Optimization Method for Integrated Energy System Considering Seasonal Hydrogen Storage and Utilization of Hydrogen-Fired Turbines

Abstract

The present invention discloses an optimization method for an integrated energy system considering seasonal hydrogen storage and utilization of hydrogen-fired gas turbines, comprising the following steps: Step 1, providing a multi-energy coupled integrated energy system with carbon-containing streams, the integrated energy system including wind power, photovoltaic power, hydropower, thermal power units, traditional gas units, hybrid fuel gas units, electrolysis water devices, hydrogen storage devices, hydrogen methanation devices, carbon capture and storage devices, absorption refrigerators, electric refrigeration equipment, and gas boilers; Step 2, establishing a two-layer planning-operation model for the integrated energy system with the comprehensive minimum of system retrofit and upgrade costs, operating costs, and penalty costs as the objective function; Step 3, using an improved differential evolution algorithm for solution to optimize the operating strategies of different units while achieving the optimal configuration of equipment capacities. The present invention solves the problem of seasonal inversion between the output of renewable energy and load demand on a medium- and long-time scale, and provides a better hydrogen-electricity coupling method.

Description

Technical Field

[0001] The present invention relates to the technical field of integrated energy system planning and operation, and particularly relates to an optimization method for an integrated energy system considering seasonal hydrogen storage and the utilization of hydrogen-fired turbines. Background Art

[0002] With the upgrading and transformation of the new energy structure and to promote the realization of low-carbon goals, the installed capacity ratio of traditional thermal power units has been continuously diluted by wind power and photovoltaic power generation, which has weakened the safety and stability of the system to a certain extent. To address the problem of insufficient regulation capacity of the power system, the state has continuously introduced relevant policy guidance for energy storage, and the research and practice of energy storage forms such as electrochemical energy storage and pumped-storage energy storage have become increasingly in-depth. However, the power generation characteristics of wind power, photovoltaic power, hydropower, and load are significantly seasonal due to the influence of the natural environment. The energy system is in short supply in winter and summer, while in spring and autumn, the supply exceeds the demand. In special extreme weather conditions, new energy may be in a low-output state for consecutive days, further expanding the energy supply gap. Pumped-storage energy storage and lithium battery energy storage are restricted by various factors such as high geographical requirements for construction, storage capacity and economic constraints, high storage energy dissipation rate, and difficult subsequent recycling, and it is difficult to participate in the operation optimization on longer time scales such as weeks and months. Therefore, it is necessary to further study large-scale, long-term, and wide-field seasonal energy storage (SES).

[0003] The main key features of seasonal energy storage are long-time-scale energy storage, cross-energy forms, and spatial transportability. With the continuous progress of electrolysis water hydrogen production technology, the cost of using renewable energy to electrolyze water to produce hydrogen has been continuously reduced. As one of the key factors in the process of energy conversion, carbon dioxide treatment, and resource utilization, hydrogen can not only meet the large-scale and long-cycle energy storage needs of renewable energy, but also, as a pollution-free green fuel, can effectively couple with electricity deeply, reduce the use of fossil energy, and reduce carbon emissions. Therefore, hydrogen energy storage is a seasonal energy storage with application potential.CAO Junwen, QIN Xiangfu, GENG Ga et al. summarized the current hydrogen storage and transportation methods and the current status of technology research in the article "Development Status and Prospect of Hydrogen Storage and Transportation Technology" published in Acta Petrolei Sinica (Petroleum Processing) Vol. 37, No. 6, pp. 1461-1478 (2021). UCHMAN W, SKOREK-OSIKOWSKA A, JURCZYK M et al. evaluated the impact of hydrogen storage on the time-domain analysis of power systems in the article "The analysis of dynamic operation of power-to-SNG system with hydrogen generator powered with renewable energy, hydrogen storage and methanation unit" published in Energy Vol. 213 (2020). ZHANG Hong, YUAN Tiejiang, TAN Jie analyzed the impact of the change in the energy system structure form and utilization mode of new energy power generation-hydrogen storage coupling in the article "Medium- and Long-Term Prediction of Hydrogen Load in a Unified Energy System" published in Proceedings of the CSEE Vol. 40, No. 10, pp. 3364-3372 (2021). PAN G, GU W, LU Y et al. proposed a robust planning model for an integrated electricity-hydrogen energy system considering the uncertainty of power generation load, the N-1 security of units, and the flexible electricity-hydrogen conversion process, and analyzed the impact of seasonal hydrogen storage on a medium- and long-term time scale of four seasons in a year in the article "The analysis of dynamic operation of power-to-SNG system with hydrogen generator powered with renewable energy, hydrogen storage and methanation unit" published in IEEE Transactions on Sustainable Energy Vol. 11, No. 4 (2020). HOU Hui, LIU Peng, HUANG Liang et al. proposed a multi-objective planning model for an integrated electricity-thermal-hydrogen energy system by combining the CHP operation mode switching strategy in the article "Planning of Integrated Electricity-Thermal-Hydrogen Energy System Considering Uncertainty" published in Transactions of China Electrotechnical Society Vol. 36, Supplement 1, pp. 133-144 (2021). DING Jian, FANG Xiaosong, SONG Yunting et al. proposed a preliminary construction plan for the near-term and long-term western integrated electricity-hydrogen energy power grid aiming at the problem of external transmission and consumption of a high proportion of new energy power sources in western China in the article "Conception of Integrated Electricity-Hydrogen Energy Network for New Energy Transmission in Western China under the Background of Carbon Neutrality" published in Automation of Electric Power Systems Vol. 45, No. 24, pp. 1-9 (2021).At present, most of the research on the operation optimization strategy of hydrogen-containing integrated energy generally focuses on relatively small industrial parks. Some research uses hydrogen fuel cells to convert hydrogen into electricity, thereby reducing the peak-valley difference of the system. However, considering the high energy loss rate in the energy conversion process of electricity-hydrogen-electricity and the high cost of fuel cells themselves, it is difficult to be actually applied in large-capacity integrated energy systems, and a better hydrogen-electric coupling method needs to be sought.

[0004] Gas turbines are important energy equipment for promoting multi-energy coupling. Li Haibo, Pan Zhiming, Huang Yaowen, etc. analyzed the technical characteristics of hydrogen fuel gas turbine power generation in the article "Analysis of the Application Prospect of Hydrogen Fuel Gas Turbine Power Generation" published on pages 94-96 of the 8th issue of "Power Equipment Management" in 2020. Qin Feng, Qin Yadi, Shan Tongwen, etc. pointed out in the article "The Current Situation and Development Prospect of Hydrogen Fuel Gas Turbine Technology under the Background of Carbon Neutrality" published on pages 10-16 of the 10th issue of Volume 34 of "Guangdong Electric Power" (2021) that the combustion of natural gas mixed with hydrogen is more complete, generates more energy and has fewer side effects of chemical products. At present, major gas turbine manufacturers have launched the research, development and production of hydrogen-rich fuel or even pure hydrogen fuel gas turbines. General Electric Power Company already has multiple mixed hydrogen fuel gas turbine projects in commercial operation. It can be seen that gas turbine units with mixed fuels have great potential to undertake multi-energy coupling and use hydrogen fuel efficiently. Replacing natural gas with hydrogen for power generation is one of the important ways to reduce carbon emissions in the process of energy transformation. Combining carbon capture, utilization and storage (CCUS) technology can achieve carbon control throughout the process from source generation to final use. Therefore, it is necessary to add research on the coupling of carbon flow in the integrated energy system framework.

[0005] Therefore, the technical personnel in this field are committed to developing an optimization method for an integrated energy system considering seasonal hydrogen storage and the utilization of hydrogen combustion turbines to overcome the above existing problems. Summary of the Invention

[0006] In view of the above-mentioned defects of the prior art, the technical problem to be solved by the present invention is how to solve the problem of seasonal inversion between the output of renewable energy and load demand on a medium- and long-term time scale, and to provide a better hydrogen-electric coupling method.

[0007] To achieve the above object, the present invention provides an optimization method for an integrated energy system considering seasonal hydrogen storage and the utilization of hydrogen combustion turbines, and the method includes the following steps:

[0008] Step 1. Provide a multi - energy coupled integrated energy system with a carbon - containing stream. The integrated energy system includes wind power, photovoltaic power, hydropower, thermal power units, traditional gas units, hybrid - fuel gas units, electrolysis water devices, hydrogen storage devices, hydrogen methanation devices, carbon capture - sequestration devices, absorption refrigerators, electric refrigeration equipment, and gas boilers;

[0009] Step 2. Establish a two - layer planning - operation model of the integrated energy system with the comprehensive minimum of system retrofit and upgrade costs, operation costs, and penalty costs as the objective function;

[0010] Step 3. Use an improved differential evolution algorithm for solution to optimize the capacity of the optimal equipment and the operation strategies of different units at the same time.

[0011] Furthermore, the energy conversion model of the electrolysis water device in Step 1 is:

[0012]

[0013]

[0014]

[0015]

[0016] In the formula, are the power consumption, hydrogen production capacity, and heat production power of the electrolysis water device at time t respectively, are the electro - hydrogen production efficiency and waste heat utilization efficiency of the electrolysis water device, is the conversion coefficient of electricity - hydrogen conversion per unit, is the calorific value of hydrogen combustion, 142500 kJ / m 3 , represents the maximum hydrogen production capacity of the electrolysis water device, and α is the conversion coefficient between kilowatts and kilojoules per hour, taking 3600.

[0017] Furthermore, the hydrogen storage device in Step 1 adopts seasonal hydrogen storage, and its constraint model is:

[0018]

[0019]

[0020]

[0021] S shs (0)=0.5Q shs (8)

[0022]

[0023] 0≤Sshs P(t) ≤ Q shs (10)

[0024] Wherein, P(t) and P'(t) respectively represent the power stored and released by the hydrogen storage device at time t, x(t) and x'(t) respectively represent the 0-1 state quantity charged and discharged at time t, V shs-max Q represents the maximum power of the hydrogen storage device, shs S represents the maximum capacity of the hydrogen storage device, shs S(0) and shs S(t) and shs S(t - 1) are respectively the initial value of the energy stored in the hydrogen storage device, the remaining energy at time t, and the remaining energy at time t - 1, η and η' respectively represent the charging and discharging efficiency of the hydrogen storage device, and Δt represents the unit time of charging and discharging of the hydrogen storage device.

[0025] Furthermore, the hydrogen methanation energy conversion model in the step 1 is as follows:

[0026]

[0027]

[0028] ξ e-gas = Q gas / α (13)

[0029]

[0030]

[0031] Wherein, Pm(t) represents the hydrogen methanation capacity and the power of the waste heat in the hydrogen methanation reaction process at time t, hm(t) and cm(t) respectively represent the hydrogen consumption and carbon dioxide consumption in hydrogen methanation at time t, and ω1 represents the mixing ratio of carbon dioxide gas in the reaction process, ηm represents the methane and heat energy conversion efficiency of hydrogen to methane, Ξ is the maximum capacity of the hydrogen methanation device, ξ e-gas α is the conversion coefficient of electricity-natural gas conversion unit, Q gas Qnet is the calorific value of natural gas combustion 33486.8kJ / m 3 .

[0032] Furthermore, the integrated energy system bi-level programming-operation model established in the step 2 includes an upper-level programming investment cost objective function and a lower-level operation optimization cost objective function;

[0033] The upper-level programming investment cost is:

[0034]

[0035] λ crf = r·(1 + r) y / ((1 + r) y - 1) (17)

[0036] In the formula, C inv represents the upper-layer planning investment cost, are respectively the unit capacity investment costs of the gas turbine unit retrofit and upgrade, electrolyzer, hydrogen storage device power, hydrogen storage device capacity, and hydrogen methanation device for the hybrid fuel; P gt-max , V shs-max Q shs , represent the maximum capacity of the gas turbine unit retrofit and upgrade, maximum hydrogen production capacity of the electrolyzer, maximum power of the hydrogen storage device, maximum capacity of the hydrogen storage device, and maximum capacity of the hydrogen methanation device obtained from the upper-layer planning model demand, providing optimization constraint conditions for the lower-layer model; λ crf is the capital recovery factor, r is the annual interest rate, and y is the average service life of the system design;

[0037] The lower-layer operation optimization cost includes the operation cost C op and the penalty cost C pw ,

[0038] The operation cost C op includes the fuel cost C opf , other device operation costs C opa and the unit startup cost C opu , where the fuel cost of the thermal power unit is processed by piecewise linearization:

[0039] C op = C opf + C opa + C opu (18)

[0040]

[0041]

[0042]

[0043]

[0044] In the formula: c gas is the natural gas price cost, is the amount of natural gas purchased by the system at time t; a1, a2, a3, b1, b2, b3 are the coefficients for the piecewise linearization of the thermal power unit generation cost, C g′ en,i (t) is the fuel cost variable of thermal power unit i at time t; P gen,i (t) is the output power of thermal power unit i at time t; are the unit - capacity operating costs of the water electrolysis device, hydrogen storage device, hydrogen methanation device, carbon capture - sequestration device, and absorption chiller; are the start - up costs of the thermal power unit, hybrid fuel gas unit, and traditional gas unit, are the start - up state variables of the thermal power unit, hybrid fuel gas unit, and traditional gas unit at time t, N gen is the number of thermal power units, are the amount of carbon dioxide gas sequestered after carbon capture and the cooling power generated by the absorption chiller at time t,

[0045] The penalty cost C pw is the penalty for wind, light, and water curtailment:

[0046]

[0047] In the formula, c cut is the unit cost of wind, light, and water curtailment penalty, is the wind, light, and water curtailment power at time t.

[0048] Furthermore, the overall objective function of the integrated energy system's two - layer planning - operation model is:

[0049] minC total = C inv + C op + C pw (24)

[0050] In the formula, C total is the total cost.

[0051] Furthermore, the constraint conditions of the integrated energy system's two - layer planning - operation model include:

[0052] System power balance equations for electricity, heat, cold, and hydrogen:

[0053]

[0054]

[0055]

[0056]

[0057]

[0058] In the formula, L e (t), Lh (t), L co (t), respectively represent the demand of electricity, heat, cold, and hydrogen conventional loads at time t.

[0059] Equation (25) represents the electric power balance of the integrated energy system. is the net output of thermal power unit i at time t, P pv (t), P wt (t), P hp (t) are the outputs of wind power, photovoltaic power, and hydropower units at time t. is the net output of traditional gas unit n at time t. is the net output of the retrofitted hybrid fuel gas unit at time t. is the electric power consumed by the electrolyzer and electric refrigeration equipment at time t.

[0060] Equation (26) represents the natural gas volume balance of the integrated energy system. is the natural gas volume burned by traditional gas unit n at time t. is the natural gas volume burned by the retrofitted hybrid fuel gas unit at time t. is the natural gas volume burned by the gas boiler at time t. is the natural gas volume produced by the hydrogen methanation device at time t.

[0061] Equation (27) represents the heat power supply-demand balance of the integrated energy system. is the collection and conversion power of the waste heat power burned by traditional gas unit n at time t. is the collection and conversion power of the waste heat power burned by the retrofitted hybrid fuel gas unit at time t. is the collection and utilization power of the heat energy in the hydrogen methanation and electrolysis reactions at time t. is the supplementary heat power of the gas boiler at time t. is the heat power absorbed by the absorption refrigeration equipment at time t. is the power discarded due to the ineffective utilization of the system heat energy at time t.

[0062] Equation (28) represents the cold power supply-demand balance of the integrated energy system. is the refrigeration power of the electric refrigeration equipment and the absorption refrigeration equipment at time t.

[0063] Equation (29) represents the balance of hydrogen production, storage, and utilization in the integrated energy system. represents the hydrogen volume generated by the electrolysis reaction at time t. represents the hydrogen volume consumed by the hydrogen methanation and the retrofitted hybrid fuel gas unit at time t. Indicates the amount of hydrogen released and stored by seasonal hydrogen storage at time t;

[0064] Renewable energy output constraint:

[0065]

[0066]

[0067]

[0068] Are the predicted maximum output values of wind power, photovoltaic power, and hydropower at time t;

[0069] Thermal power unit output constraint:

[0070] u gen,i (t)P gen-min,i ≤P gen,i (t)≤u gen,i (t)P gen-max,i (33)

[0071]

[0072]

[0073]

[0074] In the formula, P gen-max,i 、P gen-min,i Respectively represent the maximum and minimum output of thermal power unit i, u gen,i (t) represents the state variable of thermal power unit i at time t, Is the start state variable of thermal power unit i at time t, Is the shutdown state variable of thermal power unit i at time t;

[0075] Gas turbine unit output constraint, including the output constraint of the hybrid fuel gas turbine unit:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] In the formula, respectively represent the electric power and heat power output by the hybrid fuel gas unit at time t, respectively represent the input volume flow rates of natural gas and hydrogen of the hybrid fuel gas unit at time t, ξ e-gas 、 respectively represent the conversion unit conversion coefficients of electricity-natural gas and electricity-hydrogen of the hybrid fuel gas unit, and ω2 represents the mixing coefficient of hydrogen and natural gas of the hybrid fuel gas unit; respectively represent the efficiencies of converting electric energy and heat energy of the hybrid fuel gas unit; u gtc (t), are state variables from 0 to 1, respectively representing the state variable, start state variable, and shutdown state variable of the hybrid fuel gas unit at time t, P gtc-max 、P gtc-min are the maximum and minimum powers of the hybrid fuel gas unit;

[0083] Carbon capture and storage device constraint:

[0084]

[0085]

[0086]

[0087]

[0088] In the formula, is the electric power generated by the gas unit at time t, is the net output electric power of the gas unit at time t, is the operating energy consumption of the carbon capture and storage device at time t, is the basic fixed energy consumption of the carbon capture and storage device at time t, represents the amount of carbon dioxide gas captured by the carbon capture and storage device at time t, represents the amount of carbon dioxide gas sealed after capture by the carbon capture and storage device at time t, represents the amount of carbon dioxide gas added to the methanation reaction after capture by the carbon capture and storage device at time t, is the operating energy consumption coefficient for capturing unit carbon, is the capture efficiency, is the carbon emission intensity per unit energy of the gas unit;

[0089] Cooling and heating energy supplement unit constraint, and the cooling and heating energy supplement unit constraint includes an absorption chiller constraint, an electric refrigeration equipment constraint, and a gas boiler constraint.

[0090] Furthermore, the absorption chiller constraint is:

[0091]

[0092]

[0093] In the formula, represents the cooling power output by the absorption chiller at time t, represents the heat power absorbed by the absorption chiller at time t, represents the energy conversion efficiency of the absorption chiller, represents the maximum cooling power that the absorption chiller can convert and output, u ac (t) is a 0-1 variable representing the start-stop state of the absorption chiller at time t;

[0094] The constraints of the electric refrigeration equipment are as follows:

[0095]

[0096]

[0097] In the formula, represents the cooling power output by the electric refrigeration equipment at time t, represents the electric power consumed by the electric refrigeration equipment at time t, represents the energy conversion efficiency of the electric refrigeration equipment, represents the maximum cooling power that the electric refrigeration equipment can convert and output, u fr (t) is a 0-1 variable representing the start-stop state of the electric refrigeration equipment at time t;

[0098] The constraints of the gas boiler are as follows:

[0099]

[0100]

[0101] In the formula, represents the heat power output by the gas boiler at time t, represents the electric power consumed by the gas boiler at time t, represents the energy conversion efficiency of the gas boiler, represents the maximum heat power that the gas boiler can convert and output, u gb (t) is a 0-1 variable representing the start-stop state of the gas boiler at time t.

[0102] Furthermore, the improved differential evolution algorithm in step 3 specifically includes the following steps:

[0103] Step 3.1, Initialization: Determine the boundary range of the planning capacity, set the population size N p , and randomly generate the initial population. Among them, the population individuals are:

[0104]

[0105] Step 3.2, Mutation:

[0106]

[0107] F = 2 λ f0 (55)

[0108]

[0109] In the formula, are three different individuals randomly selected in the G-th generation, is an individual in the mutation population, f0 is the initially set mutation parameter; G represents the current evolution generation, G m represents the maximum evolution generation;

[0110] Step 3.3, Crossover:

[0111]

[0112] In the formula, is the n-th dimensional variable of the i-th individual in the population obtained after crossover; C r is the crossover factor, with a value between [0, 1];

[0113] Step 3.4, Competition:

[0114]

[0115] In the formula, is the fitness function of the corresponding individual, that is, the comprehensive cost objective function in the model, and the selection method for the minimization problem is adopted;

[0116] Step 3.5, When G > G m the algorithm terminates to obtain the optimal solution, otherwise, G = G + 1, return to Step 3.2 for the next optimization.

[0117] Furthermore, the improved differential evolution algorithm in Step 3 is solved by cooperating with the Gurobi solver.

[0118] The beneficial effects of the present invention are as follows: Based on the framework of the multi-energy coupling integrated energy system with carbon-containing flows proposed by the present invention, a mathematical model of key equipment for seasonal hydrogen storage is established. The hydrogen produced from surplus renewable energy not only meets the demand of conventional hydrogen loads, but can also be transported to a gas turbine unit with a hybrid fuel for power generation, and cooperate with the captured carbon dioxide to be converted into natural gas. Secondly, based on the traditional integrated energy system, an objective function model with the minimum planning investment for upgrading equipment and the annual operating cost effectively reflecting seasonal characteristics is established. Combined with an improved differential evolution algorithm, the capacity configuration of optimal upgrading is carried out, and the operation strategies of various units, the seasonal hydrogen production-storage-utilization, and the process of carbon dioxide capture and utilization on a medium- and long-term time scale are analyzed. The integrated energy system proposed in this paper can effectively cope with the seasonal fluctuations of the net load, promote the consumption of renewable energy, and reduce the carbon emissions of the overall system.

[0119] The concept, specific structure and technical effects of the present invention will be further described below in conjunction with the drawings to fully understand the purpose, features and effects of the present invention. Brief Description of the Drawings

[0120] Figure 1 is a framework diagram of the multi-energy coupling integrated energy system with carbon-containing flows of a preferred embodiment of the present invention;

[0121] Figure 2 is a solution flow chart of the improved differential evolution algorithm for system optimization of the present invention. Detailed Embodiment

[0122] The preferred embodiments of the present invention are introduced below with reference to the accompanying drawings of the specification to make its technical content clearer and easier to understand. The present invention can be embodied in many different forms of embodiments, and the protection scope of the present invention is not limited to the embodiments mentioned in the text.

[0123] The present invention establishes an optimization method for a multi-energy coupling integrated energy system with carbon-containing flows based on considering seasonal hydrogen storage and the principle of a gas turbine with a hybrid hydrogen fuel.

[0124] To promote the realization of the dual-carbon goal, the penetration rate of new energy has been continuously increasing, exacerbating the contradiction of seasonal supply-demand imbalance. Therefore, it is necessary to conduct research on the planning and operation of a new integrated energy system on a medium- and long-term time scale. First, a framework of a multi-energy coupled integrated energy system with carbon flow is proposed, the production-storage-utilization process of hydrogen as seasonal energy storage is studied, and a gas turbine unit with a hybrid hydrogen fuel is established as a model for hydrogen-electricity coupling; second, a two-layer planning-operation model of the integrated energy system with the comprehensive minimum of system retrofit cost, operation cost, and penalty cost as the objective function is established, and an improved differential evolution algorithm is used for solution to optimize the capacity of the optimal equipment while optimizing the operation strategies of different units; finally, through specific examples, it is proved that the introduction of seasonal hydrogen storage can effectively promote the consumption of renewable energy, smooth the seasonal peak-valley difference of the net load curve, improve the economy of the integrated energy system, and reduce the carbon emissions of the system.

[0125] The present invention adds an electrolytic water hydrogen production and hydrogen storage model for medium- and long-term energy storage to the traditional integrated energy system, upgrades and transforms the traditional gas turbine model so that it can efficiently use hydrogen as fuel for power generation, promotes the close coupling of hydrogen and electricity, fully considers the utilization processes of carbon capture-sequestration and hydrogen methanation reactions, and also adds the waste heat reuse of the electrolytic water reaction and hydrogen methanation reaction processes to be coupled into the thermal energy network.

[0126] I. Framework of the integrated energy system with carbon flow and key equipment models of seasonal hydrogen storage

[0127] To promote the consumption and utilization of renewable energy and smooth the seasonal fluctuations of the system net load, an electrolytic water hydrogen production and hydrogen storage device for medium- and long-term energy storage is added to the traditional integrated energy system. At the same time, the gas turbine is partially upgraded and transformed so that it can efficiently use hydrogen as fuel for power generation, promoting the close coupling of hydrogen and electricity. The utilization processes of carbon capture, sequestration, and hydrogen methanation reactions are additionally considered. Considering that both the electrolytic water reaction and the hydrogen methanation reaction are high-temperature reactions, the waste heat reuse of the reaction process is also added and coupled into the thermal energy network. The seasonal characteristics of the cooling and heating loads on a medium- and long-term time scale can be complemented by absorption refrigeration equipment, and gas boilers and electric refrigeration equipment are used as backup supplements. In this paper, a framework of a multi-energy coupled integrated energy system with carbon flow applicable to medium- and long-term time scales is constructed as Figure 1 shown.

[0128] The following introduces the key equipment models of seasonal hydrogen storage:

[0129] 1. Electrolytic water hydrogen production model

[0130] At present, electrolytic hydrogen production is mainly divided into alkaline liquid electrolysis (ALK), proton exchange membrane electrolysis (PEM), alkaline solid anion exchange membrane (AEM), and high-temperature solid oxide electrolysis (SOEC). Among them, the energy conversion efficiency of the SOEC method can reach 100%, which is an important way for future high-efficiency hydrogen production.

[0131] Energy conversion model of the electrolysis device (ED):

[0132]

[0133]

[0134]

[0135]

[0136] In the formula, are the power consumption, hydrogen production capacity, and heat production power of the electrolysis device at time t, respectively. are the electrolytic hydrogen production efficiency and waste heat utilization efficiency of the electrolysis device. is the conversion coefficient for converting electricity to hydrogen. is the calorific value of hydrogen combustion, 142500 kJ / m 3 , represents the maximum hydrogen production capacity of the electrolysis device, and α is the conversion coefficient between kilowatts and kilojoules per hour, taking 3600.

[0137] 2. Hydrogen storage model

[0138] Seasonal hydrogen storage (hydrogen storage device) is different from ordinary energy storage devices. It can be charged and discharged multiple times within a day. The seasonal hydrogen storage is determined by the supply and demand relationship in each cycle, and there is only one charging or discharging state. Considering the refinement of the large-capacity storage and charging / discharging process, analogous to lithium batteries, considering from 2 variable perspectives, the constraint model of the seasonal hydrogen storage method is:

[0139]

[0140]

[0141]

[0142] S shs (0) = 0.5Qshs (8)

[0143]

[0144] 0 ≤ S shs (t) ≤ Q shs (10)

[0145] Wherein, respectively represent the power of charging (storage) and discharging (release) of the hydrogen storage device at time t, respectively represent the 0-1 state variables of charging and discharging at time t, V shs-max represents the maximum power of the hydrogen storage device, Q shs represents the maximum capacity of the hydrogen storage device, S shs (0), S shs (t), S shs (t - 1) are respectively the initial value of the stored energy of the hydrogen storage device, the remaining energy at time t, and the remaining energy at time t - 1, respectively represent the charging and discharging efficiencies of the hydrogen storage device, and △t represents the unit time of charging and discharging of the hydrogen storage device.

[0146] 3. Hydrogen methanation

[0147] It can cooperate with the carbon capture, utilization and storage device to carry out methanation using the carbon dioxide and hydrogen generated by conventional units, further promoting the realization of the dual-carbon goal. At present, the process of CO2 hydrogenation to synthesize methane is mainly realized through gas-solid multiphase catalytic reaction or biological method, which is related to factors such as temperature, pressure, specific catalyst type and carbon-hydrogen ratio of the raw material gas. The CO2 methanation reaction is a strongly exothermic reaction, and one of the main products is pure water, which is an important raw material for electrolytic water hydrogen production. Therefore, the process products and heat can be fully utilized in the integrated energy framework.

[0148]

[0149]

[0150] ξ e-gas = Q gas / α (13)

[0151]

[0152]

[0153] Wherein, represents the hydrogen methanation capacity at time t and the power of the waste heat in the hydrogen methanation reaction process, respectively represent the hydrogen consumption and carbon dioxide consumption in hydrogen methanation at time t, and ω1 represents the mixing ratio of carbon dioxide gas in the reaction process, Indicates the methane and thermal energy conversion efficiency of hydrogen - to - methane production is the maximum capacity of the hydrogen methanation device, ξ e-gas is the conversion coefficient of electricity - to - natural gas conversion unit, Q gas is the calorific value of natural gas combustion, 33486.8 kJ / m 3 .

[0154] II. Bilevel Planning - Operation Model of Integrated Energy System

[0155] 1. Upper - level planning investment cost objective function

[0156] The planning mainly considers further development based on the existing energy system. Therefore, the investment costs of conventional wind turbines, thermal power plants, hydropower plants, gas turbines, and heating and cooling equipment are temporarily ignored. Only the retrofit and upgrade costs of gas turbines using mixed fuels, the investment costs of equipment in the hydrogen production - storage - utilization process are considered. The cost of carbon sequestration is affected by the captured carbon dioxide capacity, so it is merged into the subsequent operating costs.

[0157]

[0158] λ crf = r·(1 + r) y / ((1 + r) y - 1) (17)

[0159] In the formula, C inv represents the upper - level planning investment cost, are respectively the unit capacity investment costs of the retrofit and upgrade of gas turbines using mixed fuels, electrolyzer units, hydrogen storage device power, hydrogen storage device capacity, and hydrogen methanation device; P gt-max , V shs-max , Q shs , represent the maximum capacity of the optimal system gas turbine retrofit and upgrade, the maximum hydrogen production capacity of the electrolyzer unit, the maximum power of the hydrogen storage device, the maximum capacity of the hydrogen storage device, and the maximum capacity of the hydrogen methanation device obtained from the upper - level planning model, providing optimization constraints for the lower - level model; λ crf is the capital recovery factor, r is the annual interest rate, taking 4%, and y is the average system design life period, taking 20 years.

[0160] 2. Lower - level operation optimization cost objective function

[0161] The operating cost C op is mainly divided into fuel cost C opf , other device operating cost C opa and unit startup cost C opu, where the fuel cost of the thermal power unit is processed by piecewise linearization:

[0162] C op = C opf + C opa + C opu (18)

[0163]

[0164]

[0165]

[0166]

[0167] In the formula: c gas is the natural gas price cost, is the amount of natural gas purchased by the system at time t; a1, a2, a3, b1, b2, b3 are the coefficients for the piecewise linearization of the thermal power unit's power generation cost, C g ′ eni (t) is the fuel cost variable of thermal power unit i at time t; P gen,i (t) is the output power of thermal power unit i at time t; is the unit capacity operation cost of devices such as electrolyzed water, hydrogen storage, hydrogen methanation, carbon capture - sequestration, and thermal refrigeration (absorption refrigeration machine); is the start - up cost of thermal power units, hybrid fuel gas units, and traditional gas units, is the start - up state variable of thermal power units, hybrid fuel gas units, and traditional gas units at time t, N gen is the number of thermal power units, is the amount of carbon dioxide gas sequestered after carbon capture and the cooling power generated by the absorption refrigeration machine at time t.

[0168] To promote the consumption of new energy, a penalty cost C pw is introduced, mainly for the penalty of wind, light, and water abandonment:

[0169]

[0170] In the formula, c cut is the unit cost of wind, light, and water abandonment penalty, is the wind, light, and water abandonment power at time t.

[0171] Therefore, the overall objective function of the integrated energy system's two - layer planning - operation model considering seasonal hydrogen storage proposed in this section is:

[0172] minC total = C inv + Cop +C pw (24)

[0173] In the formula, C total is the total cost.

[0174] 3. Constraints

[0175] 1. Power balance equations of electricity, heat, cold, and hydrogen in the system

[0176]

[0177]

[0178]

[0179]

[0180]

[0181] In the formula, L e (t), L h (t), L co (t), respectively represent the demands of electricity, heat, cold, and hydrogen conventional loads at time t.

[0182] Equation (25) represents the electric power balance of the integrated energy system, is the net output of thermal power unit i at time t, P pv (t), P wt (t), P hp (t) are the outputs of wind power, photovoltaic power, and hydropower units at time t, is the net output of traditional gas unit n at time t, is the net output of the retrofitted hybrid fuel gas unit at time t, is the electric power consumed by the electrolyzer and electric refrigeration equipment at time t.

[0183] Equation (26) represents the natural gas volume balance of the integrated energy system, is the natural gas volume burned by traditional gas unit n at time t, is the natural gas volume burned by the retrofitted hybrid fuel gas unit at time t, is the natural gas volume burned by the gas boiler at time t, is the natural gas volume produced by the hydrogen methanation device at time t.

[0184] Equation (27) represents the heat power supply-demand balance of the integrated energy system, is the collection and conversion power of the waste heat power burned by traditional gas unit n at time t, is the power of collecting and converting the combustion waste heat of the hybrid fuel gas unit after transformation at time t, is the power of collecting and utilizing the heat energy during the hydrogen methanation and electrolytic water reaction processes at time t, is the supplementary heat power of the gas boiler at time t, is the heat power absorbed by the absorption chiller equipment at time t, is the power discarded due to the ineffective utilization of the system heat energy at time t.

[0185] Equation (28) represents the balance of cold power supply and demand in the integrated energy system, is the refrigeration power of the electric refrigeration equipment and the absorption chiller equipment at time t.

[0186] Equation (29) represents the balance of hydrogen production, storage, and utilization in the integrated energy system, represents the amount of hydrogen produced by the electrolytic water reaction at time t, represents the amount of hydrogen consumed by the hydrogen methanation and the hybrid fuel gas unit after transformation at time t, represents the amount of hydrogen released and stored by the seasonal hydrogen storage at time t.

[0187] 2. Renewable energy output constraint

[0188]

[0189]

[0190]

[0191] is the predicted maximum output value of wind power, photovoltaic power, and hydropower at time t.

[0192] 3. Thermal power unit output constraint

[0193] Since this section studies the long-term operation optimization in the integrated energy system, with one week as the specific optimization time granularity, the constraints such as the ramp rate of the specific output change of the unit are ignored. To ensure sufficient flexible resource scheduling for short-term operation, there are at least two or more thermal power units in the system that are turned on.

[0194] u gen,i (t)P gen-min,i ≤P gen,i (t)≤u gen,i (t)P gen-max,i (33)

[0195]

[0196]

[0197]

[0198] Wherein, P gen-maxi and P gen-mini respectively represent the maximum and minimum output of thermal power unit i, u geni (t) represents the state variable of thermal power unit i at time t, is the start state variable of thermal power unit i at time t, is the shutdown state variable of thermal power unit i at time t.

[0199] 4. Output constraint of gas turbine unit

[0200] In this section, two types of gas turbine units are set. One is the traditional type that only uses natural gas as the raw material, and the other is the type that uses a mixture of natural gas and hydrogen as the raw material. Taking the gas turbine unit model with mixed fuel as an example, it is expressed as:

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207] Wherein, respectively represent the electric power and thermal power output by the gas turbine unit with mixed fuel at time t, respectively represent the volume flow rates of natural gas and hydrogen input by the gas turbine unit with mixed fuel at time t, ξ e-gas and respectively represent the conversion unit conversion coefficients of electricity-natural gas and electricity-hydrogen of the gas turbine unit with mixed fuel, and ω2 represents the mixing coefficient of hydrogen and natural gas of the gas turbine unit with mixed fuel; respectively represent the efficiencies of converting electrical energy and thermal energy of the gas turbine unit with mixed fuel; u gtc (t), are state variables of 0-1, respectively representing the state variable, start state variable, and shutdown state variable of the gas turbine unit with mixed fuel at time t, P gtc-max and P gtc-min are the maximum and minimum powers of the gas turbine unit with mixed fuel.

[0208] 5. Constraint of carbon capture and storage device

[0209] Carbon capture and storage devices are usually directly installed near thermal power plants and gas turbine units to facilitate the local consumption of carbon emissions.

[0210] Taking a gas turbine unit as an example, the model formula is:

[0211]

[0212]

[0213]

[0214]

[0215] Wherein, is the electric power generated by the gas turbine unit at time t, is the net output electric power of the gas turbine unit at time t, is the operating energy consumption of the carbon capture and storage device at time t, is the basic fixed energy consumption of the carbon capture and storage device at time t, represents the amount of carbon dioxide gas captured by the carbon capture and storage device at time t, represents the amount of carbon dioxide gas sealed after capture by the carbon capture and storage device at time t, represents the amount of carbon dioxide gas added to the methanation reaction or other carbon utilization amounts after capture by the carbon capture and storage device at time t, is the operating energy consumption coefficient for capturing a unit of carbon, is the capture efficiency, is the carbon emission intensity per unit energy of the gas turbine unit.

[0216] 6. Constraints of the cold and heat energy supplement unit

[0217] Absorption chiller model:

[0218]

[0219]

[0220] Wherein, represents the cooling power generated by the absorption chiller at time t, represents the heat power absorbed by the absorption chiller at time t, represents the energy conversion efficiency of the absorption chiller, represents the maximum cooling power that the absorption chiller can convert and generate, u ac (t) is a 0-1 variable representing the start-stop state of the absorption chiller at time t.

[0221] Electric refrigeration equipment:

[0222]

[0223]

[0224] In the formula, represents the cooling power output by the electric refrigeration equipment at time t, represents the electric power consumed by the electric refrigeration equipment at time t, represents the energy conversion efficiency of the electric refrigeration equipment, represents the maximum cooling power output by the electric refrigeration equipment, u fr (t) is a 0-1 variable representing the start-stop state of the electric refrigeration equipment at time t.

[0225] Gas boiler:

[0226]

[0227]

[0228] In the formula, represents the heat power output by the gas boiler at time t, represents the electric power consumed by the gas boiler at time t, represents the energy conversion efficiency of the gas boiler, represents the maximum heat power output by the gas boiler, u gb (t) is a 0-1 variable representing the start-stop state of the gas boiler at time t.

[0229] III. Solving with the Modified Differential Evolution (MDE) Algorithm

[0230] The Differential Evolution (DE) algorithm is a population-based heuristic search algorithm. Similar to the genetic algorithm, it includes mutation, crossover, and selection operations. To improve the optimization efficiency, there have been many studies on improving the mutation operator F according to the principle of the mutation process in the algorithm. The basic process of this paper is as Figure 2 shown.

[0231] 1) Initialization: Determine the boundary range of the planning capacity, set the population size N p , take 50, randomly generate the initial population, and the population individuals:

[0232]

[0233] 2) Mutation:

[0234]

[0235] F = 2 λ f0 (55)

[0236]

[0237] In the formula, are three different individuals randomly selected in the G-th generation, is an individual in the mutant population, f0 is the initially set mutation parameter, taking 0.5; G represents the current evolutionary generation, and G m represents the maximum evolutionary generation, taking 80.

[0238] 3) Crossover:

[0239]

[0240] In the formula, is the n-th dimensional variable of the i-th individual in the population obtained after crossover; C r is the crossover factor, and its value is between [0, 1].

[0241] 4) Competition:

[0242]

[0243] In the formula, is the fitness function of the corresponding individual, that is, the comprehensive cost objective function in the model, and the selection method of the minimization problem is adopted.

[0244] 5) When G > G m the algorithm terminates to obtain the optimal solution; otherwise, G = G + 1, and the process returns to 2) for the next optimization.

[0245] Preferably, the Gurobi solver is used in cooperation with the improved differential evolution algorithm for solution.

[0246] Calculation example:

[0247] The present invention is used for the annual planning operation of an integrated energy system with a large load in the sending area. Usually, there are already some energy equipment of traditional integrated energy systems, as well as the planned construction of wind power, photovoltaic, hydropower, and thermal power units in this area. Based on these existing energy equipment, the present invention optimizes the capacity configuration of newly added seasonal energy storage and carbon dioxide capture and utilization equipment, etc., and analyzes the output of each equipment in the regional annual system with a week as the optimization period, which has the significance of actual participation in power grid planning and operation reference. The specific implementation method is as follows:

[0248] 1. According to the historical data of the previous 5 years and the prediction of future weather conditions, determine the output of renewable energy such as wind power, photovoltaic, and hydropower during the planned year, and determine the annual demand curve of multi-energy load according to the growth of load demand.

[0249] 2. Coordinate the parameters of conventional energy equipment such as conventional thermal power units, gas units, and gas boilers within the coordinated area, and determine the parameters and purchase costs of newly added electrolytic water devices, hydrogen storage devices, carbon capture devices, etc.

[0250] 3. According to the system architecture and double-layer model proposed above, add the constraints on the operation and energy conversion of each energy equipment, and through improving the differential optimization algorithm, optimize the model for the objective function to determine the maximum improved output of the optimal hybrid fuel gas unit, the maximum hydrogen production capacity of the electrolytic water device, the maximum capacity of the hydrogen storage device, the maximum power of the hydrogen storage device, and the maximum capacity of the hydrogen methanation device.

[0251] 4. On the basis of determining the increase in the planned equipment capacity, substitute the maximum and minimum constraint coefficients of each equipment into the model, analyze the operation optimization results, determine the annual output characteristics of conventional thermal power units and gas units, and the maintenance plan can be arranged based on this; determine the seasonal characteristics of hydrogen production and charging / discharging of hydrogen storage equipment, and analyze the effect of suppressing the seasonal fluctuations of renewable energy; determine the annual curves of the thermal output of gas units and multi-energy coupling reaction processes and the cold energy units, master the multi-energy demand in the planned year and optimize the dispatching; analyze the indicators of annual carbon emissions, analyze the feasibility of the hydrogen methanation reaction, and explore various ways to achieve future low-carbon goals.

[0252] The preferred specific embodiments of the present invention have been described in detail above. It should be understood that those of ordinary skill in the art can make many modifications and variations based on the concept of the present invention without creative labor. Therefore, all technical solutions that can be obtained by those skilled in the art in the technical field of the present invention through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art shall fall within the protection scope determined by the claims.

Claims

1. An optimization method for an integrated energy system considering seasonal hydrogen storage and the utilization of hydrogen-fired gas turbines, characterized in that The method includes the following steps: Step 1: Provide a multi - energy coupled integrated energy system with a carbon - containing stream. The integrated energy system includes a wind power generation unit, a photovoltaic power generation unit, a hydropower unit, a thermal power generation unit, a traditional gas turbine unit, a hybrid fuel gas turbine unit, an electrolyzer, a hydrogen storage device, a hydrogen methanation device, a carbon capture - storage device, an absorption chiller, an electric refrigeration device, and a gas boiler; Step 2: Establish a bi - layer planning - operation model of the integrated energy system with the comprehensive minimum of system retrofit and upgrade costs, operation costs, and penalty costs as the objective function; Step 3: Use an improved differential evolution algorithm for solving to achieve the optimal configuration of equipment capacity while optimizing the operation strategies of different units. The improved differential evolution algorithm uses the Gurobi solver for cooperation in solving; The hydrogen methanation energy conversion model in Step 1 is: In the formula, represents the hydrogen methanation capacity and the power of the waste heat in the hydrogen methanation reaction process at time t, respectively represent the hydrogen consumption and carbon dioxide consumption in hydrogen methanation at time t, ω1 represents the mixing ratio of carbon dioxide gas in the reaction process, represents the methane and heat energy conversion efficiency of hydrogen to methane, is the maximum capacity of the hydrogen methanation device, ξ e-gas is the conversion coefficient for the conversion between electricity and natural gas units, Q gas is the calorific value of natural gas combustion, 33486.8 kJ / m 3 , is the conversion coefficient for the conversion between electricity and hydrogen units, and α is the conversion coefficient between kilowatts and kilojoules per hour.

2. The optimization method of the integrated energy system considering seasonal hydrogen storage and utilization of hydrogen combustion turbines according to claim 1, characterized in that The electrolyzer energy conversion model in Step 1 is: Wherein, are respectively the power consumption, hydrogen production capacity, and heat production power of the water electrolysis device at time t, are the electro-hydrogen production efficiency and waste heat utilization efficiency of the water electrolysis device, is the conversion coefficient for converting electricity to hydrogen, is the calorific value of hydrogen combustion, 142,500 kJ / m 3 , represents the maximum hydrogen production capacity of the water electrolysis device, and α is the conversion coefficient between kilowatts and kilojoules per hour, taking 3600.

3. The optimization method of the integrated energy system considering seasonal hydrogen storage and utilization of hydrogen-fired gas turbines according to claim 1, characterized in that In Step 1, the hydrogen storage device adopts seasonal hydrogen storage, and its constraint model is: S shs (0) = 0.5Q shs (8) 0 ≤ S shs (t) ≤ Q shs (10) In the formula, respectively represent the power stored and released by the hydrogen storage device at time t, respectively represent the 0-1 state variables charged and discharged at time t, V shs-max represents the maximum power of the hydrogen storage device, Q shs represents the maximum capacity of the hydrogen storage device, S shs (0), S shs (t), S shs (t - 1) are respectively the initial value of the stored energy of the hydrogen storage device, the remaining energy at time t, and the remaining energy at time t - 1, respectively represent the charging and discharging efficiencies of the hydrogen storage device, and Δt represents the unit time of charging and discharging of the hydrogen storage device.

4. The optimization method for an integrated energy system considering seasonal hydrogen storage and utilization of hydrogen-fired turbines as claimed in claim 1, wherein The bi - layer planning - operation model of the integrated energy system established in Step 2 includes an upper - layer planning investment cost objective function and a lower - layer operation optimization cost objective function; The upper - layer planning investment cost is: λ crf = r·(1 + r) y / ((1 + r) y - 1) (17) where C inv represents the upper-level planning investment cost, are respectively the unit capacity investment costs of the gas turbine unit retrofit and upgrade, electrolyzer, hydrogen storage device power, hydrogen storage device capacity, and hydrogen methanation device for the hybrid fuel; P gt-max , V shs-max , Q shs , represent the optimal maximum capacity of the system gas turbine unit retrofit and upgrade, the maximum hydrogen production capacity of the electrolyzer, the maximum power of the hydrogen storage device, the maximum capacity of the hydrogen storage device, and the maximum capacity of the hydrogen methanation device obtained from the upper-level planning model requirements, providing optimization constraint conditions for the lower-level model; λ crf is the capital recovery factor, r is the annual interest rate, and y is the average service life of the system design; The lower-layer operation optimization cost includes the operation cost C op and the penalty cost C pw , The operating cost C op includes the fuel cost C opf , the operating cost C of other devices opa and the unit startup cost C opu , where the fuel cost of the thermal power unit is processed by piecewise linearization: C op = C opf + C opa + C opu (18) Where: c gas is the natural gas price cost, is the amount of natural gas purchased by the system at time t; a1, a2, a3, b1, b2, b3 are the coefficients for the piecewise linearization of the thermal power generation cost, C g ′ en,i (t) is the fuel cost variable of thermal power unit i at time t; P gen,i (t) is the output power of thermal power unit i at time t; are the unit capacity operation costs of the electrolyzer, hydrogen storage device, hydrogen methanation device, carbon capture and storage device, and absorption chiller; are the start-up costs of the thermal power unit, hybrid fuel gas unit, and traditional gas unit, are the start-up state variables of the thermal power unit, hybrid fuel gas unit, and traditional gas unit at time t, N gen is the number of thermal power units, are the amount of carbon dioxide gas sequestered after carbon capture and the cooling power generated by the absorption chiller at time t, The penalty cost C pw is the penalty for abandoning wind, light, water, and hydropower: where c cut is the penalty unit cost for curtailed wind, solar, and hydro power, and is the curtailed wind, solar, and hydro power at time t.

5. The optimization method for an integrated energy system considering seasonal hydrogen storage and utilization of hydrogen combustion turbines according to claim 4, characterized in that The total objective function of the bi - layer planning - operation model of the integrated energy system is: minC total = C inv + C op + C pw (24) Where C total is the total cost.

6. The optimization method for an integrated energy system considering seasonal hydrogen storage and utilization of hydrogen combustion turbines as claimed in claim 5, wherein The constraint conditions of the bi - layer planning - operation model of the integrated energy system include: System power balance equations for electricity, heat, cold, and hydrogen: where L e (t), L h (t), L co (t), respectively represent the demand for electricity, heat, cold, and hydrogen conventional loads at time t. Equation (25) represents the electric power balance of the integrated energy system. is the net output of thermal power unit i at time t, P pv (t), P wt (t), P hp (t) are the outputs of wind power, photovoltaic power, and hydropower units at time t. is the net output of traditional gas unit n at time t. is the net output of the retrofitted hybrid fuel gas unit at time t. is the electric power consumed by the electrolyzer and electric refrigeration equipment at time t. Equation (26) represents the natural gas volume balance of the integrated energy system, is the natural gas volume burned by the traditional gas turbine unit n at time t, is the natural gas volume burned by the retrofitted hybrid fuel gas turbine unit at time t, is the natural gas volume burned by the gas boiler at time t, is the natural gas volume produced by the hydrogen methanation device at time t, Equation (27) represents the thermal power supply-demand balance of the integrated energy system, is the collection and conversion power of the combustion waste heat power of the traditional gas unit n at time t, is the collection and conversion power of the combustion waste heat power of the retrofitted hybrid fuel gas unit at time t, is the collection and utilization power of thermal energy during the hydrogen methanation and electrolytic water reaction processes at time t, is the supplementary thermal power of the gas boiler at time t, is the thermal power absorbed by the absorption chiller equipment at time t, is the power discarded due to the ineffective utilization of the system thermal energy at time t, Equation (28) represents the balance between the supply and demand of cooling power in the integrated energy system. is the cooling power of the electric refrigeration equipment and the absorption chiller equipment at time t. Equation (29) represents the balance of hydrogen production, storage, and consumption in the integrated energy system. represents the amount of hydrogen produced by the electrolysis reaction at time t. represents the amount of hydrogen consumed by the hydrogen methanation and the retrofitted hybrid fuel gas turbine at time t. represents the amount of hydrogen released and stored by the seasonal hydrogen storage at time t. Renewable energy output constraints: is the predicted maximum output value of wind power, photovoltaic power, and hydropower at time t; Thermal power generation unit output constraints: u gen,i (t)P gen-min,i ≤P gen,i (t)≤u gen,i (t)P gen-max,i (33) Where, P gen-max,i , P gen-min,i represent the maximum value and the minimum value of the output of thermal power unit i respectively, u gen,i (t) represents the state variable of thermal power unit i at time t, is the start state variable of thermal power unit i at time t, is the shutdown state variable of thermal power unit i at time t; Gas turbine unit output constraints, including hybrid fuel gas turbine unit output constraints: In the formula, respectively represent the electric power and heat power output by the hybrid fuel gas unit at time t, respectively represent the volume flow rates of natural gas and hydrogen input by the hybrid fuel gas unit at time t, ξ e-gas and respectively represent the conversion unit conversion coefficients of electricity-natural gas and electricity-hydrogen of the hybrid fuel gas unit, and ω2 represents the mixing coefficient of hydrogen and natural gas of the hybrid fuel gas unit; respectively represent the efficiencies of converting electric energy and heat energy of the hybrid fuel gas unit; u gtc (t), are state variables from 0 to 1, respectively representing the state variable, start state variable, and shutdown state variable of the hybrid fuel gas unit at time t, P gtc-max and P gtc-min are the maximum and minimum powers of the hybrid fuel gas unit; Carbon capture - storage device constraints: In the formula, is the electric power generated by the gas turbine unit at time t, is the net output electric power of the gas turbine unit at time t, is the operating energy consumption of the carbon capture and storage device at time t, is the basic fixed energy consumption of the carbon capture and storage device at time t, represents the amount of carbon dioxide gas captured by the carbon capture and storage device at time t, represents the amount of carbon dioxide gas stored after capture by the carbon capture and storage device at time t, represents the amount of carbon dioxide gas added to the methanation reaction after capture by the carbon capture and storage device at time t, is the operating energy consumption coefficient for capturing a unit of carbon, is the capture efficiency, is the carbon emission intensity per unit energy of the gas turbine unit; Constraints for cold and heat energy supplement units. The constraints for cold and heat energy supplement units include absorption chiller constraints, electric refrigeration device constraints, and gas boiler constraints.

7. The optimization method of the integrated energy system considering seasonal hydrogen storage and utilization of hydrogen-fired gas turbines according to claim 6, wherein The absorption chiller constraints are: In the formula, represents the cooling power output by the absorption chiller at time t, represents the heat power absorbed by the absorption chiller at time t, represents the energy conversion efficiency of the absorption chiller, represents the maximum cooling power that the absorption chiller can convert and output, u ac (t) is a 0-1 variable representing the start-stop state of the absorption chiller at time t; The electric refrigeration device constraints are: Wherein, represents the cooling power output by the electric refrigeration equipment at time t, represents the electric power consumed by the electric refrigeration equipment at time t, represents the energy conversion efficiency of the electric refrigeration equipment, represents the maximum cooling power output by the electric refrigeration equipment, u fr (t) is a 0-1 variable representing the start-stop state of the electric refrigeration equipment at time t; The gas boiler constraints are: In the formula, represents the thermal power output by the gas boiler at time t, represents the electric power consumed by the gas boiler at time t, represents the energy conversion efficiency of the gas boiler, represents the maximum thermal power output converted by the gas boiler, u gb (t) is a 0-1 variable representing the start-stop state of the gas boiler at time t.

8. The optimization method of the integrated energy system considering seasonal hydrogen storage and utilization of hydrogen-fired gas turbines according to claim 1, characterized in that The specific steps of the improved differential evolution algorithm in Step 3 are as follows: Step 3.

1. Initialization: Determine the boundary range of the planned capacity and set the population size N p , and randomly generate an initial population, where the population individuals are: Step 3.2: Mutation: F = 2 λ f0 (55) In the formula, are three different individuals randomly selected in the G-th generation, is an individual in the mutant population, and f0 is the initially set mutation parameter; G represents the current generation of evolution, and G m represents the maximum number of generations of evolution; Step 3.3: Crossover: wherein is the n-th dimensional variable of the i-th individual in the population obtained after crossover; C r is the crossover factor, and its value is between [0, 1]; Step 3.4: Competition: wherein, is the fitness function of the corresponding individual, that is, the comprehensive cost objective function in the model, and the selection method of the minimization problem is adopted; Step 3.

5. When G > G m , the algorithm terminates and the optimal solution is obtained. Otherwise, G = G + 1, return to Step 3.2 for the next optimization.

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