Hydrogen fuel cell vehicle dispatching method based on the coupling of transportation network and power system

Abstract

The present invention discloses a method for dispatching hydrogen fuel cell vehicles based on the coupling of transportation networks and power systems, which specifically relates to the field of power system planning and operation. The model takes into account the hydrogenation behavior of hydrogen fuel cell vehicles, the congestion of the transportation network, the output of microgrid units, and the balance of the distribution network load. By establishing a comprehensive evaluation index, the hydrogen price is dynamically adjusted to alleviate the congestion of the transportation network and reduce network losses. The output of the microgrid units is optimized, the operating cost is reduced, and the load fluctuation of the distribution network is smoothed. The Bayesian algorithm is used to find the optimal hydrogen price combination scheme to improve the efficiency of model solution. The present invention can provide a complex scenario that takes into account the coupling of the transportation network and the power system, optimize the driving cost of the transportation network, adjust the output of the microgrid units, and improve the balance of the distribution network load, thereby providing a method for accelerating the construction of a new power system under the "dual carbon" goal, and has a certain engineering use value.

Description

Technical Field

[0001] The present invention relates to the field of power system planning and operation, and in particular to a hydrogen fuel cell vehicle scheduling method based on the coupling of a transportation network and a power system. Background Art

[0002] With the continuous growth of global energy demand and the increasing need to transform traditional energy into renewable clean energy, microgrids as an emerging renewable energy solution have received more and more attention. At present, hydrogen fuel cell vehicles have greater advantages in replacing gasoline vehicles and reducing carbon dioxide emissions. However, in terms of fuel replacement, the high initial investment cost of hydrogen refueling stations and the imperfect hydrogen supply chain have limited the development of fuel cell vehicles. Summary of the invention

[0003] Therefore, the present invention proposes a microgrid that introduces hydrogen energy, and uses it as a hydrogen refueling station for fuel vehicles to refuel. And the coupling effect of the transportation network and the power system is optimized by adjusting the hydrogen price. First, a comprehensive transportation evaluation strategy is proposed, and an intelligent microgrid is deployed to receive information from the transportation network. Subsequently, a hydrogen price adjustment strategy is formulated on this basis and fed back to the transportation network. Hydrogen fuel cell vehicles are affected by the hydrogen price adjustment and go to different microgrids for hydrogen refueling, thereby adjusting the unit output of each microgrid in the system. Finally, the microgrid is connected to the IEEE 33-node distribution network, and the load balance of the distribution network is analyzed within a day. After this optimization and adjustment, the congestion of the transportation network can be alleviated, the cost of the microgrid can be reduced, and the load balance of the distribution network can be optimized.

[0004] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a hydrogen fuel cell vehicle scheduling method based on the coupling of transportation network and power system, the scheduling method uses a comprehensive transportation evaluation model for hydrogenation of fuel vehicles, and optimizes the coupling effect of transportation network and power system by adjusting hydrogen price; the comprehensive transportation evaluation model is based on Hyper-box and Hyper-ellipse space theory, transportation network road service level LOS and Frank-Wolfe flow distribution model; the comprehensive transportation evaluation model design steps are as follows:

[0005] Step 101: Suppose vector ,definition The first upper and lower limits are and ,and The second upper and lower limits are and ; Define vector Among them, Defined as The difference between the first and second values ​​of The corresponding upper and lower limit values and are as follows:

[0006] (1)

[0007] Define the vector , where is the deviation between the actual value and the first value, and is calculated according to Equation (1);

[0008] (2)

[0009] As can be seen from Equation (2), according to the size of will be determined in three different regions of the Hyper-box space; when is between the first value and the second value, its corresponding will fall within the Hyper-box space; when exceeds the second value, will fall outside the Hyper-box space; when does not exceed the first value, = 0; so as to characterize the influence caused by the deviation value in the multi-dimensional case with a scalar value;

[0010] In the two-dimensional case, the edge of the inscribed Hyper-ellipse space is composed of Equation (3):

[0011] (3)

[0012] where: C is a constant;

[0013] When falls on the edge of the Hyper-ellipse space and outside the edge, it satisfies:

[0014] (4)

[0015] Step 102: According to the definitions of the first value and the second value in Step 101, introduce the definition of the traffic network road service level LOS, and redefine the traffic driving indicators into three levels: regard LOS A, LOS B, and LOS C as driving comfort indicators, that is, the flow / capacity ratio is v / c <= 0.7; regard LOS D and LOS E as driving restricted indicators, that is, 0.71 < v / c <= 1.00; regard LOS F as driving congestion indicators, that is, v / c > 1.00. Based on the Frank-Wolfe algorithm, regard the traffic network as a UE model, and according to the first principle of wardrop, the traffic flow allocation problem is described as:

[0016] (5)

[0017] (6)

[0018] In formula (5), is the traffic flow on path k, is the traffic demand on path k, represents the flow rate of section a, represents the time to pass through road section a, r and s indicate the departure point and destination respectively; Formula (6) is a widely used public road bureau function, which is used to describe the relationship between travel time and traffic flow, where: It indicates the fastest time to pass through section a. is the road capacity;

[0019] Establish comprehensive driving indicators for the traffic network: according to the road capacity of any road section , the driving comfort upper limit index , driving restriction upper limit indicator , regarded as the driving restriction value of this road section , congestion value The coefficient of is as follows:

[0020] (7)

[0021] After normalization, the deviation between road flow and driving restriction value It is expressed as:

[0022] (8)

[0023] in: is the traffic volume of section a; in a traffic network with N roads, the deviation of the traffic volume of each road from the driving restriction value It forms an N-dimensional vector space;

[0024] The form of the vector space is converted into a scalar value to reflect the comprehensive driving index of the entire traffic network. By using the approximate method of inscribing the Hyper-ellipse space in the Hyper-box space, the N-dimensional vector space consisting of the part of the road flow exceeding the comfort value is converted into a scalar representing the comprehensive driving index of the system.

[0025] Since the In the traffic driving scenario, the first value and the second value do not involve the lower limit, so only the corresponding upper limit needs to be processed; after normalizing the restricted value and congestion value of each road flow, formula (1) is rewritten as:

[0026] (9)

[0027] in, for The per-unit value in this scenario;

[0028] According to formula (3)(7)(8)(9), the comprehensive evaluation index of road is obtained: :

[0029] (10)

[0030] Combined with the driving comfort upper limit index α, driving restriction upper limit index β, congestion value , The system comprehensive driving status is divided into three categories: When , it is in a comfortable driving state; when When , it is in the driving restricted state; when When , it is a traffic congestion state;

[0031] The time periods that need to be optimized are screened out according to the comprehensive traffic assessment model. The hydrogen price adjustment results and the optimized vehicle flow data are used as input parameters of the microgrid optimization model according to the hydrogen price adjustment model. The overall unit output of the microgrid optimization model is optimized, and the load balance optimization of the distribution network is completed.

[0032] Preferably, the steps of constructing the hydrogen price adjustment model are as follows:

[0033] Step 201: Introduce a hydrogen price penalty coefficient to guide hydrogen price adjustment, as follows:

[0034] (11)

[0035] in, is the price elasticity coefficient, i.e. the sensitivity of car owners to changes in hydrogen prices; is the modified hydrogen price, is the hydrogen price before the modification; It indicates that the price of hydrogen will increase, thus leading to a reduction in road traffic; It means that the price of hydrogen will be reduced, thus leading to an increase in road traffic volume;

[0036] The hydrogen price penalty coefficient Applying this to the BPR function, modify formula (6) as follows:

[0037] (12)

[0038] Combining formulas (5) and (12), we can get the optimized traffic flow of each road in the traffic network;

[0039] Step 202: Use the Bayesian optimization algorithm to find the optimal hydrogen price penalty coefficient and formulate a plan to define

[0040] (13)

[0041] in, is a complex traffic flow allocation function. The goal of Bayesian optimization is to minimize this modified objective function , is the combination of hydrogen price penalty coefficients of each microgrid;

[0042] During the optimization process, considering that the hydrogen refueling price needs to be within a certain range and the total hydrogen filling level of each hydrogen refueling station cannot be too low, the sum of the parameters is subject to the following constraints:

[0043] (14)

[0044] And the value of each γ parameter is in the interval [0.5, 1.5]:

[0045] (15)

[0046] The traffic distribution model after hydrogen price adjustment is used to obtain the traffic flow distribution and hydrogen price within a day. According to formula (11), the hydrogen price penalty term is used to invert the hydrogen price required for time period t. Right now:

[0047] (16)

[0048] The 24h hydrogen price is combined As the input parameters of the microgrid optimization model, the microgrid units are then optimized within one day.

[0049] Preferably, adjusting the input parameters of the microgrid optimization model to construct a new microgrid optimization model;

[0050] Step 301: Establish a microgrid unit model including a hydrogen energy system consisting of a fuel cell, an electrolyzer, and a hydrogen energy storage, and introduce a waste heat recovery system to increase the electric-thermal coupling pathway of the microgrid through waste heat recovery of the fuel cell and the electrolyzer; the supply side includes a photovoltaic generator set, a wind generator set, a gas turbine, a distribution network, and a gas network; the demand side includes an electric load, a heat load, a gas load, and a hydrogen load;

[0051] The dispatching goal of the microgrid is to minimize the daily operating cost , as shown in formula (17):

[0052] (17)

[0053] In the formula, are the daily operation and maintenance cost of microgrid m, the cost of wind and solar power abandonment, the cost of purchasing electricity and gas, and the income from hydrogen sales, see equations (18)-(21) for details;

[0054] (18)

[0055] (19)

[0056] (20)

[0057] (twenty one)

[0058] Where: For microgrid m equipment The loss cost per unit output is For microgrid m equipment The loss cost of charging and discharging power; For microgrid m equipment Output at time t; and They are respectively microgrid m devices The charging power and discharging power of and are the actual power outputs of photovoltaic and wind power of microgrid m at time t, respectively; and are the photovoltaic and wind power consumed by microgrid m at time t respectively; and are the penalty costs for wind and solar abandonment respectively; and are the electricity purchase price and gas purchase price of microgrid m at time t, respectively; and are respectively the electricity purchase power and gas purchase power of microgrid m at time t; is the hydrogen selling price of microgrid m at time t, is the hydrogen sales power of microgrid m at time t;

[0059] The microgrid unit model includes microgrid m equipment and microgrid equipment ,equipment Refers to FC, GF and EL, equipment Referring to ES and HS, the microgrid unit model includes the following constraints:

[0060] 1). Equipment operation constraints:

[0061] Equipment operation constraints include equipment operation power constraints and energy storage equipment capacity constraints

[0062] (twenty two)

[0063] (twenty three)

[0064] Where: and Characterize energy storage devices A 0-1 variable for charging and discharging, where 1 means it is charging or discharging; and They are respectively microgrid m energy storage devices The capacity at time t and t-1; and Energy storage devices The minimum and maximum charge factors; and Energy storage devices The charging and discharging efficiency;

[0065] 2). Equipment climbing constraints:

[0066] The output fluctuation of the equipment cannot exceed the upper limit of its climbing capacity;

[0067] (twenty four)

[0068] Where: and They are respectively microgrid m devices Output power at time t+1 and time t; For microgrid m equipment The climbing ability For microgrid m equipment Rated power;

[0069] 3). Power balance constraints:

[0070] There are four types of loads in the microgrid m, namely, electrical load , heat load , Gas load , Hydrogen load , there are four power balance constraints respectively;

[0071] Formula (25) describes the electric power balance constraint of microgrid m:

[0072] (25)

[0073] Formula (26) describes the thermal power balance constraint of microgrid m:

[0074] (26)

[0075] in: and are the waste heat recovery efficiencies of electrolyzer and fuel cell, and They are the power of hydrogen production by electrolyzer and electricity production by fuel cell respectively;

[0076] Formula (27) describes the gas power balance constraint of microgrid m:

[0077] (27)

[0078] Formulas (28)-(30) describe the hydrogen power balance constraints of microgrid m:

[0079] (28)

[0080] (29)

[0081] (30)

[0082] in: For hydrogen energy storage and hydrogen release efficiency, and are 0-1 variables that characterize the operation of the electrolyzer and the fuel cell, respectively. Formula (30) indicates that the electrolyzer and the fuel cell cannot operate at the same time;

[0083] 4). Constraints on curtailment of wind and solar power:

[0084] (31)

[0085] Where: and are the actual power of photovoltaic and wind power generated by microgrid m in time period t, respectively;

[0086] Step 302: All microgrids are coupled with the transportation network to solve and optimize, and then the 24-hour power purchase power of each optimized microgrid is calculated. The data are transmitted to the IEEE 33-node distribution network model for power flow calculation to optimize the load balance.

[0087] The present invention has the following advantages:

[0088] Based on the coupling of the transportation network and the power system, the present invention utilizes the hydrogenation behavior of hydrogen fuel cell vehicles to couple the transportation network, distribution network and microgrid, proposes a comprehensive driving index to evaluate the level of road congestion in the transportation network, and guides the adjustment of hydrogen prices based on the index. By deploying an intelligent microgrid, it interacts with the transportation network for information, accepts the hydrogen price adjustment plan from the transportation network, and interacts with the hydrogen fuel cells in the transportation network for hydrogen energy, and then connects it to the IEEE 33-node distribution network to optimize the load balance. In the complex scenario where the transportation network and the power system are coupled, the present invention can optimize the driving cost of the transportation network, adjust the output of the microgrid units, and improve the load balance of the distribution network, thereby providing ideas and methods for accelerating the construction of a new power system under the "dual carbon" goal, and has certain engineering use value. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 A traffic network flow comparison diagram provided by the present invention;

[0090] Figure 2 A comparison chart of comprehensive evaluation indicators provided by the present invention;

[0091] Figure 3 A comparison chart of the loss time of the traffic network provided by the present invention;

[0092] Figure 4 A diagram showing the output of each unit in the microgrid 1 provided by the present invention;

[0093] Figure 5 The output diagram of each unit of the microgrid 2 provided by the present invention;

[0094] Figure 6 The output diagram of each unit of the microgrid 3 provided by the present invention;

[0095] Figure 7 The output diagram of each unit of the microgrid 4 provided by the present invention;

[0096] Figure 8 Output diagram for reference of microgrid units composed of microgrid 1, microgrid 2, microgrid 3 and microgrid 4 provided by the present invention;

[0097] Fig. 9 A comparison chart of the standard deviation and coefficient of variation of the distribution network load provided by the present invention;

[0098] Fig.10 A flow chart of the hydrogen fuel cell vehicle scheduling method based on the coupling of the transportation network and the power system provided by the present invention. DETAILED DESCRIPTION

[0099] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0100] like Fig.10 The flowchart of the hydrogen fuel cell vehicle scheduling method based on the coupling of the transportation network and the power system provided by the present invention is shown as follows: first, the demand for each time period is input According to the established traffic system model, the traffic flow is distributed for 24 hours within a day to obtain the traffic flow data of each section in the traffic network. , and according to the comprehensive evaluation strategy, the comprehensive driving index of each period is obtained Then, filter out The hydrogen price penalty coefficient of the microgrid section within the period t is adjusted , and then redistribute the traffic flow in the traffic system to obtain the optimized traffic flow data for the t period and hydrogen price .

[0101] The topology diagram of the traffic network before and after optimization is as follows Figure 1 As shown, the road congestion level is divided into four categories in the legend, where 0~0.7 corresponds to the driving comfort index established above, 0.7~1 corresponds to the driving restriction index above, >1 is the driving congestion index, and >1.5 is some particularly congested roads.

[0102] The comprehensive evaluation indicators before and after optimization are as follows: Figure 2 As shown in the data category, ABCD represent four microgrids, I1 represents before optimization, and I2 represents after optimization. The traffic network loss time is as follows Figure 3 As shown in the figure, case 2.a, 2.b, and 2.c are three different hydrogen price gradient scenarios, and case 3 is a dynamic hydrogen price scenario. The introduction of comprehensive driving indicators and the use of dynamic hydrogen prices have improved the congestion of the traffic network and reduced time loss by 23.64% compared with fixed hydrogen prices.

[0103] Subsequently, the optimized traffic flow data and hydrogen price plan are transmitted to the microgrid, and the microgrid optimizes the output of each unit under the premise of considering the traffic flow and hydrogen price plan within a day. Figure 4-8 As shown. Figure 4-7 The output of various load units (i.e., microgrid 1, microgrid 2, microgrid 3, and microgrid 4) is shown in Figure 1. Figure 8As shown in the figure below, the microgrid has a higher capacity to absorb wind and solar power after the introduction of the hydrogen energy system, and more wind and solar power output is used to produce hydrogen. However, since most vehicles are charged with hydrogen around 8h and 18h, the hydrogen load and gas load are heavier during these two periods.

[0104] Table 1 Cost table of each microgrid under different hydrogen price setting scenarios

[0105]

[0106] The costs of each microgrid are shown in Table 1. Case 1 is a microgrid without the introduction of a hydrogen energy system, cases 2.a, 2.b, and 2.c are three different hydrogen price gradient values, and case 3 is a dynamic hydrogen price. A negative cost means a positive profit on the day. By introducing a hydrogen energy system, the microgrid can cover the unit output cost on the day by selling hydrogen; and better absorb wind and solar output, improving the utilization rate of renewable resources.

[0107] Finally, the microgrid is connected to the IEEE-33 node distribution network, connected to nodes 6, 15, 25, and 33 of the distribution network respectively. The standard deviation and coefficient of variation of the load in the distribution network within a day before and after optimization are shown in Figure 2. Fig. 9 As shown. During peak hours of the traffic network, such as 8h and 18h, when the demand for hydrogen energy is high, the optimization is more obvious. After introducing hydrogen energy and coupling the traffic network, the balance of the distribution network load has been improved to a certain extent. It can be seen that connecting the microgrid to the distribution network node improves the load distribution of the distribution network and reduces the standard deviation of the average load of the distribution network by 3.4% within a day.

[0108] The above simulation results verify the effectiveness and practicality of the model constructed by the present invention. By formulating a comprehensive traffic assessment strategy, quantitatively describing the degree of traffic congestion, and conducting an overall assessment of the traffic system, a basis can be provided for the microgrid to formulate a hydrogen price plan. By formulating a hydrogen price adjustment strategy, deploying an intelligent interactive microgrid, and combining the actual situation of the traffic network on the day to guide the change in traffic volume, the congestion of the traffic network can be alleviated and the time loss of the traffic network can be reduced. A hydrogen energy system consisting of a fuel cell, an electrolyzer, and hydrogen energy storage is introduced into the microgrid, and it is used as a hydrogen refueling station to supply hydrogen to fuel vehicles. Compared with the traditional integrated energy system, the system takes into account the needs of multiple loads and is coupled with the traffic system, which can promote the coupling of energy management and traffic scheduling. In the scenario where the traffic network and the microgrid are deeply coupled, the microgrid is connected to the distribution network, which improves the load distribution of the distribution network and optimizes the network topology. At the same time, the load balance of the distribution network is improved and the reliability of the system is enhanced. The present invention can optimize the driving cost of the transportation network, adjust the output of the microgrid units, and improve the load balance of the distribution network in a complex scenario where the three networks of transportation network, microgrid, and distribution network are coupled, thereby providing a method for accelerating the construction of a new power system and having certain engineering use value.

[0109] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A hydrogen fuel cell vehicle dispatching method based on the coupling of transportation network and power system, characterized in that: The dispatching method uses a comprehensive traffic evaluation model to refuel fuel vehicles and optimize the coupling effect of the transportation network and the power system through hydrogen price adjustment; the comprehensive traffic evaluation model is based on Hyper-box and Hyper-ellipse space theory, traffic network road service level LOS and Frank-Wolfe flow distribution model; the design steps of the comprehensive traffic evaluation model are as follows: Step 101: Suppose vector ,definition The first upper and lower limits are and ,and The second upper and lower limits are and ; Define vector Among them, Defined as The difference between the first and second values ​​of The corresponding upper and lower limits and for: （1） Defining vectors ,in, is the deviation between the actual value and the first value, calculated according to formula (1); （2） From formula (2), we can see that according to The size will be corresponding Determine the three different areas in the Hyper-box space; when When between the first and second values, the corresponding will fall within the Hyper-box space; when When the second value is exceeded, will fall outside the Hyper-box space; when When the first value is not exceeded, =0; so as to use scalar values ​​to characterize the impact of deviation values ​​in multi-dimensional situations; In the two-dimensional case, the edge of the inscribed Hyper-ellipse space is formed by formula (3): (3) Where: C is a constant; when When it falls on the edge of the Hyper-ellipse space or outside the edge, it satisfies: (4) Step 102: Based on the definition of the first value and the second value in step 101, the definition of the traffic network road service level LOS is introduced, and the traffic driving index is redefined into three levels: LOS A, LOS B, and LOS C are regarded as driving comfort indexes, that is, the flow / capacity ratio is v / c<=0.7; LOS D and LOS E are regarded as driving restriction indexes, that is, 0.71< v / c <= 1.00; LOS F is regarded as a driving congestion index, that is, v / c>1.

00. Based on the Frank-Wolfe algorithm, the traffic network is regarded as a UE model. According to the warrop first principle, the traffic flow allocation problem is described as follows: （5） （6） In formula (5), is the traffic flow on path k, is the traffic demand on path k, represents the flow rate of section a, represents the time to pass through road section a, r and s indicate the departure point and destination respectively; Formula (6) is a widely used public road bureau function, which is used to describe the relationship between travel time and traffic flow, where: It indicates the fastest time to pass through section a. is the road capacity; Establish comprehensive driving indicators for the traffic network: according to the road capacity of any road section , the driving comfort upper limit index , driving restriction upper limit indicator , regarded as the driving restriction value of this road section , congestion value The coefficient of is as follows: （7） After normalization, the deviation between road flow and driving restriction value It is expressed as: （8） in: is the traffic volume of section a; in a traffic network with N roads, the deviation of the traffic volume of each road from the driving restriction value It forms an N-dimensional vector space; The form of the vector space is converted into a scalar value to reflect the comprehensive driving index of the entire traffic network. By using the approximate method of inscribing the Hyper-ellipse space in the Hyper-box space, the N-dimensional vector space consisting of the part of the road flow exceeding the comfort value is converted into a scalar representing the comprehensive driving index of the system. Since the In the traffic driving scenario, the first value and the second value do not involve the lower limit, so only the corresponding upper limit needs to be processed; after normalizing the restricted value and congestion value of each road flow, formula (1) is rewritten as: (9) in, for The per-unit value in this scenario; According to formula (3)(7)(8)(9), the comprehensive evaluation index of road is obtained: : (10) Combined with the driving comfort upper limit index α, driving restriction upper limit index β, congestion value , The system comprehensive driving status is divided into three categories: When , it is in a comfortable driving state; when When , it is in the restricted driving state; when When , it is a traffic congestion state; The time period that needs to be optimized is selected based on the comprehensive traffic evaluation model. The hydrogen price adjustment result and the optimized vehicle flow data are used as the input parameters of the microgrid optimization model according to the hydrogen price adjustment model. The overall unit output of the microgrid optimization model is optimized, and the load balance of the distribution network is optimized. The steps to construct the hydrogen price adjustment model are as follows: Step 1021: Introduce a hydrogen price penalty coefficient to guide hydrogen price adjustment, as follows: （11） in, is the price elasticity coefficient, i.e. the sensitivity of car owners to changes in hydrogen prices; is the modified hydrogen price, is the hydrogen price before the modification; It indicates that the price of hydrogen will increase, thus leading to a decrease in road traffic; It means that the price of hydrogen will be reduced, thus leading to an increase in road traffic volume; The hydrogen price penalty coefficient Applying this to the BPR function, modify formula (6) as follows: （12） Combining formulas (5) and (12), we can get the optimized traffic flow of each road in the traffic network; Step 1022: Use the Bayesian optimization algorithm to find the optimal hydrogen price penalty coefficient and formulate a plan, defining （13） in, is a complex traffic flow allocation function. The goal of Bayesian optimization is to minimize this modified objective function , is the combination of hydrogen price penalty coefficients of each microgrid; During the optimization process, considering that the hydrogen refueling price needs to be within a certain range and the total hydrogen filling level of each hydrogen refueling station cannot be too low, the sum of the parameters is subject to the following constraints: (14) And the value of each γ parameter is in the interval [0.5, 1.5]: (15) The traffic distribution model after hydrogen price adjustment is used to obtain the traffic flow distribution and hydrogen price within a day. According to formula (11), the hydrogen price penalty term is used to invert the hydrogen price required for time period t. Right now: (16) The 24h hydrogen price is combined As the input parameters of the microgrid optimization model, the microgrid units are then optimized within one day.

2. The method for dispatching hydrogen fuel cell vehicles based on the coupling of transportation network and power system according to claim 1 is characterized in that: Adjust the input parameters of the microgrid optimization model and build a new microgrid optimization model; Step 201: Establish a microgrid unit model including a hydrogen energy system consisting of a fuel cell, an electrolyzer, and a hydrogen energy storage, and introduce a waste heat recovery system to increase the electric-thermal coupling pathway of the microgrid through waste heat recovery of the fuel cell and the electrolyzer; the supply side includes a photovoltaic generator set, a wind generator set, a gas turbine, a distribution network, and a gas network; the demand side includes an electric load, a heat load, a gas load, and a hydrogen load; The dispatching goal of the microgrid is to minimize the daily operating cost , as shown in formula (17): (17) In the formula, are the daily operation and maintenance cost of microgrid m, the cost of wind and solar power abandonment, the cost of purchasing electricity and gas, and the income from hydrogen sales, see equations (18)-(21) for details; (18) (19) (20) (21) Where: For microgrid m equipment The loss cost per unit output is For microgrid m equipment The loss cost of charging and discharging power; For microgrid m equipment Output at time t; and They are respectively microgrid m devices The charging power and discharging power of and are the actual power outputs of photovoltaic and wind power of microgrid m at time t, respectively; and are the photovoltaic and wind power consumed by microgrid m at time t respectively; and are the penalty costs for wind and solar abandonment respectively; and are the electricity purchase price and gas purchase price of microgrid m at time t, respectively; and are respectively the electricity purchase power and gas purchase power of microgrid m at time t; is the hydrogen selling price of microgrid m at time t, is the hydrogen sales power of microgrid m at time t; The microgrid unit model includes microgrid m equipment and microgrid equipment ,equipment Refers to FC, GF and EL, equipment Referring to ES and HS, the microgrid unit model includes the following constraints: 1). Equipment operation constraints: Equipment operation constraints include equipment operation power constraints and energy storage equipment capacity constraints (22) (23) Where: and Characterize energy storage devices A 0-1 variable for charging and discharging, where 1 means it is charging or discharging; and They are respectively microgrid m energy storage devices The capacity at time t and t-1; and Energy storage devices The minimum and maximum charge factors; and Energy storage devices The charging and discharging efficiency; 2). Equipment climbing constraints: The output fluctuation of the equipment cannot exceed the upper limit of its climbing capacity; (24) Where: and They are respectively microgrid m devices Output power at time t+1 and time t; For microgrid m equipment The climbing ability For microgrid m equipment Rated power; 3). Power balance constraints: There are four types of loads in the microgrid m, namely, electrical load , heat load , Gas load , Hydrogen load , there are four power balance constraints respectively; Formula (25) describes the electric power balance constraint of microgrid m: (25) Formula (26) describes the thermal power balance constraint of microgrid m: (26) in: and are the waste heat recovery efficiencies of electrolyzer and fuel cell, and They are the power of hydrogen production by electrolyzer and electricity production by fuel cell respectively; Formula (27) describes the gas power balance constraint of microgrid m: (27) Formulas (28)-(30) describe the hydrogen power balance constraints of microgrid m: (28) (29) (30) in: For hydrogen energy storage and hydrogen release efficiency, and are 0-1 variables that characterize the operation of the electrolyzer and the fuel cell, respectively. Formula (30) indicates that the electrolyzer and the fuel cell cannot operate at the same time; 4). Constraints on curtailment of wind and solar power: (31) Where: and are the actual power of photovoltaic and wind power generated by microgrid m in time period t, respectively; Step 202: All microgrids are coupled with the transportation network to solve and optimize, and then the 24-hour power purchase power of each optimized microgrid is calculated. The data are transmitted to the IEEE 33-node distribution network model for power flow calculation to optimize the load balance.

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