A V2G-based scheduling method for integrated energy systems of electricity, hydrogen, and ammonia.

Abstract

This invention provides a V2G-based scheduling method for an integrated electric-hydrogen-ammonia energy system, belonging to the field of energy system scheduling optimization technology. The method includes: establishing mathematical models for each unit of the integrated electric-hydrogen-ammonia energy system; generating travel characteristic data for electric vehicles and hydrogen vehicles, and constructing a vehicle-to-grid (V2G) charging and discharging model for electric vehicles and hydrogen vehicles; dividing carbon emission intervals based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, and calculating tiered carbon trading costs; obtaining an objective function that minimizes the total operating cost of the integrated electric-hydrogen-ammonia energy system based on the mathematical models of each unit, the V2G charging and discharging model, and the tiered carbon trading costs; creating constraints for the integrated electric-hydrogen-ammonia energy system, and obtaining scheduling rules for the integrated electric-hydrogen-ammonia energy system that satisfy the objective function under the constraints, thereby optimizing the operation of the integrated electric-hydrogen-ammonia energy system, improving the renewable energy absorption rate, reducing carbon emissions, and enhancing the system's economic efficiency and low-carbon characteristics.

Description

Technical Field

[0001] This invention relates to the field of energy system scheduling and optimization technology, and in particular to a V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia. Background Technology

[0002] With the continuous growth of global energy demand and increasingly stringent environmental protection requirements, Integrated Energy Systems (IES), as a new energy management approach, can effectively improve energy efficiency, reduce carbon emissions, and achieve sustainable development of energy systems by coupling and optimizing the management of multiple energy forms (such as electricity, heat, cooling, and hydrogen). In recent years, the Electro-Hydrogen-Ammonia Integrated Energy System (EHA-IES), as a system configuration with great potential, has received some research and application. The EHA-IES integrates clean energy sources such as wind power, hydrogen, and ammonia, and utilizes technologies such as hydrogen production via electrolysis cells, ammonia synthesis in ammonia plants, and ammonia-blended combustion to achieve complementary and synergistic utilization of multiple energy forms.

[0003] However, existing integrated energy systems for electricity, hydrogen, and ammonia still have some shortcomings in terms of optimized scheduling. These shortcomings are mainly reflected in the insufficient exploitation of load-side resources and the ineffective integration of carbon trading mechanisms. These shortcomings limit the further improvement of integrated energy systems for electricity, hydrogen, and ammonia in terms of new energy consumption, system economy, and low carbon emissions. Summary of the Invention

[0004] This invention provides a V2G-based scheduling method for an integrated energy system of electricity, hydrogen, and ammonia, which solves the technical problem that existing technologies cannot improve the renewable energy absorption rate of integrated energy systems of electricity, hydrogen, and ammonia.

[0005] On one hand, this invention provides a V2G-based scheduling method for an integrated energy system of electricity, hydrogen, and ammonia, comprising:

[0006] Mathematical models for each unit of the integrated energy system of electricity, hydrogen, and ammonia are established; wherein, each unit mathematical model includes a hydrogen energy unit model, an ammonia production unit model, an ammonia-blended combustion thermal power unit model, a carbon capture device model, and an ice storage air conditioning model;

[0007] The Monte Carlo method was used to generate travel characteristic data for electric vehicles and hydrogen vehicles, and a vehicle-to-grid charging and discharging model for electric vehicles and hydrogen vehicles was constructed.

[0008] Based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, carbon emission zones are divided, and tiered carbon trading costs are calculated.

[0009] Based on the mathematical models of each unit, the charging and discharging model of the vehicle-to-grid technology, and the tiered carbon trading cost, the objective function for minimizing the total operating cost of the integrated energy system of electricity, hydrogen, and ammonia is obtained.

[0010] Create constraints for the integrated energy system of electricity, hydrogen, and ammonia. Under the condition that the constraints are met, call the Gurobi solver to obtain the scheduling rules of the integrated energy system of electricity, hydrogen, and ammonia that satisfy the objective function.

[0011] This invention provides a V2G-based scheduling method for an integrated electric-hydrogen-ammonia energy system. It establishes mathematical models for each unit of the integrated electric-hydrogen-ammonia energy system; uses the Monte Carlo method to generate travel characteristic data for electric vehicles and hydrogen vehicles, and constructs a vehicle-to-grid (V2G) charging and discharging model for electric vehicles and hydrogen vehicles; divides carbon emission intervals based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, and calculates tiered carbon trading costs; based on the mathematical models of each unit, the V2G charging and discharging model, and the tiered carbon trading costs, it obtains an objective function that minimizes the total operating cost of the integrated electric-hydrogen-ammonia energy system; creates constraints for the integrated electric-hydrogen-ammonia energy system; and, under the constraints, calls the Gurobi solver to obtain scheduling rules for the integrated electric-hydrogen-ammonia energy system that satisfy the objective function. This method optimizes the operation of the integrated electric-hydrogen-ammonia energy system, improves the renewable energy absorption rate, reduces carbon emissions, and simultaneously enhances the system's economic efficiency and low-carbon characteristics. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating the V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia provided in this embodiment of the invention.

[0014] Figure 2 This is a schematic diagram of the architecture of the V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in an embodiment of the present invention.

[0015] Figure 3 This is a schematic diagram of the wind power and electricity, heat and cooling load prediction curves of the V2G-based integrated energy system for electricity, hydrogen and ammonia provided in an embodiment of the present invention;

[0016] Figure 4 This is a schematic diagram of the cooling load balance scheduling results for scenario three of the V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in this embodiment of the invention.

[0017] Figure 5 This is a schematic diagram of the cooling load balance scheduling results for scenario four of the V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in this embodiment of the invention.

[0018] Figure 6 This is a schematic diagram of the HVs and EVs cluster scheduling results in Scenario 2 of the V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in this embodiment of the invention.

[0019] Figure 7 This is a schematic diagram of the HVs and EVs cluster scheduling results in Scenario 3 of the V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in this embodiment of the invention.

[0020] Figure 8 This is a schematic diagram of the electrical load and unit output scheduling results for scenario three of the V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in this embodiment of the invention.

[0021] Figure 9 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0023] Figure 1 This is a flowchart illustrating the scheduling method for a V2G-based integrated energy system of electricity, hydrogen, and ammonia provided in this invention. The integrated energy system can integrate clean energy sources such as wind power, hydrogen energy, and ammonia energy. Utilizing technologies such as hydrogen production via electrolysis, ammonia synthesis in ammonia plants, and ammonia-blended combustion, it achieves complementary and synergistic utilization of multiple energy forms. Vehicle-to-Grid (V2G) technology refers to electric vehicles (EVs) and hydrogen vehicles (HVs) connecting to the power grid, feeding back their electrical or hydrogen energy into the grid, thus achieving bidirectional energy flow. An Integrated Energy System (IES) is a system that couples and optimizes the management of multiple energy sources (such as electricity, heat, cooling, and hydrogen).

[0024] Figure 2This is a schematic diagram of the architecture of a V2G-based integrated energy system for electricity, hydrogen, and ammonia provided in this embodiment of the invention. Wind turbine: Utilizes wind power to generate electricity, which can be used directly for power supply or converted into hydrogen through an electrolyzer. Pressure Swing Adsorption (PSA) nitrogen production unit: Separates nitrogen (N2) from the air, providing raw materials for the ammonia plant. Ammonia plant: Uses nitrogen provided by the PSA unit and hydrogen (H2) generated by the electrolyzer to synthesize ammonia (NH3). Thermal power unit with carbon capture device: This is a thermal power unit combining carbon capture and storage (CCS) technology, which can reduce carbon dioxide (CO2) emissions. Electrolyzer (EL): Electrolyzes water to produce hydrogen, which can be stored in a hydrogen storage tank or directly used in combined heat and power (CHP) or hydrogen vehicles (HVs). Hydrogen Storage Tank (HST): Used to store the hydrogen produced by the electrolyzer for subsequent use. Combined Heat and Power (CHP): Utilizes hydrogen and natural gas to generate electricity and heat, improving energy efficiency. Electric Vehicles (EVs) and Hydrogen Vehicles (HVs): Electric vehicles are charged via the grid, while hydrogen vehicles are refueled using hydrogen storage tanks. Both can feed energy back into the grid, achieving vehicle-to-grid (V2G) technology. Ice Storage Air Conditioning: Uses electricity for cooling and stores the cold energy for later use, thus balancing the grid load. Electrical Load, Cooling Load, and Heat Load: Represent the system's electricity demand, cooling demand, and heat demand, respectively. The arrows in the diagram indicate different forms of energy flow. Solid arrows represent electrical energy flow. Dashed arrows represent hydrogen energy flow. Dotted-line arrows represent cooling energy flow. Dotted-line arrows represent heat energy flow.

[0025] See Figure 1 The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia may include the following steps 101 to 105.

[0026] Step 101: Establish mathematical models for each unit of the integrated energy system of electricity, hydrogen, and ammonia; among which, the mathematical models for each unit include the hydrogen energy unit model, the ammonia production unit model, the ammonia-blended combustion power unit model, the carbon capture device model, and the ice storage air conditioning model.

[0027] In this step, the hydrogen energy unit model includes a proton exchange membrane model unit, an HST model unit, and a hydrogen-doped CHP model unit;

[0028] The proton exchange membrane (PEM) type unit is shown in the following formula (1):

[0029] (1);

[0030] , They are respectively t Energy consumption of the electrolyzer (EL) and H2 power generated by the electrolyzer (EL); The energy conversion efficiency of EL; , These are the upper and lower limits of EL energy consumption, respectively.

[0031] The HST model unit is shown in the following formula (2):

[0032] (2);

[0033] for t H2 capacity in the hydrogen storage tank (HST); , These are the minimum and maximum values ​​of the HST capacity, respectively. for t Hydrogen charging power of HST during the time period; This is a hydrogen charging status indicator for HST, and can be either 0 or 1. This is the maximum hydrogen charging power; for t Hydrogen release power during the HST period; This represents the maximum hydrogen release power. This is the hydrogen release state flag for HST, and can be either 0 or 1. for t -1-hour hydrogen storage tank (HST) H2 capacity; , These are the hydrogen charging efficiency and hydrogen discharging efficiency of HST, respectively.

[0034] The hydrogen-doped CHP model unit is shown in the following formula (3):

[0035] (3);

[0036] for t Hydrogen doping ratio of CHP during the specified time period; for t The hydrogen power input from the upstream gas network to the combined heat and power (CHP) plant during the specified time period; for t The natural gas power input to the combined heat and power (CHP) plant from the upstream gas network during the specified time period; , The lower heating values ​​are H2 and CH4, respectively. The calorific value of the mixture of H2 and CH4; , , They are respectively tInput power, output electrical power, and output thermal power of hydrogen-doped CHP during a given time period; , The electrical efficiency and thermal efficiency of CHP are respectively. , These represent the maximum and minimum thermoelectric adjustable ratios of hydrogen-doped CHP, respectively.

[0037] The ammonia production unit model is shown in the following formula (4):

[0038] (4);

[0039] for t Overall energy consumption of the ammonia production unit during a specific time period; for t Energy consumption of a periodic ammonia production plant; for t Electrical energy consumed by time-swing adsorption (PSA); , They are respectively t The mass of ammonia and nitrogen produced during a given time period; , These are the unit energy consumption figures for the ammonia production plant and the PSA preparation unit, respectively. for t The heat generated during the production of a unit of ammonia gas in a given time period; For the heat release efficiency of ammonia production plants; The heat generated per unit of ammonia gas during the ammonia production process in an ammonia plant;

[0040] The model of the ammonia-blended combustion thermal power unit is shown in the following formula (5):

[0041] (5);

[0042] For thermal power units, for t ammonia-blended thermal power units Coal consumption generated; for t ammonia-blended thermal power units ; output electrical energy; , , thermal power units The coal consumption coefficient; , These are the lower heating values ​​of ammonia and coal, respectively. for t The ammonia blending ratio of ammonia-blended thermal power units during specific time periods; , thermal power units Upper and lower limits of energy consumption; , These are the minimum and maximum ramp power of the thermal power unit, respectively. for t-1 ammonia-blended thermal power units ; output electrical energy;

[0043] The carbon capture device model is shown in the following formula (6):

[0044] (6);

[0045] , , , They are respectively t ammonia-blended thermal power units CO2 produced, CO2 supplied by solution storage, thermal power units The CO2 absorbed by the regeneration tower of the carbon capture device, and the CO2 actually captured by the regeneration tower; The carbon emission rate of thermal power units; This refers to the amount of CO2 released per unit of coal. This refers to the flue gas split ratio; , These represent the absorption efficiency of the CCS absorption tower and the energy consumption per unit of CO2 captured, respectively. This refers to the maximum operating condition coefficient of the regeneration tower and compressor within the carbon capture device; , , They are respectively t Periodic thermal power units The stationary energy consumption, operating energy consumption, and net output of the carbon capture device; It is the regeneration rate of the regeneration tower;

[0046] The ice storage air conditioning model is shown in the following formula (7):

[0047] (7);

[0048] , , They are respectively t Cooling capacity, ice storage capacity, and ice melting cooling capacity of time-of-use ice storage air conditioners; , , They are respectively t The cooling indicator, ice storage indicator, and ice melting indicator of a time-limited ice storage air conditioner; , They are respectively t Minimum and maximum cooling power of time-limited ice storage air conditioners; for t The maximum ice-melting cooling power of a time-limited ice storage air conditioner; , They are respectively t, t-1 The amount of ice stored in the ice storage tank during a given time period; , , These represent the self-loss rate, ice storage rate, and ice melting rate of the ice storage tank, respectively. For a series-connected ice storage air conditioning model, .

[0049] Step 102: Use the Monte Carlo method to generate travel characteristic data for electric vehicles and hydrogen vehicles, and construct a vehicle-to-grid charging and discharging model for electric vehicles and hydrogen vehicles.

[0050] The travel characteristic data follows a normal distribution at both the grid connection and grid disconnection times, as shown in formulas (8) and (9) below:

[0051] (8);

[0052] (9);

[0053] and For electric vehicles or hydrogen vehicles at the time of grid connection and off-network time The probability density function of travel characteristics; and These are the mathematical expectations at the grid connection time and the grid disconnection time, respectively; and These are the standard deviations of the grid connection time and the grid disconnection time, respectively.

[0054] The mileage traveled approximately follows a log-normal distribution, and its probability density function is shown in the following formula (10):

[0055] (10);

[0056] The mathematical variance, equal to 0.88, represents the degree of dispersion in the mileage traveled. The expected value is 3.2, representing the average or expected value of the mileage traveled. For driving mileage, This represents the probability distribution of driving mileage.

[0057] The initial state of charge of EVs and HVs at the time of grid disconnection follows a uniform distribution, and its probability distribution function is shown in the following formula (11):

[0058] (11);

[0059] in, Vehicle type; for The initial state of charge of a vehicle of a certain type at the time of disconnection from the grid; for Maximum capacity of car battery type; Let be the probability distribution function of the initial state of charge of EVs or HVs at the moment of off-grid departure;

[0060] The initial charge capacity model at the time of grid connection of EVs and HVs is shown in the following formula (12):

[0061] (12);

[0062] The initial charge capacity at the moment when EVs and HVs are connected to the grid; for Energy consumption per unit mileage of similar vehicles; for Mileage of different types of vehicles; for Maximum capacity of car battery type;

[0063] The vehicle-to-the-grid (V2G) charging and discharging model includes electric vehicle model units and HVs model units;

[0064] The electric vehicle model unit is shown in the following formula (13):

[0065] (13);

[0066] i For electric vehicles, The total number of electric vehicles; , , , Electric vehicles i exist t The charging power, discharging power, charging flag, and discharging flag for each time period; , These are the maximum charging power and maximum discharging power of EVs, respectively. , These are the charging efficiency and discharging efficiency of EVs, respectively. The number of EVs participating in the scheduling; , For electric vehicle clusters t The sum of charging power and the sum of discharging power during the time period; Let represent the initial charge state of electric vehicle i in time period t-1.

[0067] The HVs model unit is shown in the following formula (14):

[0068] (14);

[0069] For hydrogen vehicles, This represents the total number of hydrogen vehicles. , , , Hydrogen cars exist t The amount of hydrogen added and released during a given period, the amount of hydrogen stored in the hydrogen storage tank, and the discharge power of the hydrogen vehicle; , These are the hydrogen charging efficiency and hydrogen degassing efficiency of HVs, respectively. The number of HVs participating in the scheduling; For hydrogen car clusters t The sum of discharge power over the time period; For hydrogen cars exist t-1 The amount of hydrogen stored in the hydrogen storage tank during a given period.

[0070] Step 103: Based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, divide the carbon emission ranges and calculate the tiered carbon trading costs.

[0071] A gas turbine (TPU) is a device used for power generation. Based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, carbon emission zones are defined, and tiered carbon trading costs are calculated, including:

[0072] The IES carbon quota is mainly provided by thermal power units with carbon capture devices and hydrogen-blended gas turbines, as shown in the following formula (15):

[0073] (15);

[0074] For gas turbines; , , These are the carbon quotas for IES, CHP, and TPU, respectively. , These are the carbon quota coefficients for CHP and TPU, respectively. For gas turbine Output power during time period t; The heat input power of the combined heat and power generation during time period t; T is the total number of time periods; t represents the electrical input power of the combined heat and power (CHP) during time period t; E represents the total number of gas turbines.

[0075] The actual carbon emissions of IES are shown in the following formula (16):

[0076] (16);

[0077] , , These are the carbon emissions of IES, CHP, and TPU, respectively. , These represent the carbon content of methane per unit calorific value and the carbon oxidation rate of methane, respectively. The heat-to-electric ratio of a gas turbine; For thermal power units Carbon dioxide emissions during time period t; The amount of carbon dioxide that the carbon capture device can capture; The number of thermal power units;

[0078] The tiered carbon trading system is shown in the following formula (17):

[0079] (17);

[0080] , , , These are the carbon trading base price, compensation factor, penalty factor, and carbon emission range length; The cost of tiered carbon trading.

[0081] Step 104: Based on the mathematical models of each unit, the vehicle-to-grid technology charging and discharging model, and the tiered carbon trading cost, the objective function for minimizing the total operating cost of the integrated energy system of electricity, hydrogen, and ammonia is obtained.

[0082] Based on the mathematical models of each unit, the charging and discharging model of the vehicle-to-grid (V2G) technology, and the tiered carbon trading cost, the objective function for minimizing the total operating cost of the integrated energy system of electricity, hydrogen, and ammonia is obtained, including the following formula (18):

[0083] (18);

[0084] , , , , These are energy procurement costs, operating costs of thermal power units with ammonia-blended combustion, carbon capture costs, EVs and HVs costs, and equipment operating costs.

[0085] The energy procurement cost is shown in the following formula (19):

[0086] (19);

[0087] , , These are the prices of gas purchased from the upstream gas network, the price coefficient for wind turbine power generation, and the price coefficient for abandoned wind power. , , , They are respectively t Gas transmission capacity of the upstream power grid, actual wind turbine power generation, wind power curtailment, and predicted wind turbine power generation for the specified time period;

[0088] The operating cost of ammonia-blended thermal power units includes coal purchase costs. and start-up / shutdown costs As shown in the following formula (20):

[0089] (20);

[0090] The price per unit of coal; for t ammonia-blended thermal power units The start / stop indicator quantity; For ammonia-blended thermal power units The start-stop cost coefficient;

[0091] Carbon capture cost Including daily depreciation costs Solution loss cost of the absorption tower Carbon sequestration costs As shown in the following formula (21):

[0092] (twenty one);

[0093] , These are the investment cost and service life of the carbon capture device; , , These are the total cost of the solution storage device, the volume of the solution storage device, and its service life; The capital cost of carbon capture equipment; , These are the economic coefficient and solvent loss coefficient of ethanolamine solvent for absorbing CO2, respectively. Cost of carbon sequestration;

[0094] V2G cost This includes all costs incurred by EVs and HVs participating in V2G, including battery degradation costs. and incentive costs As shown in the following formula (22):

[0095] (twenty two);

[0096] for t Incentive cost coefficient for a given period; It refers to battery cycle life; denoted as the depth of battery discharge, and denoted as the ratio of battery discharge capacity to battery maximum capacity; a and b are curve fitting parameters. This represents the total discharge capacity of the battery. Battery capacity;

[0097] The operating and maintenance costs per unit power for the hydrogen energy section and the ammonia production section need to be calculated, as shown in the following formula (23):

[0098] (twenty three);

[0099] For equipment y Price coefficient; For equipment y The power; This represents the total number of devices.

[0100] Step 105: Create constraints for the integrated energy system of electricity, hydrogen, and ammonia. Under the condition that the constraints are met, call the Gurobi solver to obtain the scheduling rules of the integrated energy system of electricity, hydrogen, and ammonia that satisfy the objective function.

[0101] The constraints for creating the integrated energy system of electricity, hydrogen, and ammonia include:

[0102] Energy balance mainly includes the supply and demand balance of electricity, heat, cooling and hydrogen, as shown in the following formula (24):

[0103] (twenty four);

[0104] Energy consumption of ice storage air conditioning during time period t; , , These are the electrical load, thermal load, and cooling load of the IES system, respectively. , , , These represent the hydrogen charging power, hydrogen discharging power, hydrogen power required for ammonia synthesis in the ammonia plant, and hydrogen power consumed by the hydrogen-doped CHP at time t, respectively.

[0105] Limits are imposed on the electrical storage capacity and hydrogen storage capacity used for EVs and HVs operation, as shown in the following formula (25):

[0106] (25);

[0107] , These are the minimum and maximum values ​​of the EVs power storage capacity, respectively. , These represent the minimum and maximum values ​​of the hydrogen storage capacity of HVs, respectively.

[0108] In this embodiment, mathematical models of each unit of the integrated electric-hydrogen-ammonia energy system are established; Monte Carlo methods are used to generate travel characteristic data of electric vehicles and hydrogen vehicles, and vehicle-to-grid (V2G) charging and discharging models of electric vehicles and hydrogen vehicles are constructed; carbon emission intervals are divided according to the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, and tiered carbon trading costs are calculated; based on the mathematical models of each unit, the V2G charging and discharging models, and the tiered carbon trading costs, an objective function for minimizing the total operating cost of the integrated electric-hydrogen-ammonia energy system is obtained; constraints on the integrated electric-hydrogen-ammonia energy system are created, and under the condition that the constraints are met, the Gurobi solver is called to obtain the scheduling rules of the integrated electric-hydrogen-ammonia energy system that satisfy the objective function, which can optimize the operation of the integrated electric-hydrogen-ammonia energy system, improve the renewable energy absorption rate, reduce carbon emissions, and at the same time improve the system's economy and low-carbon performance.

[0109] The invention will now be illustrated with some specific examples.

[0110] Example Design: Four different operating scenarios were constructed for comparative analysis: 1. Without considering ammonia production, thermal power units do not mix ammonia for combustion, vehicles do not participate in V2G dispatch, and ice storage air conditioning provides cooling, ice storage, and ice melting throughout the day; 2. Considering ammonia production, thermal power units mix ammonia for combustion, vehicles do not participate in V2G dispatch, and ice storage air conditioning provides cooling, ice storage, and ice melting throughout the day; 3. Considering ammonia production, thermal power units mix ammonia for combustion, vehicles participate in V2G dispatch, and ice storage air conditioning provides cooling, ice storage, and ice melting throughout the day; 4. Considering ammonia production, thermal power units mix ammonia for combustion, vehicles participate in V2G dispatch, ice storage air conditioning provides cooling throughout the day, ice storage during off-peak load periods, and ice melting during peak load periods. Table 1 shows the IES simulation results under different scenarios.

[0111] Table 1

[0112]

[0113] Analysis of IES scheduling optimization results: Table 1 shows the cost of each part of the IES in different scenarios. Scenario 2 adds an ammonia production section, which absorbs more wind power. Compared with Scenario 1, the wind power absorption rate increases by 6.88%. During the peak wind power generation period at night, the electrolyzer can operate at full load, producing more hydrogen, which is then synthesized into ammonia with the ammonia production section. Ammonia can replace a certain amount of coal for combustion in thermal power units. Under the same coal consumption, the thermal power units can provide more output by ammonia-blended combustion. The process of ammonia synthesis can release a certain amount of heat, reducing the pressure on the heating of the hydrogen-blended gas turbine, thus reducing the natural gas cost of Scenario 2 by 2.4% compared with Scenario 1. Ammonia-blended combustion in thermal power units, under the same output, reduces both coal consumption and the actual carbon dioxide emissions of the thermal power units. The corresponding energy consumption of the carbon capture device decreases. Therefore, the carbon capture cost and thermal power unit operating cost of Scenario 2 decrease by 1.3% and 1.6% respectively compared with Scenario 1. Although the ammonia production section increases the corresponding maintenance costs, it reduces the total cost of the IES by 10.3%, making the IES system more economical. Scenario 3 adds V2G scheduling between EVs and HVs clusters, discharging during peak electricity load periods and charging during peak wind power generation periods at night. Compared to Scenario 2, Scenario 3 reduces the operating costs of thermal power units, gas purchase costs, and equipment operating costs by 1.9%, 1.2%, and 0.1%, respectively. This reduces the use of fossil fuels by thermal power units and gas turbines. Furthermore, the EVs cluster, after discharging and charging, can absorb more wind power, making the cost of wind curtailment zero. The HVs cluster actively utilizes hydrogen produced by electrolyzers, making hydrogen energy utilization more diversified and maximizing the economic benefits of hydrogen. This results in a 2.1% reduction in the total IES cost compared to Scenario 2. Scenario 4 restricts ice and ice melting periods, selecting ice making during periods of high wind power output to reduce the possibility of ice making during peak electricity load periods. Ice melting occurs during peak electricity and cooling load periods, replacing the cooling load demand of refrigeration supply with the cooling load provided by ice melting. This reduces peak-on-peak situations and reduces the output of thermal power units to supply cooling power. The total IES cost is 0.9% lower than that of Scenario 3. Figure 3 This is a schematic diagram of the wind power and electricity, heat and cooling load prediction curves of the V2G-based integrated energy system for electricity, hydrogen and ammonia provided in the embodiments of the present invention.

[0114] Analysis of the working mode of ice storage air conditioners, as shown in the appendix. Figure 4 and Figure 5As shown in the diagram, comparing the cooling load balancing scheduling results of scenarios three and four, scenario three does not restrict the ice storage period, directly affecting the ice storage and melting volume. The ice storage and melting states of ice storage air conditioning are mutually exclusive. Ice storage was chosen during the peak electricity load period of 7-8 PM, causing a rise in peak load, requiring higher output from thermal power units and gas turbines, thus increasing costs. Melting occurred during the off-peak periods of 4-5 PM and 7 PM. At these times, strong wind power could fully supply the cooling load through refrigeration. Using thermal power and gas turbines for ice storage during peak load periods and melting ice during periods of high wind power output resulted in resource waste. Scenario four concentrates the melting periods during peak electricity load periods of 10-14 PM and 6-8 PM, and the peak cooling load period of 10-14 PM. Melting during these periods reduces the output of thermal power and gas turbines to meet the cooling load demand, avoiding peak-on-peak electricity load increases caused by supplying cooling load. Ice storage is carried out through wind power during off-peak periods, reducing the cost of ice storage during peak load periods. Planning ice storage and melting periods according to load conditions can maximize benefits. The IES cost of Scenario 4 is 0.9% lower than the total cost of Scenario 3.

[0115] Analysis of EVs and HVs cluster scheduling results, such as Figure 6 , Figure 7 and Figure 8 As shown. In Figure 8 In Scenario 2, without V2G participation, wind power is fully absorbed during peak electricity load periods (18-20%), with thermal power units and gas turbines handling a portion of the load. During off-peak periods (23-04), wind power is not fully absorbed, resulting in a waste of renewable resources. Scenario 3, similar to Scenario 2, fully absorbs wind power during peak load periods. However, under the same load, the output of thermal power units and gas turbines in Scenario 3 is lower than in Scenario 2. This helps reduce the operating costs and gas purchase costs of thermal power units, further reducing coal consumption. Simultaneously, the EV cluster discharges via V2G, resulting in a higher charging capacity compared to clusters without V2G participation. The EV cluster meets this increased charging power by charging during peak wind power output periods (23-04), thus absorbing wind power during off-peak periods and reducing the use of fossil fuels by IES (Environmentally Inspired Fuels), lowering IES costs.

[0116] from Figure 6As can be seen, during the wind power surplus period from 23:00 to 04:00, the EV cluster absorbs wind power with a maximum charging power of 20MW, while the electrolyzer produces hydrogen at full capacity of 200MW, converting green electricity into green hydrogen to supply the HV cluster. During the peak electricity load period from 18:00 to 20:00, the EVs and HV clusters discharge together, with a maximum output of 22.7MW of power per hour, accounting for 4.7% of the peak electricity load. This energy comes from the conversion of wind power at night, achieving the spatial and temporal reuse of clean energy. The application of V2G technology, through the flexible bidirectional adjustment of electric vehicle batteries, significantly optimizes the dynamic balance of the power system. This flexible adjustment mechanism forms an energy migration, reducing the pressure on thermal power peak shaving by 9.6%, while simultaneously pushing the wind power curtailment rate down to 0%. More importantly, the HV cluster constructs a bidirectional flow channel between electricity and hydrogen energy through V2G technology, with the HV cluster's fuel cells achieving a conversion efficiency of 60% for hydrogen to electricity. During sudden load fluctuations, 50 hydrogen-powered heavy-duty trucks can provide 39MW of inertia support, and their coordinated operation with the ammonia and hydrogen production sections ensures the full utilization of hydrogen resources. This model, through flexible scheduling of EV and HV clusters, reduces overall operating costs by 2.4% compared to Scenario 2, validating the qualitative leap of V2G technology from "load terminal" to "system hub," and providing a cost-effective and resilient solution for high-proportion renewable energy power systems.

[0117] Figure 9 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention.

[0118] like Figure 9 As shown, the electronic device may include a processor 910, a communication interface 920, a memory 930, and a communication bus 940. The processor 910, communication interface 920, and memory 930 communicate with each other via the communication bus 940. The processor 910 can call logical instructions from the memory 930 to execute a V2G-based scheduling method for an integrated electric-hydrogen-ammonia energy system.

[0119] Furthermore, the logical instructions in the aforementioned memory 930 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia provided by the above methods.

[0121] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, is implemented to perform the V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia provided by the above methods.

[0122] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0123] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia, characterized in that, include: Mathematical models for each unit of the integrated energy system of electricity, hydrogen, and ammonia are established; wherein, each unit mathematical model includes a hydrogen energy unit model, an ammonia production unit model, an ammonia-blended combustion power plant model, a carbon capture device model, and an ice storage air conditioning model; The Monte Carlo method was used to generate travel characteristic data for electric vehicles and hydrogen vehicles, and a vehicle-to-grid charging and discharging model for electric vehicles and hydrogen vehicles was constructed. Based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, carbon emission zones are divided, and tiered carbon trading costs are calculated. Based on the mathematical models of each unit, the charging and discharging model of the vehicle-to-grid technology, and the tiered carbon trading cost, the objective function for minimizing the total operating cost of the integrated energy system of electricity, hydrogen, and ammonia is obtained. Create constraints for the integrated energy system of electricity, hydrogen, and ammonia. Under the condition that the constraints are met, call the Gurobi solver to obtain the scheduling rules of the integrated energy system of electricity, hydrogen, and ammonia that satisfy the objective function. Specifically, carbon emission zones are defined based on the carbon emissions of thermal power units with carbon capture devices and hydrogen-blended gas turbines, and tiered carbon trading costs are calculated, including: IES carbon quotas are provided by thermal power units with carbon capture devices and hydrogen-blended gas turbines, as shown in the following formula: ； For gas turbines; , , These are the carbon quotas for IES, CHP, and TPU, respectively. , These are the carbon quota coefficients for CHP and TPU, respectively. For gas turbine Output power during time period t; The heat input power of the combined heat and power generation during time period t; T is the total number of time periods; t represents the electrical input power of the combined heat and power (CHP) during time period t; E represents the total number of gas turbines. The actual carbon emissions of IES are shown in the following formula: ； , , These are the carbon emissions of IES, CHP, and TPU, respectively. , These represent the carbon content of methane per unit calorific value and the carbon oxidation rate of methane, respectively. The heat-to-electric ratio of the gas turbine; For thermal power units Carbon dioxide emissions during time period t; The amount of carbon dioxide that the carbon capture device can capture; The number of thermal power units; The tiered carbon trading system is illustrated by the following formula: ； , , , These are the carbon trading base price, compensation factor, penalty factor, and carbon emission range length; The cost of tiered carbon trading.

2. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 1, characterized in that, The hydrogen energy unit model includes a proton exchange membrane model unit, an HST model unit, and a hydrogen-doped CHP model unit. The proton exchange membrane model unit is shown in the following formula: ； , These represent the energy consumption of EL and the H2 power generated by EL during time period t, respectively. The energy conversion efficiency of EL; , These are the upper and lower limits of EL energy consumption, respectively. The HST model unit is shown in the following formula: ； The capacity of H2 within the HST period t; , These are the minimum and maximum values ​​of the HST capacity, respectively. The hydrogen charging power of HST during time period t; This is a marker of the hydrogen charging status of HST; This is the maximum hydrogen charging power; for t Hydrogen release power during the HST period; This represents the maximum hydrogen release power. This is a marker of the hydrogen release state of HST; for t H2 capacity within HST period -1; , These are the hydrogen charging efficiency and hydrogen discharging efficiency of HST, respectively. The hydrogen-doped CHP model unit is shown in the following formula: ； for t Hydrogen doping ratio of CHP during the specified time period; for t The hydrogen power input from the upper-level gas network during the period; for t The natural gas output of the combined heat and power plant input from the upstream gas network during the specified time period; , The lower heating values ​​are H2 and CH4, respectively. The calorific value of the mixture of H2 and CH4; , , They are respectively t Input power, output electrical power, and output thermal power of hydrogen-doped CHP during a given time period; , The electrical efficiency and thermal efficiency of CHP are respectively. , These represent the maximum and minimum thermoelectric adjustable ratios of hydrogen-doped CHP, respectively.

3. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 2, characterized in that, The ammonia production unit model is shown in the following formula: ； for t Overall energy consumption of the ammonia production unit during a specific time period; for t Energy consumption of a periodic ammonia production plant; for t Electrical energy consumed by time-swing adsorption; , They are respectively t The mass of ammonia and nitrogen produced during the time period; , These are the unit energy consumption figures for the ammonia production plant and the PSA preparation unit, respectively. for t The heat generated during the production of a unit of ammonia gas in a given time period; For the heat release efficiency of ammonia production plants; The heat generated per unit of ammonia gas during the ammonia production process in an ammonia plant; The model of the ammonia-blended thermal power unit is shown in the following formula: ； For thermal power units, for t ammonia-blended thermal power units Coal consumption generated; for t ammonia-blended thermal power units ; output electrical energy; , , thermal power units The coal consumption coefficient; , These are the lower heating values ​​of ammonia and coal, respectively. for t The ammonia blending ratio of ammonia-blended thermal power units during specific time periods; , thermal power units Upper and lower limits of energy consumption; , These are the minimum and maximum ramp power of the thermal power unit, respectively. for t-1 ammonia-blended thermal power units ; output electrical energy; The carbon capture device model is shown in the following formula: ； , , , They are respectively t ammonia-blended thermal power units CO2 produced, CO2 supplied by solution storage, thermal power units The CO2 absorbed by the regeneration tower of the carbon capture device, and the CO2 actually captured by the regeneration tower; The carbon emission rate of thermal power units; This refers to the amount of CO2 released per unit of coal. This refers to the flue gas split ratio; , These represent the absorption efficiency of the CCS absorption tower and the energy consumption per unit of CO2 captured, respectively. This refers to the maximum operating condition coefficient of the regeneration tower and compressor within the carbon capture device; , , They are respectively t Periodic thermal power units The stationary energy consumption, operating energy consumption, and net output of the carbon capture device; It is the regeneration rate of the regeneration tower; The ice storage air conditioning model is shown in the following formula: ； In the formula: , , They are respectively t Cooling capacity, ice storage capacity, and ice melting cooling capacity of time-of-use ice storage air conditioners; , , They are respectively t The cooling indicator, ice storage indicator, and ice melting indicator of a time-limited ice storage air conditioner; , They are respectively t Minimum and maximum cooling power of time-limited ice storage air conditioners; for t The maximum ice-melting cooling power of a time-limited ice storage air conditioner; , They are respectively t, t-1 The amount of ice stored in the ice storage tank during a given time period; , , These are the self-loss rate, ice storage rate, and ice melting rate of the ice storage tank, respectively.

4. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 1, characterized in that, The travel characteristic data follows a normal distribution at both the time of grid connection and the time of grid disconnection, as shown in the following formula: ； ； and For electric vehicles or hydrogen vehicles at the time of grid connection and off-network time The probability density function of travel characteristics; and These are the mathematical expectations at the grid connection time and the grid disconnection time, respectively; and These are the standard deviations of the grid connection time and the grid disconnection time, respectively. The mileage traveled follows a log-normal distribution, and its probability density function is shown in the following formula: ； The mathematical variance represents the degree of dispersion in the mileage traveled. Mathematical expectation, representing the average or expected value of the mileage traveled; For driving mileage, This represents the probability distribution of driving mileage.

5. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 4, characterized in that, The travel characteristic data also includes: The initial state of charge of EVs and HVs at the time of grid disconnection follows a uniform distribution, and its probability distribution function is shown in the following formula: ； in, Vehicle type; for The initial state of charge of a vehicle of a certain type at the time of disconnection from the grid; for Maximum capacity of car battery type; Let be the probability distribution function of the initial state of charge of EVs or HVs at the moment of off-grid departure; The initial charge capacity model at the time of grid connection of EVs and HVs is shown in the following formula: ； The initial charge capacity at the moment when EVs and HVs are connected to the grid; for Energy consumption per unit mileage of similar vehicles; for Mileage of different types of vehicles; The vehicle-to-grid (V2G) charging and discharging model includes electric vehicle model units and HVs model units; The electric vehicle model unit is shown in the following formula: ； i For electric vehicles, The total number of electric vehicles; , , , Electric vehicles i exist t The charging power, discharging power, charging flag, and discharging flag for each time period; , These are the maximum charging power and maximum discharging power of EVs, respectively. , These are the charging efficiency and discharging efficiency of EVs, respectively. The number of EVs participating in the scheduling; , For electric vehicle clusters t The sum of charging power and the sum of discharging power during the time period; Let represent the initial charge state of electric vehicle i in time period t-1.

6. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 5, characterized in that, The HVs model unit is shown in the following formula: ； For hydrogen vehicles, This represents the total number of hydrogen vehicles. , , , Hydrogen cars exist t The amount of hydrogen added and released during a given period, the amount of hydrogen stored in the hydrogen storage tank, and the discharge power of the hydrogen vehicle; , These are the hydrogen charging efficiency and hydrogen degassing efficiency of HVs, respectively. The number of HVs participating in the scheduling; For hydrogen vehicle clusters t The sum of discharge power over the time period; For hydrogen cars exist t-1 The amount of hydrogen stored in the hydrogen storage tank during a given period.

7. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 1, characterized in that, Based on the mathematical models of each unit, the vehicle-to-grid charging and discharging model, and the tiered carbon trading cost, the objective function for minimizing the total operating cost of the integrated energy system of electricity, hydrogen, and ammonia is obtained, including: ； , , , , These are energy procurement costs, operating costs of thermal power units with ammonia-blended combustion, carbon capture costs, EVs and HVs costs, and equipment operating costs.

8. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 7, characterized in that, Energy procurement costs are shown in the following formula: ； , , These are the prices of gas purchased from the upstream gas network, the price coefficient for wind turbine power generation, and the price coefficient for abandoned wind power. , , , They are respectively t Gas transmission capacity of the upstream power grid, actual wind turbine power generation, wind power curtailment, and predicted wind turbine power generation for the specified time period; The operating cost of ammonia-blended thermal power units includes coal purchase costs. and start-up / shutdown costs As shown in the formula below: ； The unit price of coal; for t ammonia-blended thermal power units The start / stop indicator quantity; For ammonia-blended thermal power units The start-stop cost coefficient; Carbon capture cost Including daily depreciation costs Solution loss cost of the absorption tower Carbon sequestration costs As shown in the formula below: ； , These are the investment cost and service life of the carbon capture device; , , These are the total cost of the solution storage device, the volume of the solution storage device, and its service life; The capital cost of carbon capture equipment; , These are the economic coefficient and solvent loss coefficient of ethanolamine solvent for absorbing CO2, respectively. Cost of carbon sequestration; V2G cost This includes all costs incurred by EVs and HVs participating in V2G, including battery degradation costs. and incentive costs As shown in the formula below: ； for t Incentive cost coefficient for a given period; It refers to battery cycle life; denoted as the depth of battery discharge, and denoted as the ratio of battery discharge capacity to battery maximum capacity; a and b are curve fitting parameters. This represents the total discharge capacity of the battery. Battery capacity; The operating and maintenance costs per unit power for both the hydrogen energy and ammonia production sections need to be calculated, as shown in the following formula: ； For equipment y Price coefficient; For equipment y The power.

9. The V2G-based integrated energy system scheduling method for electricity, hydrogen, and ammonia as described in claim 1, characterized in that, The constraints for creating the integrated energy system of electricity, hydrogen, and ammonia include: Energy balance includes the supply and demand balance of electricity, heat, cooling, and hydrogen, as shown in the following formula: ； Energy consumption of ice storage air conditioning during time period t; , , These are the electrical load, thermal load, and cooling load of the IES system, respectively. , , , These represent the hydrogen charging power, hydrogen discharging power, hydrogen power required for ammonia synthesis in the ammonia plant, and hydrogen power consumed by the hydrogen-doped CHP at time t, respectively. Limits are imposed on the electrical storage capacity and hydrogen storage capacity used for EVs and HVs operation, as shown in the following formula: ； , These are the minimum and maximum values ​​of the EVs power storage capacity, respectively. , These represent the minimum and maximum values ​​of the hydrogen storage capacity of HVs, respectively.

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