Comprehensive energy system optimization scheduling method considering diversified application of wind, light and green power

Abstract

The invention provides a comprehensive energy system optimization scheduling method and device considering diversified application of wind, light and green power, electronic equipment and a storage medium, and relates to the technical field of new energy technology and power system integration optimization. According to the method, a comprehensive energy system mathematical model integrating wind, light, green electricity, a hydrogen and ammonia production process, thermal power ammonia-doped combustion and thermal power oxygen-enriched combustion is constructed; combining the comprehensive energy system mathematical model to construct a comprehensive energy system scheduling model considering cost effectiveness, new energy utilization rate and environmental protection; and selecting a target solution algorithm from the plurality of solution algorithms, constructing an operation scene, and performing simulation solution on the integrated energy system scheduling model to obtain an integrated energy system optimization scheduling result. According to the embodiment of the invention, wind-solar-green-electricity hydrogen production ammonia synthesis and thermal-electricity ammonia-doped combustion and oxygen-enriched combustion technologies are effectively integrated, an ecological system for efficient cyclic utilization of energy and resources is created, and green transformation and industrial upgrading of an energy structure are promoted.

Description

Technical Field

[0001] The present application relates to the field of new energy technology and power system integrated optimization technology, and in particular to an integrated energy system optimization scheduling method and device, electronic equipment and storage medium that take into account the diversified application of wind, solar and green electricity. Background Art

[0002] As the world accelerates the transformation of the energy system to low-carbon and zero-carbon, the development and utilization of wind, solar and green electricity increasingly highlight their indispensable strategic position. Hydrogen energy, as a clean, safe and efficient secondary energy source, has many sources and a wide range of applications. It is an important carrier for realizing large-scale renewable energy utilization. It can achieve the optimal configuration of multiple heterogeneous energy sources across regions and seasons, and form a sustainable and highly flexible innovative multi-energy complementary system. In 2021, the International Energy Agency's 2050 Net Zero Emission Roadmap Research Report pointed out that by 2050, global hydrogen demand will grow to 528 million tons, about 60% of which will come from hydrogen production by electrolysis of water, accounting for 20% of the global electricity supply. At the same time, the world will invest more than US$14 billion in ammonia production each year, of which 80% adopts a near-zero emission production route.

[0004] Oxygen, a byproduct of hydrogen production from water electrolysis, can be used for oxygen-enriched combustion in thermal power units. Thermal power units need to burn a large amount of coal or gas to generate steam to drive generators, and are currently the largest energy consumers in high-energy-consuming industries. The use of oxygen-enriched combustion technology in large coal-fired power station boilers can enhance combustion efficiency and reduce the ignition temperature of primary air pulverized coal. This advantage can reduce the number of times fuel is put into use and save boiler fuel consumption. The oxygen-enriched combustion technology was applied to the boiler of the 2×300MW coal-fired generator unit of Guodian Chongqing Hengtai Power Generation Co., Ltd. The results showed that after the generator unit was transformed with oxygen-enriched combustion technology, the fuel consumption was significantly reduced year-on-year (>80%), and the deep peak load of the unit was greatly reduced (<30%), showing excellent energy-saving and emission reduction results.

[0005] To sum up, the development and application of green electricity hydrogen and ammonia production technologies, combined with the oxygen-enriched combustion and ammonia-blended combustion transformation of thermal power systems, together constitute the core strategy for the transformation of integrated energy systems to low-carbon and high-efficiency, and are of great significance to promoting the use of renewable energy. Summary of the invention

[0006] In view of the above problems, this application is proposed to provide a comprehensive energy system optimization scheduling method and device, electronic equipment and storage medium that considers the diversified application of wind, solar and green electricity to overcome the above problems or at least partially solve the above problems. By building a diversified renewable energy utilization system, wind energy, solar energy, thermal power, electrochemical energy storage, hydrogen energy, ammonia energy and oxygen energy are effectively integrated to expand the application boundaries of the comprehensive energy system. While ensuring economic feasibility, the utilization rate of renewable energy is enhanced, and environmental friendliness is guaranteed, so as to obtain an economic, efficient and green optimization scheduling plan. The technical solution is as follows:

[0007] In a first aspect, a method for optimizing and dispatching an integrated energy system considering the diversified application of wind, solar and green electricity is provided, the method comprising:

[0008] Construct a mathematical model of an integrated energy system that integrates wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion;

[0009] Combined with the mathematical model of the integrated energy system, a comprehensive energy system scheduling model is constructed that takes into account cost-effectiveness, new energy utilization and environmental protection;

[0010] The target solution algorithm is selected from multiple solution algorithms, the operation scenario is constructed, the integrated energy system scheduling model is simulated and solved, and the optimized scheduling result of the integrated energy system is obtained.

[0011] In a possible implementation, the construction of a comprehensive energy system mathematical model integrating wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion includes:

[0012] Based on historical meteorological data and wind turbine operating status data, the preset statistical learning algorithm is used to predict wind power generation and build a wind power model;

[0013] Based on historical meteorological data and the operating status data of photovoltaic power plants, the preset training model is used to predict solar power generation and build a photovoltaic power model;

[0014] Construct a hydrogen energy storage system model including hydrogen production by water electrolysis and byproduct oxygen utilization strategy;

[0015] Construct a thermal power unit model including peak load regulation strategy, oxygen-enriched and ammonia-blended combustion mechanisms;

[0016] Construct an electrochemical energy storage model for an energy storage power station using lithium-ion batteries;

[0017] Construct a hydrogen-to-ammonia system model including refined ammonia synthesis process and ammonia supply to thermal power units;

[0018] The wind power model, photovoltaic power model, hydrogen energy storage system model, thermal power unit model, electrochemical energy storage model and hydrogen to ammonia system model are used as the mathematical model of the comprehensive energy system.

[0019] In a possible implementation, the integrated energy system mathematical model is combined to construct an integrated energy system scheduling model that takes into account cost-effectiveness, new energy utilization rate and environmental protection, including:

[0020] Combined with the mathematical model of the integrated energy system, a cost-benefit model is constructed that takes into account the operating costs of each generating unit, the depreciation costs of energy storage equipment, and the revenue from the sale of hydrogen and ammonia;

[0021] Combined with the mathematical model of the integrated energy system, a consumption rate model that takes into account wind and solar power abandonment is constructed;

[0022] Combined with the mathematical model of the integrated energy system, an environmental model is constructed that takes into account the carbon emissions of thermal power units and the carbon emission reduction effect of ammonia combustion;

[0023] A comprehensive energy system scheduling model is constructed by integrating cost-benefit model, absorption rate model and environmental protection model.

[0024] In a possible implementation, the step of selecting a target solution algorithm from a plurality of solution algorithms includes:

[0025] Based on the solution precision, time and accuracy of multiple solution algorithms, and considering the solution scale, complexity and nonlinearity of the integrated energy system scheduling model, the target solution algorithm is selected.

[0026] In a second aspect, a comprehensive energy system optimization and scheduling device considering the diversified application of wind, solar and green electricity is provided, the device comprising:

[0027] The first construction unit is used to construct a mathematical model of a comprehensive energy system that integrates wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion;

[0028] The second construction unit is used to combine the mathematical model of the integrated energy system to construct an integrated energy system scheduling model that takes into account cost-effectiveness, new energy utilization rate and environmental protection;

[0029] The computing unit is used to select a target solution algorithm from multiple solution algorithms, construct an operation scenario, simulate and solve the integrated energy system scheduling model, and obtain the optimized scheduling result of the integrated energy system.

[0030] In a possible implementation manner, the first building unit is further used to:

[0031] Based on historical meteorological data and wind turbine operating status data, the preset statistical learning algorithm is used to predict wind power generation and build a wind power model;

[0032] Based on historical meteorological data and the operating status data of photovoltaic power plants, the preset training model is used to predict solar power generation and build a photovoltaic power model;

[0033] Construct a hydrogen energy storage system model including hydrogen production by water electrolysis and byproduct oxygen utilization strategy;

[0034] Construct a thermal power unit model including peak load regulation strategy, oxygen-enriched and ammonia-blended combustion mechanisms;

[0035] Construct an electrochemical energy storage model for an energy storage power station using lithium-ion batteries;

[0036] Construct a hydrogen-to-ammonia system model including refined ammonia synthesis process and ammonia supply to thermal power units;

[0037] The wind power model, photovoltaic power model, hydrogen energy storage system model, thermal power unit model, electrochemical energy storage model and hydrogen to ammonia system model are used as the mathematical model of the comprehensive energy system.

[0038] In a possible implementation manner, the second building unit is further used to:

[0039] Combined with the mathematical model of the integrated energy system, a cost-benefit model is constructed that takes into account the operating costs of each generating unit, the depreciation costs of energy storage equipment, and the revenue from the sale of hydrogen and ammonia;

[0040] Combined with the mathematical model of the integrated energy system, a consumption rate model that takes into account wind and solar power abandonment is constructed;

[0041] Combined with the mathematical model of the integrated energy system, an environmental model is constructed that takes into account the carbon emissions of thermal power units and the carbon emission reduction effect of ammonia combustion;

[0042] A comprehensive energy system scheduling model is constructed by integrating cost-benefit model, absorption rate model and environmental protection model.

[0043] In a possible implementation manner, the computing unit is further configured to:

[0044] Based on the solution precision, time and accuracy of multiple solution algorithms, and considering the solution scale, complexity and nonlinearity of the integrated energy system scheduling model, the target solution algorithm is selected.

[0045] In a third aspect, an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute any of the above-mentioned methods for optimizing and scheduling an integrated energy system considering the diversified applications of wind, solar and green electricity.

[0046] In a fourth aspect, a storage medium is provided, which stores a computer program, wherein the computer program is configured to execute any of the above-mentioned methods for optimizing and scheduling an integrated energy system that considers the diversified applications of wind, solar and green electricity when running.

[0047] By means of the above technical scheme, the embodiment of the present application provides an integrated energy system optimization scheduling method and device, electronic equipment and storage medium that considers the diversified application of wind, solar and green electricity. The method constructs a mathematical model of an integrated energy system that integrates wind, solar and green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion and thermal power oxygen-enriched combustion; combined with the mathematical model of the integrated energy system, a comprehensive energy system scheduling model that considers cost-effectiveness, new energy utilization rate and environmental protection is constructed; the target solution algorithm is selected from multiple solution algorithms, an operation scenario is constructed, and the integrated energy system scheduling model is simulated and solved to obtain the optimized scheduling result of the integrated energy system. It can be seen that this embodiment effectively integrates the wind, solar and green electricity hydrogen production and ammonia synthesis with thermal power ammonia combustion and oxygen-enriched combustion technology, creating an ecosystem for efficient recycling of energy and resources; this integration of high-proportion consumption of renewable resources to produce chemical raw materials such as hydrogen and ammonia promotes the application of clean energy, reduces carbon dioxide emissions in power production, and realizes waste resource utilization; this model deepens the interactive cooperation between the new energy chemical industry and the power industry, promotes the green transformation of the energy structure and industrial upgrading, and forms a win-win circular economy system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0049] Figure 1 This is a diversified application mode of wind and solar green electricity designed by the present invention;

[0050] Figure 2 A flow chart of an integrated energy system optimization scheduling method considering diversified applications of wind, solar and green electricity provided in an embodiment of the present application is shown;

[0051] Figure 3 A flowchart of an integrated energy system optimization scheduling method considering diversified applications of wind, solar and green electricity provided by another embodiment of the present application is shown;

[0052] Figure 4 The results of wind and solar power prediction provided by the embodiments of the present application are shown;

[0053] Figure 5 The solution result of the comprehensive energy system optimization scheduling provided by the embodiment of the present application is shown;

[0054] Figure 6The structure diagram of the comprehensive energy system optimization scheduling device considering the diversified application of wind, solar and green electricity provided by the embodiment of the present application is shown;

[0055] Figure 7 A structural diagram of an electronic device provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0056] The exemplary embodiments of the present application will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present application are shown in the accompanying drawings, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present application and to fully convey the scope of the present application to those skilled in the art.

[0057] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such use is interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the term "including" and its variants are to be interpreted as open-ended terms meaning "including but not limited to".

[0058] In order to solve the above technical problems, this embodiment provides an integrated energy system optimization scheduling method considering the diversified application of wind, solar and green electricity, with the purpose of building a diversified renewable energy utilization system, effectively integrating wind energy, solar energy, thermal power, electrochemical energy storage, hydrogen energy, ammonia energy and oxygen energy, expanding the application boundary of the integrated energy system, and enhancing the utilization rate of renewable energy while ensuring economic feasibility, and ensuring environmental friendliness, so as to obtain an economic, efficient and green optimization scheduling plan. Figure 1 As shown, in Figure 1 In the process, wind and solar green electricity can be used to supply electricity load and produce hydrogen; after hydrogen production, it can be sold, synthesized ammonia and hydrogen fuel cell power generation; after ammonia synthesis, it can be sold and burned in thermal power plants with ammonia, etc.

[0059] The embodiment of the present application provides a comprehensive energy system optimization scheduling method considering the diversified application of wind, solar and green electricity, such as Figure 2 As shown, the integrated energy system optimization scheduling method considering the diversified application of wind, solar and green electricity can include the following steps S201 to S203:

[0060] Step S201, constructing a mathematical model of a comprehensive energy system integrating wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion;

[0061] Step S202, combining the comprehensive energy system mathematical model, constructing a comprehensive energy system scheduling model that considers cost-effectiveness, new energy utilization rate and environmental protection;

[0062] Step S203, selecting a target solution algorithm from a plurality of solution algorithms, constructing an operation scenario, simulating and solving the integrated energy system scheduling model, and obtaining an optimized scheduling result of the integrated energy system.

[0063] This embodiment effectively integrates the wind and solar green electricity hydrogen production and ammonia synthesis with thermal power ammonia combustion and oxygen-enriched combustion technology, creating an ecosystem for efficient recycling of energy and resources; this integration of a high proportion of renewable resources to produce hydrogen, ammonia and other chemical raw materials promotes the application of clean energy, reduces carbon dioxide emissions in electricity production, and realizes waste resource utilization; this model deepens the interactive cooperation between the new energy chemical industry and the power industry, promotes the green transformation of the energy structure and industrial upgrading, and forms a win-win circular economy system.

[0064] The present application provides a possible implementation method. The above step S201 constructs a mathematical model of a comprehensive energy system integrating wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion, which may specifically include the following steps A1 to A7:

[0065] Step A1, based on historical meteorological data and wind turbine operating status data, use a preset statistical learning algorithm to predict wind power generation and build a wind power model.

[0066] In this step, for wind power generation prediction, it is necessary to collect historical power output data of the wind farm, meteorological data such as wind speed, wind direction, temperature, air pressure, and wind turbine operation status data. Based on meteorological principles, a mathematical model is constructed to simulate atmospheric movement. Based on statistical learning methods such as ARIMA (Autoregressive Integrated Moving Average Model), SVM (Support Vector Machine), and ANN (Artificial Neural Network), historical data is statistically analyzed to obtain the correlation between wind speed or power and meteorological factors, and the predicted wind power generation is calculated. The wind power model can be approximately expressed as:

[0067]

[0068] In formula (1), P wt (t) is the wind power generation at time t; S(t) is the wind speed at time t; is the rated output of wind power; S w is the rated wind speed; S inand S out It is the cut-in and cut-out wind speed of the scenery.

[0069] The wind power rated output here is the maximum power output of the wind power component or system under standard test conditions (Standard Test Condition, STC).

[0070] Step A2, based on historical meteorological data and operating status data of the photovoltaic power station, use the preset training model to predict the solar power generation power and build a photovoltaic power model.

[0071] In this step, for photovoltaic power generation prediction, it is necessary to collect historical power data, geographic location, tilt angle, orientation, radiation intensity, temperature and other meteorological data of photovoltaic power stations and operating status data of photovoltaic power stations, predict solar radiation intensity and temperature based on cloud maps, satellite data and combined with meteorological forecasts, and use historical data to train models, such as LSTM (Long ShortTerm Memory) and SVR (Support Vector Regression), to build the relationship between radiation, temperature and power, and calculate the predicted photovoltaic power generation. The photovoltaic power model can be approximately expressed as:

[0072]

[0073] In formula (2), P pv (t) is the photovoltaic power generation at time t; A(t) is the light amplitude at time t; A is the rated output of photovoltaic power; w A is the light amplitude under standard test conditions; c is the illumination amplitude of the radiation point.

[0074] Step A3, constructing a hydrogen energy storage system model including a water electrolysis hydrogen production and by-product oxygen utilization strategy.

[0075] The hydrogen energy storage system starts with green electricity generated by wind and solar power, and integrates water electrolysis, hydrogen storage and hydrogen fuel cell technology to form a closed-loop energy cycle system. During periods of abundant wind and solar energy, the electrolyzer can convert excess clean electricity into green hydrogen and store it safely in hydrogen storage tanks. After the internal hydrogen demand of the system is met, the remaining hydrogen storage will be further diversified. It can not only be used for hydrogen fuel cell power supply and commercial hydrogen sales, but also support the production of hydrogen to ammonia, etc., fully demonstrating the broad prospects for the application of hydrogen energy. In addition, the by-product produced in the electrolysis process - high-purity oxygen, can be introduced into the combustion process of the thermal power plant boiler, and oxygen-enriched combustion technology can be adopted to improve combustion efficiency and reduce emissions, achieving the dual benefits of efficient and comprehensive utilization of resources and economic environment.

[0076] In this step, the hydrogen energy storage system model can be expressed as:

[0077]

[0078] In formula (3), are the hydrogen production, oxygen production, and hydrogen consumption of the hydrogen fuel cell at time t; P el (t), P fc (t) are the input power of the electrolyzer and the output power of the hydrogen fuel cell at time t; ρ and μ are the hydrogen production coefficient and power generation coefficient respectively; η el , η fc are the operating efficiencies of the electrolyzer and hydrogen fuel cell, respectively; are the gas storage capacity of the hydrogen storage tank and the oxygen storage tank at time t respectively; are the gas storage capacity of the hydrogen storage tank and the oxygen storage tank at time t-1 respectively; σ HST , σ OST are the leakage rates of the hydrogen storage tank and the oxygen storage tank respectively; η HST , η OST are the working efficiency of hydrogen storage tank and oxygen storage tank respectively; H cha (t), H dis (t) are the power of hydrogen input and output from the hydrogen storage tank at time t; cha (t), O dis (t) are the power of oxygen input and output of the oxygen storage tank at moment t.

[0079] Step A4, constructing a thermal power unit model including a peak load regulation strategy, oxygen-enriched combustion and ammonia-blended combustion mechanisms.

[0080] The deep regulation of thermal power units with oxygen enrichment begins with the by-product of oxygen in the electrolysis of water to produce hydrogen. It refers to a flexible operation strategy adopted by thermal power plants to adapt to changes in grid load, especially when there is an imbalance in grid supply and demand caused by large amounts of renewable energy. The ability of thermal power units to adjust power generation in different load ranges to achieve deeper load tracking and regulation. Specifically, deep peak regulation of thermal power units can be summarized into the following three stages or types:

[0081] 1) Conventional peak load regulation: In this stage, the load adjustment of thermal power units is relatively mild, usually adjusted within the range of 50% to 100% of the unit design load. This is the most common peak load regulation mode in the daily operation of thermal power units, mainly to cope with conventional grid load fluctuations.

[0082] 2) Deep peak load regulation without oil injection: As the power grid's requirements for flexibility increase, thermal power units need to further reduce their loads and enter the moderate peak load regulation range, which usually means that the units need to operate within 40% to 50% of their rated capacity. This stage requires thermal power units to have better dynamic response capabilities and thermal system adaptability.

[0083] 3) Oil injection for deep peak regulation: To achieve deep peak regulation, thermal power units must be able to operate stably at extremely low loads, usually at 30% to 40% of rated capacity. At this stage, an oil injection combustion-assisted strategy is required to ensure that the units can maintain efficient operation under low-load conditions and respond quickly to grid demand through oxygen-enriched combustion technology, effectively absorbing intermittent power generation from renewable energy sources such as wind power and photovoltaics.

[0084] In this step, the thermal power unit model can be expressed as:

[0085]

[0086] In formula (4), 1, 2, and 3 represent the conventional peak regulation, deep peak regulation without oil injection, and deep peak regulation with oil injection, respectively; h i,1 (t), h i,2 (t), h i,3 (t) are the peak load durations of different peak load stages at time t; P max,i , P a,i , P b,i , P c,i are the upper and lower limits of thermal power units in different peak load regulation stages; P the,i,1 (t), P the,i,2 (t), P the,i,3 (t) are the outputs of thermal power units at different peak load regulation stages at time t; P the,i (t) is the actual output of the thermal power unit at time t.

[0087] The combustion of ammonia in thermal power units begins with green ammonia produced from green hydrogen. It is a method of adding ammonia to thermal power units during coal-fired operation to replace part of the heat released by coal powder combustion, thereby saving coal consumption and reducing carbon emissions and nitrogen oxide emissions. In the 300MW coal-fired unit of Anhui Tongling Power Generation Co., Ltd., a subsidiary of Anhui Power, a large-proportion ammonia combustion test achieved a stable operation with a maximum ammonia blend of 35%. The test installed a pure ammonia burner in the boiler of the coal-fired unit and operated it simultaneously with the coal powder burner to achieve the effect of replacing standard coal, reducing dust and carbon dioxide emissions, and reducing energy consumption.

[0088] The ammonia combustion model can be expressed as:

[0089]

[0090] In formula (5), m coal,re (t) is the amount of coal saved at time t; L NH3 and L coal are the lower calorific values ​​of ammonia and coal respectively; m NH3 (t) is the amount of ammonia added at time t; γ(t) is the ammonia added ratio at time t; C cer (t) is the carbon emission reduction of thermal power units with ammonia combustion at time t; qCO2 Carbon emissions per unit of coal burning.

[0091] Step A5, constructing an electrochemical energy storage model of an energy storage power station composed of lithium-ion batteries.

[0092] Electrochemical energy storage refers to an energy storage power station that uses a comprehensive energy system composed of lithium-ion batteries. Assuming that the charging and discharging power of the energy storage power station is constant within a scheduling cycle, its mathematical model can be expressed as:

[0093]

[0094] In formula (6), S Pb S(t) and SOC(t) are the energy storage capacity and its per unit value of the energy storage power station at time t respectively; S Pb (t-1) is the energy storage capacity of the energy storage power station at time t-1; η Pb is the charging and discharging efficiency of the energy storage power station; P b (t) is the power of the energy storage power station at time t; S Pbmax It is the maximum storage capacity of the energy storage power station.

[0095] Each charge and discharge cycle of the energy storage power station is converted to 100% discharge depth to obtain the equivalent full cycle number, and the life loss cost of this cycle is further calculated:

[0096]

[0097] In formula (7), DOD cyc (t), N eq (t) are the discharge depth and equivalent full cycle number of the energy storage power station at time t; SOC(t-1) is the per unit value of the energy storage power station at time t-1; C bl is the life depreciation cost; C(t) is the end mark of the charge and discharge cycle of the energy storage power station at time t; k p is the battery fitting constant; N0 is the number of battery cycles when charged and discharged at 100% discharge depth; is the initial investment cost of the energy storage power station.

[0098] Step A6, constructing a hydrogen-to-ammonia system model including a refined ammonia synthesis process and ammonia supply to thermal power units.

[0099] The hydrogen-to-ammonia system starts with the production of green hydrogen by green electricity. It is a process of synthesizing ammonia (NH3) by chemical reaction of hydrogen and nitrogen, which usually uses the Haber-Bosch process technology. Hydrogen in the reaction raw materials is produced by electrolysis of water, and nitrogen is mainly extracted and separated from the air by an air separation unit (ASU) to obtain high-purity nitrogen. Under high temperature and high pressure conditions (usually about 400-500°C and 150-300 atmospheres), hydrogen and nitrogen can react to produce ammonia under the action of a catalyst (usually an iron-based catalyst). The reaction of synthesizing ammonia occurs in a synthesis tower or reactor, and the temperature, pressure and ratio of raw gas need to be precisely controlled to optimize the yield of ammonia and maintain the activity of the catalyst. The generated ammonia and the unreacted hydrogen and nitrogen mixture need to be separated by a separation unit, generally by condensation and evaporation, such as separation by an ammonia condenser and an ammonia separator, and finally obtain high-purity liquid ammonia.

[0100] In this step, the hydrogen-to-ammonia system model can be expressed as:

[0101]

[0102] In formula (8), P c (t) is the compressor power at time t; q in (t) is the flow rate of gas input to the compressor at time t; R is the thermodynamic constant; T in is the temperature of the input gas; η c is the compressor operating efficiency; M in is the molar mass of the input gas; p out 、p in are the pressure of output and input gas respectively; P AS (t) is the power consumption of synthetic ammonia at time t; is the running mark at time t; is the fixed power consumption of synthetic ammonia; k AS is the power consumption coefficient; is the ammonia production at time t.

[0103] Step A7, using the wind power model, photovoltaic power model, hydrogen energy storage system model, thermal power unit model, electrochemical energy storage model and hydrogen to ammonia system model as a comprehensive energy system mathematical model.

[0104] This embodiment can achieve a more optimized energy scheduling strategy and improve the operating efficiency of the system by comprehensively considering the output characteristics and operating rules of different energy forms.

[0105] In the embodiment of the present application, a possible implementation method is provided. The above step S202 combines the mathematical model of the integrated energy system to construct an integrated energy system scheduling model that considers cost-effectiveness, new energy utilization rate and environmental protection, which may specifically include the following steps B1 to B4:

[0106] Step B1, combining the mathematical model of the integrated energy system, constructing a cost-benefit model that takes into account the operating cost of each generator set, the depreciation cost of energy storage equipment, and the revenue from hydrogen and ammonia sales;

[0107] Step B2, combining the mathematical model of the integrated energy system to construct an absorption rate model that takes into account wind and solar power abandonment;

[0108] Step B3, combining the mathematical model of the integrated energy system, constructing an environmental model that takes into account the carbon emissions of thermal power units and the carbon emission reduction effect of ammonia combustion;

[0109] Step B4, construct a comprehensive energy system scheduling model by integrating the cost-benefit model, the absorption rate model and the environmental protection model.

[0110] The comprehensive energy system scheduling model provided in this embodiment can more comprehensively consider the economy, efficiency and environmental protection of the energy system and achieve multi-objective optimization.

[0111] A possible implementation method is provided in an embodiment of the present application. In the above step S203, a target solution algorithm is selected from multiple solution algorithms. Specifically, the target solution algorithm can be selected based on the solution accuracy, time and accuracy of each of the multiple solution algorithms, taking into account the solution scale, complexity and nonlinearity of the integrated energy system scheduling model.

[0112] This embodiment can significantly reduce the time required for solving the problem and improve the response speed and real-time performance of the system by selecting an algorithm suitable for a specific scheduling model.

[0113] The above introduces Figure 1 and Figure 2 There are various implementation methods for each link of the illustrated embodiment. The integrated energy system optimization scheduling method considering the diversified application of wind, solar and green electricity in the embodiment of the present application will be further explained through specific embodiments below.

[0114] See also Figure 3 In the embodiment of the comprehensive energy system, six system models are included, namely wind power, photovoltaic power, thermal power, electrochemical energy storage, hydrogen energy storage and synthetic ammonia. The modeling details are as introduced above and will not be repeated here.

[0115] The wind and solar power generation system includes wind turbines and photovoltaic arrays, which are the renewable energy part of the system. A prediction result of wind and solar power is as follows: Figure 4 As shown. Figure 4In the figure, the horizontal axis is time h (hours) and the vertical axis is power MW (megawatts).

[0116] The thermal power unit builds a power model based on the deep peak-shaving mechanism, comprehensively considering fuel costs, rotor losses, stable fuel injection, and operation and maintenance costs to establish a cost function. After flexibility modification or low-carbon modification, the thermal power unit is equipped with an oxygen transmission channel or a pure ammonia burner to achieve oxygen-enriched combustion or ammonia-blended combustion of the thermal power unit. During the deep peak-shaving stage of oil injection, the thermal power oxygen-enriched combustion mechanism uses the oxygen produced by hydrogen energy storage and water electrolysis to improve the combustion stability of the furnace and reduce the amount of oil injection. The thermal power ammonia-blended combustion mechanism uses the co-combustion of coal and ammonia to release heat. According to the furnace temperature, oxygen concentration, and ammonia blending ratio, an ammonia blending control strategy is designed to reduce the amount of coal and pollutant emissions while maintaining combustion stability and efficiency.

[0117] The electrochemical energy storage power station uses lithium-ion batteries as core components. Based on the battery's charge and discharge characteristics (such as open circuit voltage-state of charge curve), it takes into account factors such as charge and discharge efficiency and cycle life to simulate the dynamic behavior of the battery energy storage system.

[0118] The hydrogen energy storage system model integrates three modules: an electrolysis hydrogen production model that simulates the hydrogen production rate of the electrolyzer based on Faraday's law and takes into account current efficiency, energy consumption, etc.; a hydrogen fuel cell model that simulates the electrical energy output of the fuel cell by taking into account factors such as reaction rate, electrode overpotential, and thermal management; and a dynamic model of the hydrogen storage tank that considers the effects of pressure and temperature on hydrogen storage density, as well as hydrogen leakage rate and charging and discharging efficiency.

[0119] The synthetic ammonia system model adopts the Haber-Bosch process, produces nitrogen through a compressor and an air separator, uses a hydrogen energy storage system to produce hydrogen, and reacts with an iron-based catalyst in a synthesis tower to produce ammonia. The establishment of the synthetic ammonia model takes into account factors such as pressure, temperature, and catalyst efficiency, simulates the efficiency and output of hydrogen and nitrogen synthesis of ammonia, and finely calculates the electrical energy consumption of the ammonia production process to optimize the system energy efficiency.

[0120] Continue reading Figure 3 The optimization scheduling objectives of the integrated energy system of this embodiment are designed, including the three core considerations of maximizing economic benefits, increasing the proportion of renewable energy utilization (new energy consumption rate) and enhancing environmental protection (reducing carbon emissions and other environmental impacts). The purpose is to accurately evaluate and demonstrate the comprehensive performance and sustainable development potential of the system designed by the present invention in actual operation.

[0121] The economic goal is to minimize the total cost, including operation and maintenance costs, fuel sales revenue, equipment depreciation costs, etc. By constructing a cost function, quantifying and summing up various costs, we can get the economic objective function model:

[0122]

[0123] In formula (9), C op is the total operating cost of the integrated energy system; C the,i , C WT , C PV are the operating costs of thermal power generation, wind power generation and photovoltaic power generation respectively; C b is the charging and discharging cost of the energy storage power station; C bl C is the life loss cost of the energy storage power station; H is the comprehensive cost of hydrogen energy storage system; C A Proceeds from sale of ammonia for the synthetic ammonia system.

[0124] The goal of the new energy consumption rate is to maximize the actual utilization ratio of renewable energy such as wind energy and solar energy in the system. The difference between the predicted maximum power of wind and solar power obtained by wind and solar power prediction and the actual power generation of wind and solar power as a result of the dispatch is added with a penalty factor to obtain the penalty for abandoning wind and solar power, which is recorded as the consumption rate objective function:

[0125]

[0126] In formula (10), C ab The total penalty cost for curtailing wind and solar power; are the penalty factors for abandoning wind and abandoning solar power respectively; are the predicted maximum power of wind power and photovoltaic power at time t; P wt (t), P pv (t) are wind power and photovoltaic power generation at time t respectively.

[0127] The goal of environmental protection is to minimize the environmental pollution caused by system operation, mainly to reduce greenhouse gas emissions and other harmful substances. By establishing an emission model, calculating the emissions of carbon dioxide, sulfur dioxide, nitrogen oxides, etc. from various power generation methods (especially coal-fired power generation), adding penalties to the emission of pollutants, and obtaining the environmental protection objective function:

[0128]

[0129] In formula (11), C pe is the penalty cost for pollutant emissions; e P is the total pollutant emission factor per unit of power generation of thermal power units; the,i (t) is the actual output of the thermal power unit at time t; C cer (t) is the carbon emission reduction of thermal power units due to ammonia combustion at time t.

[0130] In order to comprehensively consider the three goals of maximizing economic benefits, increasing the rate of new energy consumption, and enhancing environmental protection, a weighted objective function can be constructed. This method allows decision makers to assign a relative importance weight to each goal based on policy orientation, corporate goals, or social needs, thereby reflecting the preferences and balance between different goals. The weighted objective function can be expressed as:

[0131] minObj=δ1C op +δ2C ab +δ3C pe (12)

[0132] In formula (12), δ1, δ2, and δ3 are the weight coefficients of the three objective functions.

[0133] See also Figure 3 When solving the scheduling model, it is necessary to compare and analyze the solving algorithms including genetic algorithm (NSGA-II, Nondominated Sorting Genetic Algorithm II), particle swarm algorithm (PSO, Particle Swarm Optimization), sparrow search algorithm (SSA, Sparrow Search Algorithm) and commercial solvers, such as CPLEX, GUROBI and other efficient solvers, and select the algorithm that is most suitable for solving the scheduling model proposed in this patent.

[0134] 1. Genetic Algorithm (NSGA-II, Nondominated Sorting Genetic Algorithm II)

[0135] Advantages:

[0136] 1) Classic swarm intelligence algorithm, which has powerful global search capabilities, is suitable for dealing with multi-objective optimization problems and can effectively find the Pareto frontier solution set.

[0137] 2) High parallelism, easy to implement parallel computing and accelerate convergence speed.

[0138] 3) It shows good solution performance for complex problems with nonlinearity and multimodality.

[0139] Disadvantages:

[0140] 1) The convergence speed is slow, especially for large-scale problems, which may require a long computing time.

[0141] 2) Parameter settings (such as population size, crossover rate, and mutation rate) have a great impact on the results, and improper selection may affect the performance of the algorithm.

[0142] 3) The accuracy of the solution is affected by the genetic encoding method and decoding strategy.

[0143] 2. Particle Swarm Optimization (PSO)

[0144] Advantages:

[0145] 1) Classic swarm intelligence algorithm, which is simple to implement, has fewer parameters, and is easy to understand and adjust.

[0146] 2) It converges quickly and is particularly suitable for solving continuous optimization problems.

[0147] 3) It has a certain global search capability and can also approach the local optimum quickly.

[0148] Disadvantages:

[0149] 1) For complex multimodal problems, it is easy to fall into the local optimal solution.

[0150] 2) Parameters such as inertia weight and acceleration constant need to be carefully adjusted to achieve optimal performance.

[0151] 3) Premature maturation may occur, causing the search to stop prematurely.

[0152] 3. Sparrow Search Algorithm (SSA)

[0153] Advantages:

[0154] 1) Emerging bio-inspired algorithms that simulate the foraging behavior of sparrow colonies and have strong exploration and exploitation capabilities.

[0155] 2) It is suitable for solving continuous and discrete optimization problems, and the exploration of the search space is more comprehensive.

[0156] 3) The algorithm has a certain degree of adaptability and can adjust the search strategy according to different environments.

[0157] Disadvantages:

[0158] 1) Compared with other mature algorithms, there are fewer research and application cases, and limited experience in parameter tuning.

[0159] 2) For very complex problems, its performance and stability need further verification.

[0160] 3) Lack of widely recognized standard parameter setting guidance.

[0161] 4. Commercial solvers (CPLEX, GUROBI, etc.)

[0162] Advantages:

[0163] 1) Efficient, mature, and particularly good at handling large-scale linear and mixed integer programming problems.

[0164] 2) High degree of automation, built-in advanced algorithms and preprocessing technology, reducing the need for manual parameter adjustment.

[0165] 3) Provides a powerful API (Application Programming Interface) for easy integration into existing systems.

[0166] Disadvantages:

[0167] 1) It is non-open source software and requires a license to use, which is very costly.

[0168] 2) The ability to handle nonlinear problems is relatively limited and may need to be combined with other algorithms.

[0169] 3) For specific complex optimization problems, the degree of customization is not as flexible as self-developed algorithms.

[0170] In summary, based on the solution scale, complexity and nonlinearity of the scheduling model proposed in this embodiment, and considering the solution precision, time and accuracy of various solution algorithms, this embodiment selects the CPLEX solver to efficiently solve the scheduling model. The solution results are as follows: Figure 5 shown.

[0171] See also Figure 5 , the horizontal axis is time h, and the vertical axis is power MW. It can be seen that during the scheduling period, the power generation of thermal power units is generally low. During the early morning 2:00-4:00 when the power load is low and wind power is high, and during the afternoon 12:00-16:00 when photovoltaic power is high, the thermal power units enter the oxygen-enriched combustion deep peak-shaving mode to better absorb renewable resources; the energy storage power station and the hydrogen energy storage system are operated at night and at noon, which has the effect of shaving the peak and filling the valley; during the night 1:00-5:00, the synthetic ammonia system is started and operated, and the comprehensive income of thermal power units from ammonia combustion is higher than the income from hydrogen and ammonia sales. The excess hydrogen produced by water electrolysis is all used to synthesize ammonia and supply thermal power units for ammonia combustion. During the operation of the synthetic ammonia system, the power consumption is at a low level, and its cost mainly comes from the cost of hydrogen raw materials; hydrogen fuel cells are subject to their excessively high operating costs and low power generation efficiency, and are always in a non-operating state during the scheduling period.

[0172] This embodiment constructs an integrated operation model of wind and solar power hydrolysis to produce hydrogen, synthesize ammonia, and combine ammonia combustion and oxygen-enriched combustion of thermal power units, effectively integrating wind and solar green electricity with hydrogen energy storage system, hydrogen-to-ammonia process, ammonia combustion and oxygen-enriched combustion technology of thermal power units, forming a closed-loop system from source green electricity production to terminal multi-energy coupling utilization; secondly, with economy, absorption rate and environmental protection as the goal, considering operating costs, depreciation of energy storage equipment, wind and solar abandonment penalties and thermal power carbon emission penalties, under the premise of ensuring energy supply and demand balance and unit operation constraints, solve the optimal operation strategy of each unit; finally, construct an operation scenario, apply the established scheduling model, and verify the feasibility of the scheduling method. The present invention innovatively utilizes the hydrogen and ammonia production process to absorb wind and solar green electricity, and through wind and solar power hydrolysis to produce hydrogen, synthesize ammonia, and combine the collaborative operation mode of ammonia combustion and oxygen-enriched combustion of thermal power units, it strengthens the linkage effect between the power industry and the chemical industry, improves the accuracy and flexibility of regional power supply and demand management, and effectively promotes the overall efficiency improvement of the power chemical system and the optimization and upgrading of sustainable development capabilities.

[0173] It should be noted that the size of the sequence number of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application. In practical applications, all the above possible implementation methods can be combined in any way to form possible embodiments of the present application, which will not be described one by one here.

[0174] Based on the integrated energy system optimization scheduling method considering the diversified applications of wind, solar and green electricity provided in the above embodiments, based on the same inventive concept, the embodiments of the present application also provide an integrated energy system optimization scheduling device considering the diversified applications of wind, solar and green electricity.

[0175] Figure 6 : is a structural diagram of a comprehensive energy system optimization scheduling device considering the diversified application of wind, solar and green electricity provided by the embodiment of the present application. Figure 6 As shown, the integrated energy system optimization and scheduling device considering the diversified application of wind, solar and green electricity can specifically include a first construction unit 610, a second construction unit 620 and a calculation unit 630.

[0176] The first construction unit 610 is used to construct a mathematical model of a comprehensive energy system integrating wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion;

[0177] The second construction unit 620 is used to construct a comprehensive energy system scheduling model that takes into account cost-effectiveness, new energy utilization rate and environmental protection in combination with the comprehensive energy system mathematical model;

[0178] The calculation unit 630 is used to select a target solution algorithm from multiple solution algorithms, construct an operation scenario, simulate and solve the integrated energy system scheduling model, and obtain an optimized scheduling result of the integrated energy system.

[0179] A possible implementation manner is provided in an embodiment of the present application, where the first constructing unit 610 is further configured to:

[0180] Based on historical meteorological data and wind turbine operating status data, the preset statistical learning algorithm is used to predict wind power generation and build a wind power model;

[0181] Based on historical meteorological data and the operating status data of photovoltaic power plants, the preset training model is used to predict solar power generation and build a photovoltaic power model;

[0182] Construct a hydrogen energy storage system model including hydrogen production by water electrolysis and byproduct oxygen utilization strategy;

[0183] Construct a thermal power unit model including peak load regulation strategy, oxygen-enriched and ammonia-blended combustion mechanisms;

[0184] Construct an electrochemical energy storage model for an energy storage power station using lithium-ion batteries;

[0185] Construct a hydrogen-to-ammonia system model including refined ammonia synthesis process and ammonia supply to thermal power units;

[0186] The wind power model, photovoltaic power model, hydrogen energy storage system model, thermal power unit model, electrochemical energy storage model and hydrogen to ammonia system model are used as the mathematical model of the comprehensive energy system.

[0187] A possible implementation manner is provided in an embodiment of the present application, where the second constructing unit 620 is further configured to:

[0188] Combined with the mathematical model of the integrated energy system, a cost-benefit model is constructed that takes into account the operating costs of each generating unit, the depreciation costs of energy storage equipment, and the revenue from the sale of hydrogen and ammonia;

[0189] Combined with the mathematical model of the integrated energy system, a consumption rate model that takes into account wind and solar power abandonment is constructed;

[0190] Combined with the mathematical model of the integrated energy system, an environmental model is constructed that takes into account the carbon emissions of thermal power units and the carbon emission reduction effect of ammonia combustion;

[0191] A comprehensive energy system scheduling model is constructed by integrating cost-benefit model, absorption rate model and environmental protection model.

[0192] A possible implementation is provided in an embodiment of the present application, wherein the calculation unit 630 is further configured to:

[0193] Based on the solution precision, time and accuracy of multiple solution algorithms, and considering the solution scale, complexity and nonlinearity of the integrated energy system scheduling model, the target solution algorithm is selected.

[0194] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a processor and a memory, wherein a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the above embodiments of the integrated energy system optimization scheduling method considering the diversified applications of wind, solar and green electricity.

[0195] In an exemplary embodiment, an electronic device is provided, such as Figure 7 As shown, Figure 7 The electronic device 700 shown includes: a processor 701 and a memory 703. The processor 701 and the memory 703 are connected, such as through a bus 702. Optionally, the electronic device 700 may also include a transceiver 704. It should be noted that in actual applications, the transceiver 704 is not limited to one, and the structure of the electronic device 700 does not constitute a limitation on the embodiments of the present application.

[0196] Processor 701 may be a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 701 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0197] The bus 702 may include a path to transmit information between the above components. The bus 702 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 702 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 7 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0198] The memory 703 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited to these.

[0199] The memory 703 is used to store the computer program code for executing the solution of the present application, and the execution is controlled by the processor 701. The processor 701 is used to execute the computer program code stored in the memory 703 to implement the contents shown in the above method embodiment.

[0200] Among them, electronic devices include but are not limited to: mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0201] Based on the same inventive concept, an embodiment of the present application also provides a storage medium, which stores a computer program, wherein the computer program is configured to execute any one of the above-mentioned embodiments of an integrated energy system optimization scheduling method considering the diversified application of wind, solar and green electricity when running.

[0202] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0203] Those skilled in the art can understand that the technical solution of the present application can be essentially or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, which includes a number of program instructions, so that an electronic device (such as a personal computer, a server, or a network device, etc.) executes all or part of the steps of the method described in each embodiment of the present application when running the program instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0204] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as electronic devices such as personal computers, servers, or network devices), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of an electronic device, the electronic device executes all or part of the steps of the methods described in the embodiments of the present application.

[0205] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present application, they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate from the protection scope of the present application.

Claims

1. A comprehensive energy system optimization scheduling method considering the diversified application of wind, solar and green electricity, characterized in that: The method comprises: Construct a mathematical model of an integrated energy system that integrates wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion; Combined with the mathematical model of the integrated energy system, a comprehensive energy system scheduling model is constructed that takes into account cost-effectiveness, new energy utilization and environmental protection; The target solution algorithm is selected from multiple solution algorithms, the operation scenario is constructed, the integrated energy system scheduling model is simulated and solved, and the optimized scheduling result of the integrated energy system is obtained.

2. The method according to claim 1, characterized in that The mathematical model of a comprehensive energy system integrating wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion and thermal power oxygen-enriched combustion includes: Based on historical meteorological data and wind turbine operating status data, the preset statistical learning algorithm is used to predict wind power generation and build a wind power model; Based on historical meteorological data and the operating status data of photovoltaic power plants, the preset training model is used to predict solar power generation and build a photovoltaic power model; Construct a hydrogen energy storage system model including hydrogen production by water electrolysis and byproduct oxygen utilization strategy; Construct a thermal power unit model including peak load regulation strategy, oxygen-enriched and ammonia-blended combustion mechanisms; Construct an electrochemical energy storage model for an energy storage power station using lithium-ion batteries; Construct a hydrogen-to-ammonia system model including refined ammonia synthesis process and ammonia supply to thermal power units; The wind power model, photovoltaic power model, hydrogen energy storage system model, thermal power unit model, electrochemical energy storage model and hydrogen to ammonia system model are used as the mathematical model of the comprehensive energy system.

3. The method according to claim 1, characterized in that The above-mentioned integrated energy system mathematical model is combined to construct an integrated energy system scheduling model that takes into account cost-effectiveness, new energy utilization rate and environmental protection, including: Combined with the mathematical model of the integrated energy system, a cost-benefit model is constructed that takes into account the operating costs of each generating unit, the depreciation costs of energy storage equipment, and the revenue from the sale of hydrogen and ammonia; Combined with the mathematical model of the integrated energy system, a consumption rate model that takes into account wind and solar power abandonment is constructed; Combined with the mathematical model of the integrated energy system, an environmental model is constructed that takes into account the carbon emissions of thermal power units and the carbon emission reduction effect of ammonia combustion; A comprehensive energy system scheduling model is constructed by integrating cost-benefit model, absorption rate model and environmental protection model.

4. The method according to claim 1, characterized in that: The step of selecting a target solution algorithm from a plurality of solution algorithms comprises: Based on the solution precision, time and accuracy of multiple solution algorithms, and considering the solution scale, complexity and nonlinearity of the integrated energy system scheduling model, the target solution algorithm is selected.

5. An integrated energy system optimization and dispatching device considering the diversified application of wind, solar and green electricity, characterized in that: The device comprises: The first construction unit is used to construct a mathematical model of a comprehensive energy system that integrates wind and solar green electricity, hydrogen and ammonia production processes, thermal power ammonia combustion, and thermal power oxygen-enriched combustion; The second construction unit is used to combine the mathematical model of the integrated energy system to construct an integrated energy system scheduling model that takes into account cost-effectiveness, new energy utilization rate and environmental protection; The computing unit is used to select a target solution algorithm from multiple solution algorithms, construct an operation scenario, simulate and solve the integrated energy system scheduling model, and obtain the optimized scheduling result of the integrated energy system.

6. The device according to claim 5, characterized in that The first building block is also used for: Based on historical meteorological data and wind turbine operating status data, the preset statistical learning algorithm is used to predict wind power generation and build a wind power model; Based on historical meteorological data and the operating status data of photovoltaic power plants, the preset training model is used to predict solar power generation and build a photovoltaic power model; Construct a hydrogen energy storage system model including hydrogen production by water electrolysis and byproduct oxygen utilization strategy; Construct a thermal power unit model including peak load regulation strategy, oxygen-enriched and ammonia-blended combustion mechanisms; Construct an electrochemical energy storage model for an energy storage power station using lithium-ion batteries; Construct a hydrogen-to-ammonia system model including refined ammonia synthesis process and ammonia supply to thermal power units; The wind power model, photovoltaic power model, hydrogen energy storage system model, thermal power unit model, electrochemical energy storage model and hydrogen to ammonia system model are used as the mathematical model of the comprehensive energy system.

7. The device according to claim 5, characterized in that The second building block is also used for: Combined with the mathematical model of the integrated energy system, a cost-benefit model is constructed that takes into account the operating costs of each generating unit, the depreciation costs of energy storage equipment, and the revenue from the sale of hydrogen and ammonia; Combined with the mathematical model of the integrated energy system, a consumption rate model that takes into account wind and solar power abandonment is constructed; Combined with the mathematical model of the integrated energy system, an environmental model is constructed that takes into account the carbon emissions of thermal power units and the carbon emission reduction effect of ammonia combustion; A comprehensive energy system scheduling model is constructed by integrating cost-benefit model, absorption rate model and environmental protection model.

8. The device according to claim 5, characterized in that The computing unit is also used for: Based on the solution precision, time and accuracy of multiple solution algorithms, and considering the solution scale, complexity and nonlinearity of the integrated energy system scheduling model, the target solution algorithm is selected.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute the integrated energy system optimization scheduling method considering the diversified application of wind, solar and green electricity as described in any one of claims 1 to 4.

10. A storage medium, characterized in that: The storage medium stores a computer program, wherein the computer program is configured to execute the integrated energy system optimization scheduling method considering the diversified application of wind, solar and green electricity according to any one of claims 1 to 4 when running.

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