Optimal operation method of hydrogen integrated energy system considering seasonal scheduling

Abstract

The application discloses a hydrogen-containing comprehensive energy system optimal operation method considering seasonal scheduling, and specifically comprises the following steps: step 1, a comprehensive energy system integrating a carbon capture power plant and hydrogen energy multi-element utilization is built, and an NBRO-Kmeans++ algorithm is used to select representative typical scenes from annual data; step 2, on the basis of step 1, a scientific carbon emission quota allocation model is established by integrating multiple principles and multiple indexes through a distance between optimal and poor solutions method combined with grey correlation analysis, and carbon emission characteristics in different seasons are analyzed; step 3, on the basis of step 2, when the PIES is subjected to low-carbon economic scheduling, optimal scheduling is carried out with the minimum total operation cost as the target. The application can realize minimization of the operation cost and carbon emission of the comprehensive energy system.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system application technology, and relates to an optimized operation method for hydrogen-containing integrated energy systems that takes into account seasonal scheduling. Background Technology

[0002] An Integrated Energy System (IES) is a system that coordinates and optimizes multiple energy forms such as electricity, natural gas, and heat, thereby improving energy utilization efficiency and flexibility and achieving the goal of low-carbon development. However, existing IES research still faces the dual challenges of insufficient equipment energy efficiency synergy and a lack of spatiotemporal scale matching. On the one hand, the operating strategies of key equipment are too simplistic, and carbon capture power plants (CCPPs) lack flexibility and deep synergy with hydrogen energy systems. They do not include the energy efficiency coupling of the entire "production, storage, and use" chain, from "electrolyzer—methane reactor—hydrogen storage tank—hydrogen fuel cell—gas blending with hydrogen," which restricts further improvement in the overall energy utilization rate of the system. On the other hand, existing models cannot accurately depict the seasonal fluctuations of source and load throughout the year, resulting in the untapped potential of hydrogen energy storage regulation across seasons and limiting the ability to cope with long-term supply and demand imbalances.

[0003] Currently, Park Integrated Energy Systems (PIES), as key hubs connecting the power grid and end users, are characterized by concentrated multi-energy loads and significant emissions, making them an ideal scenario for addressing the aforementioned issues. Therefore, how to construct a low-carbon economic dispatch method suitable for PIES, fully explore the synergistic potential of carbon capture and hydrogen energy across the entire process, and build a dispatch method that balances economic efficiency and low carbon emissions to accelerate the "low-carbon" transformation of industrial parks has become a key research focus. Summary of the Invention

[0004] The purpose of this invention is to provide an optimized operation method for hydrogen-containing integrated energy systems that takes into account seasonal scheduling. This method can minimize the operating costs and carbon emissions of the integrated energy system.

[0005] The technical solution adopted in this invention is an optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling, which specifically includes the following steps: Step 1: Build an integrated energy system that combines carbon capture power plants with diversified hydrogen energy utilization. Use the NBRO-Kmeans++ algorithm to select representative typical scenarios from the annual data. Step 2: Based on Step 1, a scientific carbon emission quota allocation model is established by integrating multiple principles and indicators through the superior-inferior solution distance method and grey relational analysis, and the carbon emission characteristics of different seasons are analyzed. Step 3: Based on Step 2, when performing low-carbon economic scheduling on PIES, optimize the scheduling with the goal of minimizing the total operating cost.

[0006] The invention is further characterized by: The specific process of step 1 is as follows: Step 1.1: Construct a framework centered on a carbon capture power plant operation model and a hydrogen energy multi-utilization structure. This framework includes a carbon capture power plant model, a hydrogen-blended cogeneration unit model, a gas boiler model, a hydrogen energy utilization model, and a hydrogen storage tank model. Step 1.2: Use the NBRO-Kmeans++ algorithm to select representative typical scenarios from the annual data.

[0007] In step 1.1, the expressions for the mathematical models of each device are as follows: Carbon capture power plant model:

[0008] In the formula: Let t be the total power generation capacity of the coal-fired unit at time t; Let t be the net output power of the carbon capture power plant. This represents the basic energy consumption of carbon capture power plants; , Let t be the carbon capture energy consumption and operating energy consumption of the carbon capture power plant. Energy consumption coefficient for carbon capture operation; For the coal-fired unit at time t Total number of units generated; Carbon emission intensity of coal-fired power units; Let t be the flue gas split ratio; , The absorption tower at time t The absorption tower and regeneration tower need to process Regeneration rate; , These are absorption efficiency and regeneration efficiency, respectively. For the rich liquid tank at time t Outflow rate: a positive value indicates flow from the rich liquid tank to the regeneration tower, and a negative value indicates flow from the absorption tower to the rich liquid tank; For the actual time t Capture quantity; Model of hydrogen-doped cogeneration unit:

[0009] In the formula, Let be the hydrogen blending ratio of the fuel gas at time t. , These represent the hydrogen power and natural gas power input to the cogeneration unit via the gas-blending device at time t, respectively. The input power of the combined heat and power unit is the mixed gas power at time t; , , The lower heating values ​​are for hydrogen, natural gas, and mixed fuel gas, respectively. Gas boiler model:

[0010] In the formula: Let be the natural gas input power of the integrated energy system at time t; The thermal efficiency coefficient of a gas-fired boiler; Let be the output power of the gas-fired boiler at time t; The models for the refined utilization of hydrogen energy include an electrolyzer model and a methane reactor model: Electrolyzer model:

[0011] In the formula: , These represent the input electrical power and hydrogen conversion power of EL at time t, respectively. The energy conversion efficiency of EL; Methane reactor model:

[0012] In the formula: , These represent the input and output power of the methane reactor at time t, respectively. The energy conversion efficiency of the methane reactor; Hydrogen fuel cell model:

[0013] In the formula: , These represent the input and output power of the hydrogen fuel cell at time t, respectively. For the power generation efficiency of fuel cells; Hydrogen storage tank model:

[0014] In the formula: Let t be the storage capacity of the hydrogen storage tank at time t; , These represent the hydrogen storage and hydrogen release power at time t, respectively. , These are hydrogen storage efficiency and hydrogen release efficiency, respectively; , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , These are variables representing the charging / discharging states, 0 and 1, respectively.

[0015] The specific process of step 1.2 is as follows: Step 1.2.1, Data preprocessing, using maximum-minimum normalization:

[0016] In the formula: where, These are the original data values; It is the minimum value of the dataset; It is the maximum value in the dataset; Step 1.2.2: Use the Kmeans++ algorithm to obtain k initial centroids, and then introduce Gaussian noise to perturb each centroid, generating m sets of candidate solutions to form the initial population; Step 1.2.3: Introduce the global search mechanism from the differential evolution algorithm to optimize the initial cluster centers. The differential evolution direction... It is composed of a linear combination of the global optimal term and the random difference term weighted by scalar random numbers, as shown in equation (9).

[0017]

[0018] In the formula: and These are the random weighting coefficients that control the direction of synthesis; It is a candidate solution that is globally optimal; It is the k-th centroid of the m-th candidate solution in the population at the t-th iteration; and It is the index of a randomly selected candidate solution; Step 1.2.4: Post-process the current centroid using Kmeans++ to construct a diagonal Hessian inverse matrix. Generate Newton's second-order correction term As shown in the following formula:

[0019] In the formula: Let be the sample set of the k-th cluster; The average position of all sample points in the k-th cluster; when class | When the number of samples in | is 0 Take 10 8 ; Steps 1.2.5 and 1.2.3 generate the differential evolution global update term, and step 1.2.4 generates the Newton second-order correction term. The weighted composite of these two terms is applied to the current centroid to obtain the composite centroid update formula, as shown in the following equation:

[0020] In the formula: This represents the maximum number of iterations. The learning rate is set to zero exponentially in the first t / 2 iterations to suppress oscillations. Step 1.2.6: Iteratively calculate the intra-cluster variance cost for all candidate solutions. The top 10% are selected as elite solutions, and their centroids are retained until the next iteration. The variance cost is shown in the equation:

[0021] The optimal solution is the one with the smallest sum of squared errors among the elite solutions in the current population, which is the typical day selected.

[0022] The specific process of step 2 is as follows: Step 2.1: Establish an indicator system for the allocation of carbon emission quotas based on multiple principles and indicators; Step 2.2: Settle the annual carbon trading allowances quarterly; Step 2.3 adopts an optimized tiered carbon pricing mechanism, which sets an initial carbon emission margin, provides subsidies and rewards to entities whose carbon emissions are below the margin, and imposes penalties on entities whose emissions exceed the margin.

[0023] The specific process of step 2.1 is as follows: Step 2.1.1: Using grey relational analysis, a carbon quota correlation model is constructed. Let N be the total number of units participating in carbon emission quota allocation, and K be the total number of carbon trading calculation periods. Three carbon quota allocation indicators are selected. The original indicator matrix at time k can then be represented as a matrix. as follows:

[0024] In the formula: Let i represent the original value of the i-th carbon quota allocation index for the i-th time unit, where i = 1, 2, ..., N represents different quota allocation units; y =1, 2, 3 represent the y-th carbon quota allocation indicator; k Indicates the time of carbon trading calculation; Step 2.1.2: Since the indicators differ in dimensions and value ranges, first determine the global extreme values ​​of each indicator across all units and all time periods. For the y-th indicator, the minimum and maximum values ​​are as follows:

[0025] In the formula, , Let represent the global minimum and global maximum values ​​of the y-th index across all units and all times, respectively; Step 2.1.3 involves performing dimensionless processing on the parameters affecting carbon trading quotas, and standardizing all indicators to unify their directions, as shown in the equation:

[0026] In the formula, Let y be the standardized value of the y-th index of unit i at time k. Step 2.1.4: The grey relational analysis method is used to calculate the correlation degree of each indicator, with the corresponding reference sequence being... Then its absolute difference is the first y The absolute difference between each indicator parameter and the reference carbon emission quota and corresponding grey relational coefficients for:

[0027] In the formula, Let the value of the y-th index of unit i at time k be after dimensionless processing; The resolution coefficient; Step 2.1.5, calculate the first... y Correlation of individual indicator parameters and weighting coefficients As shown in the following formula:

[0028] In the formula: The summation subscript represents the j-th carbon quota allocation index; Step 2.1.6: Use the TOPSIS method to calculate the closeness of each element to the ideal solution, and take the mean of the standardized index values ​​at each time point, i.e.:

[0029] right After undergoing homogenization and normalization processes, standardized decision matrix elements are obtained. Then, the index weights obtained from grey relational analysis are combined. The weighted normalized decision matrix is ​​constructed as follows:

[0030] In the formula: This represents the weighted normalized value of unit i on the y-th index; Step 2.1.7: Based on the weighted normalization matrix, determine the positive ideal solution and the negative ideal solution respectively, as shown in the following equation:

[0031] In the formula: The value is a positive ideal solution; The value is a negative ideal solution; Each unit i The distances to the positive and negative ideal solutions are shown in the following formulas:

[0032] In the formula: The value represents the distance from each cell to the positive ideal solution; The value represents the distance from each cell to the negative ideal solution; Calculate proximity As shown in the following formula:

[0033] Step 2.1.8, adjust the unit proximity. After normalization, the carbon emission quota coefficient of unit i is obtained. for:

[0034] in, Indicates the degree of closeness.

[0035] The specific process of step 2.2 is as follows: Step 2.2.1, the seasonal carbon emissions must meet the following requirements:

[0036] In the formula, Maximum carbon emissions for each season, , , , The value represents the percentage of carbon emissions by season; Step 2.2.2: Establish seasonal carbon emission constraints in PIES, specifically...

[0037] In the formula: , , This represents the carbon emission coefficient corresponding to power output, gas consumption, and heat consumption.

[0038] In step 2.3, the carbon trading cost is calculated using the following formula:

[0039] In the formula: Carbon trading costs borne by IES; This represents the actual total carbon emissions of the IES; c The benchmark price for carbon trading; These represent the price growth coefficients; h This indicates the permissible limit for exceeding carbon emission limits.

[0040] The specific process of step 3 is as follows: Step 3.1, the integrated energy system low-carbon economic dispatch model takes minimizing total cost as the optimization dispatch objective, and the objective function is expressed as follows:

[0041] The costs of coal and gas are:

[0042] In the formula: The unit power generation cost of coal-fired power units; for t Gas purchase capacity of the time-of-use system The unit price for purchasing natural gas; The amount of CO2 supplied by the carbon capture power plant for electricity-to-gas conversion is:

[0043] In the formula: The amount of CO2 required for the electro-gas conversion operation during time period t; The density of CO2 gas; The cost of carbon sequestration is:

[0044] In the formula: The cost required to seal a unit of CO2; The cost of curtailing wind and solar power is:

[0045] In the formula, The penalty cost per unit of abandoned wind and solar power; Let t be the amount of wind and solar power curtailment. Step 3.2: The electrical, thermal, gas, and hydrogen power within the integrated energy system must respectively satisfy the following balance constraints:

[0046] In the formula: , They are respectively t Actual power generation output of wind and solar power during the period; , , They are respectively t The electricity, heat, and gas load requirements of the system during different time periods; for t The output thermal power of the gas-fired boiler during a specific time period; , They are respectively t The heat storage and heat release capacity of the time-period thermal storage tank; Constraints of wind power generation:

[0047] In the formula, The predicted power output of the wind turbine generator during time period t; Carbon capture power plant operating constraints:

[0048] In the formula: , These are the upper and lower limits of the total output of coal-fired power units, respectively. , These are the upper and lower limits of the ramp power for coal-fired power units, respectively. By adjusting the flue gas split ratio, the carbon capture system can adjust the carbon capture scale at different times as needed, subject to the following constraints:

[0049] In the formula: , These are the upper and lower limits of the flow splitting ratio for the flue gas splitting device; Under the maximum operating condition of the power plant operating at full load, the regeneration tower can completely process all the CO2 absorbed by the absorption tower. The energy consumption of the carbon capture system must meet the following constraints:

[0050] In the formula: The operating energy consumption of the carbon capture system at time t; τ is the flue gas diversion ratio coefficient; τ is the maximum operating condition coefficient of the regeneration tower and compressor; The carbon emission intensity coefficient for coal-fired power units; To the maximum output of the coal-fired unit; Step 3.2.4: For the electrolyzer model, methane reactor model, hydrogen fuel cell model, combined heat and power (CHP) model, and gas boiler model, ensure that the input power first meets the equipment capacity constraints, as shown in the following formula:

[0051] In the formula: For the first i The input power of this energy conversion device; For the first i The capacity of this type of energy conversion equipment; , These are the upper and lower limits of the ramp power for the i-th type of energy conversion device, respectively. Step 3.2.5: The hydrogen storage tank model needs to consider storage capacity constraints, single hydrogen storage / release constraints, complementary storage and release states constraints, and periodic storage conservation constraints, as expressed below:

[0052] In the formula: for t Storage capacity of the time-limited hydrogen storage tank; , They are respectively t Hydrogen storage and release power over a given period of time; , These are hydrogen storage efficiency and hydrogen release efficiency, respectively; , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , The charge / discharge state is a 0-1 variable; Step 3.2.6, considering the seasonal characteristic constraints of the two energy storage systems, Hys and HS, is expressed as follows:

[0053] In the formula: Total capacity for different seasons; The expected daily capacity under typical daily scenarios in different seasons.

[0054] The beneficial effects of this invention are as follows: First, under the premise of ensuring a reliable energy supply, it fully explores and synergistically leverages the flexible operation potential of carbon capture power plants and the low-carbon value of hydrogen energy across multiple stages (production, storage, and utilization), significantly enhancing the overall system's regulation capacity and energy efficiency; it overcomes the problem of regulatory lag caused by the excessively long annual carbon settlement cycle in traditional systems, enabling carbon emission management to respond more promptly and accurately to the actual operating status of the system; second, it achieves a scientific and fair allocation of initial carbon emission quotas, improving the matching degree between quota allocation and the actual emission characteristics of the system, and on this basis, it introduces a seasonal carbon trading mechanism to effectively reduce carbon emissions; finally, it realizes the synergistic optimization of economic costs and carbon emission levels throughout the system's annual operating cycle, providing a practical and feasible operating paradigm and mechanism support for building an efficient, flexible, and sustainable new integrated energy system. Attached Figure Description

[0055] Figure 1 This is an IES operation framework diagram of the optimized operation method for hydrogen-containing integrated energy systems that takes into account seasonal scheduling, as described in this invention. Figure 2This is a flowchart of the steps for solving typical scenarios using the NBRO-Kmeans++ algorithm in the proposed method for optimizing the operation of hydrogen-containing integrated energy systems considering seasonal scheduling. Figure 3 is a structural diagram of hydrogen energy multi-utilization in the hydrogen-containing integrated energy system optimization operation method considering seasonal scheduling of the present invention. Figures 4(a) to 4(d) are day-ahead power curves of wind power, photovoltaic and electricity, heat and gas loads under typical PIES scenarios of the optimized operation method of hydrogen-containing integrated energy system considering seasonal scheduling in this invention. Figure 5 is a comparison of the effects of the tiered carbon price based on reward and punishment factors on the optimized operation method of hydrogen-containing integrated energy system considering seasonal scheduling according to the present invention. Detailed Implementation

[0056] The following detailed description is provided in conjunction with specific implementation methods.

[0057] Example 1 This invention presents an optimized operation method for a hydrogen-based integrated energy system considering seasonal scheduling. The specific steps include: constructing an integrated energy system combining carbon capture power plants and diversified hydrogen energy utilization to fully leverage the system's flexible operation capabilities and high-efficiency energy use value; obtaining wind and solar load data for typical scenarios in four seasons using the NBRO-Kmeans++ method; constructing a carbon emission quota allocation model based on multiple principles and indicators, employing a combination of TOPSIS and GRA to make the evaluation dimensions more comprehensive, and simultaneously optimizing the tiered carbon trading mechanism to adapt to seasonal changes; establishing an optimization direction that minimizes the total system operating cost, and using the CPLEX solver to verify the actual effectiveness of the scheme in balancing economic efficiency and carbon emission reduction through simulation examples, providing technical support for the low-carbon energy transition. Example 2 This invention provides an optimized operation method for hydrogen-containing integrated energy systems that consider seasonal scheduling, and is implemented according to the following steps: Step 1: Construct a comprehensive energy system framework centered on the flexible operation of carbon capture power plants and the diversified utilization of hydrogen energy. The PIES operating architecture is as follows: Figure 1 As shown. Meanwhile, to accurately reflect the operational correlations between different seasons and support annual carbon emission accounting, the NBRO-Kmeans++ algorithm is used to select representative typical scenarios from the annual data. The flowchart of the steps is shown below. Figure 2 As shown.

[0058] Step 2: Building upon Step 1, a scientific carbon emission quota allocation model is established by integrating multiple principles and indicators through the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) combined with Grey Relational Analysis (GRA). This model analyzes the carbon emission characteristics of different seasons and refines the annual total quota to each season, ensuring that PIES implements carbon emission control on a quarterly basis. Finally, an optimized tiered carbon trading mechanism is introduced to guide PIES to proactively reduce carbon emissions in system scheduling decisions.

[0059] Step 3: Building upon Step 2, optimize the low-carbon economic scheduling of PIES with the goal of minimizing overall operating costs. Simultaneously, allocate annual carbon trading allowances for PIES seasonally, and convert these seasonal allowances into daily scheduling constraints. Establish scheduling models at the energy and carbon flow layers, and implement overall low-carbon economic scheduling of PIES at the management level.

[0060] Example 3 The specific process of step 1 is as follows: Step 1.1 establishes a framework centered on a flexible operation model for carbon capture power plants and a diversified hydrogen energy utilization structure. This framework includes a carbon capture power plant, a hydrogen-blended cogeneration unit, a gas-fired boiler, a hydrogen fuel cell, an electrolyzer, a methane reactor, a thermal storage tank, and a hydrogen storage tank. Through synergistic optimization of carbon and hydrogen, the overall energy efficiency of the system is improved. The mathematical model expressions for each device are as follows: (1) Carbon capture power plant model

[0061] In the formula: Let t be the total power generation capacity of the coal-fired unit at time t; Let t be the net output power of the carbon capture power plant. This represents the basic energy consumption of carbon capture power plants; , Let t be the carbon capture energy consumption and operating energy consumption of the carbon capture power plant at time t; Energy consumption coefficient for carbon capture operation; For the coal-fired unit at time t Total number of units generated; Carbon emission intensity of coal-fired power units; Let t be the flue gas split ratio; , The absorption tower at time t The absorption tower and regeneration tower need to process Regeneration rate; , These are absorption efficiency and regeneration efficiency, respectively. For the rich liquid tank at time t Outflow rate: a positive value indicates flow from the rich liquid tank to the regeneration tower, and a negative value indicates flow from the absorption tower to the rich liquid tank; For the actual time t Capture volume.

[0062] (2) Model of hydrogen-doped cogeneration unit

[0063] In the formula: The hydrogen blending ratio of the fuel gas at time t should be controlled within 20% by volume. , These represent the hydrogen power and natural gas power input to the cogeneration unit via the gas-fueling device at time t, respectively. The input power of the combined heat and power unit is the mixed gas power at time t; , , These are the lower heating values ​​of hydrogen, natural gas, and mixed fuel gas, respectively.

[0064] (3) Gas-fired boiler:

[0065] In the formula: Let be the natural gas input power of the integrated energy system at time t; The thermal efficiency coefficient of a gas-fired boiler; Let be the output power of the gas-fired boiler at time t.

[0066] (4) Model for the refined utilization of hydrogen energy The system consists of two parts: an electrolyzer (EL) that uses electricity to produce hydrogen, and a methane reactor (MR) that uses hydrogen as a feedstock to further synthesize methane. A hydrogen storage tank is installed between the two processes to allow for flexible hydrogen distribution. The hydrogen energy multi-utilization model is as follows: Figure 3 As shown.

[0067] 1) Electrolytic cell model:

[0068] In the formula: , These represent the input electrical power and hydrogen conversion power of EL at time t, respectively. This refers to the energy conversion efficiency of EL.

[0069] 2) Methane reactor model:

[0070] In the formula: , These represent the input and output power of the methane reactor at time t, respectively. The energy conversion efficiency of the methane reactor.

[0071] 3) Hydrogen fuel cell model:

[0072] In the formula: , These represent the input and output power of the hydrogen fuel cell at time t, respectively. This refers to the power generation efficiency of fuel cells.

[0073] (5) Hydrogen storage tank model: Hydrogen storage tanks need to comprehensively consider storage capacity constraints, single hydrogen storage / release constraints, complementary storage and release states constraints, and periodic storage conservation constraints. Thermal storage tanks are similar to hydrogen storage tanks; therefore, only hydrogen storage tanks will be modeled, with the following expression:

[0074] In the formula: Let t be the storage capacity of the hydrogen storage tank at time t; , These represent the hydrogen storage and hydrogen release power at time t, respectively. , These are hydrogen storage efficiency and hydrogen release efficiency, respectively; , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , These are variables representing the charging and discharging states, ranging from 0 to 1. A value of 1 at both times indicates that the hydrogen storage tank cannot simultaneously perform hydrogen storage and hydrogen release.

[0075] Step 1.2: Select a typical day using the NBRO-Kmeans++ algorithm, and use its daytime power data for wind power, photovoltaic power, electricity load, heat load, and gas load as model input.

[0076] Step 1.2.1, data preprocessing, using maximum and minimum value normalization.

[0077]

[0078] In the formula: where, These are the original data values; It is the minimum value of the dataset; It is the maximum value of the dataset.

[0079] Step 1.2.2: The Kmeans++ algorithm is used to obtain k initial centroids. Then, Gaussian noise is introduced to perturb each centroid, generating m sets of candidate solutions to form the initial population. This ensures the diversity of the first generation and effectively avoids premature convergence of the algorithm.

[0080] Step 1.2.3: The candidate solutions of the K-means++ algorithm are prone to getting trapped in local optima during the optimization process. Therefore, the Differential Evolution (DE) algorithm is introduced to optimize the initial cluster centers using its global search mechanism. The differential evolution direction... It is composed of a linear combination of the global optimal term and the random difference term weighted by scalar random numbers, as shown in equation (9).

[0081]

[0082] In the formula: and These are the random weighting coefficients that control the direction of synthesis; It is a candidate solution that is globally optimal; It is the k-th centroid of the m-th candidate solution in the population at the t-th iteration; and It is the index of a randomly selected candidate solution.

[0083] Step 1.2.4: Post-process the current centroid using Kmeans++ to construct a diagonal Hessian inverse matrix. Generate Newton's second-order correction term As shown in the formula.

[0084]

[0085] In the formula: Let be the sample set of the k-th cluster; The average position of all sample points in the k-th cluster; when class | When the number of samples in | is 0 Take 10 8 To avoid the error of dividing by 0.

[0086] Steps 1.2.5 and 1.2.3 generate the differential evolution global update term, and step 1.2.4 generates the Newton second-order correction term. The weighted composite of these two terms is applied to the current centroid to obtain the composite centroid update formula, as shown in the following equation:

[0087] In the formula: This represents the maximum number of iterations. The learning rate is set to zero exponentially in the first t / 2 iterations to suppress oscillations.

[0088] Step 1.2.6: Iteratively calculate the intra-cluster variance cost for all candidate solutions. The top 10% are selected as elite solutions, and their centroids are retained until the next iteration. The variance cost is shown in the equation:

[0089] In the formula: The smaller the value, the better the clustering effect of the candidate solution. The remaining non-elite solutions are referenced by the elite solutions. New solutions are generated by introducing attenuated noise in the neighborhood of the elite solutions to maintain population diversity and global search capability, thereby improving the optimization performance of the algorithm while avoiding the loss of elite solutions.

[0090] Step 1.2.7: The optimal solution is the one with the minimum sum of squared errors (SSE) among the elite solutions in the current population; the optimal solution is defined as the one whose SSE changes less than 10 over 10 consecutive iterations. 6 Convergence may stop when the maximum number of iterations is reached.

[0091] Example 4 The specific process of step 2 is as follows: Step 2.1: Establish a multi-principle, multi-indicator indicator system for carbon emission quota allocation, as shown in Table 1.

[0092] Table 1 Carbon Emission Quota Indicator System

[0093] As shown in Table 1, the low-carbon principle includes two indicator systems: carbon emission efficiency and new energy absorption rate. The principle of fairness considers the external carbon emission difference indicator. When allocating carbon emission quotas, lower-level indicator coefficients can be set according to users' differentiated needs for low-carbon and fair practices, combined with the corresponding relationships in Table 1. Indicators that reduce carbon emissions are set as "+" indicators, and indicators that increase carbon emissions are set as "-" indicators.

[0094] Step 2.1.1: To ensure that the total carbon emissions of PIES do not exceed the limit, considering the temporal differences in carbon emissions across days, seasons, and years, a grey relational analysis method is used to construct a carbon quota correlation model. Let N be the total number of units participating in carbon emission quota allocation, and K be the total number of carbon trading calculation periods. Selecting three carbon quota allocation indicators, the original indicator matrix at time k can be represented as a matrix... as follows:

[0095] In the formula: Let i represent the original value of the i-th carbon quota allocation index for the i-th time unit, where i = 1, 2, ..., N represents different quota allocation units; y =1, 2, 3 represent the y-th carbon quota allocation indicator; k This indicates the time of carbon trading calculation.

[0096] Step 2.1.2: Due to differences in the dimensions and ranges of the various indicators, the global extreme values ​​of each indicator are first determined across all units and all time periods. For the y-th indicator, its minimum and maximum values ​​are as follows:

[0097] In the formula, , Let represent the global minimum and global maximum values ​​of the y-th index across all units and at all times, respectively.

[0098] Step 2.1.3: Perform dimensionless processing on the parameters affecting carbon trading quotas, and unify the direction of all indicators through standardization, as shown in the formula.

[0099]

[0100] In the formula: Let y be the standardized value of the y-th index of unit i at time k.

[0101] Step 2.1.4, to reflect the closeness of each carbon quota allocation indicator to the reference carbon emission sequence, grey relational analysis is used to calculate the correlation degree of each indicator, and the indicator weights are determined accordingly. The corresponding reference sequence is... Then its absolute difference is the first y The absolute difference between each indicator parameter and the reference carbon emission quota and corresponding grey relational coefficients for:

[0102] In the formula: Let the value of the y-th index of unit i at time k be after dimensionless processing; The resolution coefficient is typically set to 0.5.

[0103] Step 2.1.5, calculate the first... y Correlation of individual indicator parameters and weighting coefficients As shown in the formula:

[0104] In the formula: The summation subscript represents the j-th carbon quota allocation index.

[0105] Step 2.1.6, to comprehensively evaluate the performance of each unit in carbon quota allocation, uses the TOPSIS method to calculate the degree of closeness of each unit to the ideal solution. The mean of the standardized index values ​​at each time point is taken, i.e.:

[0106] right After undergoing homogenization and normalization processes, standardized decision matrix elements are obtained. Then, the indicator weights obtained from grey relational analysis are combined. The weighted normalized decision matrix is ​​constructed as follows:

[0107] In the formula: This represents the weighted normalized value of unit i on the y-th index.

[0108] Step 2.1.7: Based on the weighted normalization matrix, determine the positive and negative ideal solutions respectively. As shown in the equation:

[0109] In the formula: The value is a positive ideal solution; The value is a negative ideal solution.

[0110] Each unit i The distances to the positive ideal solution (PIS) and the negative ideal solution (NIS) are shown in the equation.

[0111]

[0112] In the formula: The value represents the distance from each cell to the positive ideal solution; The value is the distance from each cell to the negative ideal solution.

[0113] The closeness is calculated as shown in the formula.

[0114] In the formula: The larger the value, the larger the unit. i The better the overall performance.

[0115] Step 2.1.8: To obtain the carbon emission allowance allocation ratio for each unit, the unit proximity is calculated. After normalization, the carbon emission quota coefficient of unit i is obtained. for:

[0116] Step 2.2: In order to further constrain PIES carbon emissions, ensure that carbon emissions within the system are consistent with carbon trading quotas, and avoid the problem of exceeding carbon emission limits, the annual carbon trading quotas will be settled quarterly.

[0117] Step 2.2.1, the seasonal carbon emissions must meet the following requirements:

[0118] In the formula Maximum carbon emissions for each season, , , , The value for the seasonal carbon emission percentage is determined by summarizing the seasonal carbon emission characteristics from historical wind power, solar power, and load data in PIES.

[0119] Step 2.2.2: Establish seasonal carbon emission constraints in PIES, specifically...

[0120] In the formula: , , This represents the carbon emission coefficient corresponding to power output, gas consumption, and heat consumption.

[0121] Step 2.3: To avoid a sharp increase in environmental pollution due to excessive carbon emissions, PIES adopts an optimized tiered carbon pricing mechanism. This mechanism sets an initial free carbon emission margin, provides subsidies and rewards to entities whose carbon emissions are below the margin, and penalizes entities that exceed the margin. It is more refined, has more reasonable parameter settings, and is better able to guide low-carbon behavior than the traditional tiered mechanism. As shown in the formula:

[0122] In the formula: Carbon trading costs borne by IES; This represents the actual total carbon emissions of the IES; c The benchmark price for carbon trading; These represent the price growth coefficients; h This indicates the permissible limit for exceeding carbon emission limits.

[0123] Example 5 Step 3.1: The integrated energy system low-carbon economic dispatch model proposed in this invention takes minimizing total cost as the optimal dispatch objective. The objective function is expressed as follows:

[0124] Step 3.1.1, the cost of coal and gas is:

[0125] In the formula: The unit power generation cost of coal-fired power units; for t The gas purchasing power of the system during certain time periods is determined. The gas demand of the system is first met by the electricity-to-gas conversion. When the electricity-to-gas conversion capacity is insufficient, the system will purchase gas from the natural gas market to supplement it. The unit price for purchasing natural gas.

[0126] Step 3.1.2, the amount of CO2 utilized by the carbon capture power plant for electricity-to-gas conversion is:

[0127] In the formula: for t The amount of CO2 required for the periodic power-to-gas conversion operation; This represents the density of CO2 gas.

[0128] Step 3.1.3, the cost of carbon sequestration can be expressed as:

[0129] In the formula: The cost required to seal a unit of CO2.

[0130] Step 3.1.4, the cost of wind and solar power curtailment can be expressed as:

[0131] In the formula: The penalty cost per unit of abandoned wind and solar power; for t The amount of wind and light abandoned at any given moment.

[0132] Step 3.2: Based on step 3.1, add constraints.

[0133] Step 3.2.1: The electrical, thermal, gas, and hydrogen power within the integrated energy system must each satisfy the following balance constraints.

[0134]

[0135] In the formula: , They are respectively t Actual power generation output of wind and solar power during the period; , , They are respectively t The electricity, heat, and gas load requirements of the system during different time periods; for t The output thermal power of the gas-fired boiler during a specific time period; , They are respectivelyt The heat storage and heat release capacity of the time-period thermal storage tank.

[0136] Step 3.2.2, Wind power generation constraints.

[0137]

[0138] In the formula: This represents the predicted power output of the wind turbine generators during time period t.

[0139] Step 3.2.3, Operational constraints of carbon capture power plants.

[0140]

[0141] In the formula: , These are the upper and lower limits of the total output of coal-fired power units, respectively. , These represent the upper and lower limits of the ramp power for coal-fired power units.

[0142] By adjusting the flue gas split ratio, the carbon capture system can adjust the carbon capture scale at different times as needed. The constraints are as follows:

[0143] In the formula: , These are the upper and lower limits of the flow ratio for the flue gas diversion device.

[0144] Under the maximum operating condition of the power plant operating at full load, the regeneration tower can completely process all the CO2 absorbed by the absorption tower. The energy consumption of the carbon capture system must meet the following constraints:

[0145] In the formula: The operating energy consumption of the carbon capture system at time t; This is the flue gas diversion ratio coefficient; τ This represents the maximum operating condition coefficient for the regeneration tower and compressor. The carbon emission intensity coefficient for coal-fired power units; This is the maximum output of the coal-fired power unit.

[0146] Step 3.2.4: For electrolyzers, methane reactors, hydrogen fuel cells, cogeneration plants, and gas-fired boilers, ensure that their input power first meets the equipment capacity constraints (does not exceed the equipment design capacity range), and then complies with the power ramp-up constraints (considering the equipment response speed limitations to avoid rapid power changes). Finally, by simultaneously implementing these dual constraints, the safe and stable operation of the equipment can be achieved.

[0147]

[0148] In the formula: For the first i The input power of this energy conversion device; For the first i The capacity of this type of energy conversion equipment; , The first i The upper and lower limits of the ramp power of this type of energy conversion equipment.

[0149] Step 3.2.5, Energy Storage Equipment Operation Constraints. The model for the hydrogen storage tank needs to consider storage capacity constraints, single hydrogen storage / release constraints, complementary storage and release states constraints, and periodic storage conservation constraints. The expression is as follows:

[0150] In the formula: for t Storage capacity of the time-limited hydrogen storage tank; , They are respectively t Hydrogen storage and release power over a given period of time; , These are hydrogen storage efficiency and hydrogen release efficiency, respectively; , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , The variable represents the charging and discharging state (0-1). A value of 1 at both times indicates that the hydrogen storage tank cannot simultaneously perform hydrogen storage and hydrogen release.

[0151] Step 3.2.6, considering the seasonal characteristic constraints of the two energy storage systems, Hys and HS, is expressed as follows:

[0152] In the formula: Total capacity for different seasons; This represents the expected daily capacity under typical daytime scenarios in different seasons, and this value is determined by the seasonal operating characteristics.

[0153] Example 6 Taking the integrated energy system of an industrial park in Northwest China as a case study, an optimization model was constructed using the YALMIP modeling toolbox on the Matlab simulation platform, and the CPLEX solver was used for solving and benefit analysis. Regarding carbon emissions, to fully reflect the impact of seasonal operating differences on the system, carbon emission data under typical daily operating conditions was first obtained, then accumulated quarterly to form seasonal emissions, and finally integrated into the annual total carbon emissions for trading and settlement.

[0154] Experimental verification analysis was conducted on typical scenarios in the region. The day-ahead power curves of wind power, photovoltaic (Fig. 4(a)), electrical load (Fig. 4(b)), thermal load (Fig. 4(c)), and gas load (Fig. 4(d)) under typical PIES scenarios are shown in Fig. 4(a)~Fig. 4(d). The seasonal carbon emission coefficient of PIES is shown in Table 2.

[0155] Table 2 Seasonal Distribution of Net Load and Carbon Emission Demand

[0156] As shown in Figures 4(a) to 4(d), photovoltaic power generation reaches its peak during the day, but cannot generate electricity at night due to lack of sunlight, while wind power generation is often stronger at night and generates more electricity. This temporal complementarity, through the rational allocation of the ratio of wind and photovoltaic power generation, can optimize the power supply structure and improve the stability and reliability of the new energy system.

[0157] As shown in Table 2, PIES carbon emissions in northern my country exhibit seasonal characteristics, with winter and summer being peak periods, spring being a trough, and autumn being a plateau.

[0158] Based on typical scenarios, in order to effectively verify the impact of tiered carbon pricing and seasonal carbon trading policies on PIES low-carbon economic dispatch, the following 5 verification schemes were set up.

[0159] Option 1: The carbon trading mechanism adopts a fixed carbon price, and the carbon trading adopts a traditional annual trading strategy.

[0160] Option 2: The carbon trading mechanism adopts the traditional tiered carbon pricing, and the carbon trading adopts the traditional annual trading strategy.

[0161] Option 3: The carbon trading mechanism adopts an optimized tiered carbon price, and the carbon trading adopts a traditional annual trading strategy.

[0162] Option 4: The carbon trading mechanism adopts an optimized tiered carbon price, and the carbon trading adopts a four-season average allocation strategy.

[0163] Option 5: The carbon trading mechanism adopts an optimized tiered carbon pricing system, and carbon trading employs a seasonal trading strategy. The optimization results of schemes 1-3 are compared by Figure 5 As shown. By Figure 5 It is evident that Scheme 3, compared to Schemes 2 and 1, can significantly reduce PIES carbon emissions by optimizing the tiered carbon pricing mechanism.

[0164] To clarify whether the seasonal carbon trading mechanism can simultaneously promote deeper carbon emission reduction in PIES and take into account the system's economic operation objectives, a comparative analysis of the annual operating costs of PIES under both Scheme 4 and Scheme 5, where carbon emissions are reduced by 10%, is conducted. The analysis results for Schemes 4 and 5 are shown in Table 3.

[0165] Table 3. Annual operating cost distribution of Scheme 5 compared to Scheme 4

[0166] As shown in Table 3, Scheme 5, through the reasonable allocation of seasonal carbon emissions, achieves PIES The annual operating cost is reduced by an average of 1.023% compared to Plan 4, thus reducing system operating costs while further reducing carbon emissions.

[0167] This invention considers an optimized operation method for seasonal scheduling. First, an integrated energy system incorporating carbon capture power plants and diversified hydrogen energy utilization is established to fully leverage the system's flexible operation advantages and high-efficiency energy utilization potential. The NBRO-Kmeans method is used to obtain wind and solar load data for typical scenarios in four seasons. Second, a carbon emission quota allocation model is developed using TOPSIS combined with GRA to provide a more comprehensive evaluation dimension, and an optimized tiered carbon trading mechanism is introduced. Finally, with minimizing the total system operating cost as the optimization objective, simulation examples verify the effectiveness of this method in improving system economy and reducing carbon emissions, providing support for the low-carbon transformation of energy.

Claims

1. An optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling, characterized by: Specifically, the steps include the following: Step 1: Build an integrated energy system that combines carbon capture power plants with diversified hydrogen energy utilization. Use the NBRO-Kmeans++ algorithm to select representative typical scenarios from the annual data. Step 2: Based on Step 1, a scientific carbon emission quota allocation model is established by integrating multiple principles and indicators through the superior-inferior solution distance method and grey relational analysis, and the carbon emission characteristics of different seasons are analyzed. Step 3: Based on Step 2, when performing low-carbon economic scheduling on PIES, optimize the scheduling with the goal of minimizing the total operating cost.

2. The method for optimizing the operation of a hydrogen-containing integrated energy system considering seasonal scheduling as described in claim 1, characterized in that: The specific process of step 1 is as follows: Step 1.1: Construct a framework centered on a carbon capture power plant operation model and a hydrogen energy multi-utilization structure. This framework includes a carbon capture power plant model, a hydrogen-blended cogeneration unit model, a gas boiler model, a hydrogen energy utilization model, and a hydrogen storage tank model. Step 1.2: Use the NBRO-Kmeans++ algorithm to select representative typical scenarios from the annual data.

3. The optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling according to claim 2, characterized in that: In step 1.1, the mathematical models of each device are expressed as follows: Carbon capture power plant model: In the formula: Let t be the total power generation capacity of the coal-fired unit at time t; Let t be the net output power of the carbon capture power plant. This represents the basic energy consumption of carbon capture power plants; , Let t be the carbon capture energy consumption and operating energy consumption of the carbon capture power plant. Energy consumption coefficient for carbon capture operation; For the coal-fired unit at time t Total generation; Carbon emission intensity of coal-fired power units; Let t be the flue gas split ratio; , The absorption tower at time t The absorption tower and regeneration tower need to process Regeneration rate; , These are absorption efficiency and regeneration efficiency, respectively. For the rich liquid tank at time t Outflow rate: a positive value indicates flow from the rich liquid tank to the regeneration tower, and a negative value indicates flow from the absorption tower to the rich liquid tank; For the actual time t Capture quantity; Model of hydrogen-doped cogeneration unit: In the formula, Let be the hydrogen blending ratio of the fuel gas at time t. , These represent the hydrogen power and natural gas power input to the cogeneration unit via the gas-blending device at time t, respectively. The input power of the combined heat and power unit is the mixed gas power at time t; , , The lower heating values ​​are for hydrogen, natural gas, and mixed fuel gas, respectively. Gas boiler model: In the formula: Let be the natural gas input power of the integrated energy system at time t; The thermal efficiency coefficient of a gas-fired boiler; Let be the output power of the gas-fired boiler at time t; The models for the refined utilization of hydrogen energy include an electrolyzer model and a methane reactor model: Electrolyzer model: In the formula: , These represent the input electrical power and hydrogen conversion power of EL at time t, respectively. The energy conversion efficiency of EL; Methane reactor model: In the formula: , These represent the input and output power of the methane reactor at time t, respectively. The energy conversion efficiency of a methane reactor; Hydrogen fuel cell model: In the formula: , These represent the input and output power of the hydrogen fuel cell at time t, respectively. For the power generation efficiency of fuel cells; Hydrogen storage tank model: In the formula: Let t be the storage capacity of the hydrogen storage tank at time t; , These represent the hydrogen storage and hydrogen release power at time t, respectively. , These are hydrogen storage efficiency and hydrogen release efficiency, respectively. , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , These are variables representing the charging / discharging states, 0 and 1, respectively.

4. The optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling according to claim 3, characterized in that: The specific process of step 1.2 is as follows: Step 1.2.1, Data preprocessing, using maximum-minimum normalization: In the formula: where, These are the original data values; It is the minimum value of the dataset; It is the maximum value in the dataset; Step 1.2.2: Use the Kmeans++ algorithm to obtain k initial centroids, and then introduce Gaussian noise to perturb each centroid, generating m sets of candidate solutions to form the initial population; Step 1.2.3: Introduce the global search mechanism from the differential evolution algorithm to optimize the initial cluster centers. The differential evolution direction... It consists of a linear combination of the global optimal term and the random difference term weighted by scalar random numbers, as shown in equation (9): In the formula: and These are the random weighting coefficients that control the direction of synthesis; It is a candidate solution that is globally optimal; It is the k-th centroid of the m-th candidate solution in the population at the t-th iteration; and It is the index of a randomly selected candidate solution; Step 1.2.4: Post-process the current centroid using Kmeans++ to construct a diagonal Hessian inverse matrix. Generate Newton's second-order correction term As shown in the following formula: In the formula: Let be the sample set of the k-th cluster; The average position of all sample points in the k-th cluster; when class | When the number of samples in | is 0 Take 10 8 ; Steps 1.2.5 and 1.2.3 generate the differential evolution global update term, and step 1.2.4 generates the Newton second-order correction term. The weighted composite of these two terms is applied to the current centroid to obtain the composite centroid update formula, as shown in the following equation: In the formula: This represents the maximum number of iterations. The learning rate is set to zero exponentially in the first t / 2 iterations to suppress oscillations. Step 1.2.6: Iteratively calculate the intra-cluster variance cost for all candidate solutions. The top 10% are selected as elite solutions, and their centroids are retained until the next iteration. The variance cost is shown in the equation: The optimal solution is the one with the smallest sum of squared errors among the elite solutions in the current population, which is the typical day selected.

5. The method for optimizing the operation of a hydrogen-containing integrated energy system considering seasonal scheduling according to claim 4, characterized in that: The specific process of step 2 is as follows: Step 2.1: Establish an indicator system for the allocation of carbon emission quotas based on multiple principles and indicators; Step 2.2: Settle the annual carbon trading allowances quarterly; Step 2.3 adopts an optimized tiered carbon pricing mechanism, which sets an initial carbon emission margin, provides subsidies and rewards to entities whose carbon emissions are below the margin, and imposes penalties on entities whose emissions exceed the margin.

6. The optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling according to claim 5, characterized in that: The specific process of step 2.1 is as follows: Step 2.1.1: Using grey relational analysis, a carbon quota correlation model is constructed. Let N be the total number of units participating in carbon emission quota allocation, and K be the total number of carbon trading calculation periods. Three carbon quota allocation indicators are selected. The original indicator matrix at time k can then be represented as a matrix. as follows: In the formula: Let i represent the original value of the i-th carbon quota allocation index for the i-th time unit, where i = 1, 2, ..., N represents different quota allocation units; y =1, 2, 3 represent the y-th carbon quota allocation indicator; k Indicates the time of carbon trading calculation; Step 2.1.2: Since the indicators differ in dimensions and value ranges, first determine the global extreme values ​​of each indicator across all units and all time periods. For the y-th indicator, the minimum and maximum values ​​are as follows: In the formula, , Let represent the global minimum and global maximum values ​​of the y-th index across all units and all times, respectively; Step 2.1.3 involves performing dimensionless processing on the parameters affecting carbon trading quotas, and standardizing all indicators to unify their directions, as shown in the equation: In the formula, Let y be the standardized value of the y-th index of unit i at time k. Step 2.1.4: The grey relational analysis method is used to calculate the correlation degree of each indicator, with the corresponding reference sequence being... Then its absolute difference is the first y The absolute difference between each indicator parameter and the reference carbon emission quota and corresponding grey relational coefficients for: In the formula, Let the value of the y-th index of unit i at time k be after dimensionless processing; The resolution coefficient; Step 2.1.5, calculate the first... y Correlation of individual indicator parameters and weighting coefficients As shown in the following formula: In the formula: The summation subscript represents the j-th carbon quota allocation index; Step 2.1.6: Use the TOPSIS method to calculate the degree of closeness of each element to the ideal solution, and take the mean of the standardized index values ​​at each time point, i.e.: right After undergoing homogenization and normalization processes, the standardized decision matrix elements are obtained. Then, the index weights obtained from grey relational analysis are combined. The weighted normalized decision matrix is ​​constructed as follows: In the formula: This represents the weighted normalized value of unit i on the y-th index; Step 2.1.7: Based on the weighted normalization matrix, determine the positive ideal solution and the negative ideal solution respectively, as shown in the following equation: In the formula: The value is a positive ideal solution; The value is a negative ideal solution; Each unit i The distances to the positive and negative ideal solutions are shown in the following formulas: In the formula: The value represents the distance from each cell to the positive ideal solution; The value represents the distance from each cell to the negative ideal solution; Calculate proximity As shown in the following formula: Step 2.1.8, adjust the unit proximity. After normalization, the carbon emission quota coefficient of unit i is obtained. for: in, Indicates the degree of closeness.

7. The optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling according to claim 6, characterized in that: The specific process of step 2.2 is as follows: Step 2.2.1, the seasonal carbon emissions must meet the following requirements: In the formula, Maximum carbon emissions for each season, , , , This represents the percentage of carbon emissions by season. Step 2.2.2: Establish seasonal carbon emission constraints in PIES, specifically... In the formula: , , This represents the carbon emission coefficient corresponding to power output, gas consumption, and heat consumption.

8. The optimized operation method for a hydrogen-containing integrated energy system considering seasonal scheduling according to claim 7, characterized in that: In step 2.3, the carbon trading cost is calculated using the following formula: In the formula: Carbon trading costs borne by IES; This represents the actual total carbon emissions of the IES; c The benchmark price for carbon trading; These represent the price growth coefficients; h This indicates the permissible limit for exceeding carbon emission limits.

9. The method for optimizing the operation of a hydrogen-containing integrated energy system considering seasonal scheduling as described in claim 8, characterized in that: The specific process of step 3 is as follows: Step 3.1, the integrated energy system low-carbon economic dispatch model takes minimizing total cost as the optimization dispatch objective, and the objective function is expressed as follows: The costs of coal and gas are: In the formula: The unit power generation cost of coal-fired power units; for t Gas purchase capacity of the time-of-use system The unit price for purchasing natural gas; The amount of CO2 supplied by the carbon capture power plant for electricity-to-gas conversion is: In the formula: for t The amount of CO2 required for the periodic power-to-gas conversion operation; The density of CO2 gas; The cost of carbon sequestration is: In the formula: The cost required to seal a unit of CO2; The cost of curtailing wind and solar power is: In the formula, The penalty cost per unit of abandoned wind and solar power; for t The amount of wind and solar power abandoned at any given moment; Step 3.2: The electrical, thermal, gas, and hydrogen power within the integrated energy system must respectively satisfy the following balance constraints: In the formula: , They are respectively t Actual power generation output of wind and solar power during the period; , , They are respectively t The electricity, heat, and gas load requirements of the system during different time periods; for t The output thermal power of the gas-fired boiler during a specific time period; , They are respectively t The heat storage and heat release capacity of the time-period thermal storage tank; Constraints of wind power generation: In the formula, The predicted power output of the wind turbine generator during time period t; Carbon capture power plant operating constraints: In the formula: , These are the upper and lower limits of the total output of coal-fired power units, respectively. , These are the upper and lower limits of the ramp power for coal-fired power units, respectively. By adjusting the flue gas split ratio, the carbon capture system can adjust the carbon capture scale at different times as needed, subject to the following constraints: In the formula: , These are the upper and lower limits of the flow splitting ratio for the flue gas splitting device; Under the maximum operating condition of the power plant operating at full load, the regeneration tower can completely process all the CO2 absorbed by the absorption tower. The energy consumption of the carbon capture system must meet the following constraints: In the formula: The operating energy consumption of the carbon capture system at time t; This is the flue gas diversion ratio coefficient; τ This represents the maximum operating condition coefficient for the regeneration tower and compressor. The carbon emission intensity coefficient for coal-fired power units; To the maximum output of the coal-fired unit; Step 3.2.4: For the electrolyzer model, methane reactor model, hydrogen fuel cell model, combined heat and power (CHP) model, and gas boiler model, ensure that the input power first meets the equipment capacity constraints, as shown in the following formula: In the formula: For the first i The input power of this energy conversion device; For the first i The capacity of this type of energy conversion equipment; , These are the upper and lower limits of the ramp power for the i-th type of energy conversion device, respectively. Step 3.2.5: The hydrogen storage tank model needs to consider storage capacity constraints, single hydrogen storage / release constraints, complementary storage and release states constraints, and periodic storage conservation constraints, as expressed below: In the formula: for t Storage capacity of the time-limited hydrogen storage tank; , They are respectively t Hydrogen storage and release power over a given period of time; , These are hydrogen storage efficiency and hydrogen release efficiency, respectively. , These are the upper and lower limits of hydrogen storage capacity, respectively. , These are the maximum hydrogen storage and hydrogen release power per cycle, respectively. , The charge / discharge state is a 0-1 variable; Step 3.2.6, considering the seasonal characteristic constraints of the two energy storage systems, Hys and HS, is expressed as follows: In the formula: Total capacity for different seasons; The expected daily capacity is calculated for typical daily scenarios in different seasons.

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