Robust dispatch optimization method for cluster hydrogen fuel cell vehicles and integrated power system

Abstract

The application discloses a kind of robust scheduling optimization methods of cluster hydrogen fuel cell vehicle and comprehensive power system, comprising: simulate the process that single hydrogen fuel cell vehicle participates in comprehensive power system scheduling, based on the process of simulation, build the energy network that cluster hydrogen fuel cell vehicle participates in comprehensive power system scheduling;Based on energy network, construct the robust model that cluster hydrogen fuel cell vehicle is accessed to comprehensive power system, based on the VB model of hydrogen fuel cell vehicle that the energy charging and discharging behavior of hydrogen fuel cell vehicle is constructed;With the highest operation economic benefit of comprehensive power system as optimization goal, based on robust model and VB model, construct objective function, solve objective function, determine the operation economic benefit of comprehensive power system when hydrogen fuel cell vehicle and the scheduling mode of comprehensive power system is highest;The application can realize the best collaborative optimization of economy and power fluctuation in comprehensive power system, promote hydrogen energy and power system collaborative development, improve power system flexibility and stability.

Description

Technical Field

[0001] This invention relates to the field of power system dispatching technology for hydrogen fuel cell vehicles, and in particular to a robust dispatching optimization method for clustered hydrogen fuel cell vehicles and integrated power systems. Background Technology

[0002] With the widespread application of renewable energy sources (such as wind power and photovoltaics) in the current energy system, the traditional power system, mainly based on thermal power, is gradually developing into an integrated power system with energy storage and absorption capabilities, encompassing wind, solar, and thermal power. However, the volatility and uncertainty faced by the power system are intensifying, and traditional dispatch strategies are increasingly unable to effectively balance the requirements of operational economy and stability. In May 2022, the National Development and Reform Commission and the National Energy Administration issued the "Implementation Plan on Promoting High-Quality Development of New Energy in the New Era," proposing to accelerate the construction of distributed photovoltaic and wind power facilities, support green microgrids and integrated source-grid-load-storage projects, require grid companies to improve their new energy access and absorption capabilities, promote the development of new energy storage, tap the energy demand response potential of various production, transportation, and operation sectors, and enhance load-side regulation capabilities.

[0003] Hydrogen energy, as a clean energy source, can be obtained through industrial waste gas and water electrolysis, enhancing the environmental protection capabilities of production enterprises and the grid's energy storage and absorption capacity, thus achieving coordinated control of the energy network. Following the release of the "Medium- and Long-Term Plan for the Development of the Hydrogen Energy Industry (2021-2035)" in 2020, hydrogen fuel cells began commercial operation in various regions. Hydrogen fuel cell vehicles (FCEVs), as a clean energy transportation tool, can not only achieve energy conversion from electricity to hydrogen and back to electricity, but also utilize their fixed operating routes and times to absorb renewable distributed energy systems, providing flexible support for the dispatch and operation of the power system.

[0004] However, integrated power systems combining wind, solar, and thermal power, with multiple power generation methods and energy storage and absorption capabilities, exhibit complex output characteristics. The charging and discharging behavior, operational status, and energy storage of clustered hydrogen fuel cell vehicles are dynamic. With the increasing number of connected power devices, conflicts arise between the optimization objectives of operating costs and power fluctuations in the integrated power system. Designing and establishing models to achieve synergistic optimization of economics and volatility across multiple time scales presents challenges due to the increased number of optimization variables and computational complexity. Furthermore, the constraints of power systems with renewable energy sources such as wind and solar power, and hydrogen fuel cell vehicles, are nonlinear, further complicating the optimization problem. Summary of the Invention

[0005] This invention provides a robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems. This method overcomes the technical problem that, due to the complex output characteristics of integrated power systems and the dynamic charging and discharging behavior and operation of clustered hydrogen fuel cell vehicles, when clustered hydrogen fuel cell vehicles are connected to integrated power systems for scheduling, the number of optimization variables increases and the calculation becomes more complex, making it impossible to achieve optimal synergistic optimization of economy and power fluctuation.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: A robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated electric systems includes: S1: Simulate the process of a single hydrogen fuel cell vehicle participating in the integrated power system dispatch. Based on the simulated process, build an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch. The energy network includes an electrical energy network, a thermal energy network, a hydrogen energy network, and a signal network, which are used to refer to the various devices in the integrated power system and the hydrogen fuel cell vehicles. S2: Based on the energy network, construct a robust model for the clustered hydrogen fuel cell vehicles to access the integrated power system. Based on the charging and discharging behavior of the hydrogen fuel cell vehicles, construct a VB model for the hydrogen fuel cell vehicles. The robust model is used to describe the energy transfer relationship between various devices in the power grid, thermal grid, and hydrogen grid. S3: Taking the highest economic efficiency of the integrated power system as the optimization objective, construct the objective function based on the robust model and VB model, solve the objective function, and determine the scheduling mode of hydrogen fuel cell vehicles and the integrated power system when the economic efficiency of the integrated power system is maximized, considering the power generation efficiency and power load of the integrated power system and the power cost of the equipment participating in the scheduling.

[0007] Furthermore, the robust model includes a heat relationship model and a power relationship model based on a thermal energy network and a power relationship model based on an electric energy network and a hydrogen energy network. The VB model for hydrogen fuel cell vehicles includes VB models for single hydrogen fuel cell vehicles and cluster hydrogen fuel cell vehicles.

[0008] Furthermore, the power grid includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries, and user terminals; the thermal grid includes electric heaters and thermal storage tanks; the hydrogen grid includes electrolyzers; and the signaling grid includes the integrated power system dispatch and control center and hydrogen fuel cell vehicle controllers.

[0009] Furthermore, the heat relationship model and power relationship model based on the thermal energy network include: Based on the relationship of heat energy utilization, a heat relationship model between the electric heater and the heat storage tank is established. The heat relationship model between the electric heater and the heat storage tank considers the heat lost from the heat storage tank to the environment. The calculated heat supply of the electric heater and the heat lost from the heat storage tank to the environment are shown in formulas (1) and (2). (1) (2) In the formula, , They are time points t The l The heat supplied by the electric heater and the first l The heat lost from the heat storage tank to the environment For the first l The mass of the heat storage medium in each heat storage tank The specific heat capacity of the heat storage medium. , They are time points t , t -1 of l The temperature of the heat storage medium inside each heat storage tank , They are time points t , t -1 of l The ambient temperature of each thermal storage tank U The heat transfer coefficient of the thermal storage tank is expressed in W / (m²·K). The surface area of ​​the thermal storage tank; Based on the heat supplied by the electric heater, a power relationship model for the electric heater is constructed, that is, the power consumption of the electric heater is calculated, as shown in formula (3). (3) In the formula, For a moment t The l The power consumption of each electric heater, The sampling time period.

[0010] Furthermore, the power relationship model based on the electric power grid and the hydrogen power grid includes: Based on the relationship of hydrogen energy utilization, a power relationship model of electrolyzer-hydrogen storage tank-hydrogen fuel cell is constructed, that is, to calculate the power consumption of electrolyzer, the chemical energy of hydrogen in hydrogen storage tank and the power generation and power consumption of hydrogen fuel cell. The formula for calculating the power consumption of the electrolytic cell is shown in (4). (4) In the formula, For a moment t No.m The power consumption of each electrolytic cell For a moment t No. m Hydrogen production rate of each electrolyzer, This refers to the calorific value of hydrogen. The efficiency of the electrolytic cell; The hydrogen storage tank stores the hydrogen produced by the electrolysis of water in the electrolyzer. The chemical energy of the hydrogen produced by the hydrogen storage tank is shown in formula (5). (5) In the formula, , They are time points t , t The chemical energy of hydrogen in a hydrogen storage tank at -1 For a moment t The total chemical energy of hydrogen produced by the electrolyzer, For a moment t The power generation capacity of all hydrogen fuel cell vehicles; For a moment t The mass of hydrogen produced by the electrolyzer; The formula for calculating the power generation of a hydrogen fuel cell is shown in (6). (6) In the formula, For a moment t The n The power generation capacity of the hydrogen fuel cells installed in Taiwan's hydrogen fuel cell vehicles. For a moment t Inner n The hydrogen consumption rate of Taiwan's hydrogen fuel cell vehicles The efficiency of hydrogen-to-electricity conversion in hydrogen fuel cells; The electrical power consumed by a hydrogen fuel cell vehicle is obtained from its power generation capacity, i.e., its electrical power consumption, as shown in formula (7). (7) In the formula, For a moment t The n The electrical power consumed by Taiwan's hydrogen fuel cell vehicles This represents the road condition coefficient for the route taken by the hydrogen fuel cell vehicle.

[0011] Furthermore, VB models for single and clustered hydrogen fuel cell vehicles include: The energy boundary of a single hydrogen fuel cell vehicle is constructed as shown in formulas (8)-(11). (8) (9) (10) (11) In the formula, , and They represent the first i The maximum, minimum, and expected energy output of hydrogen fuel cell vehicles in Taiwan during the time they are connected to the power grid. , and These represent the times when the hydrogen fuel cell vehicle connects to and disconnects from the power grid, respectively. and They represent the first i The minimum charge of a hydrogen fuel cell vehicle before and after leaving the grid. and They represent the first i The maximum and minimum power of charging and discharging of hydrogen fuel cell vehicles in Taiwan. and They represent the first i The minimum allowable energy capacity and the energy capacity when connected to the grid for hydrogen fuel cell vehicles in Taiwan. Indicates the sampling time period; Based on the energy boundary of a single hydrogen fuel cell vehicle, the power boundary of the hydrogen fuel cell vehicle is constructed as shown in formulas (12) and (13). (12) (13) In the formula, , They represent the first i The maximum and minimum power of Taiwan's hydrogen fuel cell vehicles; , The moment just before leaving the power grid;

[0012] A VB model of a single hydrogen fuel cell vehicle is constructed based on the power boundary description of the hydrogen fuel cell vehicle, as shown in formulas (14)-(16). (14) (15) (16) In the formula, For the first i Taiwan's hydrogen fuel cell vehicles in the time cycle t power, , They represent the first i Taiwan's hydrogen fuel cell vehicles in the time cycle t ,t +1 electrical energy; By summing the values ​​of individual hydrogen fuel cell vehicles, the power and energy boundaries of the cluster of hydrogen fuel cell vehicles are obtained, as shown in formulas (17)-(20). (17) (18) (19) (20) In the formula, , , , These represent the time periods of the cluster hydrogen fuel cell vehicles. t The upper and lower limits of the battery capacity and power. The number of vehicles in a cluster of hydrogen fuel cell vehicles; The connection status of the cluster of hydrogen fuel cell vehicles to and from the power grid is quantified into state coefficients, and the change in electricity is determined based on the state coefficients, as shown in formula (21). (twenty one) In the formula, The VB model shows the changes in electricity consumption caused by the connection and disconnection of clustered hydrogen fuel cell vehicles from the power grid. , They are respectively in t , t The state coefficient of the cluster hydrogen fuel cell vehicle at time +1, where the state coefficient is 1 when the cluster hydrogen fuel cell vehicle is connected to the grid and 0 when the cluster hydrogen fuel cell vehicle is disconnected from the grid. Based on the VB model of a single hydrogen fuel cell vehicle and the power and energy boundaries of a cluster of hydrogen fuel cell vehicles, a VB model of a cluster of hydrogen fuel cell vehicles is constructed, as shown in formulas (22)-(24). (twenty two) (twenty three) (twenty four) In the formula, , These represent the time periods of the cluster hydrogen fuel cell vehicles. t Power and electricity.

[0013] Furthermore, with the goal of maximizing the operational economic efficiency of the integrated power system, an objective function is constructed based on the robust model and the VB model, including: S31. Generate a dataset of the electricity consumption of the clustered hydrogen fuel cell vehicles based on the VB model of the clustered hydrogen fuel cell vehicles. S32. Predict wind power and solar power output data based on data-driven methods; S33. Based on the robust model, VB model, power output data of wind power and photovoltaic power, and the data set of electricity of clustered hydrogen fuel cell vehicles, construct the objective function as shown in formulas (25)-(27). (25) (26) (27) In the formula, The calculated economic benefits generated by the operation of the integrated power system. For the integrated power system at time t The output power, For the integrated power system at time t The load power, For a moment t The load of the power user in the user client, For real-time electricity prices, To assess the electricity market benefits of different power generation methods; Indicates time t The j Power output forecast for a wind or solar power plant Indicates time t The k The output of a conventional thermal power unit Indicates time t The power of electricity purchased from the external grid, Indicates time t The l Power consumption of each electric heater; Indicates time t No. m The power consumption of each electrolytic cell; This indicates that the cluster of hydrogen fuel cell vehicles is in the time period t power, , , , These represent the number of wind and solar power units, conventional thermal power units, electric heaters, and electrolytic cells participating in the integrated power system dispatch; This indicates the sampling time period; S34. Using the concept of power balance, set dispatch constraints for the integrated power system to achieve dynamic balance between power output and load power. The constraints are as follows: The power balance constraint is constructed as shown in formula (28). (28) In the formula, M For flexibility margin; The processing constraints for conventional thermal power units are constructed as shown in formula (29). (29) In the formula, , They are time points t The k The lower and upper limits of the output of a conventional thermal power unit; The ramp rate constraint for conventional thermal power units is constructed as shown in formulas (30) and (31). (30) (31) In the formula, , The first k The maximum and minimum ramp rate of output of a conventional thermal power unit. Representing time respectively t -1 and time t No. k The output of a conventional thermal power unit; The output constraints for wind power and photovoltaic power are constructed as shown in formula (32). (32) In the formula, , They are time points t The j The lower and upper limits of the predicted output of a wind or solar power plant. , They are time points t The j The lower and upper limits of the permissible output of a wind or solar power plant. For a moment t The j Power output forecast for a wind or solar power plant; The power consumption constraint of the clustered hydrogen fuel cell vehicles is constructed as shown in formula (33). (33) In the formula, , They represent the first i Minimum and maximum power for charging and discharging of hydrogen fuel cell vehicles in Taiwan. For a moment t The n The electrical power consumed by a hydrogen fuel cell vehicle in Taiwan; The output constraint of the electrolytic cell is constructed as shown in formula (34). (34) In the formula, , They are time points t The m The lower and upper limits of the output of each electrolytic cell; For a moment t No. m The power consumption of each electrolytic cell; The output constraint of the thermal storage device is constructed as shown in formula (35). (35) In the formula, For a moment t The l The power consumption of each electric heater, , They are time points t The l The lower and upper limits of the permissible output of a thermal storage device; The constraints for purchasing electricity from the external grid are constructed as shown in formula (36). (36) In the formula, For a moment t The power of electricity purchased from external grids, For a moment t The upper limit on the power capacity for purchasing electricity from external grids; The electrical constraints for constructing the hydrogen storage tank are shown in formula (37). (37) In the formula, Indicates time t The chemical energy of hydrogen in the hydrogen storage tank, for n The total volume of hydrogen storage tanks installed on Taiwanese hydrogen fuel cell vehicles. The density of hydrogen gas under standard conditions. This is the calorific value of hydrogen.

[0014] Beneficial Effects: This invention provides a robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems. It proposes a process for hydrogen fuel cell vehicles to participate in the scheduling of integrated power systems. Energy utilization and system flexibility are optimized through energy conversion between the integrated power system's scheduling control center and the vehicle controllers of the hydrogen fuel cell vehicles. Considering the energy transfer relationships within the system, a robust optimization model for clustered hydrogen fuel cell vehicles is established. An objective function is constructed with the goal of maximizing economic benefits. The objective function is solved, and the scheduling mode corresponding to the hydrogen fuel cell vehicles and integrated power systems when the operating economic benefits of the integrated power system are maximized is determined. This achieves optimal synergistic optimization of economic efficiency and power fluctuations within the integrated power system, promoting the coordinated development of hydrogen energy and power systems, and improving the flexibility and stability of the power system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems provided by the present invention; Figure 2 A diagram illustrating the energy utilization structure of hydrogen fuel cell vehicles; Figure 3 A structural diagram of the integrated power system scheduling framework for access cluster hydrogen fuel cell vehicles and new energy provided by the present invention; Figure 4 The calculation flowchart of the example analysis method provided by the present invention; Figure 5 The real-time electricity price curve provided for this invention; Figure 6 Fluctuating power output data curves for wind and solar power provided for this invention; Figure 7 The load forecast data curve for power users in the user client provided by this invention. Detailed Implementation

[0017] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This embodiment provides a robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems, such as... Figure 1 As shown, it includes: S1: Simulate the process of a single hydrogen fuel cell vehicle participating in the integrated power system dispatch. Based on the simulated process, build an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch. The energy network includes an electrical energy network, a thermal energy network, a hydrogen energy network, and a signal network, which are used to refer to the various devices in the integrated power system and the hydrogen fuel cell vehicles. S2: Based on the energy network, construct a robust model for the clustered hydrogen fuel cell vehicles to access the integrated power system. Based on the charging and discharging behavior of the hydrogen fuel cell vehicles, construct a VB model for the hydrogen fuel cell vehicles. The robust model is used to describe the energy transfer relationship between various devices in the power grid, thermal grid, and hydrogen grid. S3: Taking the highest economic efficiency of the integrated power system as the optimization objective, construct the objective function based on the robust model and VB model, solve the objective function, and determine the scheduling mode of hydrogen fuel cell vehicles and the integrated power system when the economic efficiency of the integrated power system is maximized, considering the power generation efficiency and power load of the integrated power system and the power cost of the equipment participating in the scheduling.

[0019] Specifically, the process of a single hydrogen fuel cell vehicle participating in the integrated power system dispatch is first simulated. This process includes the grid charging the hydrogen fuel cell vehicle, the operation of the hydrogen fuel cell vehicle, and the hydrogen fuel cell vehicle releasing energy back to the grid. Simulating the process of hydrogen fuel cell vehicles participating in the integrated power system dispatch can determine the basis of energy flow in the integrated power system, which facilitates the subsequent determination of the optimal dispatch optimization scheme. Based on the simulated process, an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch is constructed. The energy network includes an electrical grid, a thermal grid, a hydrogen grid, and a signal grid, which are used to refer to the various devices in the integrated power system and the hydrogen fuel cell vehicles. Constructing an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch allows each device in the hydrogen fuel cell vehicle to participate in the dispatch of the integrated power system. By controlling the vehicle to enter and maintain the charging, operation, and energy release process through signal control, the hydrogen fuel cell vehicles can meet the usage requirements and improve the flexibility and power balance of the power system. Secondly, a robust model for the integration of clustered hydrogen fuel cell vehicles into the integrated power system is constructed based on the energy network. A VB model for hydrogen fuel cell vehicles is also constructed based on their charging and discharging behavior. This robust model describes the energy transfer relationships between devices in the power grid, thermal grid, and hydrogen grid. A heat relationship model and a power relationship model are constructed based on the thermal grid, and a power relationship model is constructed based on the power grid and hydrogen grid. This allows for the coordinated use of electrical energy, hydrogen energy, and thermal energy, enhancing the flexibility of the integrated power system and achieving global optimization. The VB model is used to describe the clustered hydrogen fuel cell vehicles, describing their charging and discharging behavior as multiple key parameters, reducing the solution time of the robust optimization algorithm. Therefore, during the charging phase, the required hydrogen energy is converted into electrical energy for calculation. VB models (virtual battery models) for a single hydrogen fuel cell vehicle and the clustered hydrogen fuel cell vehicles are constructed to generate a dataset of hydrogen fuel cell vehicle electrical energy, providing a data foundation for constructing the objective function. Finally, with the goal of maximizing the operational economic benefits of the integrated power system, an objective function is constructed based on the robust model and the VB model. The objective function is solved, and considering the power generation benefits and load of the integrated power system, as well as the electricity costs of the equipment participating in the dispatch, the dispatching mode of hydrogen fuel cell vehicles and the integrated power system that maximizes the operational economic benefits of the integrated power system is determined. The result is the optimal dispatching mode for clustered hydrogen fuel cell vehicles to participate in the integrated power system.

[0020] In a specific embodiment, the process of a single hydrogen fuel cell vehicle participating in the integrated power system dispatch is simulated. Based on the simulated process, an energy network for clustered hydrogen fuel cell vehicles participating in the integrated power system dispatch is constructed. The energy network includes an electrical grid, a thermal grid, a hydrogen grid, and a signal grid. The scheme used to refer to the various devices in the integrated power system and the hydrogen fuel cell vehicles is as follows: S11. Establish the energy utilization structure for hydrogen fuel cell vehicles, such as... Figure 2 As shown, Figure 2 This diagram illustrates the energy utilization structure of a hydrogen fuel cell vehicle. Thin solid lines represent electrical energy, thick solid lines represent mechanical energy, and dashed lines represent hydrogen energy. The electrical energy required for the vehicle's operation comes from the hydrogen fuel cell and lithium-ion battery. The hydrogen in the fuel cell comes from a hydrogen storage tank, while the lithium-ion battery receives its electrical energy from the power grid. An electrolyzer electrolyzes water to produce hydrogen, which is stored in the storage tank. The hydrogen from the storage tank is then mixed with air and fed into the hydrogen fuel cell for a redox reaction to generate electricity. The electrical energy generated by the lithium-ion battery and the hydrogen fuel cell is boosted and inverted by DC / DC and DC / AC converters to drive the vehicle's electric motor. The motor then drives the transmission mechanism to rotate the wheels. The electrolyzer electrolyzes water to produce hydrogen, which is stored in the storage tank. The lithium-ion battery is charged and discharged to the power grid via a charging station connected to the grid. The hydrogen fuel cell is equipped with a discharge terminal connected to the grid for discharging into the grid. S12. Simulate the process of a single hydrogen fuel cell vehicle participating in the integrated power system dispatch. This process includes the grid charging the hydrogen fuel cell vehicle, the operation of the hydrogen fuel cell vehicle, and the hydrogen fuel cell vehicle releasing energy back to the grid. The specific process is as follows: Hydrogen fuel cell vehicles participate in integrated power system dispatch by charging and discharging hydrogen fuel cells and lithium batteries. Their operation is related to power fluctuations in the grid interconnection lines and the vehicle's off-grid and on-grid status. Specific operating states include: (1) Power grid charges the car: When the car is parked, the power grid charges the lithium battery through the charging pile, the electrolyzer works and delivers hydrogen to the hydrogen storage tank; (2) Vehicle operation: The vehicle is in normal operation and consumes electrical energy generated by lithium batteries and hydrogen fuel cells; (3) Vehicle discharges energy to the grid: The vehicle is parked but no power-off operation is performed. The hydrogen fuel cell vehicle controller performs charging and discharging operations on the on-board hydrogen fuel cell and lithium battery according to the wireless signal sent by the integrated power system dispatch center. Because hydrogen fuel cell vehicles have fixed operating routes, single loads, and small differences in load weight, the fluctuations in the vehicle's power consumption during operation are small, which can reduce the degree of power fluctuations in the grid interconnection caused by the vehicle during charging. S13. Establish an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch. Figure 3 This diagram illustrates the power system dispatch framework for connecting clustered hydrogen fuel cell vehicles and new energy sources. Thin solid lines represent the power grid, thin dashed lines represent the signaling network, thick solid lines represent the thermal network, and thick dashed lines represent the hydrogen network. The integrated power system's power grid includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries, and user clients; the thermal network includes electric heaters and thermal storage tanks; the hydrogen network includes electrolyzers; and the signaling network includes the integrated power system dispatch control center and hydrogen fuel cell vehicle controllers. Distributed power sources such as wind turbines and photovoltaic batteries transmit electrical energy to the power grid. Electric heaters consume excess electrical energy from the grid, convert it into heat energy, and store it in energy storage tanks. Electrolyzers electrolyze water to produce hydrogen, which is supplied to hydrogen fuel cells and generates electricity. Lithium batteries and hydrogen fuel cells work together to transmit electricity to the power system of hydrogen fuel cell vehicles. Thermal storage equipment, with electric heaters and energy storage tanks as its main components, is used for energy optimization of the entire integrated power system. Because thermal storage equipment has electrothermal conversion and thermal storage capabilities, the volatility of the integrated power system can be reduced by adopting flexible control strategies. Hydrogen fuel cell vehicles can obtain energy from the power grid and hydrogen grid through hydrogen energy grid. Through the discharge characteristics of the battery, electrical energy can be fed back to the integrated power system, enhancing the flexibility of integrated power system dispatch. In the signal network, the integrated power system dispatch and control center, as a primary dispatching system, can acquire in real time the output status of new energy sources such as wind power, solar power, and electrolyzers, the equipment status of electric heaters, and the energy storage status of vehicles issued by hydrogen fuel cell vehicle controllers. It then issues power control signals to control the flow of electrical energy, thermal energy, and hydrogen energy, thereby achieving optimized system dispatch. The hydrogen fuel cell vehicle controller, as a secondary dispatching system, monitors the energy storage status of hydrogen fuel cells and lithium batteries. Based on the current power system dispatch and freight load requirements, it issues power control signals to control the vehicles to enter and maintain the charging, operation, and energy dissipation processes, ensuring that hydrogen fuel cell vehicles can meet usage needs, improving the flexibility and power balance of the power system, and achieving efficient utilization of power resources.

[0021] This scheme simulates the process of hydrogen fuel cell vehicles participating in the integrated power system dispatch, which can determine the basis of energy flow of hydrogen fuel cell vehicles in the integrated power system, facilitating the subsequent determination of the optimal dispatch optimization scheme. Based on the simulation process, an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch is built, enabling each device in the hydrogen fuel cell vehicle to participate in the integrated power system dispatch. Through signal control, the vehicle enters and maintains the charging, operation and energy dissipation process, so that the hydrogen fuel cell vehicle can meet the usage requirements and improve the flexibility and power balance of the power system.

[0022] In a specific embodiment, a robust model for the integration of clustered hydrogen fuel cell vehicles into the integrated power system is constructed based on the energy network. A VB model for the hydrogen fuel cell vehicles is constructed based on their charging and discharging behavior. The robust model is used to describe the energy transfer relationships between various devices in the power grid, thermal grid, and hydrogen grid. The robust models include heat relationship models and power relationship models based on thermal energy networks and power relationship models based on electric energy networks and hydrogen energy networks. The VB models for hydrogen fuel cell vehicles include VB models for single hydrogen fuel cell vehicles and cluster hydrogen fuel cell vehicles. The specific formulas are as follows: (1) Heat relationship model and power relationship model based on thermal energy network, including: refer to Figure 3 Based on the relationship of heat energy utilization, a heat relationship model between the electric heater and the heat storage tank is established. The heat relationship model between the electric heater and the heat storage tank considers the heat lost from the heat storage tank to the environment. The calculated heat supply of the electric heater and the heat lost from the heat storage tank to the environment are shown in formulas (38) and (39). (38) (39) In the formula, , They are time pointst The l The heat supplied by the electric heater and the first l The heat lost from the heat storage tank to the environment For the first l The mass of the heat storage medium in each heat storage tank The specific heat capacity of the heat storage medium. , They are time points t , t -1 of l The temperature of the heat storage medium inside each heat storage tank , They are time points t , t -1 of l The ambient temperature of each thermal storage tank U The heat transfer coefficient of the thermal storage tank is expressed in W / (m²·K). The surface area of ​​the thermal storage tank; Based on the heat supplied by the electric heater, a power relationship model for the electric heater is constructed, that is, the power consumption of the electric heater is calculated, as shown in formula (40). (40) In the formula, For a moment t The l The power consumption of each electric heater, The sampling time period; (2) A power relationship model based on the electric power grid and the hydrogen power grid, including: Based on the relationship of hydrogen energy utilization, a power relationship model of electrolyzer-hydrogen storage tank-hydrogen fuel cell is constructed, that is, to calculate the power consumption of electrolyzer, the chemical energy of hydrogen in hydrogen storage tank and the power generation and power consumption of hydrogen fuel cell. The formula for calculating the power consumption of the electrolytic cell is shown in (41). (41) In the formula, For a moment t No. m The power consumption of each electrolytic cell For a moment t No. m Hydrogen production rate of each electrolyzer, This refers to the calorific value of hydrogen. The efficiency of the electrolytic cell; The hydrogen storage tank stores the hydrogen produced by the electrolysis of water in the electrolyzer. The chemical energy of the hydrogen produced by the hydrogen storage tank is shown in formula (42). (42) In the formula, , They are time points t , t The chemical energy of hydrogen in a hydrogen storage tank at -1 For a moment t The total chemical energy of hydrogen produced by the electrolyzer, For a moment t The power generation capacity of all hydrogen fuel cell vehicles; For a moment t The mass of hydrogen produced by the electrolyzer; The formula for calculating the power generation of a hydrogen fuel cell is shown in equation (43). (43) In the formula, For a moment t The n The power generation capacity of the hydrogen fuel cells installed in Taiwan's hydrogen fuel cell vehicles. For a moment t Inner n The hydrogen consumption rate of Taiwan's hydrogen fuel cell vehicles The efficiency of hydrogen-to-electricity conversion in hydrogen fuel cells; The electrical power consumed by a hydrogen fuel cell vehicle is obtained from its power generation capacity, i.e., its electrical power consumption, as shown in formula (44). (44) In the formula, For a moment t The n The electrical power consumed by Taiwan's hydrogen fuel cell vehicles This refers to the road condition coefficient for the driving route of hydrogen fuel cell vehicles. In this scheme, the road condition coefficient includes the road's slope, curvature, rolling resistance coefficient of the road surface type, road surface smoothness, and traffic conditions. The specific value is determined based on the actual road conditions on which the hydrogen fuel cell vehicles drive. (3) VB models of single and clustered hydrogen fuel cell vehicles constructed based on the charging and discharging behavior of hydrogen fuel cell vehicles, including: At multiple time scales, the usage scenarios are divided according to the time of vehicle grid connection and disconnection. When the vehicle is connected to the power system, it is charged at the maximum charging power until the vehicle reaches its maximum power. Before the vehicle is connected to the power system, if its power is less than the minimum charging power allowed by the power grid, the vehicle is charged first. After the vehicle's power reaches the required power, it performs charging and discharging behavior according to the instructions issued by the hydrogen fuel cell vehicle controller and the integrated power system dispatch control center. Therefore, the power boundary of a single hydrogen fuel cell vehicle is constructed as shown in formulas (45)-(48). (45) (46) (47) (48) In the formula, , and They represent the first i The maximum, minimum, and expected energy output of hydrogen fuel cell vehicles in Taiwan during the time they are connected to the power grid. , and These represent the times when the hydrogen fuel cell vehicle connects to and disconnects from the power grid, respectively. and They represent the first i The minimum charge of a hydrogen fuel cell vehicle before and after leaving the grid. and They represent the first i The maximum and minimum power of charging and discharging of hydrogen fuel cell vehicles in Taiwan. and They represent the first i The minimum allowable energy capacity and the energy capacity when connected to the grid for hydrogen fuel cell vehicles in Taiwan. Indicates the sampling time period; Based on the energy boundary of a single hydrogen fuel cell vehicle, the power boundary of the hydrogen fuel cell vehicle is constructed as shown in formulas (49) and (50). (49) (50) In the formula, , They represent the first i The maximum and minimum power of Taiwan's hydrogen fuel cell vehicles; , The moment just before leaving the power grid; A VB model of a single hydrogen fuel cell vehicle is constructed based on the power boundary description of hydrogen fuel cell vehicles, as shown in formulas (51)-(53). (51) (52) (53) In the formula, For the first i Taiwan's hydrogen fuel cell vehicles in the time cycle t power, , They represent the first i Taiwan's hydrogen fuel cell vehicles in the time cyclet , t +1 electrical energy; By summing the values ​​of individual hydrogen fuel cell vehicles, the power and energy boundaries of the cluster of hydrogen fuel cell vehicles are obtained, as shown in formulas (54)-(57). (54) (55) (56) (57) In the formula, , , , These represent the time periods of the cluster hydrogen fuel cell vehicles. t The upper and lower limits of the battery capacity and power. The number of vehicles in a cluster of hydrogen fuel cell vehicles; The connection and disconnection of the cluster of hydrogen fuel cell vehicles from the grid will cause a sudden change in the power of the VB model. The connection state of the cluster of hydrogen fuel cell vehicles to and from the grid is quantified into state coefficients, and the power change is determined based on the state coefficients, as shown in formula (58). (58) In the formula, The VB model shows the changes in electricity consumption caused by the connection and disconnection of clustered hydrogen fuel cell vehicles from the power grid. , They are respectively in t , t The state coefficient of the cluster hydrogen fuel cell vehicle at time +1, where the state coefficient is 1 when the cluster hydrogen fuel cell vehicle is connected to the grid and 0 when the cluster hydrogen fuel cell vehicle is disconnected from the grid. Based on the VB model of a single hydrogen fuel cell vehicle and the power and energy boundaries of a cluster of hydrogen fuel cell vehicles, a VB model of a cluster of hydrogen fuel cell vehicles is constructed, as shown in formulas (59)-(61). (59) (60) (61) In the formula, , These represent the time periods of the cluster hydrogen fuel cell vehicles. t Power and electricity.

[0023] In this scheme, a heat relationship model and a power relationship model are constructed based on the thermal energy network, and a power relationship model is constructed based on the electric energy network and the hydrogen energy network. This can coordinate electric energy, hydrogen energy and thermal energy, enhance the flexibility of the integrated power system, and achieve the global optimum of the optimization results. In different application scenarios, the working process of hydrogen fuel cell vehicles can be divided into charging and discharging stages. However, in a cluster of hydrogen fuel cell vehicles, the charging and discharging time and operating requirements of each vehicle are different. Therefore, modeling only a single vehicle would increase the complexity and difficulty of scheduling the integrated power system. Here, the VB model (virtual battery model) is used to describe the cluster of hydrogen fuel cell vehicles. The charging and discharging behavior of the vehicles is described as multiple key parameters, which reduces the solution time of the robust optimization algorithm. Therefore, in the charging stage, the required hydrogen energy is converted into electricity for calculation. VB models (virtual battery models) of a single hydrogen fuel cell vehicle and a cluster of hydrogen fuel cell vehicles are constructed to generate a dataset of hydrogen fuel cell vehicle electricity, providing a data foundation for constructing the objective function.

[0024] In a specific embodiment, with the optimization objective of maximizing the operational economic efficiency of the integrated power system, an objective function is constructed based on the robust model and the VB model. The objective function is solved, and considering the power generation efficiency and load of the integrated power system, as well as the electricity costs of the equipment participating in the dispatching, the dispatching scheme for hydrogen fuel cell vehicles and the integrated power system that maximizes the operational economic efficiency of the integrated power system is determined as follows: S31. Generate a dataset of the electric power of clustered hydrogen fuel cell vehicles based on the VB model of clustered hydrogen fuel cell vehicles: S311: Obtain the initial charge within the charge boundary of the clustered hydrogen fuel cell vehicle, usually taking the median value of the boundary data; S312: Calculate at each time step and Generate time series datasets; S32. Predicted wind and solar power output data: S321: Collect historical wind power and solar power output data, clean the data, extract key features, perform feature transformations on the data, and form a dataset; divide the dataset into training set, test set, and validation set; S322: Select a pre-trained model, including but not limited to machine learning algorithms and deep learning methods, input the training set into the training model to train it, form a prediction model, use the test set and validation set to evaluate and optimize the model, and obtain the predicted wind power and solar power output data. S33. Based on the robust model, VB model, power output data of wind power and photovoltaic power, and the data set of electricity of clustered hydrogen fuel cell vehicles, construct the objective function as shown in formulas (62)-(64). (62) (63) (64) In the formula, The calculated economic benefits generated by the operation of the integrated power system. For the integrated power system at time t The output power, For the integrated power system at time t The load power, For a moment t The load of the power user in the user client, For real-time electricity prices, To assess the electricity market benefits of different power generation methods; Indicates time t The j Power output forecast for a wind or solar power plant Indicates time t The k The output of a conventional thermal power unit Indicates time t The power of electricity purchased from the external grid, Indicates time t The l Power consumption of each electric heater; Indicates time t No. m The power consumption of each electrolytic cell; This indicates that the cluster of hydrogen fuel cell vehicles is in the time period t power, , , , These represent the number of wind and solar power units, conventional thermal power units, electric heaters, and electrolytic cells participating in the integrated power system dispatch; This indicates the sampling time period; S34. The dispatch flexibility of an integrated power system is reflected in its ability to respond to changes in the system's sources and loads. Using the concept of power balance, dispatch constraints are set for the integrated power system to achieve a dynamic balance between power output and load. The constraints are as follows: The power balance constraint is constructed as shown in formula (65). (65) In the formula, M For flexibility margin; The processing constraints for conventional thermal power units are constructed as shown in formula (66). (66) In the formula, , They are respectively time t The k The lower and upper limits of the output of a conventional thermal power unit; The ramp rate constraints for conventional thermal power units are constructed as shown in formulas (67) and (68). (67) (68) In the formula, , The first k The maximum and minimum ramp rate of output of a conventional thermal power unit. Representing time respectively t -1 and time t No. k The output of a conventional thermal power unit; The output constraints for wind power and photovoltaic power are constructed as shown in formula (69). (32) In the formula, , They are time points t The j The lower and upper limits of the predicted output of a wind or solar power plant. , They are time points t The j The lower and upper limits of the permissible output of a wind or solar power plant. For a moment t The j Power output forecast for a wind or solar power plant; The power consumption constraint of the clustered hydrogen fuel cell vehicles is constructed as shown in formula (70). (70) In the formula, , They represent the first i Minimum and maximum power for charging and discharging of hydrogen fuel cell vehicles in Taiwan. For a moment t The n The electrical power consumed by a hydrogen fuel cell vehicle in Taiwan; The output constraint of the electrolytic cell is constructed as shown in formula (71). (71) In the formula, , They are time points t The mThe lower and upper limits of the output of each electrolytic cell; For a moment t No. m The power consumption of each electrolytic cell; The output constraint of the thermal storage device is constructed as shown in formula (72). (72) In the formula, For a moment t The l The power consumption of each electric heater, , They are time points t The l The lower and upper limits of the permissible output of a thermal storage device; The constraints for purchasing electricity from the external grid are constructed as shown in formula (73). (73) In the formula, For a moment t The power of electricity purchased from external power grids, For a moment t The upper limit on the power capacity for purchasing electricity from external grids; The electrical constraints for constructing the hydrogen storage tank are shown in formula (74). (74) In the formula, Indicates time t The chemical energy of hydrogen in the hydrogen storage tank for n The total volume of hydrogen storage tanks installed on Taiwanese hydrogen fuel cell vehicles. The density of hydrogen gas under standard conditions. This refers to the calorific value of hydrogen. S35. Using simulation software, input the objective function and constraints into the simulation software for calculation, generate scheduling curves, and use the signal network to schedule each device in the entire system according to the scheduling curves. Based on the current scheduling calculations, the economic benefits are analyzed, and the time periods in which economic benefits decrease are generated to produce an economic benefit characteristic curve. Based on the scheduling situation, the adjustable time distribution and capacity are determined, and hydrogen fuel cell vehicles are scheduled at maximum capacity within the adjustable time distribution.

[0025] In this scheme, the actual, maximum, and minimum values ​​of power and energy of the VB model and the cluster hydrogen fuel cell vehicle are simulated and verified. The specific calculation and analysis are as follows: The simulation software and its solver were used to solve the example problem. The calculation process is as follows: Figure 4 As shown: First, set the simulation verification parameters, setting the time interval to 15 minutes and dividing the day into 96 time periods.

[0026] Taking the power system in a certain region of western Liaoning as an example, the output and load of the integrated power system are set up, such as the rated capacity and number of conventional thermal power generating units, the rated parameters and number of clustered hydrogen fuel cell vehicles and thermal storage equipment, etc., and the real-time electricity price of the local power supply company is used. The electricity market situation is determined, and the resulting real-time electricity price curve is as follows: Figure 5 As shown; Secondly, a data-driven approach is adopted to preprocess and predict the output data of wind and solar power, reducing the impact of deviations between real-time and day-ahead scheduling on the calculated economic benefits. The output data curves of fluctuating wind and solar power sources are shown below. Figure 6 As shown, the load forecast data curves for electricity users in the user client are as follows: Figure 7 As shown; Next, the effectiveness of the VB model applied to the cluster hydrogen fuel cell vehicle was verified. Under the conditions of the conventional model obtained by equation (51)-(53) and the VB model obtained by equation (59)-(61), the dataset of hydrogen fuel cell vehicle power was generated according to the VB model. The objective function was compared and verified to determine whether the simulation results were consistent. According to equations (45)-(61), the actual, maximum and minimum values ​​of power and energy of the cluster hydrogen fuel cell vehicle are simulated and verified to verify whether the power boundary and energy boundary in the VB model conform to the actual cluster behavior. If they do not conform, the boundary data are redefined. Next, based on the objective function calculated by the simulation software and its solver, a scheduling curve is generated according to the power conditions of thermal power generating units, electrolyzers, external power purchases, thermal storage equipment, and hydrogen fuel cells. The signal network is then used to schedule according to the scheduling curve. The economic benefits are calculated based on the scheduling situation. The time periods in which economic benefits decrease are analyzed to generate an economic benefit characteristic curve generated by the system operation. Based on the current scheduling situation, the adjustable time distribution and capacity are determined. Within the adjustable time, hydrogen fuel cell vehicles are scheduled at the maximum capacity to improve economic benefits.

[0027] 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 or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated electric systems, characterized in that, include: S1: Simulate the process of a single hydrogen fuel cell vehicle participating in the integrated power system dispatch. Based on the simulated process, build an energy network for clustered hydrogen fuel cell vehicles to participate in the integrated power system dispatch. The energy network includes an electrical grid, a thermal grid, a hydrogen grid, and a signal network, which are used to refer to the various devices in the integrated power system and the hydrogen fuel cell vehicles. The electrical grid of the integrated power system includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries, and user clients. The thermal grid includes electric heaters and thermal storage tanks. The hydrogen grid includes an electrolyzer. The signal network includes the integrated power system dispatch control center and the hydrogen fuel cell vehicle controller. S2: Based on the energy network, construct a robust model for the integration of clustered hydrogen fuel cell vehicles into the integrated power system. Based on the charging and discharging behavior of hydrogen fuel cell vehicles, construct a VB model for hydrogen fuel cell vehicles. The robust model is used to describe the energy transfer relationship between various devices in the power grid, thermal grid, and hydrogen grid. The robust model includes a heat relationship model and a power relationship model constructed based on the thermal grid and a power relationship model constructed based on the power grid and hydrogen grid. The VB model for hydrogen fuel cell vehicles includes VB models for single units and clustered hydrogen fuel cell vehicles. The power relationship model based on the electric power grid and the hydrogen energy grid includes: Based on the relationship of hydrogen energy utilization, a power relationship model of electrolyzer-hydrogen storage tank-hydrogen fuel cell is constructed, that is, to calculate the power consumption of electrolyzer, the chemical energy of hydrogen in hydrogen storage tank and the power generation and power consumption of hydrogen fuel cell. The formula for calculating the power consumption of the electrolytic cell is shown in (4). （4） In the formula, For a moment t No. m The power consumption of each electrolytic cell For a moment t No. m Hydrogen production rate of each electrolyzer, This refers to the calorific value of hydrogen. The efficiency of the electrolytic cell; The hydrogen storage tank stores the hydrogen produced by the electrolysis of water in the electrolyzer. The chemical energy of the hydrogen produced by the hydrogen storage tank is shown in formula (5). （5） In the formula, , They are time points t , t The chemical energy of hydrogen in a hydrogen storage tank at -1 For a moment t The total chemical energy of hydrogen produced by the electrolyzer, For a moment t The power generation capacity of all hydrogen fuel cell vehicles; For a moment t The mass of hydrogen produced by the electrolyzer; The formula for calculating the power generation of a hydrogen fuel cell is shown in (6). （6） In the formula, For a moment t The n The power generation capacity of the hydrogen fuel cells installed in Taiwan's hydrogen fuel cell vehicles. For a moment t Inner n The hydrogen consumption rate of Taiwan's hydrogen fuel cell vehicles The efficiency of hydrogen-to-electricity conversion in hydrogen fuel cells; The electrical power consumed by a hydrogen fuel cell vehicle is obtained from its power generation capacity, i.e., its electrical power consumption, as shown in formula (7). （7） In the formula, For a moment t The n The electrical power consumed by Taiwan's hydrogen fuel cell vehicles This refers to the road condition coefficient for the hydrogen fuel cell vehicle's driving route; VB models for single and clustered hydrogen fuel cell vehicles include: The energy boundary of a single hydrogen fuel cell vehicle is constructed as shown in formulas (8)-(11). （8） （9） （10） （11） In the formula, , and They represent the first i The maximum, minimum, and expected energy output of hydrogen fuel cell vehicles in Taiwan during the time they are connected to the power grid. , and These represent the times when the hydrogen fuel cell vehicle connects to and disconnects from the power grid, respectively. and They represent the first i The minimum charge of a hydrogen fuel cell vehicle before and after leaving the grid. and They represent the first i The maximum and minimum power of charging and discharging of hydrogen fuel cell vehicles in Taiwan. and They represent the first i The minimum allowable energy capacity and the energy capacity when connected to the grid for hydrogen fuel cell vehicles in Taiwan. Indicates the sampling time period; Based on the energy boundary of a single hydrogen fuel cell vehicle, the power boundary of the hydrogen fuel cell vehicle is constructed as shown in formulas (12) and (13). （12） （13） In the formula, , They represent the first i The maximum and minimum power of Taiwan's hydrogen fuel cell vehicles; , The moment just before leaving the power grid; A VB model of a single hydrogen fuel cell vehicle is constructed based on the power boundary description of the hydrogen fuel cell vehicle, as shown in formulas (14)-(16). （14） （15） （16） In the formula, For the first i Taiwan's hydrogen fuel cell vehicles in the time cycle t power, , They represent the first i Taiwan's hydrogen fuel cell vehicles in the time cycle t , t +1 electrical energy; By summing the values ​​of individual hydrogen fuel cell vehicles, the power and energy boundaries of the cluster of hydrogen fuel cell vehicles are obtained, as shown in formulas (17)-(20). （17） （18） （19） （20） In the formula, , , , These represent the time periods of the cluster hydrogen fuel cell vehicles. t The upper and lower limits of the battery capacity and power. The number of vehicles in a cluster of hydrogen fuel cell vehicles; The connection status of the cluster of hydrogen fuel cell vehicles to and from the power grid is quantified into state coefficients, and the change in electricity is determined based on the state coefficients, as shown in formula (21). （21） In the formula, The VB model shows the changes in electricity consumption caused by the connection and disconnection of clustered hydrogen fuel cell vehicles from the power grid. , They are respectively in t , t The state coefficient of the cluster hydrogen fuel cell vehicle at time +1, where the state coefficient is 1 when the cluster hydrogen fuel cell vehicle is connected to the grid and 0 when the cluster hydrogen fuel cell vehicle is disconnected from the grid. Based on the VB model of a single hydrogen fuel cell vehicle and the power and energy boundaries of a cluster of hydrogen fuel cell vehicles, a VB model of a cluster of hydrogen fuel cell vehicles is constructed, as shown in formulas (22)-(24). （22） （23） （24） In the formula, , These represent the time periods of the cluster hydrogen fuel cell vehicles. t S3: Taking the highest economic efficiency of the integrated power system as the optimization objective, construct the objective function based on the robust model and VB model, solve the objective function, and determine the scheduling mode of hydrogen fuel cell vehicles and integrated power system when the economic efficiency of the integrated power system is maximized, considering the power generation efficiency and power load of the integrated power system and the power cost of the equipment participating in the scheduling.

2. The robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems according to claim 1, characterized in that, The power grid includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries, and user terminals; the thermal grid includes electric heaters and thermal storage tanks; the hydrogen grid includes electrolyzers; and the signaling grid includes the integrated power system dispatch and control center and hydrogen fuel cell vehicle controllers.

3. The robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems according to claim 2, characterized in that, The heat relationship model and power relationship model based on the thermal energy network include: Based on the relationship of heat energy utilization, a heat relationship model between the electric heater and the heat storage tank is established. The heat relationship model between the electric heater and the heat storage tank considers the heat lost from the heat storage tank to the environment. The calculated heat supply of the electric heater and the heat lost from the heat storage tank to the environment are shown in formulas (1) and (2). （1） （2） In the formula, , They are time points t The l The heat supplied by the electric heater and the first l The heat lost from the heat storage tank to the environment For the first l The mass of the heat storage medium in each heat storage tank The specific heat capacity of the heat storage medium. , They are time points t , t -1 of l The temperature of the heat storage medium inside each heat storage tank , They are time points t , t -1 of l The ambient temperature of each thermal storage tank U The heat transfer coefficient of the thermal storage tank is expressed in W / (m²·K). The surface area of ​​the thermal storage tank; Based on the heat supplied by the electric heater, a power relationship model for the electric heater is constructed, that is, the power consumption of the electric heater is calculated, as shown in formula (3). （3） In the formula, For a moment t The l The power consumption of each electric heater, The sampling time period.

4. The robust scheduling optimization method for clustered hydrogen fuel cell vehicles and integrated power systems according to claim 1, characterized in that, With the goal of maximizing the operational economic efficiency of the integrated power system, an objective function is constructed based on the robust model and the VB model, including: S31. Generate a dataset of the electricity consumption of the clustered hydrogen fuel cell vehicles based on the VB model of the clustered hydrogen fuel cell vehicles. S32. Predict wind power and solar power output data based on data-driven methods; S33. Based on the robust model, VB model, power output data of wind power and photovoltaic power, and the data set of electricity of clustered hydrogen fuel cell vehicles, construct the objective function as shown in formulas (25)-(27). （25） （26） （27） In the formula, The calculated economic benefits generated by the operation of the integrated power system. For the integrated power system at time t The output power, For the integrated power system at time t The load power, For a moment t The load of the power user in the user client, For real-time electricity prices, To assess the electricity market benefits of different power generation methods; Indicates time t The j Power output forecast for a wind or solar power plant Indicates time t The k The output of a conventional thermal power unit Indicates time t The power of electricity purchased from the external grid, Indicates time t The l Power consumption of each electric heater; Indicates time t No. m The power consumption of each electrolytic cell; This indicates that the cluster of hydrogen fuel cell vehicles is in the time period t power, , , , These represent the number of wind and solar power units, conventional thermal power units, electric heaters, and electrolytic cells participating in the integrated power system dispatch; This indicates the sampling time period; S34. Using the concept of power balance, set dispatch constraints for the integrated power system to achieve dynamic balance between power output and load power. The constraints are as follows: The power balance constraint is constructed as shown in formula (28). （28） In the formula, M For flexibility margin; The processing constraints for conventional thermal power units are constructed as shown in formula (29). （29） In the formula, , They are time points t The k The lower and upper limits of the output of a conventional thermal power unit; The ramp rate constraint for conventional thermal power units is constructed as shown in formulas (30) and (31). （30） （31） In the formula, , The first k The maximum and minimum ramp rate of output of a conventional thermal power unit. Representing time respectively t -1 and time t No. k The output of a conventional thermal power unit; The output constraints for wind power and photovoltaic power are constructed as shown in formula (32). （32） In the formula, , They are time points t The j The lower and upper limits of the predicted output of a wind or solar power plant. , They are time points t The j The lower and upper limits of the allowable output of a wind or solar power plant. For a moment t The j Power output forecast for a wind or solar power plant; The power consumption constraint of the clustered hydrogen fuel cell vehicles is constructed as shown in formula (33). （33） In the formula, , They represent the first i Minimum and maximum power for charging and discharging of hydrogen fuel cell vehicles in Taiwan. For a moment t The n The electrical power consumed by a hydrogen fuel cell vehicle in Taiwan; The output constraint of the electrolytic cell is constructed as shown in formula (34). （34） In the formula, , They are time points t The m The lower and upper limits of the output of each electrolytic cell; For a moment t No. m The power consumption of each electrolytic cell; The output constraint of the thermal storage device is constructed as shown in formula (35). （35） In the formula, For a moment t The l The power consumption of each electric heater, , They are time points t The l The lower and upper limits of the permissible output of a thermal storage device; The constraints for purchasing electricity from the external grid are constructed as shown in formula (36). （36） In the formula, For a moment t The power of electricity purchased from external power grids, For a moment t The upper limit on the power capacity for purchasing electricity from external grids; The electrical constraints for constructing the hydrogen storage tank are shown in formula (37). （37） In the formula, Indicates time t The chemical energy of hydrogen in the hydrogen storage tank for n The total volume of hydrogen storage tanks installed on Taiwanese hydrogen fuel cell vehicles. The density of hydrogen gas under standard conditions. This is the calorific value of hydrogen.

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