Comprehensive energy system optimization scheduling method considering flexible regulation and control of hydrogen for storage and transportation

Abstract

The invention provides a comprehensive energy system optimization scheduling method considering flexible regulation and control of storage and transportation hydrogen, and belongs to the field of power system scheduling. The method comprises the following steps: constructing a hydrogen fuel cell power generation model and a hydrogen storage system and electric energy storage coordinated operation model in a hydrogen production park in an off-grid mode by utilizing water electrolysis hydrogen production of new energy output; establishing a supply-demand relation model including the hydrogen production park, the hydrogen energy demand customer and the charging station based on the hydrogen energy transportation scheduling platform of the hydrogen production park; the method comprises the following steps: constructing an electric hydrogen transport vehicle real-time variation model, an electric hydrogen transport vehicle path planning model, a hydrogen-electricity transfer model, a flexible hydrogen utilization model, and constructing a function which takes the lowest total operation cost of a hydrogen production park as an objective function, and based on flexible hydrogen energy production-storage-transport-utilization links, meeting new energy output fluctuation and ammonia production target capacity, and reducing wind and light abandoning. According to the method, the hydrogen transportation cost of the hydrogen production park in the isolated network operation mode is reduced, and the schedulable potential of the hydrogen production park is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system operation and planning, and relates to an optimization scheduling method for an integrated energy system taking into account flexible regulation, storage and utilization of hydrogen. Background Art

[0002] With the growing global demand for clean energy and the increasing environmental protection requirements, hydrogen energy, as an efficient and clean energy carrier, has received more and more attention and application in the field of transportation. As a key link in the hydrogen energy transportation and utilization system, the operating efficiency and energy management of electric hydrogen transport vehicles have become the focus of research and development.

[0003] In the actual operation scenarios of electric hydrogen transport vehicles, such as in various industrial parks, logistics parks and other areas, electric hydrogen transport vehicles need to shuttle between different nodes (such as hydrogen refueling stations, hydrogen production park points, etc.) to achieve the transportation and distribution of hydrogen while meeting part of the electricity demand in the park. However, the power consumption of electric hydrogen transport vehicles is affected by a variety of complex factors. On the one hand, the overall weight of electric hydrogen transport vehicles includes the vehicle's own weight and the mass of hydrogen carried, and changes with different transportation tasks, which is directly related to the energy consumption level of the vehicle during driving. On the other hand, factors such as vehicle speed fluctuations, differences in ambient temperature, road conditions (such as flatness, slope, etc.) and the driver's driving habits will have a significant impact on the power consumption of electric hydrogen transport vehicles. The traditional vehicle operation management model is difficult to accurately consider the combined effect of these complex factors on power consumption, resulting in low energy utilization efficiency of electric hydrogen transport vehicles during operation, and unable to achieve accurate energy planning and scheduling.

[0004] In the park's energy supply system, the coordinated and complementary supply of hydrogen energy and electric energy is crucial to ensure the stable operation of the park. However, there is currently a lack of effective methods to reasonably plan the driving route of the electric hydrogen transport vehicle according to its operating status and remaining energy situation, and to efficiently dispatch the remaining hydrogen-electric energy to meet the diverse energy needs in the park, such as replenishing electricity for other electrical equipment in the park and replenishing hydrogen reserves for hydrogen refueling stations. The existing energy management system often views hydrogen energy and electric energy in isolation, fails to fully tap the potential of electric hydrogen transport vehicles in the park's hydrogen-electric comprehensive energy management, and is difficult to achieve the smooth and efficient operation of the entire park's energy system and the optimal configuration of energy. Therefore, there is an urgent need for a technical solution that can comprehensively consider the factors affecting the power consumption of electric hydrogen transport vehicles, perform path planning through advanced algorithms, and realize accurate dispatch of hydrogen-electric energy, so as to improve the operating efficiency of electric hydrogen transport vehicles in the park's energy management system and ensure the stable and reliable operation of the park's energy system. Summary of the invention

[0005] The present invention is aimed at hydrogen production parks that contain green electricity hydrogen production and local hydrogen energy consumption. Due to the fluctuation of new energy output, the shortage of hydrogen-electric energy is caused. The invention explores the flexible control capabilities of each link of the hydrogen industry composed of hydrogen production, hydrogen storage, transportation, and hydrogen use, and constructs an optimization scheduling method for a comprehensive energy system that takes into account the flexible control, storage, and use of hydrogen. For the energy, transportation route, transportation time, hydrogen demand customer demand time window, hydrogen demand, road conditions, and other conditions involved in the hydrogen energy delivery process, the planning algorithm is used to iteratively optimize and solve with the goal of minimizing the total transportation cost to reduce the hydrogen transportation cost. Based on this, the remaining power and remaining hydrogen energy of electric hydrogen transport vehicles and mobile hydrogen storage tanks are used to supplement the hydrogen-electric energy for the hydrogen production park by analogy with hydrogen-electric energy storage. Through flexible hydrogen production, flexible hydrogen storage, and flexible control of hydrogen and electricity consumption of hydrogen-ammonia equipment, the hydrogen transportation cost of the hydrogen production park in the isolated grid operation mode is reduced and the dispatchable potential of the park is improved.

[0006] The present invention is implemented by the following technical solutions: A method for optimizing the scheduling of an integrated energy system considering the flexible control, storage and use of hydrogen, the method comprising the following steps:

[0007] Step S1: constructing a hydrogen production park in an off-grid mode that uses renewable energy to electrolyze water to produce hydrogen, a hydrogen fuel cell power generation model, and a hydrogen storage system and electric energy storage coordinated operation model;

[0008] Step S2: establishing a supply and demand relationship model including the hydrogen production park, hydrogen energy demand customers and charging stations based on the hydrogen production park hydrogen energy transportation scheduling platform;

[0009] Step S3: construct a real-time variation model of the electric hydrogen transport vehicle, replace the instantaneous speed, the electric vehicle power consumption, the variation of the hydrogen energy load, and the transportation rate of the electric hydrogen transport vehicle with the average speed, so as to accurately plan the energy consumption, load, and driving time of the electric hydrogen transport vehicle;

[0010] Step S4: constructing a path planning model for an electric hydrogen transport vehicle, and using a genetic algorithm to perform path planning based on a supply-demand relationship model and a real-time variation model of the electric hydrogen transport vehicle;

[0011] Step S5: construct a hydrogen-electricity transfer model, and perform hydrogen-electricity energy conversion through the park charging piles and hydrogen storage system based on the number of electric hydrogen transport vehicles, transportation time, transportation roads, remaining power of hydrogen transport vehicles, and remaining hydrogen energy results;

[0012] Step S6: construct a flexible hydrogen use model, based on the flexible control characteristics of hydrogen-to-ammonia equipment, to achieve flexible production under a certain ammonia output to meet the power fluctuation and hydrogen energy balance of the park;

[0013] Step S7: Construct an objective function with the lowest total operating cost of the hydrogen production park as the objective function, based on the flexible production-storage-transportation-use of hydrogen energy, to meet the fluctuation of new energy output and the target capacity of ammonia production, and reduce the abandonment of wind and solar power.

[0014] Further preferably, step S1 constructs a water electrolysis hydrogen production model using new energy output, a hydrogen fuel cell power generation model, and a hydrogen storage system and electric energy storage coordinated operation model, including: an electrolyzer water electrolysis hydrogen production model with the park's new energy output, a hydrogen fuel cell model, electric energy storage, and a mathematical model of fixed hydrogen energy storage equipment.

[0015] Further preferably, in step S2, based on the hydrogen energy transportation scheduling platform of the hydrogen production park, relevant data such as the demand, geographical location, demand time window and geographical location of charging stations in the area of ​​hydrogen energy demand customers are clarified, and a supply and demand relationship model including the hydrogen production park, hydrogen energy demand customers and charging stations is established.

[0016] Further optimization is performed to facilitate the subsequent use of genetic algorithms for route planning of electric hydrogen transport vehicles. According to the number of vehicles given by the hydrogen production park, the departure time of each vehicle is randomly generated within the operating time window of the hydrogen energy transportation scheduling platform of the hydrogen production park, and node constraints for the electric hydrogen transport vehicles to and from the hydrogen production park, hydrogen energy demand customers, and charging stations during the transportation cycle are constructed.

[0017] More preferably, in step S3, a section of the route traveled by the electric hydrogen transport vehicle is further decomposed into a uniform acceleration stage, a uniform speed travel stage, and a uniform deceleration braking stage, so as to construct a real-time variation model.

[0018] Step S4 includes the following sub-steps:

[0019] Step S41: Hydrogen is delivered through electric hydrogen transport vehicles, and a single distribution center and multiple distribution locations transportation mode is constructed with the hydrogen production park as the starting point and each hydrogen user as the transportation point. Considering the power consumption of electric hydrogen transport vehicles, electric hydrogen transport vehicles are allowed to choose charging stations to charge due to long routes and then continue to complete the transportation task, and the power of electric hydrogen transport vehicles is monitored in real time. Based on this, a path planning model for electric hydrogen transport vehicles is constructed, and a genetic algorithm is used to solve it, and the objective function is constructed;

[0020] Step S42: Based on steps S1 and S2, the hydrogen transportation route, power consumption, remaining hydrogen energy, and transportation time constraints in the transportation plan are constructed to lay the foundation for planning using genetic algorithms.

[0021] In step S5, the number of electric hydrogen transport vehicles participating in the hydrogen transport task can be determined based on the hydrogen transport scheduling platform of the hydrogen production park, which can be further divided into two parts: one part participates in the transportation scheduling task to meet the needs of hydrogen demand customers; the other part is the electric hydrogen transport vehicles that do not participate in the transportation task and stay in the hydrogen production park, thus constructing a full-time scale hydrogen-electricity scheduling model.

[0022] The present invention provides a terminal, which includes a memory, a processor and a computer program stored in the memory. When the computer program is executed by the processor, it implements the various steps of the above-mentioned method for optimizing scheduling of an integrated energy system that takes into account flexible control, storage and utilization of hydrogen.

[0023] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the various steps of the above-mentioned method for optimizing scheduling of an integrated energy system that takes into account flexible control, storage and use of hydrogen.

[0024] Advantages of the present invention: In order to address the problem of power shortage in hydrogen production parks due to fluctuations in new energy output and insufficient adjustable potential in green electricity hydrogen production parks, the present invention conducts detailed modeling of the power consumption of hydrogen transport vehicles based on the energy, transportation routes, transportation time, hydrogen demand customer demand time window, hydrogen demand, road conditions and other conditions involved in the hydrogen energy delivery process, and constructs a real-time change model of electric hydrogen transport vehicles. Based on this, the remaining power and remaining hydrogen energy of electric vehicles and hydrogen storage tanks can be used to supplement hydrogen-electric energy for hydrogen production parks by analogy with hydrogen-electric energy storage. Through flexible hydrogen production, flexible hydrogen storage and flexible regulation of hydrogen and power consumption of hydrogen-to-ammonia equipment, the hydrogen transportation cost of hydrogen production parks in isolated grid operation mode is reduced and the dispatchable potential of the parks is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 This is a schematic diagram of the flow of hydrogen energy and electricity in the park.

[0026] Figure 2 Schematic diagram of the hydrogen-electricity transfer scheduling process based on electric hydrogen transport vehicles.

[0027] Figure 3 Schematic diagram of hydrogen-electricity transfer between electric hydrogen transport vehicles and the park. DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in 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. 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.

[0029] See also Figure 1 , Figure 2 and Figure 3 The present invention aims at the hydrogen production park with green electricity hydrogen production and on-site hydrogen energy consumption, and proposes a comprehensive energy system optimization scheduling method considering flexible regulation, storage and utilization of hydrogen, which is caused by the shortage of hydrogen-electricity energy due to the fluctuation of new energy output. The method includes the following steps:

[0030] Step S1: constructing a hydrogen production park in an off-grid mode that uses renewable energy to electrolyze water to produce hydrogen, a hydrogen fuel cell power generation model, and a hydrogen storage system and electric energy storage coordinated operation model;

[0031] Step S2: establishing a supply and demand relationship model including the hydrogen production park, hydrogen energy demand customers and charging stations based on the hydrogen production park hydrogen energy transportation scheduling platform;

[0032] Step S3: construct a real-time variation model of the electric hydrogen transport vehicle, replace the instantaneous speed, the electric vehicle power consumption, the variation of the hydrogen energy load, and the transportation rate of the electric hydrogen transport vehicle with the average speed, so as to accurately plan the energy consumption, load, and driving time of the electric hydrogen transport vehicle;

[0033] Step S4: constructing a path planning model for an electric hydrogen transport vehicle, and using a genetic algorithm to perform path planning based on a supply-demand relationship model and a real-time variation model of the electric hydrogen transport vehicle;

[0034] Step S5: construct a hydrogen-electricity transfer model, and perform hydrogen-electricity energy conversion through the park charging piles and hydrogen storage system based on the number of electric hydrogen transport vehicles, transportation time, transportation roads, remaining power of hydrogen transport vehicles, and remaining hydrogen energy results;

[0035] Step S6: construct a flexible hydrogen use model, based on the flexible control characteristics of hydrogen-to-ammonia equipment, to achieve flexible production under a certain ammonia output to meet the power fluctuation and hydrogen energy balance of the park;

[0036] Step S7: Construct an objective function with the lowest total operating cost of the hydrogen production park as the objective function, based on the flexible production-storage-transportation-use of hydrogen energy, to meet the fluctuation of new energy output and the target capacity of ammonia production, and reduce the abandonment of wind and solar power.

[0037] The output power of new energy is partly consumed by the electrolyzer to produce hydrogen, and partly consumed by hydrogen-to-ammonia, rigid loads and electric hydrogen transport vehicles. The produced hydrogen is partly transported to hydrogen-demand customers by electric hydrogen transport vehicles using mobile hydrogen storage tanks, and partly consumed by hydrogen-to-ammonia equipment and hydrogen fuel cells. The electricity generated by hydrogen fuel cells can be fed to the power system of the hydrogen production park. Step S1 constructs a model for using new energy output to electrolyze water to produce hydrogen, a hydrogen fuel cell power generation model, and a coordinated operation model of the hydrogen storage system and electric energy storage. The model is as follows:

[0038] (1) The electrolyzer (EL) produces hydrogen by electrolyzing water with the output of new energy in the park. The model is as follows:

[0039]

[0040] Where: P e,EL The electrical energy input to the electrolyzer for period t; is the hydrogen energy output by the electrolyzer during period t; η EL is the energy conversion efficiency of the electrolyzer; are the upper and lower limits of the electric energy input into the electrolyzer, respectively; They are the upper and lower limits of the electrolytic cell's climbing slope respectively.

[0041] (2) Hydrogen fuel cell model

[0042] Hydrogen fuel cells (HFC) generate electricity through chemical reactions and use the waste heat generated during the power generation process to supply thermal loads. Hydrogen fuel cells with adjustable heat-to-electricity ratio can adjust the power and heat output according to the real-time power and heat energy demand, further optimizing the operating efficiency. Its working model is:

[0043]

[0044] Where: P is the hydrogen energy input into the hydrogen fuel cell during period t; HFC,e (t), P HFC,h (t) are the electricity and heat energy output by the hydrogen fuel cell during each period; are the efficiency of hydrogen fuel cells converting into electricity and heat energy respectively; They are the upper and lower limits of hydrogen energy input into hydrogen fuel cells; They are the upper and lower limits of the climbing capacity of hydrogen fuel cells; They are the upper and lower limits of the thermal-to-electricity ratio of hydrogen fuel cells, respectively.

[0045] The mathematical models of electric energy storage and fixed hydrogen energy storage equipment are similar and can be modeled uniformly. The model is as follows:

[0046]

[0047] Where: are the charging and discharging powers of the nth type of energy storage device during period t respectively; The maximum single charge and discharge power of the nth energy storage device; are binary variables, which are the charging and discharging state parameters of the nth energy storage device in period t. Indicates that it is in charging state, B ES,n cha (t) = 0, B ES,n dis (t) = 1 means in the energy release state; P ES,n (t) is the final output power of the nth energy storage device during period t; are the charging and discharging efficiencies of the nth energy storage device; S n (t) is the capacity of the nth energy storage device in period t, S n (1) is the capacity of the nth energy storage at the initial moment, S n (T) is the capacity of the nth energy storage at the last moment, and T is the total time, which is generally set to 24 hours; is the rated capacity of the nth type of energy storage device; They are the upper and lower limits of the capacity of the nth type of energy storage device. n refers to electric energy storage and fixed hydrogen energy storage equipment respectively.

[0048] In step S2, based on the hydrogen energy transportation dispatching platform of the hydrogen production park, the needs, geographical location, demand time window and geographical location of the charging stations in the area to which the hydrogen energy demand customers belong are clarified, and a supply and demand relationship model including the hydrogen production park, hydrogen energy demand customers and charging stations is established:

[0049] In order to facilitate the subsequent use of genetic algorithms for electric hydrogen transport vehicle route planning, the hydrogen transport scheduling platform of the hydrogen production park adopts natural number coding, sets the numbers of n hydrogen demand customers to 1, 2, 3, ..., n, and sets the numbers of m charging stations to n+1, n+2, ..., n+m. And further, the starting point and end point of the electric hydrogen transport vehicle are respectively inserted into the hydrogen production park code 0. The k electric hydrogen transport vehicles are regarded as a set K = {1, 2, 3, ..., k}, the set of n hydrogen demand customers is N = {1, 2, 3, ..., n}, the set of m charging stations is M = {n+1, n+2, ..., n+m}, and the hydrogen production park is represented by o. The three sets of hydrogen production park, charging station, and hydrogen demand customers can be combined into a large set (full node) L (L = o∪M∪N) during planning. The second part uses real number coding to randomly generate the departure time of each vehicle within the operating time window of the hydrogen energy transportation scheduling platform of the hydrogen production park according to the number of vehicles given by the hydrogen production park. The calculation formula is as follows:

[0050] y=T E +(T E (max)-T E )*rand (4)

[0051] Where: T E Arrange the earliest dispatch time for the hydrogen transportation platform in the hydrogen production park; T L Arrange the latest dispatch time for the hydrogen transportation platform in the hydrogen production park; T E (max) is the latest scheduling time of the hydrogen energy transportation platform in the hydrogen production park; rand is a random number ranging from (0-1).

[0052] Construct node constraints for the transportation of electric hydrogen vehicles between hydrogen production parks, hydrogen demand customers, and charging stations during the transportation cycle:

[0053]

[0054] Formula (5) indicates that all hydrogen demand customers can only be served once in a transportation cycle, x is a binary variable, x ijkThe kth electric hydrogen transport vehicle travels from node i to node j, which is 1, otherwise it is 0; Formula (6) indicates that all electric hydrogen transport vehicles start from the hydrogen production park and return to the hydrogen production park, x is a binary variable, x oik If the kth electric hydrogen transport vehicle travels from node o to node i in the hydrogen production park, it is 1, otherwise it is 0; jok If the kth electric hydrogen transport vehicle returns from node j to node o in the hydrogen production park, it is represented as 1, otherwise it is 0.

[0055] The power consumption of electric hydrogen transport vehicles is affected by multiple factors including vehicle weight, hydrogen weight, vehicle speed, road smoothness, driving habits, etc. Based on this, considering the weight change of electric hydrogen transport vehicles, road smoothness, acceleration, braking, and constant speed driving states, the real-time change model is constructed in step S3 as follows:

[0056] The route traveled by the electric hydrogen transport vehicle is further decomposed into a uniform acceleration phase, a uniform speed driving phase, and a uniform deceleration braking phase. The time required for each phase of the electric hydrogen transport vehicle is:

[0057]

[0058] Formula (7), t1 represents the time the electric hydrogen transport vehicle travels in the acceleration phase, t2 represents the time the electric hydrogen transport vehicle travels in the deceleration and braking phase, t ij is the driving time of the electric hydrogen transport vehicle from point i to point j. d1 is the driving distance in the acceleration phase, d2 is the driving distance in the braking phase, and d ij is the distance between nodes i and j. u1(t) is the initial velocity of the electric hydrogen transport vehicle, u2(t) is the final velocity of the electric hydrogen transport vehicle, u"(t) is the uniform speed of the electric hydrogen transport vehicle, a1(t) represents the acceleration of the electric hydrogen transport vehicle when it starts, and a2(t) represents the acceleration of the electric hydrogen transport vehicle when it brakes.

[0059] The energy consumption expression of the electric hydrogen transport vehicle in each stage of the uniform acceleration stage, uniform speed driving stage, and uniform deceleration braking stage is:

[0060]

[0061] In formula (8), R is the air resistance coefficient, A is the windward area of ​​the electric hydrogen transport vehicle, ρ is the air density, f is the rolling resistance coefficient, G is the weight of the electric hydrogen transport vehicle, and w ij is the total hydrogen capacity transported by the electric hydrogen transport vehicle from point i to point j, i, j∈(0, m+n), θ' is the average slope of the road, g is the acceleration of gravity, φ d Indicates the output efficiency of the drive motor. Battery output efficiency.

[0062] The energy consumption of all stages is:

[0063] b ij =b1+b2+b3 i,j∈(0,m+n) (9)

[0064] In formula (9), b1 represents the electric energy consumed in the acceleration and starting stage of the electric hydrogen transport vehicle, b2 represents the electric energy consumed in the uniform speed driving stage of the electric hydrogen transport vehicle, b3 represents the electric energy consumed in the deceleration and braking stage of the electric hydrogen transport vehicle, and b ij It is the total energy consumption of a single electric hydrogen transport vehicle during the three stages of acceleration, constant speed and braking from node i to node j.

[0065] Step S4 includes the following sub-steps:

[0066] Step S41: Hydrogen is delivered through electric hydrogen transport vehicles, and a single distribution center and multiple distribution locations transportation mode is constructed with the hydrogen production park as the starting point and each hydrogen user as the transportation point. Considering the power consumption of electric hydrogen transport vehicles, electric hydrogen transport vehicles are allowed to choose charging stations to charge due to long routes and then continue to complete the transportation task, and the power of electric hydrogen transport vehicles is monitored in real time. Based on this, a path planning model for electric hydrogen transport vehicles is constructed and solved using a genetic algorithm. The objective function is as follows:

[0067]

[0068] Among them, C0 is the total cost of transporting hydrogen energy by electric hydrogen transport vehicles; C fk is the fixed cost of the electric hydrogen transport vehicle k; C tk is the driving cost of the electric hydrogen transport vehicle k; C mk is the charging cost of the electric hydrogen transport vehicle k; C pk The penalty fee for the early or late arrival of the electric hydrogen transport vehicle k; E, E' mk are the initial capacity of the battery of the electric hydrogen vehicle and the battery capacity of the kth hydrogen vehicle when it enters the m node; c1 is the fixed cost of the electric hydrogen vehicle driving once, c2 is the unit driving cost of the electric hydrogen vehicle, c3 is the unit charging cost of the electric hydrogen vehicle when charging, c4 is the penalty cost of the electric hydrogen vehicle arriving at the hydrogen energy demand customer point too early, and c5 is the penalty cost of the electric hydrogen vehicle arriving at the hydrogen energy demand customer point too late; t' ik is the time when the kth electric hydrogen transport vehicle arrives at node i, e i is the earliest time when node i needs hydrogen energy, l i is the latest hydrogen energy demand time of node i, d ij is the distance between nodes i and j. mk is a binary variable, where the kth electric hydrogen transport vehicle is charged at node m, which is 1, otherwise it is 0, m∈M(n, n+1, ..., m); x ojk It is 1 if the kth electric hydrogen transport vehicle travels from hydrogen production park o to node j, otherwise it is 0.

[0069] Step S42: Based on steps S1 and S2, the hydrogen transportation route, power consumption, remaining hydrogen energy, and transportation time constraints in the transportation plan are constructed to lay the foundation for planning using genetic algorithms. The model is as follows:

[0070] (1) Node access constraints are shown in formula (5)-formula (6).

[0071] (2) Load constraints

[0072] When the electric hydrogen transport vehicle departs from the hydrogen production park, it loads the mobile hydrogen storage tank to deliver hydrogen to hydrogen demand customers. The load of the electric hydrogen transport vehicle to the next hydrogen demand customer point is equal to the total weight at the previous hydrogen demand customer point minus the demand at the previous hydrogen demand customer point. The formula is as follows:

[0073]

[0074] Formula (21) represents the relationship between the weight of the electric hydrogen transport vehicle when it leaves the hydrogen production park and the weight of the electric hydrogen transport vehicle at the node j and the previous node i. ok is the weight of the electric hydrogen transport vehicle when it leaves the hydrogen production park o, W max Indicates the maximum weight of the electric hydrogen transport vehicle, w i is the hydrogen energy demand of node i, w ik is the weight of the kth electric hydrogen transport vehicle arriving at node i (without unloading hydrogen at node i), w jk is the weight of the kth electric hydrogen truck arriving at node j (without unloading hydrogen at node j).

[0075] The remaining hydrogen energy in the mobile hydrogen storage tank is the capacity at the time of departure minus the hydrogen energy demand of the hydrogen energy demanding customer. The model is as follows:

[0076]

[0077] S' k is the capacity of the kth mobile hydrogen storage tank leaving the hydrogen production park; S max is the maximum capacity of the mobile hydrogen storage tank; S k The capacity of the kth mobile hydrogen storage tank returned to the hydrogen production park;

[0078] (5) The power consumption constraint of electric hydrogen transport vehicles is:

[0079] E ok =E (14)

[0080]

[0081] b ij =b1+b2+b3 i,j∈(0,m+n) (16)

[0082] E' jk =E" ik -b ijk x ijk +E(1-x ijk )i,j∈N(1,n)k∈K(1,k) (17)

[0083] E" jk ≥min{b jok x jok ,b jmk x jmk}j∈N(1,n)m∈M(n+1,n+m) (18)

[0084]

[0085] Formula (14) indicates that the kth electric hydrogen transport vehicle is fully charged when it leaves the hydrogen production park; Formula (15) indicates that the kth electric hydrogen transport vehicle does not consume electricity during the process of unloading hydrogen at the hydrogen energy demand customer point; Formula (17) indicates that the power of the kth electric hydrogen transport vehicle when it arrives at node j is equal to the power it leaves node i minus the power consumed during driving; Formula (18) indicates that the power of the kth electric hydrogen transport vehicle leaving node j can meet the power required to return directly from point j to the hydrogen production park or to the nearest charging station; Formula (19) indicates that the power of the kth electric hydrogen transport vehicle when it arrives at node m for charging is the power it leaves node j minus the power consumed on the road; Formula (20) indicates that the kth electric hydrogen transport vehicle is fully charged when it leaves the charging station; Formula (21) indicates the remaining power of the kth electric hydrogen transport vehicle when it returns to the hydrogen production park; E ok is the power consumption of the kth electric hydrogen transport vehicle leaving the hydrogen production park, E' ok The amount of electricity that the electric hydrogen transport vehicle k returns to the hydrogen production park; E' ik is the power of the electric hydrogen transport vehicle k when it arrives at node i, E” ik is the power of the kth electric hydrogen transport vehicle when it leaves the i node; E' jk is the power of the kth electric hydrogen transport vehicle when it arrives at node j, E" jk E' is the power of the kth electric hydrogen transport vehicle when it leaves the jth node; mk is the power of the kth electric hydrogen transport vehicle when it arrives at node m, E" mk is the power of the kth electric hydrogen transport vehicle when it leaves node m; b ijk b is the power consumption of the kth electric hydrogen transport vehicle traveling from node i to node j; jok b is the power consumption of the kth electric hydrogen transport vehicle traveling from node j to the hydrogen production park node; jmk b is the power consumption of the kth electric hydrogen transport vehicle traveling from node j to node m; mok b is the power consumption of the kth electric hydrogen transport vehicle traveling from node m to the hydrogen production park;jok is the power consumption of the kth electric hydrogen transport vehicle traveling from node j to the hydrogen production park; x is a binary variable, x ijk If the kth electric hydrogen transport vehicle travels from node i to node j, it is 1, otherwise it is 0; jok If the kth electric hydrogen transport vehicle travels from node j to the hydrogen production park, it is 1, otherwise it is 0; x jmk If the kth electric hydrogen transport vehicle travels from node j to node m, it is 1, otherwise it is 0; mok The kth electric hydrogen transport vehicle travels from node m to the hydrogen production park, which is 1, otherwise it is 0;

[0086] (3) Time constraints

[0087] Each hydrogen energy demand customer has a demand time window, so if the electric hydrogen truck arrives at the hydrogen refueling station early, it needs to wait. If it arrives within the time window constraint, it does not need to wait and there is no penalty fee. The waiting time model of the hydrogen truck is as follows:

[0088]

[0089] In formula (22), is the waiting time of the kth electric hydrogen transport vehicle at node i, t′ ik is the time when the kth electric hydrogen transport vehicle arrives at node i.

[0090] The travel time of an electric hydrogen transport vehicle, including the stop time at each node, driving time, and charging time, is as follows:

[0091]

[0092]

[0093] t" ik The time when the kth electric hydrogen transport vehicle leaves the i-th node, The time when the kth electric hydrogen transport vehicle unloads hydrogen at node i is t mk is the charging time of the kth electric hydrogen transport vehicle at node m, t k is the total driving time of the kth electric hydrogen transport vehicle, t0 is the time when the electric hydrogen transport vehicle loads the mobile hydrogen storage tank after receiving the command from the hydrogen energy transportation dispatching platform of the hydrogen production park, and t ijk is the travel time of the kth electric hydrogen transport vehicle at nodes i and j.

[0094] The hydrogen production park’s hydrogen transport dispatching platform has fixed operating hours every day, and vehicles must depart and return within the distribution center’s operating hours.

[0095]

[0096] Formula (26) represents the earliest departure time and the latest departure time of the kth electric hydrogen transport vehicle, T E Arrange the earliest dispatch time for the hydrogen transport dispatch platform of the hydrogen production park; T L The latest dispatch time of the hydrogen transport dispatch platform in the hydrogen production park, T″ ok is the time when the kth electric hydrogen transport vehicle leaves the hydrogen production park, T′ ok It is the time for the kth electric hydrogen transport vehicle to return to the hydrogen production park.

[0097] In step S5, the number of electric hydrogen transport vehicles participating in the hydrogen transport task can be determined based on the hydrogen transport dispatching platform of the hydrogen production park, and can be further divided into two parts. One part participates in the transport dispatching task to meet the needs of hydrogen demand customers; the other part is the electric hydrogen transport vehicles that do not participate in the transport task and stay in the hydrogen production park.

[0098] For the electric hydrogen transport vehicles that participate in the transportation task, based on the planning results of the previous steps, the remaining hydrogen, electricity and return time of the electric hydrogen transport vehicles when they return to the hydrogen production park are clarified, and the mobile hydrogen storage tanks are connected to the hydrogen storage system and the electric hydrogen transport vehicles are connected to the charging piles in the hydrogen production park to build a hydrogen-electricity scheduling model after the hydrogen transport vehicles return; for the electric hydrogen transport vehicles that do not participate in the transportation task, since all electric hydrogen transport vehicles are guaranteed to be fully charged and the mobile hydrogen storage tanks are full of hydrogen at 24 hours a day, the mobile hydrogen storage tanks are connected to the hydrogen storage system and the electric hydrogen transport vehicles are connected to the charging piles in the hydrogen production park, and a full-time scale hydrogen-electricity scheduling model can be built, as follows:

[0099] The scheduling time formula after the mobile hydrogen storage tank is connected to the hydrogen storage system is as follows:

[0100]

[0101] t dk is the time when the kth electric hydrogen transport vehicle starts hydrogen-electricity dispatch. When the kth electric hydrogen transport vehicle returns from the hydrogen transport mission, the dispatching time is calculated as t k +t0 is rounded up to start scheduling; when the kth electric hydrogen transport vehicle does not participate in the hydrogen transport task, its scheduling time starts from 1:00.

[0102] The electric hydrogen transport vehicle uses the mobile hydrogen storage tank to connect to the hydrogen storage system to replenish hydrogen for the park, and ensures that the mobile hydrogen storage tank is full of hydrogen 24 hours a day. The hydrogen energy scheduling model is:

[0103]

[0104] Where: are the charging and discharging powers of the kth mobile hydrogen storage tank at time t respectively; is the maximum power of a single charge and discharge of the kth mobile hydrogen storage tank; are binary variables, which are the charging and discharging state parameters of the kth mobile hydrogen storage tank in period t. Indicates that it is in charging state. Indicates that it is in the state of releasing energy; P ES,k (t) is the final output power of the kth mobile hydrogen storage tank during period t; are the charging and discharging efficiencies of the kth mobile hydrogen storage tank; S k (t) is the capacity of the kth mobile hydrogen storage tank in period t, S k (1) is the initial capacity of the kth mobile hydrogen storage tank, S k (T) is the final capacity of the kth mobile hydrogen storage tank, T is 24; are the upper and lower limits of the capacity of the kth mobile hydrogen storage tank respectively.

[0105] The electric hydrogen transport vehicle uses the charging piles in the park to replenish the power for the park and ensures that the electric hydrogen transport vehicle is fully charged 24 hours a day. The power dispatch model is as follows:

[0106]

[0107] E ok is the power of the kth electric hydrogen transport vehicle in the park, E' ok The remaining power of the kth electric hydrogen transport vehicle returning to the park; and The charging and discharging efficiency of electric hydrogen transport vehicles; and is the charging and discharging power of the kth electric hydrogen transport vehicle in the hydrogen production park at time t.

[0108] Ammonia's physical properties such as high stability and high heat make it a new energy source with great potential. Green hydrogen synthesis of ammonia often uses the Haber-Bosch process. The synthesis of 1kg of ammonia consumes 7.4kW·h of electricity and releases heat at the same time. Flexible scheduling can meet part of the heat load. The hydrogen ammonia equipment operates through variable load and can be regarded as a flexible load. The relationship between its energy consumption and output can be approximated to be linear. The flexible hydrogen use model described in step S6 is as follows:

[0109]

[0110] Where: They are the energy consumption and thermal power provided by the hydrogen-to-ammonia equipment during period t respectively; The unit energy consumption of hydrogen-to-ammonia equipment; is the mass of ammonia produced during period t; η heat The proportion of heat released by the hydrogen-to-ammonia equipment used for heating; The heat released to generate a unit mass of ammonia; is the hydrogen consumption of the hydrogen-to-ammonia equipment during period t; The ratio of hydrogen consumed when producing ammonia for hydrogen-to-ammonia equipment; The target ammonia production capacity of the hydrogen-to-ammonia equipment within one day; They are the upper and lower limits of hydrogen energy input into the hydrogen-to-ammonia equipment; They are the upper and lower limits of the climbing range of the hydrogen-to-ammonia equipment respectively.

[0111] The hydrogen production park operates in an isolated grid mode and does not interact with the large power grid, nor does it interact with the municipal thermal network. In order to ensure the balance of hydrogen, electricity and heat in the park, a comprehensive energy optimization strategy based on production, storage, transportation and use of hydrogen has been constructed by deeply exploring the flexible control capabilities of each link of the hydrogen industry consisting of hydrogen production, storage, transportation and use, so as to further improve the reliability and decarbonization capabilities of the entire system. Among them, the electrolyzer produces hydrogen by electrolyzing water with the output of new energy. Part of the produced hydrogen is transported to hydrogen energy demand customers by electric hydrogen transport vehicles using mobile hydrogen storage tanks, and part is consumed by hydrogen ammonia production equipment and hydrogen fuel cells. The electricity generated by hydrogen fuel cells can be fed to the power system of the hydrogen production park. In order to cope with the volatility of the park's new energy output, a hydrogen-electricity transfer model based on electric hydrogen transport vehicles and a flexible hydrogen use model have been constructed. Based on the flexible control characteristics of hydrogen ammonia production equipment, flexible production can be achieved under certain ammonia production conditions to meet the power fluctuations in the park. In addition, the heat released by the flexible production of the adjustable thermoelectric ratio of hydrogen fuel cells and the hydrogen-to-ammonia equipment can meet the heat load of the park, and the hydrogen energy remaining when the hydrogen transport vehicle participating in the transportation task returns and the hydrogen energy of the mobile hydrogen storage tank that does not participate in the transportation task are coordinated to meet the hydrogen energy balance. Considering the economy at the same time, in step S7, the model is built with the lowest total operating cost of the hydrogen production park as the objective function, and the model is as follows:

[0112]

[0113] C d,x (t) is the depreciation cost of each equipment at time t; C m,x (t) represents the maintenance cost of each equipment at time t; t represents the time scale of scheduling; C0 is the total cost of hydrogen transported by electric hydrogen transport vehicles t∈{1, 2..., T}; x represents the electrolyzer, hydrogen-to-ammonia, hydrogen fuel cell equipment, and electrical energy storage and fixed hydrogen energy storage.

[0114]

[0115] Where: C INS,x is the installation cost of the xth device; f x,cr is the capital recovery factor of the xth equipment; k x is the capacity factor of each device; P rated,x is the rated power of the xth device; P x (t) is the power of each device at time t; D R,x、n x are the depreciation rate and service life of the x-th equipment respectively.

[0116] C m,x (t) = C UM,x P x (t) (34)

[0117] C UM,x It is the maintenance cost of each device under unit output power.

[0118] The system collaborative operation constraint model is as follows:

[0119] 1) Constraints on new energy output

[0120]

[0121] P PV (t), P PW (t) are the output power of photovoltaic and wind turbine at time t respectively; are the maximum output powers of photovoltaic and wind turbine at time t respectively.

[0122] 2) The planning constraints of electric hydrogen transport vehicles and the operating constraints of electrolyzers, hydrogen-to-ammonia, and hydrogen fuel cell equipment are as mentioned above.

[0123] 3) Electric power balance constraints

[0124] This hydrogen production park operates in an isolated grid mode and does not sell electricity to the grid. Considering the large randomness and volatility of new energy output, in order to ensure that there is no power shortage in the hydrogen production park, a flexible production-storage-transportation-use hydrogen optimization scheduling strategy is constructed to meet the power consumption and ammonia production target capacity of the hydrogen production park. The power constraints are as follows:

[0125]

[0126] P is the charging and discharging power of the energy storage at time t; e,Load (t) is the rigid load of the park at time t.

[0127] 4) Thermal power balance constraints

[0128] The heat of this hydrogen production park is only used by this hydrogen production park. Based on the flexible regulation of the thermoelectric adjustable ratio of hydrogen fuel cells and the heat released by the flexible production of hydrogen-ammonia production equipment, the heat load of the park is met. The thermal power constraints are as follows:

[0129]

[0130] P h,Load (t) is the heat load of the hydrogen production park.

[0131] 5) Hydrogen power balance constraints

[0132] Part of the produced hydrogen is transported to hydrogen demand customers by hydrogen transport vehicles using mobile hydrogen storage tanks, and part is consumed by hydrogen-to-ammonia equipment and hydrogen fuel cells. Based on fixed hydrogen energy storage and the use of the remaining hydrogen energy when the hydrogen transport vehicles participating in the transportation mission return and the hydrogen energy of the hydrogen storage tanks that do not participate in the transportation mission, the hydrogen energy balance of the hydrogen production park is met. The hydrogen power constraints are as follows:

[0133]

[0134] To fix the charging and discharging power of hydrogen storage at time t.

[0135] This embodiment provides a terminal, which includes a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the various steps of the method for optimizing scheduling of an integrated energy system that takes into account flexible control, storage and use of hydrogen as described in any of the above-mentioned implementation modes.

[0136] This embodiment provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the various steps of the method for optimizing scheduling of an integrated energy system that takes into account the flexible control, storage and use of hydrogen as described in any of the above-mentioned embodiments.

[0137] The present invention is not limited to what is described in the specification and implementation modes, and therefore additional advantages and modifications can be easily realized by those skilled in the art. Therefore, without departing from the spirit and scope of the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details, representative devices, and illustrative examples shown and described herein.

Claims

1. A method for optimizing the scheduling of an integrated energy system taking into account the flexible control, storage and utilization of hydrogen, characterized in that: The following steps are involved: Step S1: constructing a hydrogen production park in an off-grid mode that uses renewable energy to electrolyze water to produce hydrogen, a hydrogen fuel cell power generation model, and a hydrogen storage system and electric energy storage coordinated operation model; Step S2: establishing a supply and demand relationship model including the hydrogen production park, hydrogen energy demand customers and charging stations based on the hydrogen production park hydrogen energy transportation scheduling platform; Step S3: construct a real-time variation model of the electric hydrogen transport vehicle, replace the instantaneous speed, the electric vehicle power consumption, the variation of the hydrogen energy load, and the transportation rate of the electric hydrogen transport vehicle with the average speed, so as to accurately plan the energy consumption, load, and driving time of the electric hydrogen transport vehicle; Step S4: constructing a path planning model for an electric hydrogen transport vehicle, and using a genetic algorithm to perform path planning based on a supply-demand relationship model and a real-time variation model of the electric hydrogen transport vehicle; Step S5: construct a hydrogen-electricity transfer model, and perform hydrogen-electricity energy conversion through the park charging piles and hydrogen storage system based on the number of electric hydrogen transport vehicles, transportation time, transportation roads, remaining power of hydrogen transport vehicles, and remaining hydrogen energy results; Step S6: construct a flexible hydrogen use model, based on the flexible control characteristics of hydrogen-to-ammonia equipment, to achieve flexible production under a certain ammonia output to meet the power fluctuation and hydrogen energy balance of the park; Step S7: Construct an objective function with the lowest total operating cost of the hydrogen production park as the objective function, based on the flexible production-storage-transportation-use of hydrogen energy, to meet the fluctuation of new energy output and the target capacity of ammonia production, and reduce the abandonment of wind and solar power.

2. The method for optimizing the dispatching of an integrated energy system considering the flexible control, storage and utilization of hydrogen according to claim 1 is characterized in that: Step S1 constructs a model of hydrogen production by electrolyzing water using new energy output, a hydrogen fuel cell power generation model, and a coordinated operation model of a hydrogen storage system and electric energy storage, including: a model of hydrogen production by electrolyzing water using new energy output of an electrolyzer, a hydrogen fuel cell model, electric energy storage, and a mathematical model of fixed hydrogen energy storage equipment.

3. The method for optimizing the dispatching of an integrated energy system considering the flexible control, storage and utilization of hydrogen according to claim 1 is characterized in that: In step S2, the hydrogen energy transportation scheduling platform of the hydrogen production park adopts the natural number coding method to set the numbers of n hydrogen energy demand customers to 1, 2, 3, ..., n, and the numbers of m charging stations to n+1, n+2, ..., n+m; and further insert the starting point and the end point of the electric hydrogen transport vehicle into the hydrogen production park code 0 respectively; k electric hydrogen transport vehicles are taken as a set K = {1, 2, 3, ..., k}, the set of n hydrogen energy demand customers is N = {1, 2, 3, ..., n}, the set of m charging stations is M = {n+1, n+2, ..., n+m}, the hydrogen production park is represented by o, and the three sets of hydrogen production park, charging station and hydrogen energy demand customers are combined into a large set L during planning; the second part adopts the real number coding method, and according to the number of vehicles given by the hydrogen production park, the departure time of each vehicle is randomly generated within the operating time window of the hydrogen energy transportation scheduling platform of the hydrogen production park, and the calculation formula is as follows: y=T E +(T E (max)-T E )*rand (4) Where: T E Arrange the earliest dispatch time for the hydrogen transportation platform in the hydrogen production park; T L Arrange the latest dispatch time for the hydrogen transportation platform in the hydrogen production park; T E (max) is the latest scheduling time of the hydrogen energy transportation platform in the hydrogen production park; rand is a random number ranging from (0-1); Construct node constraints for electric hydrogen vehicles to travel between hydrogen production parks, hydrogen energy demand customers, and charging stations during the transportation cycle.

4. The method for optimizing the dispatching of an integrated energy system considering the flexible control, storage and utilization of hydrogen according to claim 3 is characterized in that: The real-time variation model is constructed in step S3 as follows: The route traveled by the electric hydrogen transport vehicle is further decomposed into a uniform acceleration phase, a uniform speed driving phase, and a uniform deceleration braking phase. The time required for each phase of the electric hydrogen transport vehicle is: Formula (7), t1 represents the time the electric hydrogen transport vehicle travels in the acceleration phase, t2 represents the time the electric hydrogen transport vehicle travels in the deceleration and braking phase, t ij is the driving time of the electric hydrogen transport vehicle from point i to point j. d1 is the driving distance in the acceleration phase, d2 is the driving distance in the braking phase, and d ij is the distance between nodes i and j. u1(t) is the initial velocity of the electric hydrogen transport vehicle, u2(t) is the final velocity of the electric hydrogen transport vehicle, u"(t) is the uniform speed of the electric hydrogen transport vehicle, a1(t) represents the acceleration of the electric hydrogen transport vehicle when it starts, and a2(t) represents the acceleration of the electric hydrogen transport vehicle when it brakes; The energy consumption expression of the electric hydrogen transport vehicle in each stage of the uniform acceleration stage, uniform speed driving stage, and uniform deceleration braking stage is: In formula (8), R is the air resistance coefficient, A is the windward area of ​​the electric hydrogen transport vehicle, ρ is the air density, f is the rolling resistance coefficient, G is the weight of the electric hydrogen transport vehicle, and w ij is the total hydrogen capacity transported by the electric hydrogen transport vehicle from point i to point j, i, j∈(0, m+n), θ' is the average slope of the road, g is the acceleration of gravity, φ d Indicates the output efficiency of the drive motor. Battery output efficiency; The energy consumption of all stages is: b ij =b1+b2+b3 i,j∈(0,m+n) (9) In formula (9), b1 represents the electric energy consumed in the acceleration and starting stage of the electric hydrogen transport vehicle, b2 represents the electric energy consumed in the uniform speed driving stage of the electric hydrogen transport vehicle, b3 represents the electric energy consumed in the deceleration and braking stage of the electric hydrogen transport vehicle, and b ij It is the total energy consumption of a single electric hydrogen transport vehicle during the three stages of acceleration, constant speed and braking from node i to node j.

5. The method for optimizing the dispatching of an integrated energy system considering the flexible control, storage and utilization of hydrogen according to claim 3 is characterized in that: Step S4 includes: Step S41: Hydrogen is delivered through electric hydrogen transport vehicles, and a single distribution center and multiple distribution locations transportation mode is constructed with the hydrogen production park as the starting point and each hydrogen user as the transportation point; considering the power consumption of the electric hydrogen transport vehicle, the electric hydrogen transport vehicle is allowed to choose a charging station to charge due to the long route and then continue to complete the transportation task, and the power of the electric hydrogen transport vehicle is monitored in real time; based on this, a path planning model for the electric hydrogen transport vehicle is constructed, and a genetic algorithm is used to solve it. The objective function is as follows: Among them, C0 is the total cost of transporting hydrogen energy by electric hydrogen transport vehicles; C fk is the fixed cost of the electric hydrogen transport vehicle k; C tk is the driving cost of the electric hydrogen transport vehicle k; C mk is the charging cost of the electric hydrogen transport vehicle k; C pk The penalty fee for the early or late arrival of the electric hydrogen transport vehicle k; E, E' mk are the initial capacity of the battery of the electric hydrogen vehicle and the battery capacity of the kth hydrogen vehicle when it enters the m node; c1 is the fixed cost of the electric hydrogen vehicle driving once, c2 is the unit driving cost of the electric hydrogen vehicle, c3 is the unit charging cost of the electric hydrogen vehicle when charging, c4 is the penalty cost of the electric hydrogen vehicle arriving at the hydrogen energy demand customer point too early, and c5 is the penalty cost of the electric hydrogen vehicle arriving at the hydrogen energy demand customer point too late; t i ' k is the time when the kth electric hydrogen transport vehicle arrives at node i, e i is the earliest time when node i needs hydrogen energy, l i is the latest hydrogen energy demand time of node i, d ij is the distance between nodes i and j. mk is a binary variable, where the kth electric hydrogen transport vehicle is charged at node m, which is 1, otherwise it is 0, m∈M(n, n+1, ..., m); x ojk It is 1 if the kth electric hydrogen transport vehicle travels from hydrogen production park o to node j, otherwise it is 0.

6. The method for optimizing the dispatching of an integrated energy system taking into account the flexible control, storage and utilization of hydrogen according to claim 5 is characterized in that: Step S4 also includes: Step S42: Based on steps S1 and S2, the hydrogen transportation route, power consumption, remaining hydrogen energy, and transportation time constraints in the transportation plan are constructed to lay the foundation for planning using genetic algorithms.

7. The method for optimizing the dispatching of an integrated energy system taking into account the flexible control, storage and utilization of hydrogen according to claim 5, characterized in that: In step S5, a full-time scale hydrogen-electricity scheduling model is constructed, as follows: The scheduling time formula after the mobile hydrogen storage tank is connected to the hydrogen storage system is as follows: t dk is the time when the kth electric hydrogen transport vehicle starts hydrogen-electricity dispatch. When the kth electric hydrogen transport vehicle returns from the hydrogen transport mission, the dispatching time is calculated as t k +t0 is rounded up to start scheduling; when the kth electric hydrogen transport vehicle does not participate in the hydrogen transport task, its scheduling time starts from 1:00; The electric hydrogen transport vehicle uses the mobile hydrogen storage tank to connect to the hydrogen storage system to replenish hydrogen for the park, and ensures that the mobile hydrogen storage tank is full of hydrogen 24 hours a day. The hydrogen energy scheduling model is: Where: are the charging and discharging powers of the kth mobile hydrogen storage tank at time t respectively; is the maximum power of a single charge and discharge of the kth mobile hydrogen storage tank; are binary variables, which are the charging and discharging state parameters of the kth mobile hydrogen storage tank in period t. Indicates that it is in charging state. Indicates that it is in the state of releasing energy; P ES,k (t) is the final output power of the kth mobile hydrogen storage tank during period t; are the charging and discharging efficiencies of the kth mobile hydrogen storage tank; S k (t) is the capacity of the kth mobile hydrogen storage tank in period t, S k (1) is the initial capacity of the kth mobile hydrogen storage tank, S k (T) is the final capacity of the kth mobile hydrogen storage tank, T is 24; are the upper and lower limits of the capacity of the kth mobile hydrogen storage tank respectively; The electric hydrogen transport vehicle uses the charging piles in the park to replenish the power for the park and ensures that the electric hydrogen transport vehicle is fully charged 24 hours a day. The power dispatch model is as follows: E ok is the power of the kth electric hydrogen transport vehicle in the park, E o ' k The remaining power of the kth electric hydrogen transport vehicle returning to the park; and The charging and discharging efficiency of electric hydrogen transport vehicles; and is the charging and discharging power of the kth electric hydrogen transport vehicle in the hydrogen production park at time t.

8. The method for optimizing the dispatching of an integrated energy system considering the flexible control, storage and utilization of hydrogen according to claim 5 is characterized in that: The flexible hydrogen use model in step S6 is as follows: Where: They are the energy consumption and thermal power provided by the hydrogen-to-ammonia equipment during period t respectively; The unit energy consumption of hydrogen-to-ammonia equipment; is the mass of ammonia produced during period t; η heat The proportion of heat released by the hydrogen-to-ammonia equipment used for heating; The heat released to generate a unit mass of ammonia; is the hydrogen consumption of the hydrogen-to-ammonia equipment during period t; The ratio of hydrogen consumed when producing ammonia for hydrogen-to-ammonia equipment; The target ammonia production capacity of the hydrogen-to-ammonia equipment within one day; They are the upper and lower limits of hydrogen energy input into the hydrogen-to-ammonia equipment; They are the upper and lower limits of the climbing range of the hydrogen-to-ammonia equipment respectively.

9. A terminal comprising a memory, a processor, and a computer program stored in the memory, characterized in that: When the computer program is executed by the processor, the steps of the method for optimizing the scheduling of an integrated energy system taking into account the flexible control, storage and utilization of hydrogen as described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by the processor, each step of the method for optimizing the scheduling of an integrated energy system taking into account the flexible control, storage and utilization of hydrogen as described in any one of claims 1 to 8 is implemented.

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