A multi-energy complementary power generation system optimization scheduling method based on carbon emission minimization

Abstract

The application discloses a kind of multi-energy complementary power generation system optimization scheduling method based on carbon emission minimization, to solve the problems such as insufficient carbon emission reduction pertinence, multi-objective imbalance and multi-constraint coupling conflict of existing scheduling scheme.The method realizes optimization through six-step core process: clear system boundary and build "source-net-load" whole chain carbon flow transfer function, establish dynamic weight driven carbon emission-economic-stability multidimensional objective function, quantize each sub-objective function and multidimensional constraint condition, design carbon flow dominant double-layer nested iteration algorithm and realize scheduling result real-time update output through programmed platform.The application integrates carbon flow dynamic tracking, adaptive weight adjustment and constraint priority mechanism, maximally reduces whole cycle carbon emission under the premise of guaranteeing system safety and stability operation, considers economy and operability, provides technical support for multi-energy complementary power generation system low-carbon and efficient operation, and is suitable for complex energy system scheduling scene containing renewable energy, fossil energy, energy storage and coupling unit.

Description

Technical Field

[0001] This invention relates to the field of power grid dispatching, and more particularly to an optimized dispatching method for multi-energy complementary power generation systems based on minimizing carbon emissions. Background Technology

[0002] Guided by the "dual carbon" goal, the low-carbon transformation of energy systems has become a global consensus. Multi-energy complementary power generation systems, with their advantages of synergistic operation of renewable and fossil fuels, have become the core carrier for reducing carbon emissions and improving energy efficiency. However, this system encompasses intermittent renewable energy sources such as wind and solar power, traditional fossil fuels such as coal and gas power, and coupling and storage units such as power-to-gas (P2G) and energy storage devices. The output characteristics, carbon emission intensity, and operating costs of each energy form differ significantly, leading to the core challenge of "balancing carbon emission reduction targets with system stability and economic efficiency" during dispatch.

[0003] Traditional dispatching methods often prioritize economic efficiency, achieving carbon emission reduction simply by increasing the proportion of renewable energy installed capacity. They lack precise tracking and quantitative control of the entire carbon flow chain from source to grid to load, which can easily lead to problems such as severe wind and solar curtailment, excessive output of high-carbon energy, and insufficient multi-energy coupling and coordination, making it difficult to meet the needs of deep decarbonization.

[0004] With the gradual improvement of carbon market mechanisms and the enhancement of energy digitalization, carbon emission constraints have become a rigid indicator for dispatching decisions, necessitating the construction of an integrated optimized dispatching system that balances carbon emission reduction, economic efficiency, and stability. Existing research has seen some scholars focus on single-dimensional carbon emission reduction optimization, neglecting multi-energy coupling constraints and dynamic operational characteristics; other studies, while introducing multi-objective optimization frameworks, use fixed weight coefficients, failing to adapt to load fluctuations and the randomness of renewable energy output, resulting in insufficient practicality of dispatching schemes. Furthermore, traditional solution algorithms suffer from slow convergence and insufficient solution space exploration when dealing with multi-constraint coupling conflicts, making it difficult to efficiently find the global optimal solution.

[0005] To address this, this paper proposes an optimal scheduling method for multi-energy complementary power generation systems based on minimizing carbon emissions. By constructing a full-chain carbon flow transfer function from source to grid to load, dynamic quantification and tracking of carbon emission intensity are achieved. A multi-dimensional objective function driven by dynamic weights is established, balancing the three optimization objectives through real-time linkage of load characteristics, renewable energy output, and carbon emission reduction demand. Improved global optimization using a genetic algorithm and constraint correction using the interior-point method enhance the solution efficiency and accuracy. Finally, a programmed closed-loop platform enables real-time updating and output of the scheduling scheme, ensuring the timeliness and practicality of the optimization results.

[0006] This method breaks through the limitations of traditional scheduling that prioritizes economics over carbon emissions and focuses on local factors over global impact. It can minimize carbon emissions throughout the entire lifecycle while ensuring the safe and stable operation of the system. It provides technical support for the low-carbon and efficient operation of multi-energy complementary power generation systems and has important theoretical and engineering significance for promoting the deep decarbonization of the energy system and helping to achieve the "dual carbon" goal. Summary of the Invention

[0007] The purpose of this invention is to provide an optimized scheduling method for multi-energy complementary power generation systems based on minimizing carbon emissions, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention adopts the following technical solution: an optimal scheduling method for a multi-energy complementary power generation system based on minimizing carbon emissions, characterized by comprising the following steps to implement the proposed optimal scheduling method for a multi-energy complementary power generation system based on minimizing carbon emissions: S1: Define the physical boundaries of the scheduling system and construct the carbon flow transfer function; S2: Establish a multidimensional objective function that considers carbon emissions, economics, and stability; S3: Calculate each sub-objective function for the multidimensional objective function; S4: Constructing multi-dimensional constraints for a multi-energy complementary power generation system; S5: Design a double-layer nested iterative structure to achieve global optimization for minimizing carbon emissions; S6: Program the above steps and update the solution results in real time; As a further improvement to this technical solution, the following steps are included to clarify the physical boundaries of the scheduling system and construct the carbon flow transfer function: The physical boundary of the dispatch system covers all aspects of energy production (renewable energy and fossil fuel power generation), energy conversion (electricity to gas P2G, gas to electricity G2P), energy storage (electricity storage ES, thermal storage HS), and energy consumption (total end-user load), while the time boundary is the dispatch cycle. The spatial boundary is a multi-energy complementary power generation network within the target area; Carbon transfer functions are used to quantify the carbon emission intensity at each stage of the "source-grid-load" chain, enabling full-chain carbon footprint tracking; power supply unit i Instantaneous carbon emission intensity represents the amount of carbon emissions per unit of power generation: (1) in, f i Indicates energy i The carbon emission coefficient per unit calorific value; m i Indicates energy i Energy conversion efficiency;P i s Indicates power supply i The rated output power; Taking power-to-gas (P2G) conversion as an example, considering the carbon intensity of grid input and the carbon equivalent of conversion losses, the carbon emission intensity formula is as follows: (2) in, e grid This represents the average carbon intensity of electrical energy input into the power grid; or P2G Indicates the efficiency of electro-gas conversion; e loss,P2G This represents the carbon equivalent lost during the P2G conversion process. The combined carbon emission intensity of total terminal load reflects the average carbon footprint of the electricity consumed by the load: (3) in, P i ( t ) represents a unit i At any moment t Actual output, energy storage charging P i ( t The value is negative when released and positive when released. e i ( t ) represents a unit i At any moment t Dynamic carbon emission intensity; P L ( t () indicates time t The total system load is the equivalent electrical load after converting the terminal electrical, heat, and gas loads; As a further improvement to this technical solution, the following steps are included to construct an objective function with minimizing carbon emissions throughout the entire lifecycle as the primary objective: With “minimizing carbon emissions throughout the entire lifecycle” as the primary goal, while taking into account the economic efficiency of system operation and the stability of multi-energy complementarity, a three-dimensional optimization goal of “prioritizing carbon emission reduction, being economically feasible, and being stable and controllable” is achieved through a dynamic weighting mechanism and a carbon intensity correction model. The overall objective function of the multi-energy complementary power generation system optimal scheduling method based on minimizing carbon emissions is expressed as follows: (4) in, F C This represents the total carbon emissions over the entire lifecycle; F E Indicates the economic cost of operating throughout the entire lifecycle; FS This indicates a system stability penalty. oh C ( t ) represents the weight of the carbon emission reduction target. oh E ( t ) represents the weight of economic objectives. oh S ( t ) represents the stability objective weight; satisfying oh C ( t )+ oh E ( t )+ oh S ( t )=1, and oh C ( t ≥0.5 (to ensure priority for carbon emission reduction); As a further improvement to this technical solution, the following steps are included for calculating the sub-objective functions in the overall objective function: 1) Carbon reduction targets with dynamic carbon intensity correction F C Represented as, (5) in, RE FE indicates renewable energy, such as wind power and solar power; FE indicates fossil fuels, such as coal power and gas power. CU Representing coupling units, such as power-to-gas (P2G) and gas-to-electricity (G2P); dynamic correction of carbon intensity of renewable energy. e RE ( t Considering the hidden carbon costs of wind and solar power curtailment, the formula is as follows: (6) in, e RE 0 Indicates the benchmark carbon intensity for renewable energy; α Indicates the carbon penalty coefficient for abandoned energy; P RE,avail ( t () indicates time t Maximum renewable energy generation capacity; P RE,used ( t () indicates time t Actual renewable energy consumption capacity; Furthermore, the dynamic carbon intensity of fossil fuels e i (t ) is represented as: (7) in, e i 0 Indicates the baseline carbon intensity of fossil fuels; β This represents the load sensitivity coefficient; the higher the load, the greater the carbon intensity penalty. c This represents the sensitivity coefficient to the proportion of fossil fuels; the higher the proportion of fossil fuel output, the greater the carbon intensity penalty. P FE,total ( t () indicates time t Total output of fossil fuels; P total ( t () indicates time t Total system output; 2) Economic objective item including cost allocation of coupling units F E Represented as, (8) Cost of coupling unit c i ( t Dynamic allocation, meaning that P2G costs are linked to the amount of renewable energy curtailed, is expressed as: (9) in, c P2G 0 This represents the benchmark unit cost of P2G; d This represents the cost-sharing coefficient for energy curtailment; P RE,surplus ( t () indicates time t Residual power from renewable energy sources; P P2G max This indicates the maximum conversion power of the P2G. 3) Taking into account both output fluctuations and deviations from the optimal energy storage state, and avoiding frequent system adjustments, the stability objective of multi-dimensional fluctuations is quantified. F S Represented as, (10) in, k 1 represents the output fluctuation penalty coefficient; k 2 represents the energy storage deviation penalty coefficient; E i opt Indicates energy storage i Optimal power ( Ei opt =( E i min + E i max ) / 2); DE Indicates the allowable deviation range; As a further improvement to this technical solution, the following steps are included to construct an objective function with minimizing carbon emissions throughout the entire lifecycle as the primary objective: The weighting coefficients are calculated in real time based on a three-dimensional indicator of "load characteristics + renewable energy output + carbon emission reduction demand", as shown in the following formula: (11) in, P L max Indicates the maximum load within the scheduling period; P L min Indicates the minimum load within the scheduling period; P total max This indicates the system's maximum total output. c FE,avg ( t () indicates time t Average unit cost of fossil fuels; c FE,max This represents the maximum unit cost of fossil fuels; As a further improvement to this technical solution, the following steps are included to construct multi-dimensional constraints for a multi-energy complementary power generation system: With "physical feasibility + carbon emission reduction rigidity + multi-energy synergy" as the three core dimensions, carbon flow-related constraints and dynamic coupling constraints are added on the basis of traditional power and capacity constraints to ensure that the optimization results not only meet the system operation safety and carbon emission minimization goals, but also adapt to the dynamic operation characteristics of multi-energy complementary units. The power balance constraint with carbon flow optimization correction is expressed as follows: (12) Introducing carbon flow to regulate power P carbon,adjust ( t Carbon intensity can be achieved by flexibly adjusting power distribution, as shown in the following formula: (13) in, CU in This represents a set of coupled units that consume energy. CU out The set of coupling units representing the output energy;P loss ( t () indicates network loss; e L max Indicates the maximum permissible carbon intensity on the load side; k c Indicates the carbon flow adjustment coefficient; The upper limit for renewable energy output is dynamically adjusted in real time based on carbon intensity; the lower the carbon intensity, the higher the allowed output, thus incentivizing the consumption of clean energy. The renewable energy output constraint is expressed as follows: (14) in, P i avail ( t ) indicates renewable energy i time t Maximum transmit power; k RE This represents the incentive coefficient for renewable energy output; The upper and lower limits of fossil fuel output are linked to carbon intensity; the higher the carbon intensity, the lower the upper limit and the higher the lower limit, thus curbing excessive output of high-carbon energy sources. The fossil fuel output constraint is expressed as follows: (15) in, P i min / P i max Represents fossil energy i Minimum / maximum rated output; e L min Indicates the minimum carbon intensity on the load side; k FE Indicates the power output suppression coefficient of fossil fuels; Dynamic coupling constraints of coupled elements include: P2G element constraints and G2P element constraints; P2G element constraints (electro-electric coordination) are expressed as follows: (16) Dynamic coupling coefficient c P2G ( t The formula is as follows, which adjusts for carbon intensity and gas storage capacity: (17) in, P P2G max This indicates the maximum conversion power of P2G. P RE,load ( tThe power supplied directly to the load by renewable energy sources; G storage ( t This indicates the amount of natural gas stored. G storage max Maximum gas storage capacity; c P2G 0 Represents the P2G reference coupling coefficient; G2P unit constraints (gas-electric coordination) are expressed as follows: (18) Dynamic coupling coefficient c G2P ( t ) is represented as, (19) in, c G2P 0 The G2P reference coupling coefficient, P total max This represents the system's maximum total output. As a further improvement to this technical solution, the following steps are included to design a double-nested iterative structure for global optimization to minimize carbon emissions: With "carbon flow priority, constraint feedback, and adaptive convergence" as the core, a two-layer nested iterative structure is designed: the upper layer uses an improved genetic algorithm (GA) to achieve global optimization to minimize carbon emissions, and the lower layer uses the interior point method (IPM) to handle the coupling conflict of multiple constraints. At the same time, "carbon flow deviation feedback coefficient" and "constraint relaxation adaptive mechanism" are introduced to ensure that the algorithm converges quickly to the global optimum under the premise of satisfying all constraints, taking into account both solution efficiency and optimization accuracy. As a further improvement to this technical solution, the following steps are included to implement an improved genetic algorithm dominated by upper-layer carbon flow: Define the core parameters of the algorithm, including: population size N pop Number of iterations K max Crossover probability p c Probability of mutation p m Initialize the population so that each individual corresponds to a set of unit output matrices within a scheduling cycle. P ( t )=[ P 1( t ), P 2( t ), ..., PN ( t )] T×N This satisfies basic constraints (such as upper and lower limits of output and preliminary conditions for power balance); the upper-level objective function takes minimizing carbon emissions as the sole objective and focuses on the globally optimal output allocation, as shown in the following formula: (20) The fitness function design for carbon flow adaptation incorporates constraint satisfaction weights: (twenty one) in, ΔC carbon = max (0,∑ P i ( t ) e i ( t )- C carbon total ) is a penalty item for exceeding carbon emission standards; ΔP balance = |∑ P in ( t )-∑ P out ( t )- P L ( t | represents the power imbalance penalty term; l 1. l 2 represents the penalty coefficient; For high carbon units ( e i ( t )>0.5 e i max The output gene adopts a "low-carbon biased crossover", as follows: (twenty two) in, α For cross weights, P i p ( t ), P i q ( t To contribute to one's father's cause. P i c ( t To contribute to the next generation; The "carbon intensity-incentivized variation" formula is used to apply to the output genes of renewable energy units, as follows: (twenty three) in, β To vary the time of change, we must ensure that the solution space of the low-carbon region is fully explored.

[0009] As a further improvement to this technical solution, the following steps are included to correct constraint conflicts in lower-level optimization: Define the core parameters of the algorithm and the obstacle parameters. m attenuation coefficient r Precision threshold e ipm Carbon emission reduction rate deviation e carbon Objective function deviation e F The initial optimal solution output by the upper layer. P * ( t There may be constraint conflicts (such as excessive carbon intensity or energy storage exceeding limits). The lower layer is corrected using the interior point method. The objective function is: (twenty four) For carbon strength rigid constraint ( g j ( P ( t ))= e i ( t )- e i max ≤0), constraint barrier function f ( g j ( P ( t Carbon confinement strengthening design: (25) For other constraints, the following conditions are met: (26) in, i carbon This is the carbon constraint weighting coefficient, which strengthens the priority of carbon intensity constraints.

[0010] When a certain constraint is not satisfied, the relaxation factor is dynamically adjusted. s j ( t To avoid the algorithm getting trapped in local optima: (27) in, s j 0The initial relaxation factor, k σ The attenuation coefficient is... K This represents the current iteration number; The modified constraint form is expressed as: (28) Later stages of iteration s j ( t The value approaches 0 to ensure that the constraints are strictly satisfied; As a further improvement to this technical solution, the following steps are included to determine whether the model solution iteration meets the requirements: When the convergence condition is met or the maximum number of iterations is reached... K max At that time, the final optimal scheduling scheme is output: unit output matrix. P opt ( t Total carbon emissions throughout the entire lifecycle F C,opt Carbon strength compliance rate or carbon , (29) in, I (·) is an indicator function. N FE The number of fossil energy units; As a further improvement to this technical solution, the following steps are included to program the above steps and update the solution results in real time: A programmatic solution platform is built using Python or MATLAB. Input parameters are collected in real time through the platform interface and automatically matched to the corresponding mathematical model. Next, a nested solution module combining an improved genetic algorithm and the interior-point method is invoked to perform iterative calculations according to preset parameters, simultaneously outputting intermediate iteration results and constraint satisfaction indices. Subsequently, the optimal scheduling scheme is displayed through a visual interface, and a standardized scheduling report is generated. Finally, a real-time update mechanism with a time step Δt of 15~30 minutes is set up. The latest operating data is collected again at intervals of Δt, triggering a second solution of the model to dynamically correct the output commands. At the same time, an interface with the power grid dispatching system is reserved to support the real-time issuance and execution feedback of dispatching commands, ensuring the timeliness and operability of the optimized scheduling.

[0011] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention discloses an optimized scheduling method for multi-energy complementary power generation systems based on minimizing carbon emissions, aiming to solve problems such as insufficient carbon emission reduction targeting, imbalance of multiple objectives, and coupling conflicts of multiple constraints in existing scheduling schemes. This method achieves optimization through a six-step core process: clarifying the system boundary and constructing a carbon flow transfer function for the entire "source-grid-load" chain; establishing a dynamic weight-driven multi-dimensional objective function of carbon emissions, economy, and stability; quantifying each sub-objective function and multi-dimensional constraints; designing a carbon flow-dominated double-layer nested iterative algorithm; and implementing real-time updates and output of scheduling results through a programmatic platform. This invention incorporates dynamic carbon flow tracking, adaptive weight adjustment, and constraint priority mechanisms, minimizing full-cycle carbon emissions while ensuring the safe and stable operation of the system, balancing economic efficiency and practicality. It provides technical support for the low-carbon and efficient operation of multi-energy complementary power generation systems and is applicable to complex energy system scheduling scenarios containing renewable energy, fossil energy, energy storage, and coupling units.

[0012] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it according to the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings. Specific embodiments of the present invention are given in detail below with reference to the accompanying drawings. Attached Figure Description

[0013] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart of the optimal scheduling method for a multi-energy complementary power generation system based on minimizing carbon emissions, as studied in this invention. Figure 2 These are the output optimization scheduling curves for each unit studied in this invention; Figure 3 This is the carbon emission intensity versus carbon emission curve studied in this invention; Figure 4 This invention compares the renewable energy absorption rates studied. Detailed Implementation

[0014] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are for illustrative purposes only and are not intended to limit the scope of the invention. The invention is described more specifically in the following paragraphs by way of example with reference to the accompanying drawings. The advantages and features of the invention will become clearer from the following description and claims. It should be noted that the drawings are in a very simplified form and use non-precise proportions, and are only used to facilitate and clarify the illustration of the embodiments of the invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0016] This embodiment is based on Figure 1 The flowchart shown is a method for optimizing the scheduling of multi-energy complementary power generation systems based on minimizing carbon emissions. The method is analyzed.

[0017] Please see the appendix Figure 1 A method for optimizing the scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized by the following steps to implement the proposed method: S1: Define the physical boundaries of the scheduling system and construct the carbon flow transfer function; S2: Establish a multidimensional objective function that considers carbon emissions, economics, and stability; S3: Calculate each sub-objective function for the multidimensional objective function; S4: Constructing multi-dimensional constraints for a multi-energy complementary power generation system; S5: Design a double-layer nested iterative structure to achieve global optimization for minimizing carbon emissions; S6: Program the above steps and update the solution results in real time; Furthermore, the following steps are included to define the physical boundaries of the scheduling system and construct the carbon flow transfer function: The physical boundary of the dispatch system covers all aspects of energy production (renewable energy and fossil fuel power generation), energy conversion (electricity to gas P2G, gas to electricity G2P), energy storage (electricity storage ES, thermal storage HS), and energy consumption (total end-user load), while the time boundary is the dispatch cycle. The spatial boundary is a multi-energy complementary power generation network within the target area; Carbon transfer functions are used to quantify the carbon emission intensity at each stage of the "source-grid-load" chain, enabling full-chain carbon footprint tracking; power supply unit i Instantaneous carbon emission intensity represents the amount of carbon emissions per unit of power generation: (1) in, f i Indicates energy i The carbon emission coefficient per unit calorific value; m i Indicates energy i Energy conversion efficiency; P i sIndicates power supply i The rated output power; Taking power-to-gas (P2G) conversion as an example, considering the carbon intensity of grid input and the carbon equivalent of conversion losses, the carbon emission intensity formula is as follows: (2) in, e grid This represents the average carbon intensity of electrical energy input into the power grid; or P2G Indicates the efficiency of electro-gas conversion; e loss,P2G This represents the carbon equivalent lost during the P2G conversion process. The combined carbon emission intensity of total terminal load reflects the average carbon footprint of the electricity consumed by the load: (3) in, P i ( t ) represents a unit i At any moment t Actual output, energy storage charging P i ( t The value is negative when released and positive when released. e i ( t ) represents a unit i At any moment t Dynamic carbon emission intensity; P L ( t () indicates time t The total system load is the equivalent electrical load after converting the terminal electrical, heat, and gas loads; Furthermore, the following steps are included to construct an objective function with minimizing carbon emissions throughout the entire lifecycle as the primary objective: With “minimizing carbon emissions throughout the entire lifecycle” as the primary goal, while taking into account the economic efficiency of system operation and the stability of multi-energy complementarity, a three-dimensional optimization goal of “prioritizing carbon emission reduction, being economically feasible, and being stable and controllable” is achieved through a dynamic weighting mechanism and a carbon intensity correction model. The overall objective function of the multi-energy complementary power generation system optimal scheduling method based on minimizing carbon emissions is expressed as follows: (4) in, F C This represents the total carbon emissions over the entire lifecycle; F E Indicates the economic cost of operating throughout the entire lifecycle; F S This indicates a system stability penalty. ohC ( t ) represents the weight of the carbon emission reduction target. oh E ( t ) represents the weight of economic objectives. oh S ( t ) represents the stability objective weight; satisfying oh C ( t )+ oh E ( t )+ oh S ( t )=1, and oh C ( t ≥0.5 (to ensure priority for carbon emission reduction); Furthermore, the following steps are included for calculating the sub-objective functions in the overall objective function: 1) Carbon reduction targets with dynamic carbon intensity correction F C Represented as, (5) in, RE FE indicates renewable energy, such as wind power and solar power; FE indicates fossil fuels, such as coal power and gas power. CU Representing coupling units, such as power-to-gas (P2G) and gas-to-electricity (G2P); dynamic correction of carbon intensity of renewable energy. e RE ( t Considering the hidden carbon costs of wind and solar power curtailment, the formula is as follows: (6) in, e RE 0 Indicates the benchmark carbon intensity for renewable energy; α Indicates the carbon penalty coefficient for abandoned energy; P RE,avail ( t () indicates time t Maximum renewable energy generation capacity; P RE,used ( t () indicates time t Actual renewable energy consumption capacity; Furthermore, the dynamic carbon intensity of fossil fuels e i ( t ) is represented as: (7) in, e i 0 Indicates the baseline carbon intensity of fossil fuels; β This represents the load sensitivity coefficient; the higher the load, the greater the carbon intensity penalty. c This represents the sensitivity coefficient to the proportion of fossil fuels; the higher the proportion of fossil fuel output, the greater the carbon intensity penalty. P FE,total ( t () indicates time t Total output of fossil fuels; P total ( t () indicates time t Total system output; 2) Economic objective item including cost allocation of coupling units F E Represented as, (8) Cost of coupling unit c i ( t Dynamic allocation, meaning that P2G costs are linked to the amount of renewable energy curtailed, is expressed as: (9) in, c P2G 0 This represents the benchmark unit cost of P2G; d This represents the cost-sharing coefficient for energy curtailment; P RE,surplus ( t () indicates time t Residual power from renewable energy sources; P P2G max This indicates the maximum conversion power of the P2G. 3) Taking into account both output fluctuations and deviations from the optimal energy storage state, and avoiding frequent system adjustments, the stability objective of multi-dimensional fluctuations is quantified. F S Represented as, (10) in, k 1 represents the output fluctuation penalty coefficient; k 2 represents the energy storage deviation penalty coefficient; E i opt Indicates energy storage i Optimal power ( E i opt =( Ei min + E i max ) / 2); DE Indicates the allowable deviation range; Furthermore, the following steps are included to construct an objective function with minimizing carbon emissions throughout the entire lifecycle as the primary objective: The weighting coefficients are calculated in real time based on a three-dimensional indicator of "load characteristics + renewable energy output + carbon emission reduction demand", as shown in the following formula: (11) in, P L max Indicates the maximum load within the scheduling period; P L min Indicates the minimum load within the scheduling period; P total max This indicates the system's maximum total output. c FE,avg ( t () indicates time t Average unit cost of fossil fuels; c FE,max This represents the maximum unit cost of fossil fuels; Furthermore, the following steps are included to construct multi-dimensional constraints for a multi-energy complementary power generation system: With "physical feasibility + carbon emission reduction rigidity + multi-energy synergy" as the three core dimensions, carbon flow-related constraints and dynamic coupling constraints are added on the basis of traditional power and capacity constraints to ensure that the optimization results not only meet the system operation safety and carbon emission minimization goals, but also adapt to the dynamic operation characteristics of multi-energy complementary units. The power balance constraint with carbon flow optimization correction is expressed as follows: (12) Introducing carbon flow to regulate power P carbon,adjust ( t Carbon intensity can be achieved by flexibly adjusting power distribution, as shown in the following formula: (13) in, CU in This represents a set of coupled units that consume energy. CU out The set of coupling units representing the output energy; P loss ( t () indicates network loss; eL max Indicates the maximum permissible carbon intensity on the load side; k c Indicates the carbon flow adjustment coefficient; The upper limit for renewable energy output is dynamically adjusted in real time based on carbon intensity; the lower the carbon intensity, the higher the allowed output, thus incentivizing the consumption of clean energy. The renewable energy output constraint is expressed as follows: (14) in, P i avail ( t ) indicates renewable energy i time t Maximum transmit power; k RE This represents the incentive coefficient for renewable energy output; The upper and lower limits of fossil fuel output are linked to carbon intensity; the higher the carbon intensity, the lower the upper limit and the higher the lower limit, thus curbing excessive output of high-carbon energy sources. The fossil fuel output constraint is expressed as follows: (15) in, P i min / P i max Represents fossil energy i Minimum / maximum rated output; e L min Indicates the minimum carbon intensity on the load side; k FE Indicates the power output suppression coefficient of fossil fuels; Dynamic coupling constraints of coupled elements include: P2G element constraints and G2P element constraints; P2G element constraints (electro-electric coordination) are expressed as follows: (16) Dynamic coupling coefficient c P2G ( t The formula is as follows, which adjusts for carbon intensity and gas storage capacity: (17) in, P P2G max This indicates the maximum conversion power of P2G. P RE,load ( t The power supplied directly to the load by renewable energy sources; G storage ( tThis indicates the amount of natural gas stored. G storage max Maximum gas storage capacity; c P2G 0 Represents the P2G reference coupling coefficient; G2P unit constraints (gas-electric coordination) are expressed as follows: (18) Dynamic coupling coefficient c G2P ( t ) is represented as, (19) in, c G2P 0 The G2P reference coupling coefficient, P total max This represents the system's maximum total output. Furthermore, the following steps are included for designing a two-level nested iterative structure to achieve global optimization for minimizing carbon emissions: With "carbon flow priority, constraint feedback, and adaptive convergence" as the core, a two-layer nested iterative structure is designed: the upper layer uses an improved genetic algorithm (GA) to achieve global optimization to minimize carbon emissions, and the lower layer uses the interior point method (IPM) to handle the coupling conflict of multiple constraints. At the same time, "carbon flow deviation feedback coefficient" and "constraint relaxation adaptive mechanism" are introduced to ensure that the algorithm converges quickly to the global optimum under the premise of satisfying all constraints, taking into account both solution efficiency and optimization accuracy. Furthermore, the following steps are included for implementing an improved genetic algorithm dominated by upper-layer carbon flow: Define the core parameters of the algorithm, including: population size N pop Number of iterations K max Crossover probability p c Probability of mutation p m Initialize the population so that each individual corresponds to a set of unit output matrices within a scheduling cycle. P ( t )=[ P 1( t ), P 2( t ), ..., P N ( t )] T×NThis satisfies basic constraints (such as upper and lower limits of output and preliminary conditions for power balance); the upper-level objective function takes minimizing carbon emissions as the sole objective and focuses on the globally optimal output allocation, as shown in the following formula: (20) The fitness function design for carbon flow adaptation incorporates constraint satisfaction weights: (twenty one) in, ΔC carbon = max (0,∑ P i ( t ) e i ( t )- C carbon total ) is a penalty item for exceeding carbon emission standards; ΔP balance = |∑ P in ( t )-∑ P out ( t )- P L ( t | represents the power imbalance penalty term; l 1. l 2 represents the penalty coefficient; For high carbon units ( e i ( t )>0.5 e i max The output gene adopts a "low-carbon biased crossover", as follows: (twenty two) in, α For cross weights, P i p ( t ), P i q ( t To contribute to one's father's cause. P i c ( t To contribute to the next generation; The "carbon intensity-incentivized variation" formula is used to apply to the output genes of renewable energy units, as follows: (twenty three) in, β To vary the time of change, we must ensure that the solution space of the low-carbon region is fully explored.

[0018] Furthermore, the following steps are included to correct constraint conflicts for lower-level optimization: Define the core parameters of the algorithm and the obstacle parameters. m attenuation coefficient r Precision threshold e ipm Carbon emission reduction rate deviation e carbon Objective function deviation e F The initial optimal solution output by the upper layer. P * ( t There may be constraint conflicts (such as excessive carbon intensity or energy storage exceeding limits). The lower layer is corrected using the interior point method. The objective function is: (twenty four) For carbon strength rigid constraint ( g j ( P ( t ))= e i ( t )- e i max ≤0), constraint barrier function f ( g j ( P ( t Carbon confinement strengthening design: (25) For other constraints, the following conditions are met: (26) in, i carbon This is the carbon constraint weighting coefficient, which strengthens the priority of carbon intensity constraints.

[0019] When a certain constraint is not satisfied, the relaxation factor is dynamically adjusted. s j ( t To avoid the algorithm getting trapped in local optima: (27) in, s j 0 The initial relaxation factor, k σ The attenuation coefficient is...K This represents the current iteration number; The modified constraint form is expressed as: (28) Later stages of iteration s j ( t The value approaches 0 to ensure that the constraints are strictly satisfied; Furthermore, the following steps are included to determine whether the model solution iterations meet the requirements: When the convergence condition is met or the maximum number of iterations is reached... K max At that time, the final optimal scheduling scheme is output: unit output matrix. P opt ( t Total carbon emissions throughout the entire lifecycle F C,opt Carbon strength compliance rate or carbon , (29) in, I (·) is an indicator function. N FE The number of fossil energy units; Furthermore, the following steps are included to program the above steps and update the output with the solution results in real time: A programmatic solution platform is built using Python or MATLAB. Input parameters are collected in real time through the platform interface and automatically matched to the corresponding mathematical model. Next, a nested solution module combining an improved genetic algorithm and the interior-point method is invoked to perform iterative calculations according to preset parameters, simultaneously outputting intermediate iteration results and constraint satisfaction indices. Subsequently, the optimal scheduling scheme is displayed through a visual interface, and a standardized scheduling report is generated. Finally, a real-time update mechanism with a time step Δt of 15~30 minutes is set up. The latest operating data is collected again at intervals of Δt, triggering a second solution of the model to dynamically correct the output commands. At the same time, an interface with the power grid dispatching system is reserved to support the real-time issuance and execution feedback of dispatching commands, ensuring the timeliness and operability of the optimized scheduling.

[0020] This embodiment takes a multi-energy complementary power generation system in a certain region as the research object, with a scheduling cycle of 24 hours (time step of 1 hour). The system includes: wind power (W), photovoltaic (PV), coal power (C), gas power (G), energy storage (ES), and power-to-gas (P2G) units. The core objective is to verify that the proposed scheduling method achieves the effects of increasing renewable energy consumption, optimizing fossil energy output, and reducing system carbon intensity under the premise of minimizing carbon emissions. Basic parameters: Maximum output of wind power / solar power: 1000kW, 800kW; Upper and lower limits of coal power / gas power output: 200~1500kW, 100~1000kW; Energy storage capacity: 0~1000kWh, charge and discharge efficiency 0.9; Maximum P2G conversion power: 500kW; Baseline carbon intensity of coal power: 0.82kgCO2 / kWh, gas power: 0.41kgCO2 / kWh, wind power / solar power close to 0; Load curve: Peak at 8 am and 6 pm (1800kW), trough at 3 am (800kW).

[0021] Please see the appendix Figure 2 The figure shows the 24-hour output changes and load matching of each unit after optimized scheduling: wind power / solar power output varies with natural conditions (solar power only outputs during the day, while wind power output is relatively stable at night), reaching its peak around noon to maximize the absorption of renewable energy; coal power moderately increases output during peak load periods (8 am and 6 pm), but the overall output is significantly lower than traditional scheduling (average reduction of about 30%), gas power serves as a peak-shaving supplement with stable output; energy storage charges during peak renewable energy output (10 am-2 pm) and discharges during peak load / renewable energy off-peak periods (8 am and 6 pm), achieving "peak shaving and valley filling"; P2G operates only during the midday renewable energy surplus period, consuming excess wind power / solar power output and further improving the absorption rate.

[0022] Please see the appendix Figure 3 Carbon emissions drop to their lowest point (approximately 280 kg CO2) between 10:00 AM and 2:00 PM, corresponding to the peak output of renewable energy and the lowest output of fossil fuels. Emissions rise slightly during peak load periods (8:00 AM and 6:00 PM), but remain lower than traditional dispatching. The optimized carbon intensity is lower than traditional dispatching throughout the day, with an average reduction of approximately 35% (traditional dispatching averages 0.82 kg CO2 / kWh, optimized method 0.53 kg CO2 / kWh), validating the carbon reduction effect of the proposed method. Carbon intensity changes are negatively correlated with renewable energy output; the carbon intensity drops to 0.38 kg CO2 / kWh when renewable energy output is highest at midday, reaching the optimal level for the day.

[0023] Please see the appendix Figure 4The figure compares the renewable energy absorption rate of traditional dispatching and the proposed optimized dispatching method: Under traditional dispatching, the renewable energy absorption rate is only 65.4%, with a large amount of wind / solar power being abandoned due to output fluctuations and insufficient system regulation capacity; after optimized dispatching, the absorption rate increases to 98.5%, and through multi-energy complementary means such as energy storage charging and discharging and P2G conversion, almost all renewable energy is absorbed; the significant improvement in absorption rate is the core reason for the reduction in carbon emissions, verifying the effectiveness of the proposed method in promoting the absorption of clean energy.

[0024] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Those skilled in the art can readily implement the present invention based on the accompanying drawings and the above description. However, any modifications, alterations, or variations made by those skilled in the art without departing from the scope of the present invention, utilizing the disclosed technical content, are equivalent embodiments of the present invention. Furthermore, any modifications, alterations, or variations made to the above embodiments based on the essential technology of the present invention are still within the protection scope of the present invention.

Claims

1. A method for optimal scheduling of multi-energy complementary power generation systems based on minimizing carbon emissions, characterized in that, The following steps are included to implement the proposed optimal scheduling method for multi-energy complementary power generation systems based on minimizing carbon emissions: S1: Define the physical boundaries of the scheduling system and construct the carbon flow transfer function; S2: Establish a multidimensional objective function that considers carbon emissions, economics, and stability; S3: Calculate each sub-objective function for the multidimensional objective function; S4: Constructing multi-dimensional constraints for a multi-energy complementary power generation system; S5: Design a double-layer nested iterative structure to achieve global optimization for minimizing carbon emissions; S6: Program the above steps and update the solution results in real time.

2. Based on claim 1, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to define the physical boundaries of the scheduling system and construct the carbon flow transfer function: The physical boundary of the dispatch system covers all aspects of energy production (renewable energy and fossil fuel power generation), energy conversion (electricity to gas P2G, gas to electricity G2P), energy storage (electricity storage ES, thermal storage HS), and energy consumption (total end-user load), while the time boundary is the dispatch cycle. The spatial boundary is a multi-energy complementary power generation network within the target area; Carbon transfer functions are used to quantify the carbon emission intensity at each stage of the "source-grid-load" chain, enabling full-chain carbon footprint tracking; power supply unit i Instantaneous carbon emission intensity represents the amount of carbon emissions per unit of power generation: (1) in, f i Indicates energy i The carbon emission coefficient per unit calorific value; μ i Indicates energy i Energy conversion efficiency; P i s Indicates power supply i The rated output power; Taking power-to-gas (P2G) conversion as an example, considering the carbon intensity of grid input and the carbon equivalent of conversion losses, the carbon emission intensity formula is as follows: (2) in, e grid This represents the average carbon intensity of electrical energy input into the power grid; η P2G Indicates the efficiency of electro-gas conversion; e loss,P2G This represents the carbon equivalent lost during the P2G conversion process; The combined carbon emission intensity of total terminal load reflects the average carbon footprint of the electricity consumed by the load: (3) in, P i ( t ) represents a unit i At any moment t Actual output, energy storage charging P i ( t The value is negative when released and positive when released. e i ( t ) represents a unit i At any moment t Dynamic carbon emission intensity; P L ( t () indicates time t The total system load is the equivalent electrical load after converting the terminal electrical, heat, and gas loads.

3. Based on claim 1, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to construct an objective function with minimizing carbon emissions throughout the entire lifecycle as the primary objective: With "minimizing carbon emissions throughout the entire lifecycle" as the primary goal, while taking into account the economic efficiency of system operation and the stability of multi-energy complementarity, a three-dimensional optimization goal of "prioritizing carbon emission reduction, being economically feasible, and being stable and controllable" is achieved through a dynamic weighting mechanism and a carbon intensity correction model. The overall objective function of the optimal scheduling method for multi-energy complementary power generation systems based on minimizing carbon emissions is expressed as follows: (4) in, F C This represents the total carbon emissions over the entire lifecycle; F E Indicates the economic cost of operating throughout the entire lifecycle; F S This indicates a system stability penalty. ω C ( t ) represents the weight of the carbon emission reduction target. ω E ( t ) represents the weight of economic objectives. ω S ( t ) represents the stability objective weight; satisfying ω C ( t )+ ω E ( t )+ ω S ( t )=1, and ω C ( t ≥0.5 (to ensure priority for carbon emission reduction).

4. Based on claim 3, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included for calculating the sub-objective functions in the overall objective function: 1) Carbon reduction targets with dynamic carbon intensity correction F C Represented as, (5) in, RE FE indicates renewable energy, such as wind power and solar power; FE indicates fossil fuels, such as coal power and gas power. CU Representing coupling units, such as power-to-gas (P2G) and gas-to-electricity (G2P); dynamic correction of carbon intensity of renewable energy. e RE ( t Considering the hidden carbon costs of wind and solar power curtailment, the formula is as follows: (6) in, e RE 0 Indicates the benchmark carbon intensity for renewable energy; α Indicates the carbon penalty coefficient for abandoned energy; P RE,avail ( t () indicates time t Maximum renewable energy generation capacity; P RE,used ( t () indicates time t Actual renewable energy consumption capacity; Furthermore, the dynamic carbon intensity of fossil fuels e i ( t ) is represented as: (7) in, e i 0 Indicates the baseline carbon intensity of fossil fuels; β This represents the load sensitivity coefficient; the higher the load, the greater the carbon intensity penalty. γ This represents the sensitivity coefficient to the proportion of fossil fuels; the higher the proportion of fossil fuel output, the greater the carbon intensity penalty. P FE,total ( t () indicates time t Total output of fossil fuels; P total ( t () indicates time t Total system output; 2) Economic objective item including cost allocation of coupling units F E Represented as, (8) Cost of coupling unit c i ( t Dynamic allocation, meaning that P2G costs are linked to the amount of renewable energy curtailed, is expressed as: (9) in, c P2G 0 This represents the benchmark unit cost of P2G; δ This represents the cost-sharing coefficient for energy curtailment; P RE,surplus ( t () indicates time t Residual power from renewable energy sources; P P2G max This indicates the maximum conversion power of the P2G. 3) Taking into account both output fluctuations and deviations from the optimal energy storage state, and avoiding frequent system adjustments, the stability objective of multi-dimensional fluctuations is quantified. F S Represented as, (10) in, k 1 represents the output fluctuation penalty coefficient; k 2 represents the energy storage deviation penalty coefficient; E i opt Indicates energy storage i Optimal power ( E i opt =( E i min + E i max ) / 2); ΔE Indicates the allowable deviation range.

5. Based on claim 3, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to construct an objective function with minimizing carbon emissions throughout the entire lifecycle as the primary objective: The weighting coefficients are calculated in real time based on a three-dimensional indicator of "load characteristics + renewable energy output + carbon emission reduction demand", as shown in the following formula: (11) in, P L max Indicates the maximum load within the scheduling period; P L min Indicates the minimum load within the scheduling period; P total max This indicates the system's maximum total output. c FE,avg ( t () indicates time t Average unit cost of fossil fuels; c FE,max This indicates the maximum unit cost of fossil fuels.

6. Based on claim 1, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to construct multi-dimensional constraints for multi-energy complementary power generation systems: With "physical feasibility + carbon emission reduction rigidity + multi-energy synergy" as the three core dimensions, carbon flow-related constraints and dynamic coupling constraints are added on the basis of traditional power and capacity constraints to ensure that the optimization results not only meet the system operation safety and carbon emission minimization goals, but also adapt to the dynamic operation characteristics of multi-energy complementary units. The power balance constraint with carbon flow optimization correction is expressed as follows: (12) Introducing carbon flow to regulate power P carbon,adjust ( t Carbon intensity can be achieved by flexibly adjusting power distribution, as shown in the following formula: (13) in, CU in This represents a set of coupled units that consume energy. CU out The set of coupling units representing the output energy; P loss ( t () indicates network loss; e L max Indicates the maximum permissible carbon intensity on the load side; k c Indicates the carbon flow adjustment coefficient; The upper limit for renewable energy output is dynamically adjusted in real time based on carbon intensity; the lower the carbon intensity, the higher the allowed output, thus incentivizing the consumption of clean energy. The renewable energy output constraint is expressed as follows: (14) in, P i avail ( t ) indicates renewable energy i time t Maximum transmit power; k RE This represents the incentive coefficient for renewable energy output; The upper and lower limits of fossil fuel output are linked to carbon intensity; the higher the carbon intensity, the lower the upper limit and the higher the lower limit, thus curbing excessive output of high-carbon energy sources. The fossil fuel output constraint is expressed as follows: (15) in, P i min / P i max Represents fossil energy i Minimum / maximum rated output; e L min Indicates the minimum carbon intensity on the load side; k FE Indicates the power output suppression coefficient of fossil fuels; Dynamic coupling constraints of coupled elements include: P2G element constraints and G2P element constraints; P2G element constraints (electro-electric coordination) are expressed as follows: (16) Dynamic coupling coefficient γ P2G ( t The formula is as follows, which adjusts for carbon intensity and gas storage capacity: (17) in, P P2G max This indicates the maximum conversion power of P2G. P RE,load ( t The power supplied directly to the load by renewable energy sources; G storage ( t This indicates the amount of natural gas stored. G storage max Maximum gas storage capacity; γ P2G 0 Represents the P2G reference coupling coefficient; G2P unit constraints (gas-electric coordination) are expressed as follows: (18) Dynamic coupling coefficient γ G2P ( t ) is represented as, (19) in, γ G2P 0 The G2P reference coupling coefficient, P total max This represents the system's maximum total output.

7. Based on claim 1, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included for designing a two-level nested iterative structure to achieve global optimization for minimizing carbon emissions: With "carbon flow priority, constraint feedback, and adaptive convergence" as the core, a two-layer nested iterative structure is designed: the upper layer uses an improved genetic algorithm (GA) to achieve global optimization for minimizing carbon emissions, and the lower layer uses the interior point method (IPM) to handle the coupling conflict of multiple constraints. At the same time, "carbon flow deviation feedback coefficient" and "constraint relaxation adaptive mechanism" are introduced to ensure that the algorithm converges quickly to the global optimum under the premise of satisfying all constraints, taking into account both solution efficiency and optimization accuracy.

8. Based on claim 7, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to implement an improved genetic algorithm dominated by upper-layer carbon flow: Define the core parameters of the algorithm, including: population size N pop Number of iterations K max Crossover probability p c Probability of mutation p m Initialize the population so that each individual corresponds to a set of unit output matrices within a scheduling cycle. P ( t )=[ P 1( t ), P 2( t ), ..., P N ( t )] T×N This satisfies basic constraints (such as upper and lower limits of output and preliminary conditions for power balance); the upper-level objective function takes minimizing carbon emissions as the sole objective and focuses on the globally optimal output allocation, as shown in the following formula: (20) The fitness function design for carbon flow adaptation incorporates constraint satisfaction weights: (21) in, Penalties for exceeding carbon emission standards; This is a penalty term for power imbalance; λ 1. λ 2 represents the penalty coefficient; For high carbon units ( e i ( t )>0.5 e i max The output gene of ) adopts "low-carbon biased crossover", and the formula is as follows: (22) in, α For cross weights, P i p ( t ), P i q ( t To contribute to one's father's cause. P i c ( t To contribute to the next generation; The "carbon intensity-incentivized variation" formula is used to apply the output gene of renewable energy units, as follows: (23) in, β To vary the time of change, we must ensure that the solution space of the low-carbon region is fully explored.

9. Based on claim 7, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, This includes the following steps for constraint conflict correction in lower-level optimization: Define the core parameters of the algorithm and the obstacle parameters. μ attenuation coefficient ρ Precision threshold ε ipm Carbon emission reduction rate deviation ε carbon Objective function deviation ε F The initial optimal solution output by the upper layer. P * ( t There may be constraint conflicts, which are corrected at the lower level using the interior point method. The objective function is: (24) For carbon strength rigid constraint ( g j ( P ( t ))= e i ( t )- e i max ≤0), constraint barrier function φ ( g j ( P ( t Carbon-constrained reinforcement design: (25) For other constraints, the following conditions are met: (26) in, θ carbon This is the carbon constraint weighting coefficient, which strengthens the priority of carbon intensity constraints; When a certain constraint is not satisfied, the relaxation factor is dynamically adjusted. σ j ( t To avoid the algorithm getting trapped in local optima: (27) in, σ j 0 The initial relaxation factor, k σ The attenuation coefficient is... K This represents the current iteration number; The modified constraint form is expressed as: (28) Later iterations σ j ( t The value approaches 0 to ensure that the constraints are strictly met.

10. Based on claim 7, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to determine whether the model solution iteration meets the requirements: When the convergence condition is met or the maximum number of iterations is reached... K max At that time, the final optimal scheduling scheme is output: unit output matrix. P opt ( t Total carbon emissions throughout the entire lifecycle F C,opt Carbon strength compliance rate η carbon , (29) in, I (·) is an indicator function. N FE This refers to the number of fossil fuel units.

11. Based on claim 1, a method for optimal scheduling of a multi-energy complementary power generation system based on minimizing carbon emissions, characterized in that, The following steps are included to program the above steps and update the output of the solution results in real time: A programmatic solution platform is built using Python or MATLAB. Input parameters are collected in real time through the platform interface and automatically matched to the corresponding mathematical model. Next, a nested solution module combining an improved genetic algorithm and the interior-point method is invoked to perform iterative calculations according to preset parameters, simultaneously outputting intermediate iteration results and constraint satisfaction indices. Subsequently, the optimal scheduling scheme is displayed through a visual interface, and a standardized scheduling report is generated. Finally, a real-time update mechanism with a time step Δt of 15~30 minutes is set up. The latest operating data is collected again at intervals of Δt, triggering a second solution of the model to dynamically correct the output commands. At the same time, an interface with the power grid dispatching system is reserved to support the real-time issuance and execution feedback of dispatching commands, ensuring the timeliness and operability of the optimized scheduling.

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