Multi-game comprehensive energy system low-carbon economic optimization scheduling considering hydrogen energy multi-element utilization

Abstract

The invention belongs to the field of integrated energy systems, and provides a method and a system for processing multi-energy coupling and improving the transaction flexibility and low-carbon property of an energy market through cooperation of a generalized energy storage system and an integrated energy system. According to the method, a multi-game model of a supply side, a demand side and a storage side is established according to a mathematical model of a comprehensive energy seller, a generalized energy storage sharer and a load aggregator, and the multi-game energy transaction model taking subjects as the duplication of a leader and a gamer is established. The method can balance the interests of all subjects, and increases the overall economic benefits and environmental benefits of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of low-carbon collaborative optimization of integrated energy systems, and in particular to a low-carbon economic optimization scheduling of a multi-game integrated energy system considering the diversified utilization of hydrogen energy. Background Art

[0002] Clean, low-carbon, efficient and safe are the mainstream directions of energy development in today's world. Integrated Energy System (IES), with more diverse energy-consuming equipment and decision-making entities, and more complex energy coupling relationships and interactive decision-making behaviors, has become a new business model that is an inevitable choice for energy reform.

[0003] After searching the existing technical literature, it was found that the literature [1]: "Optimal operation of integrated energy system considering diversified utilization of hydrogen energy and green certificate-carbon joint trading" (Ge Shuona, Zhang Cailing, Wang Shuang, etc. Optimal operation of integrated energy system considering diversified utilization of hydrogen energy and green certificate-carbon joint trading [J]. Electric Power Automation Equipment, 2023, 43(12): 231-237.) considers the remaining heat recovery process on the basis of the P2G refined model, and combines the green certificate-carbon joint trading to achieve the low-carbon economic goal of IES; however, the above literature [1] all verified the important role of efficient utilization of hydrogen energy in the low-carbon and economic benefits of IES, but did not consider the interest coupling relationship between different decision-making entities within IES.

[0004] Reference [2]: "Research on Optimal Operation of Shared Energy Storage and Integrated Energy Microgrid Based on Master-Slave Game Theory" (Shuai Xuanyue, Ma Zhicheng, Wang Xiuli, et al. Research on Optimal Operation of Shared Energy Storage and Integrated Energy Microgrid Based on Master-Slave Game Theory [J]. Power System Technology, 2023, 47(02): 679-690.) The game relationship between microgrids and users is used to achieve optimal benefits, and load demand response and shared energy storage are introduced in the game process to enhance the flexibility of regulatory transactions. The above reference [2] enables multiple subjects to obtain optimal economic benefits in IES, but does not consider the impact of carbon emissions on system transaction costs and the environment.

[0005] Reference [3]: "Low-carbon economic optimization dispatch of regional integrated energy system based on multi-agent master-slave game" (Wang Rui, Cheng Shan, Wang Yeqiao, Dai Jiang, Zuo Xianwang. Low-carbon economic optimization dispatch of regional integrated energy system based on multi-agent master-slave game [J]. Power System Protection and Control, 2022, 50(05): 12-21.) The reward and punishment ladder carbon trading mechanism is applied to the energy trading process, and a multi-agent game model is established with energy agents as leaders and energy suppliers, energy storage companies and users as followers, so that each subject can obtain the optimal benefits while taking into account the impact on the environment;

[0006] Existing research on the coordinated optimization game of integrated energy multi-agents, on the one hand, most studies adopt a one-way game model, which does not consider the direct interaction between users and followers such as energy storage, and lacks the active coordination of demand response to energy storage strategies and energy supply strategies; on the other hand, in the research on the optimization of IES multi-agent game, the established energy supply structure does not consider the diversified utilization model of hydrogen energy. Summary of the Invention

[0007] In order to solve the technical problems in the above background, the present invention provides a low-carbon economic optimization scheduling of a multi-game integrated energy system considering the diversified utilization of hydrogen energy. The proposed method can balance the interests of various entities and increase the overall economic benefits of the system and the benefits of each link.

[0008] Considering the low-carbon economic optimization scheduling of the multi-game integrated energy system with multiple utilization of hydrogen energy, the technical solution adopted by the present invention is:

[0009] To further reduce carbon emissions, a P2G, CCS and hydrogen blending model is constructed based on the consideration of tiered carbon trading;

[0010] Build a comprehensive energy economic transaction model based on a multi-game model, with the integrated energy seller IEM and the generalized energy storage sharing provider GESS as the leaders, and the load aggregator LA as the follower;

[0011] Based on multiple games, an economic optimization model including integrated energy marketers (IEMs), load aggregators (LAs) and generalized energy storage sharers (GESSs) is established.

[0012] The model solving module is configured to use a two-layer optimization algorithm to solve the master-slave game model on the three sides of supply, demand and storage to obtain the optimal operation strategy of the integrated energy system.

[0013] Furthermore, considering the tiered carbon trading model, the P2G, CCS and hydrogen blending models are constructed as follows:

[0014] IEM's carbon trading costs adopt a tiered carbon trading mechanism as follows:

[0015]

[0016] Where λ is the carbon trading base price, l is the length of the carbon emission interval, and α is the price growth rate. The above terms are expressed as:

[0017] E IEM,a =E total,a -E MR,a

[0018]

[0019]

[0020] Where E IEM is the carbon emission quota of IEM, E MT is the carbon emission quota of the micro-turbine, E total,a is the actual total carbon emissions of the micro-turbine unit, E MR,a is the amount of CO2 actually absorbed by MR, E IEM,t is the carbon emission rights trading amount of IEM;

[0021] To fully improve the P2G conversion efficiency, this paper breaks down P2G into two processes: electricity-to-hydrogen and methanation. A hydrogen energy utilization system consisting of an electrolyzer, a methane reactor, a hydrogen fuel cell, and a hydrogen storage system is established. Combining the two conversion relationships of amount of substance and molar mass, and gas volume flow rate and density, the relationships between hydrogen power and hydrogen amount, and between natural gas power and natural gas amount, are derived, respectively. This achieves a unified dimensional conversion for power trading calculations. The energy coupling model can be described as follows:

[0022] Dimensionally unified EL model

[0023]

[0024] In the formula is the electrolyzer efficiency function; The rated value of the electric power consumed by the electrolytic cell; a EL 、b EL 、c EL are the efficiency function coefficients respectively; and are the amount of hydrogen substance produced by the electrolyzer and the rated capacity of hydrogen energy; and are the upper and lower limits of the electrolytic cell power consumption respectively; and They are the upper and lower limits of the electric power ramp rate respectively.

[0025]

[0026] From the above formula, we can deduce

[0027]

[0028] In the formula the quality of hydrogen produced for the electrolyser; is the molar mass of hydrogen produced by the electrolyzer; is the volume flow rate of hydrogen; is the density of hydrogen; is the hydrogen energy power generated by the electrolyzer; Δh is the scheduling time unit.

[0029] Dimensionally unified MR model

[0030]

[0031] From the above formula, we can deduce

[0032]

[0033] In the formula The quality of CO2 supplied to the MR equipment for carbon capture; The amount of CO2 material supplied to the MR equipment for carbon capture; and are the molar mass and density of CO2 respectively.

[0034] The amount of H2 substance input to the MR device and the amount of CH4 substance generated by the MR device are further converted into hydrogen power and natural gas power according to the above formula, as shown in the following formula:

[0035]

[0036]

[0037] In the formula and are the hydrogen power and amount of H2 substance supplied to the methane reactor by the electrolyzer, respectively; The natural gas power output of the methane reactor; is the methane reactor conversion efficiency; The amount of CH4 produced by the methane reactor; is the molar mass of CH4; is the density of CH4.

[0038] The hydrogen fuel cell model uses hydrogen energy to generate electricity and heat, and adopts an adjustable electricity-to-heat ratio model to reduce the step-by-step energy consumption. The model is shown below:

[0039]

[0040] In the formula is the hydrogen power input to the hydrogen fuel cell; are the electrical energy and thermal energy output by the hydrogen fuel cell respectively; are the efficiency of hydrogen fuel cells converting into electrical energy and thermal energy, respectively; and They are the upper and lower limits of the ramp rate of hydrogen energy input into the hydrogen fuel cell; and They are the upper and lower limits of the electric-to-heat ratio of hydrogen fuel cells respectively.

[0041] Furthermore, an economic optimization model including integrated energy marketers (IEMs), load aggregators (LAs) and generalized energy storage sharing providers (GESSs) is established as follows:

[0042] 1. The Integrated Energy Merchant (IEM) model takes maximizing revenue as the optimization objective and can be expressed as:

[0043]

[0044] Where H is the total number of time periods in the scheduling cycle, and Δh is the time length; and are the revenues generated by IEM's supply of electricity, heating, cooling, and gas to LA; The transaction fee between IEM and GESS hydrogen energy; F EG and F MT are the interaction costs between IEM and the upper-level power grid and natural gas grid, respectively; is the tiered carbon trading cost; F DG,cut is the penalty cost for wind curtailment; the above formulas are expressed as:

[0045]

[0046] Where, and are the prices for electricity, heating, cooling and gas power sold by IEM respectively; are the electricity and gas sales prices of the upper-level network respectively; and The prices for IEM to purchase hydrogen from GESS and store hydrogen, respectively; and are the electricity, heating, cooling and gas power sold by IEM to LA respectively; and They are the electricity and gas purchases from the superior network by IEM respectively; and are the hydrogen power stored in IEM and the hydrogen power purchased; δ DG Penalty price for wind curtailment; P DG,cut is the wind power curtailment;

[0047] The IEM side includes four energy couplings: electricity, heat, and hydrogen. To ensure the supply and demand balance within the system and the system, the power balance constraints are as follows:

[0048]

[0049] In the formula and are the electricity and heat production power of the gas turbine respectively; is the electrical power consumed by EL; and are the electrical power and thermal power generated by the hydrogen fuel cell, respectively; is the actual consumption of wind power; electricity generated for hydrogen fuel cells; Consume electrical power for carbon capture; and are the natural gas power and hydrogen power consumed by the gas turbine, respectively; Output gas power for MR equipment; is the waste heat power of the micro-turbine; Power for hydrogen produced by the electrolyzer; Consume hydrogen power for hydrogen-blended gas turbines; is the hydrogen power consumed by the methane reactor; The hydrogen power consumed by the hydrogen fuel cell;

[0050] Natural gas and hydrogen produced by the electrolyzer are mixed in a certain proportion as fuel for the gas turbine to generate electricity and heat. When the hydrogen content is between 10% and 20%, the change in hydrogen content can maintain the safe and stable operation of the gas turbine. The hydrogen-blended gas turbine model is shown below:

[0051]

[0052] In the formula and are the electricity and heat production power of the gas turbine respectively; and are the electricity and heat generation efficiencies of the gas turbine, respectively; and are the volume flow rates of hydrogen and natural gas input to the gas turbine, respectively; is the calorific value of hydrogen, which is 10779 kJ / m 3 , L g is the calorific value of natural gas, which is 35807 kJ / m 3 ; are the upper and lower limits of gas turbine electrical output respectively; are the upper and lower limits of the gas turbine thermal output respectively; is the hydrogen blending ratio of the gas turbine;

[0053] 2. The mathematical model of the generalized energy storage sharing provider (GESS) aims to maximize revenue, specifically:

[0054] In the formula, let H be the total number of time periods in the scheduling cycle, Δh is the unit time length; F GESS is the total daily income of GESS; Gains from charging and discharging energy between GESS, LA and IEM; The cost of energy charging and discharging needs to be paid for GESS; the above items are expressed as:

[0055]

[0056]

[0057] In the formula are the prices of electricity, heat, cooling and gas sold by GESS respectively; Store electricity, heating, cooling and gas prices to GESS for LA respectively; and They are the power of stored electricity, heat, cold and gas energy respectively; and are the power of electricity, heat, cooling and gas sold respectively; μ ES The price of GESS charging and discharging energy operation and maintenance cost.

[0058] 3. The mathematical model of the load aggregator (LA) is optimized to maximize revenue, specifically:

[0059]

[0060] Where F LA is the total revenue of LA in one day, indicating the user's satisfaction with energy purchase; and These are the transaction fees for purchasing electricity, heat, cooling and gas from IEM on the user side; Energy transaction fees between the user side and GESS; Penalty fees for LA due to reduced comfort caused by reducing heating and cooling loads; is the energy utility function of LA, which is usually non-decreasing and convex. This paper uses a quadratic function to describe the function. The above formulas can be expressed as:

[0061]

[0062] Where a i 、b i (i∈{1,2,3,4}) is the utility function coefficient of LA’s electricity (heating, cooling and gas); δ t and δ c are the penalty coefficients for reducing heating load and cooling load respectively;

[0063] Time-shifting electrical loads Gas load and interruptible heat loads Cooling load As an adjustable strategy to participate in load-side energy demand response, the corresponding constraints are:

[0064]

[0065] The load side power balance constraint is as follows:

[0066]

[0067] In the formula and They are non-translatable electric load and gas load respectively; and are the electric power consumed by electric heating and electric cooling respectively; Predicting output for photovoltaic installations; and are the original heating load and cooling load powers respectively; The heat output of the electric heating equipment; The cooling capacity of the electric refrigeration equipment;

[0068] In order to describe the competition and master-slave interaction among multiple subjects in IES, the following are introduced: Figure 2 The multiple master-slave game model shown in the figure, in which the game entities such as IEM, LA and GESS obtain the optimal economic benefits through power trading, and improve the environmental benefits through the diversified utilization mechanism of hydrogen energy. The system multiple game model is specifically as follows:

[0069]

[0070] In the formula, the sales strategy of the top leaders is The selling strategy of lower-level leaders is The follower's energy strategy is When all game players cannot unilaterally change the equilibrium solution strategy to obtain benefits, the game reaches the Stackelberg equilibrium. When it is a game equilibrium solution, it must satisfy:

[0071]

[0072] The GESS, the upper-level leader, maximizes profits by exploiting the price differential based on the energy storage strategies of the IEM and LA through low charging and high discharging. The IEM, the lower-level leader, is the primary energy supplier and leader in carbon emission reduction for the system. Taking into account carbon emissions during CCHP operation, it sets dynamic energy prices and hydrogen storage capacity for the LA and GESS, respectively, with the goal of maximizing economic benefits. The LA and GESS then develop energy dispatch plans based on these dynamic energy prices. The LA, the system's follower, adjusts a certain proportion of flexible load based on external energy price information, comprehensively considering energy user comfort and energy purchase costs to improve economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1This is a schematic diagram of the integrated energy system architecture proposed by the present invention;

[0074] Figure 2 It is a schematic diagram of the multiple games proposed by the present invention;

[0075] Figure 3 It is a scheme for producing electric energy;

[0076] Figure 4 It is a heat energy production scheme;

[0077] Figure 5 It is a cold energy production solution;

[0078] Figure 6 It is a gas energy production scheme;

[0079] Figure 7 It is a hydrogen production solution; DETAILED DESCRIPTION

[0080] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0081] It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0082] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0083] In the absence of conflict, the embodiments of the present invention and the features thereof may be combined with each other.

[0084] Example 1

[0085] like Figure 1 As shown, this embodiment provides a low-carbon economic optimization scheduling of a multi-game integrated energy system considering the diversified utilization of hydrogen energy. The specific transaction optimization framework of this embodiment is: the integrated energy system model containing the diversified utilization of hydrogen energy is as follows: Figure 1As shown in the figure, the IEM purchases electricity and natural gas from the upstream network. In addition to its own wind power generation, it utilizes energy coupling equipment such as combined cooling, heating and power (CCHP), electrolyzers (EL), methane reactors (MR), and hydrogen fuel cells (HFC) to supply electricity, heat, cooling, and gas to users based on their energy needs. The LA utilizes photovoltaic power generation, electric cooling, and electric heating equipment to develop energy scheduling strategies based on energy demand. The GESS participates in the game process by leveraging the energy price information and energy use strategies of each trading entity, providing energy storage and access services.

[0086] Figure 2 This is a schematic diagram of the multiple games proposed in the present invention. In this model, the game entities such as IEM, LA and GESS obtain the optimal economic benefits through power trading, and improve the environmental benefits through the diversified utilization mechanism of hydrogen energy. GESS is the upper-level leader. According to the energy storage strategies of IEM and LA, it obtains the price difference through low charging and high discharging to achieve maximum benefits. IEM is the lower-level leader and is the dominant force in the system's main energy supply and carbon emission reduction. Considering the carbon emissions during the operation of CCHP, it formulates dynamic energy prices and hydrogen storage power for LA and GESS respectively with the goal of maximizing economic benefits. LA and GESS formulate energy scheduling plans accordingly. LA is a system game follower. It adjusts a certain proportion of flexible loads according to external energy price information, comprehensively considers energy comfort and energy purchase costs, and improves economic benefits.

[0087] Figure 3-7 This is a schematic diagram of energy production after game optimization. LA acts as a follower in the game process, using flexible load as a game variable. It adjusts its energy use strategy based on the leader's price game results. It adjusts the load that can be shifted and the load that can be reduced accordingly according to the peak and valley of energy prices to obtain more benefits. At the same time, LA needs to comprehensively consider the energy supply cost and penalty fees that can be reduced by reducing the load. During peak electricity price periods, the energy supply cost reduced by adjusting the load is greater than the penalty fee incurred. Therefore, if Figure 3 As shown in Figure 2, the load reduction during peak electricity price periods is greater than during off-peak periods. The heat, cooling, and gas loads are adjusted similarly to the electricity load.

[0088] Combine Figure 3-7 Analysis shows that during the 00:00-09:00 period, due to low load demand, the IEM reduces energy prices during this period, encouraging users to increase their energy purchase demand. At the same time, LA prioritizes purchasing energy from the IEM during this period and transmitting it to the GESS for storage. During peak energy consumption periods, the GESS sells energy to users, realizing profits from the peak-to-valley difference in energy prices.

[0089] Between 10:00 and 20:00, load demand gradually increased and reached its peak. For both heating and cooling loads, when self-generating equipment and the IEM were insufficient, the GESS partially covered the shortfall. From 1:00 to 17:00, the cooling load peaked again. Due to its coupled response, the increase in cooling load led to an increase in heating demand. The system met this demand through heat release from energy storage and increased heat production from HFCs. Regarding electricity, wind power generation gradually decreased, while user-side photovoltaics began to produce power, fully absorbing renewable energy. Due to the coupled nature of heat and electricity demand on the energy supply side, the IEM continued to supply heat to users while also generating significant amounts of electricity. Therefore, the P2G equipment consumed some of this electricity, enabling hydrogen energy to complement electricity, heat, and gas. Regarding gas, the continuous supply of electricity and heat by the gas turbine, coupled with the resulting carbon emissions, kept the CCS equipment operating continuously, and thus the MR equipment operating continuously. The LA met the gas demand through upstream gas purchases and energy storage. The efficient use of hydrogen energy can meet part of the energy demand and reduce carbon emissions. Therefore, EL hydrogen production equipment mainly provides hydrogen energy needs for HFC, MR and gas turbine hydrogen blending equipment during peak load periods.

[0090] During the period from 20:00 to 24:00, the photovoltaic devices on the user side stop generating electricity, wind power generation gradually increases, and the P2G equipment begins to consume part of the electricity, reducing the amount of wind curtailment.

[0091] The present invention sets up the following comparative scenarios to verify the effectiveness of the multiple game model and tiered carbon trading in improving the economic benefits and low-carbon benefits of the game entities.

[0092]

[0093]

[0094] Compared to Scenario 1, Scenario 2 reduces IEM revenue by 137.64 yuan and discharge by 361 kg. Scenario 2 introduces the GESS into the game, creating a competitive energy supply relationship with the IEM. This leverages its low-charge, high-discharge characteristics to enhance load demand responsiveness. Furthermore, the inclusion of the hydrogen storage system improves the system's hydrogen blending and regulation capabilities, reducing carbon emissions from the gas turbine. Therefore, the multi-game model has a positive impact on carbon emissions reduction and overall system profitability. Compared to Scenario 1, Scenario 3 reduces IEM revenue by 726.46 yuan and carbon emissions by 983 kg. Because Scenario 3 incorporates tiered carbon trading costs, it encourages the IEM to proactively improve hydrogen efficiency, increase P2G equipment output, reduce gas turbine natural gas consumption, and lower carbon emissions. Scenario 4 increases overall revenue by 336.85 yuan compared to Scenario 3, while reducing carbon emissions by 883 kg and 261 kg, respectively, compared to Scenario 2 and Scenario 3. Because the multiple game models in Scenario 4 maximize the flexible load regulation capability of the LA side, to maximize revenue, the LA adjusts its energy consumption based on the dynamic energy price generated by the competitive relationship between the IEM and GESS. The IEM, taking into account carbon trading and the regulation capabilities of the hydrogen storage system, reduces carbon emissions due to reduced natural gas consumption in the gas turbine. Therefore, Scenario 4 offers clear advantages in improving the overall economic benefits of the system and reducing carbon emissions.

Claims

1. Considering the multiple utilization of hydrogen energy, the multi-game integrated energy system is characterized by low-carbon economic optimization scheduling. Including the following steps: Step 1: Considering the multi-state energy coupling mechanism of tiered carbon trading, two-stage power to gas (P2G), carbon capture system (CCS), and hydrogen blending model, a low-carbon trading mechanism for an integrated energy system that considers the diversified utilization of hydrogen energy is constructed; Step 2: Based on multiple games, an economic optimization mathematical model is constructed, which includes an integrated energy marketer (IEM), a load aggregator (LA), and generalized energy storage sharers (GESS). Step 3: Use the genetic algorithm-quadratic programming solution algorithm to calculate the multiple game model and complete the low-carbon optimal scheduling of the integrated energy system.

2. The low-carbon economic optimization scheduling of the multi-game integrated energy system with diversified utilization of hydrogen energy according to claim 1 is characterized by: In step 1, the integrated energy system includes three parts: energy supply side, energy consumption side and energy storage side; On the energy supply side, IEM purchases electricity and natural gas from the upstream network. In addition to its own wind power generation, it uses energy coupling equipment such as combined cooling, heating and power (CCHP), electrolytic cells (EL), methane reactors (MR), and hydrogen fuel cells (HFC) to supply electricity, heat, cooling, and gas to users according to their energy needs. Among them, LA uses photovoltaic power generation, electric refrigeration and electric heating equipment to formulate energy scheduling strategies based on energy demand. GESS participates in the game process through the energy price information and energy use strategies of each trading entity and provides energy storage and access services.

3. The low-carbon economic optimization scheduling of the multi-game integrated energy system with diversified utilization of hydrogen energy according to claim 1 is characterized in that: The mathematical model of the IEM, with maximum benefit as the optimization goal, can be expressed as: Where H is the total number of time periods in the scheduling cycle, and Δh is the time length; and are the revenues generated by IEM's supply of electricity, heating, cooling, and gas to LA; The transaction fee between IEM and GESS hydrogen energy; F EG and F MT are the interaction costs between IEM and the upper power grid and natural gas grid respectively; F CO2 is the tiered carbon trading cost; F DG,cut is the penalty cost for wind curtailment; the above formulas are expressed as: Where, and are the prices for electricity, heating, cooling and gas power sold by IEM respectively; are the electricity and gas sales prices of the upper-level network respectively; and The prices for IEM to purchase hydrogen from GESS and store hydrogen, respectively; and are the electricity, heating, cooling and gas power sold by IEM to LA respectively; and They are the electricity and gas purchases from the superior network by IEM respectively; and are the hydrogen power stored in IEM and the hydrogen power purchased; δ DG Penalty price for wind curtailment; P DG,cut is the wind power curtailment; The IEM side includes four energy couplings: electricity, heat, and hydrogen. To ensure the supply and demand balance within the system and the system, the power balance constraints are as follows: In the formula and are the electricity and heat production power of the gas turbine respectively; is the electrical power consumed by EL; and are the electrical power and thermal power generated by the hydrogen fuel cell, respectively; is the actual consumption of wind power; electricity generated for hydrogen fuel cells; Consume electrical power for carbon capture; and are the natural gas power and hydrogen power consumed by the gas turbine, respectively; Output gas power for MR equipment; is the waste heat power of the micro-turbine; Power for hydrogen produced by the electrolyzer; Consume hydrogen power for hydrogen-blended gas turbines; is the hydrogen power consumed by the methane reactor; The hydrogen power consumed by the hydrogen fuel cell; Natural gas and hydrogen produced by the electrolyzer are mixed in a certain proportion as fuel for the gas turbine to generate electricity and heat. When the hydrogen content is between 10% and 20%, the change in hydrogen content can maintain the safe and stable operation of the gas turbine. The hydrogen-blended gas turbine model is shown below: In the formula and are the electricity and heat production power of the gas turbine respectively; and are the electricity and heat generation efficiencies of the gas turbine, respectively; and are the volume flow rates of hydrogen and natural gas input to the gas turbine, respectively; is the calorific value of hydrogen, which is 10779 kJ / m 3 , L g is the calorific value of natural gas, which is 35807 kJ / m 3 ; are the upper and lower limits of gas turbine electrical output respectively; are the upper and lower limits of the gas turbine thermal output respectively; is the hydrogen blending ratio of the gas turbine; IEM's carbon trading costs adopt a tiered carbon trading mechanism as follows: Where λ is the carbon trading base price, l is the length of the carbon emission interval, and α is the price growth rate. The above terms are expressed as: AND IEM,t =And IEM,a -AND IEM AND IEM,a =And total,a -AND MR,a Where E IEM is the carbon emission quota of IEM, E MT is the carbon emission quota of the micro-turbine, E total,a is the actual total carbon emissions of the micro-turbine unit, E MR,a is the amount of CO2 actually absorbed by MR, E IEM,t is the carbon emission rights trading amount of IEM.

4. The low-carbon economic optimization scheduling of the multi-game integrated energy system with diversified utilization of hydrogen energy according to claim 1 is characterized in that: The optimization goal of the mathematical model of GESS is to maximize the benefit, specifically: In the formula, let H be the total number of time periods in the scheduling cycle, Δh is the unit time length; F GESS is the total daily income of GESS; Gains from charging and discharging energy between GESS, LA and IEM; The cost of energy charging and discharging needs to be paid for GESS; the above items are expressed as: In the formula are the prices of electricity, heat, cooling and gas sold by GESS respectively; Store electricity, heating, cooling and gas prices to GESS for LA respectively; and They are the power of stored electricity, heat, cold and gas energy respectively; and are the power of electricity, heat, cooling and gas sold respectively; μ ES The price of GESS charging and discharging energy operation and maintenance cost.

5. The low-carbon economic optimization scheduling of the multi-game integrated energy system with diversified utilization of hydrogen energy according to claim 1 is characterized in that: The optimization goal of the mathematical model of LA is to maximize the benefit, specifically: Where F LA is the total revenue of LA in one day, indicating the user's satisfaction with energy purchase; and These are the transaction fees for purchasing electricity, heat, cooling and gas from IEM on the user side; Energy transaction fees between the user side and GESS; Penalty fees for LA due to reduced comfort caused by reducing heating and cooling loads; is the energy utility function of LA, which is usually non-decreasing and convex. This paper uses a quadratic function to describe the function. The above formulas can be expressed as: Where a i 、b i (i∈{1,2,3,4}) is the utility function coefficient of LA’s electricity (heating, cooling and gas); δ t and δ c are the penalty coefficients for reducing heating load and cooling load respectively; Time-shifting electrical loads Gas load and interruptible heat loads Cooling load As an adjustable strategy to participate in load-side energy demand response, the corresponding constraints are: The load side power balance constraint is as follows: In the formula and They are non-translatable electric load and gas load respectively; and are the electric power consumed by electric heating and electric cooling respectively; Predicting output for photovoltaic installations; and are the original heating load and cooling load powers respectively; The heat output of the electric heating equipment; It is the cooling capacity of electric refrigeration equipment.

6. The low-carbon economic optimization scheduling of the multi-game integrated energy system with diversified utilization of hydrogen energy according to claim 1 is characterized in that: The system multi-game model is specifically: In the formula, the sales strategy of the top leaders is The selling strategy of lower-level leaders is The follower's energy strategy is When all game players cannot unilaterally change the equilibrium solution strategy to obtain benefits, the game reaches the Stackelberg equilibrium. When it is a game equilibrium solution, it must satisfy:

7. The low-carbon economic optimization scheduling of the multi-game integrated energy system with diversified utilization of hydrogen energy according to claim 1 is characterized in that: The optimal operation strategy of the integrated energy system is obtained, specifically: In order to solve the master-slave game model on the three sides of supply, demand and storage, Yalmip modeling is adopted and CPLEX solver is called to solve the multi-slave game model. The genetic algorithm-quadratic programming solution algorithm is used to enable the energy supply side to obtain the optimal energy sales strategy and hydrogen storage strategy, the energy measurement to obtain the optimal energy consumption strategy, and the energy storage side to obtain the optimal energy sales strategy, until the energy sales strategies of IEM and GESS, and the energy purchasing strategy of LA are stable and unchanged, reaching the game equilibrium, thereby ensuring that each game subject obtains the optimal benefit.

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