load fluctuations on the equipment. Specifically, it is determined based on the position of the boiler's second stability threshold on the load efficiency curve and the impact of load fluctuations on the equipment. The specific calculation formula is as follows:

[0022] P1 = Δ P+P

[0023] in Δ P is the increased load, P is the second stability threshold, and Δ P satisfies the following conditions:

[0024] 0 < Δ P≤P limit

[0025] σP2P1-(B+K1(B+K 2Δ P) ΔP ) Δ P > 0

[0026] Where P limit for Δ The maximum value of P is determined based on the type of boiler, and its value ranges between 2%P3 and 5%P3. P3 is the maximum load of the boiler, P2 is the amount of coal consumption reduced per unit load under load condition P1 compared to load condition P, σ is the unit price of coal consumption, and B is the economic loss generated by the impact of unit load on the equipment when the load fluctuates. Specifically, it is determined by expert scoring. P1 is the first economic threshold, and K1 and K2 are constants.

[0027] By setting a first economic threshold based on the boiler's load efficiency curve and the impact of load fluctuations on the equipment, the load regulation target can not only be maintained above the target value for good stable operation, but also have good economic efficiency, while reducing equipment wear to a minimum.

[0028] A further technical solution is that the formula for calculating the carbon capture benefit is:

[0029]

[0030] in Δ C represents the carbon capture volume, σ1 represents the economic benefit per unit of carbon capture volume, σ2 represents the economic benefit derived from the environmental benefit per unit of carbon capture volume, and the specific value is determined by expert scoring based on local carbon emission policies. K3 is a constant, and C represents the carbon capture benefit.

[0031] By constructing a dynamic carbon capture benefit function, not only the economic benefits of carbon capture are considered, but also the environmental benefits. In order to more objectively reflect the environmental benefits of carbon capture, the greater the amount of carbon captured, the greater the environmental benefit per unit. By setting supplementary terms, the final carbon capture benefit calculation result can promote the capture of more carbon dioxide and have better environmental benefits.

[0032] A further technical solution is that the formula for calculating the revenue from the electro-hydrogen production is as follows:

[0033]

[0034] Where σ3 represents the economic benefit per unit of hydrogen production. Δ H represents the hydrogen production capacity, σ4 represents the economic benefit generated by the unit load under the unit hydrogen production capacity, and K5 and K4 are constants.

[0035] By setting the calculation formula for the revenue of dynamic hydrogen production by electricity, not only is the economic benefit per unit of hydrogen production considered, but also the benefit generated by the load balance of the unit due to the increase in hydrogen production. This promotes the increase in the amount of hydrogen produced by electricity, improves the load balance of the unit, and reduces the volatility of the unit.

[0036] A further technical solution is that when the load adjustment target is greater than the boiler's economic load, the adjustment target is determined based on the power of the hydrogen-to-electricity system and the boiler power under the boiler's economic load.

[0037] A further technical solution includes a heating system, where the specific adjustment steps for load regulation are as follows:

[0038] S31 constructs an operating cost function based on the fuel cost and depreciation cost of the thermal power unit to obtain the operating cost of the thermal power unit;

[0039] S32 constructs a carbon capture revenue function based on the operating cost, depreciation cost, and carbon capture revenue of the carbon capture system to obtain the economic benefits of the carbon capture system.

[0040] S33 constructs a carbon-to-gas revenue function based on the operating cost, depreciation cost, and carbon-to-gas revenue of the electric gas generation system, and obtains the economic benefits of the electric gas generation system.

[0041] S34 constructs an electric hydrogen production revenue function based on the operating cost, depreciation cost, and electric hydrogen production revenue of the electric hydrogen production system, and obtains the economic benefits of the electric hydrogen production system.

[0042] S35 obtains the basic revenue of the unit based on the heating revenue of the heating system and the electricity sales revenue of the thermal power unit;

[0043] S36 constructs a hydrogen power generation revenue function based on the operating cost, depreciation cost, and hydrogen power generation revenue of the hydrogen power generation system to obtain the economic benefits of the hydrogen power generation system;

[0044] S37 constructs a comprehensive load forecast result based on the predicted load result of the heating system and the predicted load result. Based on the comprehensive load forecast result, the minimum processing target of the unit is obtained. With the minimum output target of the unit, power balance, heat balance, and carbon balance as constraints, an optimization model based on a multi-objective differential evolution algorithm is adopted. An economic benefit function is constructed with the basic benefit of the unit, the economic benefit of the hydrogen-to-electricity system, the economic benefit of the electric hydrogen-to-gas system, the economic benefit of the carbon capture system, and the operating cost of the thermal power unit. The objective function is to maximize the economic benefit, and the heating system, thermal power unit, carbon capture system, electric hydrogen-to-electricity system, electric gas-to-gas system, and hydrogen-to-electricity system are adjusted.

[0045] By comprehensively considering various factors, load regulation including the heating system is achieved, which not only makes the operation of the unit safer and more reliable, but also greatly reduces carbon emissions, resulting in good environmental benefits.

[0046] On the other hand, this application provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed in a computer, it causes the computer to execute the above-mentioned smart power plant load dispatching method based on carbon capture and P2G.

[0047] On the other hand, this application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-mentioned smart power plant load dispatching method based on carbon capture and P2G. Attached Figure Description

[0048] The above and other features and advantages of the present invention will become more apparent from a detailed description of exemplary embodiments thereof with reference to the accompanying drawings.

[0049] Figure 1 This is a flowchart of a smart power plant load dispatching method based on carbon capture and P2G according to Example 1.

[0050] Figure 2 This is a framework diagram of a smart power plant load dispatching system based on carbon capture and P2G, according to Embodiment 1.

[0051] Figure 3 This is a framework diagram of a smart power plant load dispatching system based on carbon capture and P2G, which includes a heating system, according to Embodiment 1.

[0052] Figure 4This is a flowchart of a smart power plant load dispatching method based on carbon capture and P2G, which includes a heating system, according to Embodiment 1. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, they are provided so that the invention will be thorough and complete, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore their detailed description will be omitted.

[0054] The terms “a,” “one,” “the,” and “the” are used to indicate the existence of one or more elements / components / etc.; the terms “including” and “having” are used to indicate an open-ended meaning of inclusion and that other elements / components / etc. may exist in addition to the listed elements / components / etc.

[0055] In the original load dispatching model, when the boiler output reaches a certain limit, it is no longer possible to maintain a stable output, which may lead to an unstable combustion state and cause the final stable operation to collapse. At the same time, during the oil-assisted combustion stage, the boiler's combustion economy and stability are relatively poor. Therefore, if the minimum output adjustment target is not set in stages, the final combustion stability and safety will be relatively low.

[0056] Example 1

[0057] To solve the above problems, according to one aspect of the present invention, such as Figure 2 The diagram shown is a framework diagram of a smart power plant load dispatching system based on carbon capture and P2G. Figure 1 As shown, a smart power plant load dispatching method based on carbon capture and P2G is provided, characterized by specifically including:

[0058] S1 constructs load forecasting results based on load influencing factors, and obtains load adjustment targets based on the load forecasting results;

[0059] For example, load influencing factors include factors such as temperature, wind speed, and solar intensity that affect the power generation capacity of new energy sources and the load. Based on combined prediction models, a prediction model is constructed to generate load prediction results.

[0060] S2 determines whether the load adjustment target is less than the first stability threshold. If so, the minimum output target of the unit is set to the second stability threshold, which is greater than the first stability threshold. The first stability threshold is the minimum stable output of the boiler during the oil-assisted combustion stage, and the second stability threshold is the minimum stable combustion load of the boiler when oil-assisted combustion is not required.

[0061] For example, if the load regulation target is 250MW, the first stability threshold is 300MW, and the second stability threshold is 400MW, then the minimum processing target of the unit needs to be set at 400MW.

[0062] S3 determines whether the load adjustment target is less than the second stability threshold. If so, the minimum output target of the unit is set to the first economic threshold, which is greater than the second stability threshold.

[0063] Specifically, if the load regulation target is 350MW, then the minimum processing target of the unit needs to be set at 420MW, which is the first economic threshold.

[0064] S4 takes the minimum output target of the unit and the power balance as constraints, considers the economic losses caused by carbon capture revenue, hydrogen production revenue, and the impact of load fluctuations on the equipment, and takes the highest economic benefit as the objective function to adjust the thermal power unit, the hydrogen production system, the gas production system, and the hydrogen production system.

[0065] Based on load influencing factors, load forecasting results are obtained through a prediction model, and these results are used as load adjustment targets. It is determined whether the load adjustment target is less than the first stability threshold, i.e., whether it is less than the minimum output of the thermal power unit. If so, the minimum processing target of the unit is set to the second stability threshold, i.e., in the non-oil-assisted combustion stage. When the load adjustment target is less than the second stability threshold, the minimum output target of the unit is set to the first economic threshold, i.e., above the second stability value. Based on this, with the minimum output target of the unit and power balance as constraints, considering carbon capture revenue, hydrogen production revenue, and the economic losses resulting from load fluctuations on the equipment, the thermal power unit, hydrogen production system, gas production system, and hydrogen-to-electricity system are adjusted with the objective function of maximizing economic benefits.

[0066] By setting a first economic threshold based on the boiler's load efficiency curve and the impact of load fluctuations on the equipment, the load regulation target can not only be maintained above the target value for good stable operation, but also have good economic efficiency, while reducing equipment wear to a minimum.

[0067] By setting the first stability threshold, the second stability threshold, and the first economic threshold, the stability, reliability, and economy of the unit operation are further improved, ensuring that the load control target of the unit is highly economical and that the unit operation is more reliable.

[0068] By factoring in the economic losses resulting from the impact of load fluctuations on equipment, we not only consider the benefits generated by a single load fluctuation, but also take into account the equipment losses caused by load fluctuations. This makes the load regulation objectives more comprehensive and avoids the impact of drastic load fluctuations on equipment.

[0069] In another possible embodiment, the load influencing factors include at least wind speed, temperature, and humidity.

[0070] In another possible embodiment, the specific steps for constructing the load forecast results are as follows:

[0071] S21 Based on the aforementioned load forecasting influencing factors, construct the load forecasting input set;

[0072] S22 feeds the input set of the load into a prediction model based on a neural network algorithm to obtain the prediction result;

[0073] S23 constructs load prediction results based on the prediction results.

[0074] By setting the load adjustment target to the second stable threshold, i.e. the minimum stable combustion load of the boiler when no oil injection is required for combustion, not only is the load economy of the thermal power unit further improved, but its operational stability is also guaranteed, promoting the reliable and stable operation of the thermal power unit.

[0075] In another possible embodiment, the first economic threshold is determined based on the boiler's load efficiency curve and the impact of load fluctuations on the equipment. Specifically, it is determined based on the position of the boiler's second stability threshold on the load efficiency curve and the impact of load fluctuations on the equipment. The specific calculation formula is as follows:

[0076] P1 = Δ P+P

[0077] in Δ P is the increased load, P is the second stability threshold, and Δ P satisfies the following conditions:

[0078] 0 < Δ P≤P limit

[0079] σP2P1-(B+K1(B+K 2Δ P) ΔP ) Δ P > 0

[0080] Where P limit for Δ The maximum value of P is determined based on the type of boiler, and its value ranges between 2%P3 and 5%P3. P3 is the maximum load of the boiler, P2 is the amount of coal consumption reduced per unit load under load condition P1 compared to load condition P, σ is the unit price of coal consumption, and B is the economic loss generated by the impact of unit load on the equipment when the load fluctuates. Specifically, it is determined by expert scoring. P1 is the first economic threshold, and K1 and K2 are constants.

[0081] In another possible embodiment, the carbon capture benefit is calculated using the following formula:

[0082]

[0083] in Δ C represents the carbon capture volume, σ1 represents the economic benefit per unit of carbon capture volume, σ2 represents the economic benefit derived from the environmental benefit per unit of carbon capture volume, and the specific value is determined by expert scoring based on local carbon emission policies. K3 is a constant, and C represents the carbon capture benefit.

[0084] By constructing a dynamic carbon capture benefit function, not only the economic benefits of carbon capture are considered, but also the environmental benefits. In order to more objectively reflect the environmental benefits of carbon capture, the greater the amount of carbon captured, the greater the environmental benefit per unit. By setting supplementary terms, the final carbon capture benefit calculation result can promote the capture of more carbon dioxide and have better environmental benefits.

[0085] In another possible embodiment, the formula for calculating the benefit of electro-hydrogen production is:

[0086]

[0087] Where σ3 represents the economic benefit per unit of hydrogen production. Δ H represents the hydrogen production capacity, σ4 represents the economic benefit generated by the unit load under the unit hydrogen production capacity, and K5 and K4 are constants.

[0088] By setting the calculation formula for the revenue of dynamic hydrogen production by electricity, not only is the economic benefit per unit of hydrogen production considered, but also the benefit generated by the load balance of the unit due to the increase in hydrogen production. This promotes the increase in the amount of hydrogen produced by electricity, improves the load balance of the unit, and reduces the volatility of the unit.

[0089] In another possible embodiment, when the load adjustment target is greater than the boiler economic load, the adjustment target is determined based on the power of the hydrogen-to-electricity system and the boiler power under the boiler economic load.

[0090] In another possible embodiment, such as Figure 3 The diagram shows a framework of a smart power plant load dispatching system based on carbon capture and P2G, including a heating system. Figure 4 As shown, this also includes the heating system. The specific adjustment steps for load regulation in this case are as follows:

[0091] S31 constructs an operating cost function based on the fuel cost and depreciation cost of the thermal power unit to obtain the operating cost of the thermal power unit;

[0092] For example, the operating costs of thermal power units must consider not only fuel consumption but also depreciation costs. Therefore, both factors must be taken into account when calculating the operating costs of thermal power units.

[0093] For example, the formula for calculating the operating cost of a thermal power unit is:

[0094] f he =f rm +f zj

[0095]

[0096] Among them, P he,i (t) and u m,i (t) represents the electrical power output and operating state variables of the i-th thermal power unit at time t; L 1i L 2i L 3i f is the fuel cost coefficient for the i-th thermal power unit; n1 is the number of thermal power units; zj Let f be the depreciation cost of the thermal power unit, T be the total time, and f be the total time. rm This refers to the fuel cost of thermal power units.

[0097] For a specific example, the fuel cost of the unit is adjusted.

[0098]

[0099] Where Y represents the service life of the thermal power unit, Y S Given the design life of the thermal power unit, when Y is greater than 3 years, the above formula is used to construct the fuel cost of the thermal power unit.

[0100] S32 constructs a carbon capture revenue function based on the operating cost, depreciation cost, and carbon capture revenue of the carbon capture system to obtain the economic benefits of the carbon capture system.

[0101] For example, the operating cost of a carbon capture system includes two parts: raw material cost and power consumption cost. The raw material cost refers to the cost of carbon raw materials that the carbon capture system still needs to purchase in addition to the CO2 captured by CCS when synthesizing methane.

[0102]

[0103] S33 constructs a carbon-to-gas revenue function based on the operating cost, depreciation cost, and carbon-to-gas revenue of the electric gas generation system, and obtains the economic benefits of the electric gas generation system.

[0104] S34 constructs an electric hydrogen production revenue function based on the operating cost, depreciation cost, and electric hydrogen production revenue of the electric hydrogen production system, and obtains the economic benefits of the electric hydrogen production system.

[0105] S35 obtains the basic revenue of the unit based on the heating revenue of the heating system and the electricity sales revenue of the thermal power unit;

[0106] S36 constructs a hydrogen power generation revenue function based on the operating cost, depreciation cost, and hydrogen power generation revenue of the hydrogen power generation system to obtain the economic benefits of the hydrogen power generation system;

[0107] S37 constructs a comprehensive load forecast result based on the predicted load result of the heating system and the predicted load result. Based on the comprehensive load forecast result, the minimum processing target of the unit is obtained. With the minimum output target of the unit, power balance, heat balance, and carbon balance as constraints, an optimization model based on a multi-objective differential evolution algorithm is adopted. An economic benefit function is constructed with the basic benefit of the unit, the economic benefit of the hydrogen-to-electricity system, the economic benefit of the electric hydrogen-to-gas system, the economic benefit of the carbon capture system, and the operating cost of the thermal power unit. The objective function is to maximize the economic benefit, and the heating system, thermal power unit, carbon capture system, electric hydrogen-to-electricity system, electric gas-to-gas system, and hydrogen-to-electricity system are adjusted.

[0108] For a specific example, the multi-objective evolutionary algorithm used in this application can simultaneously optimize multiple objectives. It calculates the satisfaction level of each Pareto solution using fuzzy membership, compares the solutions to obtain the Pareto solution with the highest satisfaction, and obtains the optimal compromise solution among multiple objectives, thus helping operators select the optimal solution. First, the single-objective function value of each individual can be fuzzified according to the following membership function:

[0109]

[0110] Among them, F ξ F is the function value of the ξ-th objective; ξ,min and F ξ,max These represent the upper and lower limits of the ξ-th objective, respectively. The fuzzy single-objective function values ​​are then weighted and summed according to the objective weight preferences, i.e.

[0111]

[0112] Where μ is the satisfaction value and M is the number of objective functions to be optimized.

[0113] By comprehensively considering various factors, load regulation including the heating system is achieved, which not only makes the operation of the unit safer and more reliable, but also greatly reduces carbon emissions, resulting in good environmental benefits.

[0114] Example 2

[0115] This application provides a computer-readable storage medium storing a computer program. When the computer program is executed in a computer, it causes the computer to execute the aforementioned smart power plant load dispatching method based on carbon capture and P2G.

[0116] Example 3

[0117] This application provides a computer program product, characterized in that the computer program product stores instructions, which, when executed by a computer, cause the computer to implement the above-described smart power plant load dispatching method based on carbon capture and P2G.

[0118] In this embodiment of the invention, the term "multiple" refers to two or more, unless otherwise explicitly defined. The terms "install," "connect," and "fix" should be interpreted broadly. For example, "connect" can mean a fixed connection, a detachable connection, or an integral connection. Those skilled in the art can understand the specific meaning of the above terms in this embodiment of the invention based on the specific circumstances.

[0119] In the description of the embodiments of the present invention, it should be understood that the terms "upper" and "lower" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the device or unit referred to must have a specific orientation or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of the present invention.

[0120] In the description of this specification, the terms "an embodiment," "a preferred embodiment," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0121] The above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. For those skilled in the art, the embodiments of the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present invention should be included within the protection scope of the embodiments of the present invention.

Claims

1. A smart power plant load dispatching method based on carbon capture and P2G, characterized in that, Specifically, it includes: S1 constructs load forecasting results based on load influencing factors, and obtains load adjustment targets based on the load forecasting results; S2 determines whether the load adjustment target is less than the first stability threshold. If so, the minimum output target of the unit is set to the second stability threshold, which is greater than the first stability threshold. The first stability threshold is the minimum stable output of the boiler during the oil-assisted combustion stage, and the second stability threshold is the minimum stable combustion load of the boiler when oil-assisted combustion is not required. S3 determines whether the load adjustment target is less than the second stability threshold. If so, the minimum output target of the unit is set to the first economic threshold, which is greater than the second stability threshold. S4 takes the minimum output target of the unit and the power balance as constraints, considers the economic losses caused by carbon capture revenue, hydrogen production revenue, and the impact of load fluctuations on the equipment, and takes the highest economic benefit as the objective function to adjust the thermal power unit, hydrogen production system, gas production system, and hydrogen production system. The first economic threshold is determined based on the boiler's load efficiency curve and the impact of load fluctuations on the equipment. Specifically, it is determined based on the position of the boiler's second stability threshold on the load efficiency curve and the impact of load fluctuations on the equipment. The specific calculation formula is as follows: ； in For the increased load, P is the second stability threshold, and The following conditions must be met: ； Where P limit for The maximum value is determined based on the type of boiler, and the value ranges between 2%P3 and 5%P3, where P3 is the boiler's maximum load, and P2 is the reduction in coal consumption per unit load under load conditions P1 compared to load conditions P. B is the unit price of coal consumption, and B is the economic loss caused by the impact of unit load on equipment during load fluctuations. The specific value is determined by expert scoring. P1 is the first economic threshold, and K1 and K2 are constants.

2. The smart power plant load dispatching method based on carbon capture and P2G as described in claim 1, characterized in that, The load-affecting factors include at least wind speed, temperature, and humidity.

3. The smart power plant load dispatching method based on carbon capture and P2G as described in claim 1, characterized in that, The specific steps for constructing load forecasting results are as follows: S21 Based on the aforementioned load forecasting influencing factors, construct the load forecasting input set; S22 feeds the input set of the load into a prediction model based on a neural network algorithm to obtain the prediction result; S23 constructs load prediction results based on the prediction results.

4. The smart power plant load dispatching method based on carbon capture and P2G as described in claim 1, characterized in that, The formula for calculating the carbon capture revenue is as follows: ； in Carbon capture amount, The economic benefit per unit of carbon capture. The economic benefit is calculated by converting the environmental benefits per unit of carbon capture. The specific amount is determined by expert scoring based on local carbon emission policies. K3 is a constant, and C is the carbon capture benefit.

5. The smart power plant load dispatching method based on carbon capture and P2G as described in claim 1, characterized in that, The formula for calculating the revenue from electro-hydrogen production is as follows: ； in The economic benefit per unit of hydrogen production. For hydrogen production capacity, K5 and K4 represent the economic benefits generated by balancing the unit load per unit hydrogen production, and are constants.

6. The smart power plant load dispatching method based on carbon capture and P2G as described in claim 1, characterized in that, When the load adjustment target is greater than the boiler's economic load, the adjustment target is determined based on the power of the hydrogen-to-electricity system and the boiler power under the boiler's economic load.

7. The smart power plant load dispatching method based on carbon capture and P2G as described in claim 1, characterized in that, This also includes the heating system. The specific adjustment steps for load regulation in this case are as follows: S31 constructs an operating cost function based on the fuel cost and depreciation cost of the thermal power unit to obtain the operating cost of the thermal power unit; S32 constructs a carbon capture revenue function based on the operating cost, depreciation cost, and carbon capture revenue of the carbon capture system to obtain the economic benefits of the carbon capture system. S33 constructs a carbon-to-gas revenue function based on the operating cost, depreciation cost, and carbon-to-gas revenue of the electric gas generation system, and obtains the economic benefits of the electric gas generation system. S34 constructs an electric hydrogen production revenue function based on the operating cost, depreciation cost, and electric hydrogen production revenue of the electric hydrogen production system, and obtains the economic benefits of the electric hydrogen production system. S35 obtains the basic revenue of the unit based on the heating revenue of the heating system and the electricity sales revenue of the thermal power unit; S36 constructs a hydrogen power generation revenue function based on the operating cost, depreciation cost, and hydrogen power generation revenue of the hydrogen power generation system to obtain the economic benefits of the hydrogen power generation system; S37 constructs a comprehensive load forecast result based on the predicted load result of the heating system and the predicted load result. Based on the comprehensive load forecast result, the minimum processing target of the unit is obtained. With the minimum output target of the unit, power balance, heat balance, and carbon balance as constraints, an optimization model based on a multi-objective differential evolution algorithm is adopted. An economic benefit function is constructed with the basic benefit of the unit, the economic benefit of the hydrogen-to-electricity system, the economic benefit of the electric hydrogen-to-gas system, the economic benefit of the carbon capture system, and the operating cost of the thermal power unit. The objective function is to maximize the economic benefit, and the heating system, thermal power unit, carbon capture system, electric hydrogen-to-electricity system, electric gas-to-gas system, and hydrogen-to-electricity system are adjusted.

8. A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform a smart power plant load dispatching method based on carbon capture and P2G as described in any one of claims 1-7.

9. A computer program product, characterized in that, The computer program product stores instructions that, when executed by the computer, cause the computer to implement the smart power plant load dispatching method based on carbon capture and P2G as described in any one of claims 1-7.

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