Robust scheduling optimization method for cluster hydrogen fuel cell vehicle and integrated power system

Abstract

The invention discloses a robust scheduling optimization method for a cluster hydrogen fuel cell vehicle and an integrated power system, and the method comprises the steps: simulating a process in which a single hydrogen fuel cell vehicle participates in the scheduling of the integrated power system, and building an energy network in which the cluster hydrogen fuel cell vehicle participates in the scheduling of the integrated power system based on the simulated process; constructing a robust model for accessing the cluster hydrogen fuel cell vehicle into the integrated power system based on the energy network, and constructing a VB model of the hydrogen fuel cell vehicle based on the charging and discharging behaviors of the hydrogen fuel cell vehicle; taking the highest operation economic benefit of the integrated power system as an optimization target, constructing a target function based on the robust model and the VB model, solving the target function, and determining a scheduling mode of the hydrogen fuel cell vehicle and the integrated power system when the operation economic benefit of the integrated power system is highest; according to the method, optimal collaborative optimization of power fluctuation in the economical efficiency and the comprehensive power system can be realized, collaborative development of the hydrogen energy and the power system is promoted, and the flexibility and stability of the power system are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system scheduling of hydrogen fuel cell vehicles, and in particular to a robust scheduling optimization method for clustered hydrogen fuel cell vehicles and an integrated power system. Background Art

[0002] With the widespread application of renewable energy (such as wind power and photovoltaics) in the current energy system, the traditional power system dominated by thermal power has gradually developed into an integrated wind, solar, and thermal power system with energy storage and absorption capabilities. However, the volatility and uncertainty faced by the power system are becoming increasingly severe, and traditional dispatching strategies are gradually unable to effectively balance the requirements of operational economy and stability.

[0003] Hydrogen, a clean energy source, can be obtained from industrial waste gas and water electrolysis. It improves the environmental protection of manufacturers and the energy storage and absorption capabilities of power grids, enabling coordinated control of energy networks. Hydrogen fuel cells are beginning to enter commercial operation across various regions. As a clean energy vehicle, hydrogen fuel cell vehicles (FCEVs) not only enable "electricity-hydrogen-electricity" energy conversion but also leverage their fixed routes and operating hours to absorb renewable distributed energy systems, providing flexible support for power system scheduling and operation.

[0004] However, integrated power systems with multiple power generation forms and energy storage and absorption capabilities have complex output characteristics. The charging and discharging behavior, operating conditions, and energy storage of clustered hydrogen fuel cell vehicles are dynamic. As more power equipment is connected, the operating costs of the integrated power system conflict with the optimization objectives of power fluctuations. Designing and building models to achieve coordinated optimization of economic efficiency and power fluctuations at multiple time scales presents challenges such as an increase in optimization variables and computational complexity. The constraints of power systems with renewable energy sources such as wind and photovoltaic power and hydrogen fuel cell vehicles are nonlinear, further complicating the optimization problem. Summary of the Invention

[0005] The present invention provides a robust scheduling optimization method for cluster hydrogen fuel cell vehicles and an integrated power system to overcome the technical problem that due to the complex output characteristics of the integrated power system and the dynamic charging and discharging behavior and operation of the cluster hydrogen fuel cell vehicles, when the cluster hydrogen fuel cell vehicles are connected to the integrated power system for scheduling, the optimization variables increase, the calculation becomes complex, and the optimal coordinated optimization of economy and power fluctuation cannot be achieved.

[0006] In order to achieve the above object, the technical solution of the present invention is:

[0007] A robust dispatch optimization method for clustered hydrogen fuel cell vehicles and an integrated power system, comprising:

[0008] 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 cluster hydrogen fuel cell vehicles to participate in the integrated power system dispatch. The energy network includes the electric energy network, thermal energy network, hydrogen energy network and signal network, which are used to refer to hydrogen fuel cell vehicles and various devices in the integrated power system.

[0009] S2: Based on the energy network, a robust model for clustered hydrogen fuel cell vehicles connected to the integrated power system is constructed. Based on the charging and discharging behaviors of hydrogen fuel cell vehicles, a VB model of hydrogen fuel cell vehicles is constructed. The robust model is used to describe the energy transfer relationship between various devices in the electric energy network, thermal energy network, and hydrogen energy network;

[0010] S3: Taking the highest economic benefit of the operation of the integrated power system as the optimization goal, construct an objective function based on the robust model and the VB model, solve the objective function, and determine the scheduling method of hydrogen fuel cell vehicles and the integrated power system when the economic benefit of the operation of the integrated power system is the highest, taking into account the power generation efficiency and power load of the integrated power system and the electricity cost of the equipment involved in the scheduling.

[0011] Furthermore, the robust model includes a heat relationship model and a power relationship model constructed based on the thermal energy network and a power relationship model constructed based on the electric energy network and the hydrogen energy network. The VB model of the hydrogen fuel cell vehicle includes the VB model of a single hydrogen fuel cell vehicle and a cluster hydrogen fuel cell vehicle.

[0012] Furthermore, the electric energy network includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries and user clients, the thermal energy network includes electric heaters and heat storage tanks, the hydrogen energy network includes electrolyzers, and the signal network includes an integrated power system dispatching and control center and a hydrogen fuel cell vehicle controller.

[0013] Furthermore, the heat relationship model and power relationship model constructed based on the thermal energy network include:

[0014] Based on the relationship between thermal 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 takes into account the heat lost to the environment by the heat storage tank. The calculated heat supply of the electric heater and the heat lost to the environment by the heat storage tank are shown in formulas (1) and (2).

[0015]

[0016] Where, are the heat supplied by the lth electric heater at time t and the heat lost to the environment by the lth heat storage tank, m l is the mass of the heat storage medium in the lth heat storage tank, c is the specific heat capacity of the heat storage medium, are the temperatures of the heat storage medium in the lth heat storage tank at time t and t-1 respectively, are the ambient temperatures of the lth heat storage tank at time t and t-1 respectively, and U is the heat transfer coefficient of the heat storage tank, in W / (m 2 K), A l is the surface area of ​​the heat storage tank;

[0017] The power relationship model of the electric heater is constructed based on the heat supplied by the electric heater, that is, the power consumption of the electric heater is calculated, as shown in formula (3):

[0018]

[0019] Where, P TES,l,t is the power consumption of the lth electric heater at time t, and Δt is the sampling time period.

[0020] Furthermore, the power relationship model constructed based on the electric energy grid and the hydrogen energy grid includes:

[0021] Based on the relationship between hydrogen energy utilization, a power relationship model of the electrolyzer-hydrogen storage tank-hydrogen fuel cell is constructed, that is, the power consumption of the electrolyzer, the chemical energy of hydrogen in the hydrogen storage tank, and the power generation and power consumption of the hydrogen fuel cell are calculated;

[0022] The calculation formula of the electric power consumption of the electrolytic cell is shown in (4).

[0023]

[0024] Where, P H,m,t is the power consumption of the mth electrolytic cell at time t, n m,t is the hydrogen production rate of the mth electrolyzer at time t, is the calorific value of hydrogen, η ecc is the efficiency of the electrolyzer;

[0025] The hydrogen storage tank stores the hydrogen generated by the electrolysis of water in the electrolyzer. The chemical energy of the hydrogen generated by the hydrogen storage tank is shown in formula (5):

[0026]

[0027] Where, E sto,t 、E sto,t-1 are the chemical energy of hydrogen in the hydrogen storage tank at time t and t-1, is the total chemical energy of hydrogen produced by the electrolyzer at time t, is the power generation of all hydrogen fuel cell vehicles at time t; n t is the mass of hydrogen produced by the electrolyzer at time t;

[0028] The calculation formula for the power generation of a hydrogen fuel cell is shown in (6).

[0029]

[0030] Where, is the power generation of the hydrogen fuel cell installed in the nth hydrogen fuel cell vehicle at time t, m n,t is the hydrogen consumption rate of the nth hydrogen fuel cell vehicle at time t, η fc The efficiency of hydrogen-to-electricity conversion in hydrogen fuel cells;

[0031] The electric power consumed by the hydrogen fuel cell is obtained based on the power generated by the hydrogen fuel cell, that is, the electric power, as shown in formula (7):

[0032]

[0033] Where, is the electric power consumed by the nth hydrogen fuel cell vehicle at time t, λ t is the road condition coefficient of the hydrogen fuel cell vehicle's driving route.

[0034] Furthermore, the VB models of single and cluster hydrogen fuel cell vehicles include:

[0035] Construct the power boundary of a single hydrogen fuel cell vehicle, as shown in formulas (8)-(11):

[0036]

[0037] Where, and They represent the maximum, minimum and expected power of the i-th hydrogen fuel cell vehicle during the time it is connected to the grid, t∈(T a ,T o ), T a and T o They represent the time when hydrogen fuel cell vehicles connect to and leave the power grid, and They represent the minimum power of the i-th hydrogen fuel cell vehicle before and after leaving the grid, and They represent the maximum and minimum power of charging and discharging of the i-th hydrogen fuel cell vehicle, and They represent the minimum allowed power of the i-th hydrogen fuel cell vehicle and the power when connected to the grid, and Δt represents the sampling time period;

[0038] Based on the power 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).

[0039]

[0040] Where, Respectively represent the maximum and minimum power of the i-th hydrogen fuel cell vehicle; t∈(T a ,T0-1), T0-1 is the moment before leaving the grid;

[0041] Based on the power boundary description of hydrogen fuel cell vehicles, a VB model of a single hydrogen fuel cell vehicle is constructed, as shown in formulas (14)-(16).

[0042]

[0043]

[0044] Where, is the power of the i-th hydrogen fuel cell vehicle in time period t, They represent the electric energy of the i-th hydrogen fuel cell vehicle in time period t and t+1 respectively;

[0045] By summing the power and electricity of a single hydrogen fuel cell vehicle, we can obtain the power and electricity boundaries of a cluster of hydrogen fuel cell vehicles, as shown in formulas (17)-(20).

[0046]

[0047] Where, They represent the upper and lower limits of the electricity and power of the cluster hydrogen fuel cell vehicles in time period t, N F is the number of vehicles in the cluster of hydrogen fuel cell vehicles;

[0048] The connection state of cluster hydrogen fuel cell vehicles connecting to and leaving the grid is quantified as a state coefficient, and the power change is determined according to the state coefficient, as shown in formula (21):

[0049]

[0050] Where, W is the power change of the VB model caused by the cluster hydrogen fuel cell vehicles connecting to and leaving the grid. i,t+1 、W i,t are the state coefficients of the cluster hydrogen fuel cell vehicles at time t and t+1, respectively. When the cluster hydrogen fuel cell vehicles are connected to the grid, the state coefficient is 1, and when the cluster hydrogen fuel cell vehicles are off the grid, the state coefficient is 0;

[0051] Based on the VB model of a single hydrogen fuel cell vehicle and the power and electricity boundaries of a cluster of hydrogen fuel cell vehicles, a cluster hydrogen fuel cell vehicle VB model is constructed, as shown in formulas (22)-(24).

[0052]

[0053] Where, They represent the power and electricity of the cluster hydrogen fuel cell vehicles in the time period t respectively.

[0054] Furthermore, taking the highest economic benefit of the operation of the integrated power system as the optimization goal, an objective function is constructed based on the robust model and the VB model, including:

[0055] S31. Generate a data set of power of cluster hydrogen fuel cell vehicles according to the cluster hydrogen fuel cell vehicle VB model;

[0056] S32. Predict wind power and photovoltaic output data based on data-driven methods;

[0057] S33. Based on the robust model, VB model, wind power, photovoltaic power output data and cluster hydrogen fuel cell vehicle power data set, the objective function is constructed as shown in formulas (25)-(27).

[0058]

[0059] Where C E The economic benefits generated by the calculated comprehensive power system operation, is the output power of the integrated power system at time t, is the load power of the integrated power system at time t, P user,t is the load of the electricity user in the user client at time t, C d,t is the real-time electricity price, C a,t is the electricity market benefits of different power generation methods; P WP,j,t represents the output forecast of the jth wind power or photovoltaic field at time t, P G,k,t represents the output of the kth conventional thermal power unit at time t, P EG,t P represents the power purchased from the external network at time t. TES,l,t represents the power consumption of the lth electric heater at time t; P H,m,t represents the power consumption of the mth electrolytic cell at time t; N represents the power of cluster hydrogen fuel cell vehicles in time period t, WP , N G , N TES , N H They represent the number of wind power and photovoltaic power generation units, conventional thermal power generation units, electric heaters and electrolyzers participating in the integrated power system dispatch; Δt represents the sampling time period;

[0060] S34. Using the idea of ​​power balance, set the dispatch constraints of the integrated power system to achieve a dynamic balance between output and load power. The constraints are as follows:

[0061] Construct the electric power balance constraint as shown in formula (28),

[0062]

[0063] Where M is the flexibility margin;

[0064] Construct the processing constraints of conventional thermal power units, as shown in formula (29),

[0065]

[0066] Where, are the lower and upper limits of the output of the kth conventional thermal power unit at time t respectively;

[0067] Construct the ramp rate constraint of conventional thermal power units, as shown in formulas (30) and (31),

[0068] -R u,k Δt≤P k,t -P k,t-1 ≤R u,k Δt (30)

[0069] -R d,k Δt≤P k,t -P k,t-1 ≤R d,k Δt (31)

[0070] Where R u,k 、R d,k are the maximum and minimum ramp rates of the kth conventional thermal power unit output, P k,t-1 、P k,t represent the output of the kth conventional thermal power unit at time t-1 and time t respectively;

[0071] Construct the output constraints of wind power and photovoltaic power, as shown in formula (32),

[0072]

[0073] Where, are the lower and upper limits of the output forecast of the j-th wind farm or photovoltaic farm at time t, are the lower and upper limits of the output of the j-th wind power or photovoltaic field at time t, respectively. WP,j,t is the output of the j-th wind farm or photovoltaic farm at time t;

[0074] Construct the electric power constraint of cluster hydrogen fuel cell vehicles, as shown in formula (33),

[0075]

[0076] Where, They represent the minimum and maximum power of charging and discharging of the i-th hydrogen fuel cell vehicle, is the electric power consumed by the nth hydrogen fuel cell vehicle at time t;

[0077] Construct the output constraint of the electrolyzer, as shown in formula (34),

[0078]

[0079] Where, are the lower and upper limits of the output of the mth electrolytic cell at time t; P H,m,t is the power consumption of the mth electrolytic cell at time t;

[0080] Construct the output constraint of the heat storage equipment, as shown in formula (35),

[0081]

[0082] Where, P TES,l,t is the power consumption of the lth electric heater at time t, are the lower and upper limits of the allowable output of the lth heat storage device at time t respectively;

[0083] Construct the constraints for purchasing electricity from the external network, as shown in formula (36),

[0084]

[0085] Where, is the power of electricity purchased from the external network at time t, is the upper limit of the power purchased from the external network at time t;

[0086] Construct the power constraint of the hydrogen storage tank, as shown in formula (37),

[0087]

[0088] Where, E sto,t represents the chemical energy of hydrogen in the hydrogen storage tank at time t, V is the total volume of hydrogen storage tanks loaded on n hydrogen fuel cell vehicles, is the density of hydrogen under standard conditions, is the calorific value of hydrogen.

[0089] Beneficial effects: The present invention provides a robust scheduling optimization method for cluster hydrogen fuel cell vehicles and integrated power systems, proposes a process for hydrogen fuel cell vehicles to participate in the scheduling of integrated power systems, and optimizes energy utilization and system flexibility through the energy converted by the scheduling control center of the integrated power system and the vehicle controller of the hydrogen fuel cell vehicle; considering the energy transfer relationship in the system, a robust optimization model for cluster hydrogen fuel cell vehicles is established, an objective function with the goal of maximizing economic benefits is constructed, the objective function is solved, and the scheduling mode corresponding to the hydrogen fuel cell vehicle and the integrated power system is determined when the operating economic benefits of the integrated power system are the highest, so as to achieve the best coordinated optimization of economy and power fluctuation in the integrated power system, promote the coordinated development of hydrogen energy and the power system, and enhance the flexibility and stability of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0091] 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;

[0092] Figure 2 This is the energy utilization structure diagram of hydrogen fuel cell vehicles;

[0093] Figure 3 This is a diagram of the integrated power system dispatching framework structure for accessing clustered hydrogen fuel cell vehicles and new energy provided by the present invention;

[0094] Figure 4 A calculation flow chart of the example analysis method provided by the present invention;

[0095] Figure 5 A real-time electricity price curve provided by the present invention;

[0096] Figure 6 The fluctuating power output data curves of wind and photovoltaic power provided by the present invention;

[0097] Figure 7 This is a load forecast data curve for electricity users in the user client provided by the present invention. DETAILED DESCRIPTION

[0098] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0099] This embodiment provides a robust scheduling optimization method for cluster hydrogen fuel cell vehicles and integrated power systems, such as Figure 1 As shown, including:

[0100] 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 cluster hydrogen fuel cell vehicles to participate in the integrated power system dispatch. The energy network includes the electric energy network, thermal energy network, hydrogen energy network and signal network, which are used to refer to hydrogen fuel cell vehicles and various devices in the integrated power system.

[0101] S2: Based on the energy network, a robust model for clustered hydrogen fuel cell vehicles connected to the integrated power system is constructed. Based on the charging and discharging behaviors of hydrogen fuel cell vehicles, a VB model of hydrogen fuel cell vehicles is constructed. The robust model is used to describe the energy transfer relationship between various devices in the electric energy network, thermal energy network, and hydrogen energy network;

[0102] S3: Taking the highest economic benefit of the operation of the integrated power system as the optimization goal, construct an objective function based on the robust model and the VB model, solve the objective function, and determine the scheduling method of hydrogen fuel cell vehicles and the integrated power system when the economic benefit of the operation of the integrated power system is the highest, taking into account the power generation efficiency and power load of the integrated power system and the electricity cost of the equipment involved in the scheduling.

[0103] Specifically, first, a process of a single hydrogen fuel cell vehicle participating in the dispatch of an integrated power system is simulated, which includes the grid charging the hydrogen fuel cell vehicle, the operation of the hydrogen fuel cell vehicle, and the release of energy from the hydrogen fuel cell vehicle to the grid. By simulating the process of hydrogen fuel cell vehicles participating in the dispatch of an integrated power system, the basis for the energy flow of hydrogen fuel cell vehicles in the integrated power system can be determined, which facilitates the subsequent determination of the optimal dispatch optimization plan. Based on the simulation process, an energy network for cluster hydrogen fuel cell vehicles to participate in the dispatch of an integrated power system is constructed. The energy network includes an electric energy network, a thermal energy network, a hydrogen energy network, and a signal network, which are used to refer to hydrogen fuel cell vehicles and various devices in the integrated power system. By constructing an energy network for cluster hydrogen fuel cell vehicles to participate in the dispatch of an integrated power system, various devices in the hydrogen fuel cell vehicles can be involved in the dispatch of the integrated power system. The vehicle is controlled by signal control to enter and maintain the working process of charging, running, and releasing energy, so that hydrogen energy battery vehicles can meet usage needs and improve the flexibility and power balance of the power system.

[0104] Secondly, a robust model for cluster hydrogen fuel cell vehicles accessing the integrated power system is constructed based on the energy network, and a VB model of hydrogen fuel cell vehicles is constructed based on the charging and discharging behavior of hydrogen fuel cell vehicles. The robust model is used to describe the energy transfer relationship between various devices in the electric energy network, thermal energy network, and hydrogen energy network. 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. These models can coordinate electric energy, hydrogen energy, and thermal energy, enhance the flexibility of the integrated power system, and achieve global optimization of the optimization results. The VB model is used to describe cluster hydrogen fuel cell vehicles, and the charging and discharging behavior of the vehicles is described as multiple key parameters to reduce the solution time of the robust optimization algorithm. Therefore, in the charging stage, the required hydrogen energy is converted into electricity for calculation, and VB models (virtual battery models) of single hydrogen fuel cell vehicles and cluster hydrogen fuel cell vehicles are constructed to generate a data set of hydrogen fuel cell vehicle electricity, providing a data basis for constructing the objective function.

[0105] Finally, taking the highest economic benefit of the operation of the integrated power system as the optimization goal, the objective function is constructed based on the robust model and the VB model, and the objective function is solved. Taking into account the power generation efficiency and power load of the integrated power system and the electricity cost of the participating dispatching equipment, the dispatching method of hydrogen fuel cell vehicles and the integrated power system is determined when the economic benefit of the operation of the integrated power system is the highest. The result is the optimal dispatching method for cluster hydrogen fuel cell vehicles participating in the integrated power system.

[0106] In a specific embodiment, a process of a single hydrogen fuel cell vehicle participating in the dispatch of an integrated power system is simulated, and an energy network for clustered hydrogen fuel cell vehicles participating in the dispatch of the integrated power system is constructed based on the simulated process. The energy network includes an electric energy network, a thermal energy network, a hydrogen energy network, and a signal network. The scheme used to refer to the hydrogen fuel cell vehicles and various devices in the integrated power system is:

[0107] S11. Establish the energy utilization structure of hydrogen fuel cell vehicles, such as Figure 2 As shown, Figure 2 This is the energy utilization structure of a hydrogen fuel cell vehicle, where the thin solid line represents electrical energy, the thick solid line represents mechanical energy, and the dotted line represents hydrogen energy. The electrical energy required for vehicle operation comes from hydrogen fuel cells and lithium batteries. The hydrogen energy of the hydrogen fuel cell comes from the hydrogen storage tank, and the electrical energy of the lithium battery comes from the power grid. The electrolyzer electrolyzes water to produce hydrogen and stores it in the energy storage tank. The hydrogen output from the hydrogen storage tank is mixed with air and enters the hydrogen fuel cell for an oxidation-reduction reaction to generate electrical energy. The electrical energy generated by the lithium battery and hydrogen fuel cell is boosted and inverted by the DC / DC and DC / AC units to drive the vehicle's motor, which in turn drives the transmission mechanism to rotate the wheels. The electrolyzer electrolyzes water to produce hydrogen and stores it in the energy storage tank. The lithium battery is charged and discharged to the power grid via the charging terminal on the charging pile device connected to the power grid. The hydrogen fuel cell is equipped with a discharge terminal connected to the power grid to discharge to the power grid.

[0108] 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 hydrogen fuel cell vehicle operating, and the hydrogen fuel cell vehicle releasing energy to the grid. The specific process is as follows:

[0109] Hydrogen fuel cell vehicles participate in integrated power system dispatching through the charging and discharging of hydrogen fuel cells and lithium batteries. Their operating process is related to the power fluctuations of the grid's tie lines and the vehicle's off-grid and on-grid status. Specific operating states include:

[0110] (1) The grid charges the car: When the car stops, the grid charges the lithium battery through the charging pile, and the electrolyzer works and transports hydrogen to the hydrogen storage tank;

[0111] (2) Vehicle operation: The vehicle operates normally, consuming the electricity generated by the lithium battery and hydrogen fuel cell;

[0112] (3) The vehicle releases energy to the grid: The vehicle stops but does not perform power-off operations. The hydrogen fuel cell vehicle controller charges and discharges the onboard hydrogen fuel cell and lithium battery according to the wireless signal sent by the integrated power system dispatching center;

[0113] Since hydrogen fuel cell vehicles have fixed routes and carry a single load with minimal weight variation, the fluctuation in the vehicle's power consumption during operation is small, which can reduce the fluctuation in grid interconnection line power caused by the vehicle being charged.

[0114] S13. Build an energy network where clustered hydrogen fuel cell vehicles participate in the dispatch of integrated power systems. Figure 3 This is a diagram of the power system dispatch framework structure for accessing cluster hydrogen fuel cell vehicles and new energy sources. The thin solid line represents the electric energy grid, the thin dashed line represents the signal network, the thick solid line represents the thermal energy grid, and the thick dashed line represents the hydrogen energy grid. The electric energy grid of the integrated power system includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries, and user clients. The thermal energy grid includes electric heaters and heat storage tanks. The hydrogen energy grid includes electrolyzers. The signal network includes the integrated power system dispatch control center and hydrogen fuel cell vehicle controllers.

[0115] Distributed power sources such as wind turbines and photovoltaic batteries transmit electricity to the power grid. Electric heaters consume excess power from the power grid, convert it into heat 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 for operation.

[0116] Heat storage equipment, primarily consisting of electric heaters and energy storage tanks, is used to optimize the energy of the entire integrated power system. Because the heat storage equipment has both electrical-to-heat conversion and heat storage capabilities, flexible control strategies can be used to reduce the volatility of the integrated power system. The hydrogen fuel cells and lithium batteries in hydrogen energy battery vehicles can obtain energy from both the electricity and hydrogen energy grids. Through the battery's discharge characteristics, these batteries can be used to feed back electrical energy to the integrated power system, enhancing its dispatch flexibility.

[0117] In the signal network, the integrated power system dispatching and control center, as a first-level dispatching system, can obtain in real time the new energy output status of wind power, photovoltaic power and electrolyzers, the equipment status of electric heaters and the vehicle energy storage status issued by the hydrogen fuel cell vehicle controller, and send power control signals to control the flow of electrical energy, thermal energy and hydrogen energy to achieve optimized dispatching of the system; the hydrogen fuel cell vehicle controller, as a second-level dispatching system, monitors the energy storage status of hydrogen energy batteries and lithium batteries, and sends power control signals according to the current power system dispatch and freight load requirements to control the vehicle to enter and maintain the charging, running and discharging working process, so that hydrogen energy battery vehicles can meet the usage needs, improve the flexibility and power balance of the power system, and achieve efficient utilization of power resources.

[0118] In this scheme, the process of hydrogen fuel cell vehicles participating in the dispatch of the integrated power system is simulated, which can determine the basis of energy flow of hydrogen fuel cell vehicles in the integrated power system, facilitate the subsequent determination of the optimal dispatch optimization plan, and build an energy network for cluster hydrogen fuel cell vehicles to participate in the dispatch of the integrated power system based on the simulation process. This can enable each device in the hydrogen fuel cell vehicle to participate in the dispatch of the integrated power system, and control the vehicle to enter and maintain the working process of charging, running and discharging energy through signal control, so that hydrogen energy battery vehicles can meet usage needs and improve the flexibility and power balance of the power system.

[0119] In a specific embodiment, a robust model for cluster hydrogen fuel cell vehicles connected to an integrated power system is constructed based on the energy network, and a VB model of hydrogen fuel cell vehicles is constructed based on the charging and discharging behaviors of hydrogen fuel cell vehicles. The robust model is used to describe the energy transfer relationship between various devices in the electric energy network, thermal energy network, and hydrogen energy network. The scheme is:

[0120] The robust model includes a heat relationship model and a power relationship model based on the thermal energy network and a power relationship model based on the electric energy network and the hydrogen energy network. The VB model of the hydrogen fuel cell vehicle includes the VB model of a single hydrogen fuel cell vehicle and a cluster hydrogen fuel cell vehicle. The specific formula is as follows:

[0121] (1) The heat relationship model and power relationship model constructed based on the thermal energy network include:

[0122] refer to Figure 3 Based on the heat energy utilization relationship in the equation, 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 takes into account the heat lost to the environment by the heat storage tank. The calculated heat supply of the electric heater and the heat lost to the environment by the heat storage tank are as shown in formulas (38) and (39).

[0123]

[0124] Where, are the heat supplied by the lth electric heater at time t and the heat lost to the environment by the lth heat storage tank, m l is the mass of the heat storage medium in the lth heat storage tank, c is the specific heat capacity of the heat storage medium, are the temperatures of the heat storage medium in the lth heat storage tank at time t and t-1 respectively, are the ambient temperatures of the lth heat storage tank at time t and t-1 respectively, and U is the heat transfer coefficient of the heat storage tank, in W / (m 2 K), A l is the surface area of ​​the heat storage tank;

[0125] The power relationship model of the electric heater is constructed based on the heat supplied by the electric heater, that is, the power consumption of the electric heater is calculated, as shown in formula (40):

[0126]

[0127] Where, P TES,l,t is the power consumption of the lth electric heater at time t, and Δt is the sampling time period;

[0128] (2) The power relationship model based on the electric energy grid and the hydrogen energy grid includes:

[0129] Based on the relationship between hydrogen energy utilization, a power relationship model of the electrolyzer-hydrogen storage tank-hydrogen fuel cell is constructed, that is, the power consumption of the electrolyzer, the chemical energy of hydrogen in the hydrogen storage tank, and the power generation and power consumption of the hydrogen fuel cell are calculated;

[0130] The calculation formula of the electric power consumption of the electrolytic cell is shown in (41).

[0131]

[0132] Where, P H,m,t is the power consumption of the mth electrolytic cell at time t, n m,t is the hydrogen production rate of the mth electrolyzer at time t, is the calorific value of hydrogen, η ecc is the efficiency of the electrolyzer;

[0133] The hydrogen storage tank stores the hydrogen generated by the electrolysis of water in the electrolyzer. The chemical energy of the hydrogen generated by the hydrogen storage tank is shown in formula (42):

[0134]

[0135] Where, E sto,t 、E sto,t-1 are the chemical energy of hydrogen in the hydrogen storage tank at time t and t-1, is the total chemical energy of hydrogen produced by the electrolyzer at time t, is the power generation of all hydrogen fuel cell vehicles at time t; n t is the mass of hydrogen produced by the electrolyzer at time t;

[0136] The calculation formula for the power generation of a hydrogen fuel cell is shown in formula (43):

[0137]

[0138] Where, is the power generation of the hydrogen fuel cell installed in the nth hydrogen fuel cell vehicle at time t, m n,t is the hydrogen consumption rate of the nth hydrogen fuel cell vehicle at time t, ηfc The efficiency of hydrogen-to-electricity conversion in hydrogen fuel cells;

[0139] The electric power consumed by the hydrogen fuel cell is obtained based on the power generated by the hydrogen fuel cell, that is, the electric power, as shown in formula (44):

[0140]

[0141] Where, is the electric power consumed by the nth hydrogen fuel cell vehicle at time t, λ t The road condition coefficient of the hydrogen fuel cell vehicle's route; in this solution, the road condition coefficient includes the road slope, curvature, rolling resistance coefficient of the road surface type, road surface flatness, and traffic conditions. The specific value is determined based on the actual road the hydrogen fuel cell vehicle is traveling on.

[0142] (3) VB models for single and cluster hydrogen fuel cell vehicles based on the charging and discharging behavior of hydrogen fuel cell vehicles, including:

[0143] At multiple time scales, the usage scenarios are divided according to the time when the car is connected to the grid and off the grid. When the car is connected to the power system, it is charged according to the maximum charging power until the car reaches the maximum power. When the car's power is less than the minimum charging power allowed by the grid before it is connected to the power system, the car is charged first. After the car's power reaches the power requirement, it is charged and discharged according to the instructions issued by the hydrogen fuel cell vehicle controller and the integrated power system dispatching control center. Therefore, the power boundary of a single hydrogen fuel cell vehicle is constructed, as shown in formulas (45)-(48).

[0144]

[0145] Where, and They represent the maximum, minimum and expected power of the i-th hydrogen fuel cell vehicle during the time it is connected to the grid, t∈(T a ,T o ), T a and T o They represent the time when hydrogen fuel cell vehicles connect to and leave the power grid, and They represent the minimum power of the i-th hydrogen fuel cell vehicle before and after leaving the grid, and They represent the maximum and minimum power of charging and discharging of the i-th hydrogen fuel cell vehicle, and They represent the minimum allowed power of the i-th hydrogen fuel cell vehicle and the power when connected to the grid, and Δt represents the sampling time period;

[0146] Based on the power 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).

[0147]

[0148] Where, Respectively represent the maximum and minimum power of the i-th hydrogen fuel cell vehicle; t∈(T a ,T0-1), T0-1 is the moment before leaving the grid;

[0149] Based on the power boundary description of hydrogen fuel cell vehicles, a VB model of a single hydrogen fuel cell vehicle is constructed, as shown in formulas (51)-(53).

[0150]

[0151] Where, is the power of the i-th hydrogen fuel cell vehicle in time period t, They represent the electric energy of the i-th hydrogen fuel cell vehicle in time period t and t+1 respectively;

[0152] By summing the power and electricity of a single hydrogen fuel cell vehicle, we can obtain the power and electricity boundaries of a cluster of hydrogen fuel cell vehicles, as shown in formulas (54)-(57).

[0153]

[0154] Where, They represent the upper and lower limits of the electricity and power of the cluster hydrogen fuel cell vehicles in time period t, N F is the number of vehicles in the cluster of hydrogen fuel cell vehicles;

[0155] When cluster hydrogen fuel cell vehicles are connected to or leave the grid, the amount of electricity in the VB model will change suddenly. The connection state of cluster hydrogen fuel cell vehicles when they are connected to or leave the grid is quantified as a state coefficient, and the amount of electricity change is determined based on the state coefficient, as shown in formula (58):

[0156]

[0157] Where, W is the power change of the VB model caused by the cluster hydrogen fuel cell vehicles connecting to and leaving the grid. i,t+1 、W i,t are the state coefficients of the cluster hydrogen fuel cell vehicles at time t and t+1, respectively. When the cluster hydrogen fuel cell vehicles are connected to the grid, the state coefficient is 1, and when the cluster hydrogen fuel cell vehicles are off the grid, the state coefficient is 0;

[0158] Based on the VB model of a single hydrogen fuel cell vehicle and the power and electricity boundaries of a cluster of hydrogen fuel cell vehicles, a cluster hydrogen fuel cell vehicle VB model is constructed, as shown in formulas (59)-(61).

[0159]

[0160] Where, They represent the power and electricity of the cluster hydrogen fuel cell vehicles in the time period t respectively.

[0161] In this solution, 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 optimal optimization result.

[0162] 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, if only a single vehicle is modeled, its complexity will increase the difficulty of integrated power system scheduling. The VB model (virtual battery model) is now used to describe cluster hydrogen fuel cell vehicles, and the charging and discharging behaviors of the vehicle are described as multiple key parameters to reduce the solution time of the robust optimization algorithm. Therefore, in the charging stage, the required hydrogen energy is converted into electricity for calculation, and VB models (virtual battery models) of single hydrogen fuel cell vehicles and cluster hydrogen fuel cell vehicles are constructed to generate a data set of hydrogen fuel cell vehicle electricity, providing a data basis for constructing the objective function.

[0163] In a specific embodiment, the optimization goal is to maximize the economic benefits of the operation of the integrated power system. An objective function is constructed based on the robust model and the VB model. The objective function is solved. Under the conditions of considering the power generation benefits and power load of the integrated power system and the electricity costs of the equipment involved in the scheduling, the scheduling method of hydrogen fuel cell vehicles and the integrated power system when the economic benefits of the operation of the integrated power system are maximized is determined as follows:

[0164] S31. Generate a data set of the power of cluster hydrogen fuel cell vehicles based on the cluster hydrogen fuel cell vehicle VB model:

[0165] S311: Obtaining an initial power within the power boundary of the cluster hydrogen fuel cell vehicles, typically taking the middle value of the boundary data;

[0166] S312: Calculate the and Generate time series datasets;

[0167] S32. Forecast output data of wind power and photovoltaic power:

[0168] S321: Collect historical wind power and photovoltaic output data, clean the data, extract key features of the data, perform feature transformation on the data, and form a data set; divide the data set into a training set, a test set, and a validation set;

[0169] S322: Select a pre-trained model, including but not limited to a machine learning algorithm and a deep learning method, input the training set into the training model for training to form a prediction model, evaluate and optimize the model using a test set and a validation set, and obtain predicted wind power and photovoltaic output data;

[0170] S33. Based on the robust model, VB model, wind power, photovoltaic power output data and cluster hydrogen fuel cell vehicle power data set, the objective function is constructed as shown in formulas (62)-(64).

[0171]

[0172]

[0173] Where C E The economic benefits generated by the calculated comprehensive power system operation, is the output power of the integrated power system at time t, is the load power of the integrated power system at time t, P user,t is the load of the electricity user in the user client at time t, C d,t is the real-time electricity price, C a,t is the electricity market benefits of different power generation methods; P WP,j,t represents the output forecast of the jth wind power or photovoltaic field at time t, P G,k,t represents the output of the kth conventional thermal power unit at time t, P EG,t P represents the power purchased from the external network at time t. TES,l,t represents the power consumption of the lth electric heater at time t; P H,m,t represents the power consumption of the mth electrolytic cell at time t; N represents the power of cluster hydrogen fuel cell vehicles in time period t, WP , N G , N TES , N H They represent the number of wind power and photovoltaic power units, conventional thermal power units, electric heaters and electrolyzers participating in the integrated power system dispatch; Δt represents the sampling time period;

[0174] S34. The dispatch flexibility of the integrated power system is reflected in the system's ability to cope with changes in the system's source and load. Using the concept of power balance, the dispatch constraints of the integrated power system are set to achieve a dynamic balance between output and load power. The constraints are as follows:

[0175] Construct the electric power balance constraint as shown in formula (65),

[0176]

[0177] Where M is the flexibility margin;

[0178] Construct the processing constraints of conventional thermal power units, as shown in formula (66),

[0179]

[0180] Where, are the lower and upper limits of the output of the kth conventional thermal power unit at time t respectively;

[0181] Construct the ramp rate constraint of conventional thermal power units, as shown in formulas (67) and (68),

[0182] -R u,k Δt≤P k,t -P k,t-1 ≤R u,k Δt (67)

[0183] -R d,k Δt≤P k,t -P k,t-1 ≤R d,k Δt (68)

[0184] Where R u,k 、R d,k are the maximum and minimum ramp rates of the kth conventional thermal power unit output, P k,t-1 、P k,t represent the output of the kth conventional thermal power unit at time t-1 and time t respectively;

[0185] Construct the output constraints of wind power and photovoltaic power, as shown in formula (69),

[0186]

[0187] Where, are the lower and upper limits of the output forecast of the j-th wind farm or photovoltaic farm at time t, are the lower and upper limits of the output of the j-th wind power or photovoltaic field at time t, respectively. WP,j,t is the output of the j-th wind farm or photovoltaic farm at time t;

[0188] Construct the electric power constraint of cluster hydrogen fuel cell vehicles, as shown in formula (70),

[0189]

[0190] Where, They represent the minimum and maximum power of charging and discharging of the i-th hydrogen fuel cell vehicle, is the electric power consumed by the nth hydrogen fuel cell vehicle at time t;

[0191] Construct the output constraint of the electrolyzer, as shown in formula (71),

[0192]

[0193] Where, are the lower and upper limits of the output of the mth electrolytic cell at time t; P H,m,t is the power consumption of the mth electrolytic cell at time t;

[0194] Construct the output constraint of the heat storage device, as shown in formula (72),

[0195]

[0196] Where, P TES,l,t is the power consumption of the lth electric heater at time t, are the lower and upper limits of the allowable output of the lth heat storage device at time t respectively;

[0197] Construct the constraints for purchasing electricity from the external network, as shown in formula (73),

[0198]

[0199] Where, is the power of electricity purchased from the external network at time t, is the upper limit of the power purchased from the external network at time t;

[0200] Construct the power constraint of the hydrogen storage tank, as shown in formula (74),

[0201]

[0202] Where, E sto,t represents the chemical energy of hydrogen in the hydrogen storage tank at time t, V is the total volume of hydrogen storage tanks loaded on n hydrogen fuel cell vehicles, is the density of hydrogen under standard conditions, is the calorific value of hydrogen;

[0203] S35. Using simulation software, input the objective function and constraints into the simulation software for calculation, generate a scheduling curve, and use the signal network to schedule each device in the entire system according to the scheduling curve;

[0204] Calculate the economic benefits based on the current scheduling, analyze the time period when the economic benefits decrease, generate an economic benefit characteristic curve, determine the adjustable time distribution and capacity based on the scheduling situation, and schedule hydrogen fuel cell vehicles at the maximum capacity within the adjustable time distribution.

[0205] In this solution, the actual, maximum, and minimum values ​​of the power and electricity of the VB model and cluster hydrogen fuel cell vehicles are simulated and verified. The specific calculation and analysis are as follows:

[0206] Use simulation software and its solver to solve the example. The calculation process is as follows: Figure 4 As shown:

[0207] First, set the simulation verification parameters, set the time interval to 15 minutes, and divide the whole day into 96 time periods.

[0208] Taking the power system in a certain area of ​​western Liaoning as an example, the output and load conditions of the integrated power system are set, such as the rated capacity and number of conventional thermal power generating units, the rated parameters and number of cluster hydrogen fuel cell vehicles and heat storage equipment, etc., and the real-time electricity price C of the local power supply company is used to calculate the power consumption of the integrated power system. a,t Formulate the power market situation, and the real-time electricity price curve is as follows Figure 5 As shown;

[0209] Secondly, a data-driven approach is used to pre-process and predict wind and photovoltaic power output data, reducing the impact of the deviation between real-time scheduling and day-ahead scheduling on the calculation of economic benefits. The fluctuating power output data curves of wind and photovoltaic power are as follows: Figure 6 As shown, the load forecast data curve of the power user in the user client is as follows Figure 7 As shown;

[0210] Thirdly, the validity of the VB model applied to cluster hydrogen fuel cell vehicles is verified. Under the conditions of the conventional model obtained by equations (51)-(53) and the VB model obtained by equations (59)-(61), a data set of hydrogen fuel cell vehicle power is generated according to the VB model. The objective function is compared and verified to determine whether the simulation results are consistent.

[0211] According to equations (45)-(61), the actual, maximum, and minimum values ​​of the power and electricity of the cluster hydrogen fuel cell vehicles are simulated and verified to verify whether the power boundary and electricity boundary in the VB model are consistent with the actual cluster behavior. If not, the boundary data is redefined;

[0212] Thirdly, based on the objective function calculated by the simulation software and its solver, a dispatch curve is generated according to the power conditions of the thermal power generating units, electrolyzers, electricity purchased from the external grid, heat storage equipment, and hydrogen fuel cells. The signal network is used to perform dispatch according to the dispatch curve.

[0213] The economic benefits are calculated based on the scheduling situation, and the time periods when the 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 dispatched at the maximum capacity to improve economic benefits.

[0214] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements 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 dispatch optimization method for clustered hydrogen fuel cell vehicles and integrated power 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 cluster hydrogen fuel cell vehicles to participate in the integrated power system dispatch. The energy network includes the electric energy network, thermal energy network, hydrogen energy network and signal network, which are used to refer to hydrogen fuel cell vehicles and various devices in the integrated power system. S2: Based on the energy network, a robust model for clustered hydrogen fuel cell vehicles connected to the integrated power system is constructed. Based on the charging and discharging behaviors of hydrogen fuel cell vehicles, a VB model of hydrogen fuel cell vehicles is constructed. The robust model is used to describe the energy transfer relationship between various devices in the electric energy network, thermal energy network, and hydrogen energy network; S3: Taking the highest economic benefit of the operation of the integrated power system as the optimization goal, construct an objective function based on the robust model and the VB model, solve the objective function, and determine the scheduling method of hydrogen fuel cell vehicles and the integrated power system when the economic benefit of the operation of the integrated power system is the highest, taking into account the power generation efficiency and power load of the integrated power system and the electricity cost of the equipment involved in the scheduling.

2. The robust scheduling optimization method for cluster hydrogen fuel cell vehicles and integrated power systems according to claim 1 is characterized in that: The robust model includes a heat relationship model and a power relationship model based on the thermal energy network and a power relationship model based on the electric energy network and the hydrogen energy network. The VB model of the hydrogen fuel cell vehicle includes the VB model of a single hydrogen fuel cell vehicle and a cluster hydrogen fuel cell vehicle.

3. The robust scheduling optimization method for cluster hydrogen fuel cell vehicles and integrated power systems according to claim 2 is characterized in that: The electric energy network includes wind turbines, photovoltaic batteries, hydrogen fuel cells, lithium batteries and user clients; the thermal energy network includes electric heaters and heat storage tanks; the hydrogen energy network includes electrolyzers; and the signal network includes an integrated power system dispatching and control center and a hydrogen fuel cell vehicle controller.

4. The robust scheduling optimization method for cluster hydrogen fuel cell vehicles and integrated power systems according to claim 3 is characterized in that: The heat relationship model and power relationship model built based on the thermal energy network include: Based on the relationship between thermal 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 takes into account the heat lost to the environment by the heat storage tank. The calculated heat supply of the electric heater and the heat lost to the environment by the heat storage tank are shown in formulas (1) and (2). Where, are the heat supplied by the lth electric heater at time t and the heat lost to the environment by the lth heat storage tank, m l is the mass of the heat storage medium in the lth heat storage tank, c is the specific heat capacity of the heat storage medium, are the temperatures of the heat storage medium in the lth heat storage tank at time t and t-1 respectively, are the ambient temperatures of the lth heat storage tank at time t and t-1 respectively, and U is the heat transfer coefficient of the heat storage tank, in W / (m 2 K), A l is the surface area of ​​the heat storage tank; The power relationship model of the electric heater is constructed based on the heat supplied by the electric heater, that is, the power consumption of the electric heater is calculated, as shown in formula (3): Where, P TES,l,t is the power consumption of the lth electric heater at time t, and Δt is the sampling time period.

5. The robust dispatch optimization method for cluster hydrogen fuel cell vehicles and integrated power systems according to claim 3 is characterized in that: The power relationship model built based on the electric energy grid and the hydrogen energy grid includes: Based on the relationship between hydrogen energy utilization, a power relationship model of the electrolyzer-hydrogen storage tank-hydrogen fuel cell is constructed, that is, the power consumption of the electrolyzer, the chemical energy of hydrogen in the hydrogen storage tank, and the power generation and power consumption of the hydrogen fuel cell are calculated; The calculation formula of the electric power consumption of the electrolytic cell is shown in (4). Where, P H,m,t is the power consumption of the mth electrolytic cell at time t, n m,t is the hydrogen production rate of the mth electrolyzer at time t, is the calorific value of hydrogen, η ecc is the efficiency of the electrolyzer; The hydrogen storage tank stores the hydrogen generated by the electrolysis of water in the electrolyzer. The chemical energy of the hydrogen generated by the hydrogen storage tank is shown in formula (5): Where, E sto,t 、E sto,t-1 are the chemical energy of hydrogen in the hydrogen storage tank at time t and t-1, is the total chemical energy of hydrogen produced by the electrolyzer at time t, is the power generation of all hydrogen fuel cell vehicles at time t; n t is the mass of hydrogen produced by the electrolyzer at time t; The calculation formula for the power generation of a hydrogen fuel cell is shown in (6). Where, is the power generation of the hydrogen fuel cell installed in the nth hydrogen fuel cell vehicle at time t, m n,t is the hydrogen consumption rate of the nth hydrogen fuel cell vehicle at time t, η fc The efficiency of hydrogen-to-electricity conversion in hydrogen fuel cells; The electric power consumed by the hydrogen fuel cell is obtained based on the power generated by the hydrogen fuel cell, that is, the electric power, as shown in formula (7): Where, is the electric power consumed by the nth hydrogen fuel cell vehicle at time t, λ t is the road condition coefficient of the hydrogen fuel cell vehicle's driving route.

6. The robust dispatch optimization method for cluster hydrogen fuel cell vehicles and integrated power systems according to claim 2, characterized in that: VB models for single and cluster hydrogen fuel cell vehicles, including: Construct the power boundary of a single hydrogen fuel cell vehicle, as shown in formulas (8)-(11): Where, and They represent the maximum, minimum and expected power of the i-th hydrogen fuel cell vehicle during the time it is connected to the grid, t∈(T a ,T o ), T a and T o They represent the time when hydrogen fuel cell vehicles connect to and leave the power grid, and They represent the minimum power of the i-th hydrogen fuel cell vehicle before and after leaving the grid, and They represent the maximum and minimum power of charging and discharging of the i-th hydrogen fuel cell vehicle, and They represent the minimum allowed power of the i-th hydrogen fuel cell vehicle and the power when connected to the grid, and Δt represents the sampling time period; Based on the power 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). Where, Respectively represent the maximum and minimum power of the i-th hydrogen fuel cell vehicle; t∈(T a ,T0-1), T0-1 is the moment before leaving the grid; Based on the power boundary description of hydrogen fuel cell vehicles, a VB model of a single hydrogen fuel cell vehicle is constructed, as shown in formulas (14)-(16). Where, is the power of the i-th hydrogen fuel cell vehicle in time period t, They represent the electric energy of the i-th hydrogen fuel cell vehicle in time period t and t+1 respectively; By summing the power and electricity of a single hydrogen fuel cell vehicle, we can obtain the power and electricity boundaries of a cluster of hydrogen fuel cell vehicles, as shown in formulas (17)-(20). Where, They represent the upper and lower limits of the electricity and power of the cluster hydrogen fuel cell vehicles in time period t, N F is the number of vehicles in the cluster of hydrogen fuel cell vehicles; The connection state of cluster hydrogen fuel cell vehicles connecting to and leaving the grid is quantified as a state coefficient, and the power change is determined according to the state coefficient, as shown in formula (21): Where, W is the power change of the VB model caused by the cluster hydrogen fuel cell vehicles connecting to and leaving the grid. i,t+1 、W i,t are the state coefficients of the cluster hydrogen fuel cell vehicles at time t and t+1, respectively. When the cluster hydrogen fuel cell vehicles are connected to the grid, the state coefficient is 1, and when the cluster hydrogen fuel cell vehicles are off the grid, the state coefficient is 0; Based on the VB model of a single hydrogen fuel cell vehicle and the power and electricity boundaries of a cluster of hydrogen fuel cell vehicles, a cluster hydrogen fuel cell vehicle VB model is constructed, as shown in formulas (22)-(24). Where, They represent the power and electricity of the cluster hydrogen fuel cell vehicles in the time period t respectively.

7. The robust dispatch optimization method for cluster hydrogen fuel cell vehicles and integrated power systems according to claim 6, characterized in that: Taking the highest economic benefit of the integrated power system operation as the optimization goal, an objective function is constructed based on the robust model and the VB model, including: S31. Generate a data set of power of cluster hydrogen fuel cell vehicles according to the cluster hydrogen fuel cell vehicle VB model; S32. Predict wind power and photovoltaic output data based on data-driven methods; S33. Based on the robust model, VB model, wind power, photovoltaic power output data and cluster hydrogen fuel cell vehicle power data set, the objective function is constructed as shown in formulas (25)-(27). Where C E The economic benefits generated by the calculated comprehensive power system operation, is the output power of the integrated power system at time t, is the load power of the integrated power system at time t, P user,t is the load of the electricity user in the user client at time t, C d,t is the real-time electricity price, C a,t is the electricity market benefits of different power generation methods; P WP,j,t represents the output forecast of the jth wind power or photovoltaic field at time t, P G,k,t represents the output of the kth conventional thermal power unit at time t, P EG,t P represents the power purchased from the external network at time t. TES,l,t represents the power consumption of the lth electric heater at time t; P H,m,t represents the power consumption of the mth electrolytic cell at time t; N represents the power of cluster hydrogen fuel cell vehicles in time period t, WP , N G , N TES , N H They represent the number of wind power and photovoltaic power units, conventional thermal power units, electric heaters and electrolyzers participating in the integrated power system dispatch; Δt represents the sampling time period; S34. Using the idea of ​​power balance, set the dispatch constraints of the integrated power system to achieve a dynamic balance between output and load power. The constraints are as follows: Construct the electric power balance constraint as shown in formula (28), Where M is the flexibility margin; Construct the processing constraints of conventional thermal power units, as shown in formula (29), Where, are the lower and upper limits of the output of the kth conventional thermal power unit at time t respectively; Construct the ramp rate constraint of conventional thermal power units, as shown in formulas (30) and (31), -R u,k Δt≤P k,t -P k,t-1 ≤R u,k Δt (30) -R d,k Δt≤P k,t -P k,t-1 ≤R d,k Δt (31) Where R u,k 、R d,k are the maximum and minimum ramp rates of the kth conventional thermal power unit output, P k,t-1 、P k,t represent the output of the kth conventional thermal power unit at time t-1 and time t respectively; Construct the output constraints of wind power and photovoltaic power, as shown in formula (32), Where, are the lower and upper limits of the output forecast of the j-th wind farm or photovoltaic farm at time t, are the lower and upper limits of the allowable output of the j-th wind power or photovoltaic field at time t, respectively. WP,j,t is the output of the j-th wind farm or photovoltaic farm at time t; Construct the electric power constraint of cluster hydrogen fuel cell vehicles, as shown in formula (33), Where, They represent the minimum and maximum power of charging and discharging of the i-th hydrogen fuel cell vehicle, is the electric power consumed by the nth hydrogen fuel cell vehicle at time t; Construct the output constraint of the electrolyzer, as shown in formula (34), Where, are the lower and upper limits of the output of the mth electrolytic cell at time t; P H,m,t is the power consumption of the mth electrolytic cell at time t; Construct the output constraint of the heat storage equipment, as shown in formula (35), Where, P TES,l,t is the power consumption of the lth electric heater at time t, are the lower and upper limits of the allowable output of the lth heat storage device at time t respectively; Construct the constraints for purchasing electricity from the external network, as shown in formula (36), Where, is the power of electricity purchased from the external network at time t, is the upper limit of the power purchased from the external network at time t; Construct the power constraint of the hydrogen storage tank, as shown in formula (37), Where, E sto,t represents the chemical energy of hydrogen in the hydrogen storage tank at time t, V is the total volume of hydrogen storage tanks loaded on n hydrogen fuel cell vehicles, is the density of hydrogen under standard conditions, is the calorific value of hydrogen.

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