Emergency power supply dispatching method for distribution network under sudden disaster by flexible hydrogen resource multi-path participation

Abstract

The present application relates to a kind of flexible hydrogen resource multi-path under sudden disaster Participate in power distribution network emergency power supply dispatching method, belong to power distribution network, microgrid and integrated energy system field, including the following steps: S1: establish power distribution network-transport network-hydrogen pipeline network coupling framework, constructs power distribution network two-stage hydrogen-electric conversion operation strategy, and consider the dynamic load flow model of multiple distributed resources access of power distribution network;S2: in pre-dispatching model, introduce two-stage robust optimization strategy, construct flexible hydrogen resource robust pre-dispatching model;S3: construct flexible hydrogen tank truck emergency dispatching model, further construct pipeline hydrogen emergency dispatching model considering pipe storage effect on this basis;S4: flexible hydrogen resource robust pre-dispatching model and pipeline hydrogen emergency dispatching model are solved, realize electric hydrogen resource coupling collaborative optimization, output emergency power supply dispatching strategy.

Description

Technical Field

[0001] This invention belongs to the field of power distribution networks, microgrids and integrated energy systems, and relates to a method for flexible hydrogen resource participation in emergency power distribution network dispatching through multiple pathways under sudden disasters. Background Technology

[0002] Climate change has exacerbated the frequency and intensity of extreme events, while the relatively weak structure of power distribution networks makes them highly vulnerable to large-scale power outages caused by disasters. Therefore, building resilient power distribution networks with resilience has become an urgent need when power outages occur due to emergency events.

[0003] Current research on emergency power supply dispatching for distribution networks in the face of extreme disasters mainly focuses on improving the resilience of distribution networks through network reconfiguration. Some literature proposes differentiated resilience enhancement methods for lines and energy storage devices within the distribution network, given its susceptibility to extreme weather events. Other literature proposes methods to enhance distribution network resilience by pre-deploying personnel and forming preventative islands through network reconfiguration, and then dynamically dispatching personnel again after a disaster and combining this with network reconfiguration to form restorative islands. Some literature first uses the Monte Carlo method to determine the scale of faults caused to the distribution network by typhoons, proposing a tiered load reduction strategy that simultaneously increases the stock of primary loads and equivalent loads in islands. Some literature considers the rapid recovery and component repair of active distribution network faults during typhoon disasters, proposing a multi-cycle recovery method to improve the resilience of active distribution networks and the overall load recovery level. Other literature considers user load decision-making behavior during the distribution network recovery process after an emergency event from multiple perspectives and compares and analyzes two proposed distribution network recovery methods based on different indicators. However, distribution network reconfiguration is very costly, while the development of hydrogen energy provides an effective way to improve the flexibility of the hydrogen-electric energy network and enhance the resilience of the distribution network after disasters. The power grid can supply electricity to the hydrogen system to produce hydrogen to meet the hydrogen load demand, and achieve efficient hydrogen-to-electricity conversion through hydrogen-based fuel cells (HFCs) and hydrogen gas turbines (HGTs).

[0004] There are already successful cases of using hydrogen energy to supply electricity loads in emergency situations in power distribution networks. Current research has focused on utilizing hydrogen resources to improve the power supply capacity of power distribution networks, and some literature has summarized the core value of hydrogen-electric charging stations. Other literature has explored the collaborative optimization of hydrogen energy resource transportation via road networks and hydrogen-to-electricity conversion under worst-case damage scenarios to meet load survival needs. Simultaneously, with the gradual improvement of gas pipeline networks, hydrogen resources can be dynamically dispatched using existing gas pipeline and road networks, eliminating the need for new infrastructure and significantly reducing investment costs. Some literature has verified the feasibility of using pipelines to supply hydrogen to HGTs for power generation by constructing a hydrogen energy storage unit model based on HGTs, with an energy utilization efficiency of approximately 75%.

[0005] However, the aforementioned studies only focus on strategies for hydrogen energy to participate in power distribution network load supply through a single pathway, neglecting the potential for enhanced resilience of the distribution network brought about by flexible hydrogen resources participating in hydrogen-to-electricity conversion through multiple pathways. Using hydrogen pipelines as an important means of hydrogen supply via hydrogen gas utilities (HGT), and further combining this with mobile hydrogen resources, multi-path hydrogen energy transmission and distribution technologies can achieve flexible deployment and efficient coordinated scheduling of hydrogen resources.

[0006] Currently, mainstream emergency power supply measures for distribution networks can be divided into three stages: pre-disaster prevention, in-disaster emergency response, and post-disaster recovery. Pre-disaster prevention is operational assurance, in-disaster emergency response is a key means, and post-disaster recovery is a necessary method. Regarding the timing of emergency power supply for distribution networks, scholars have conducted some research. One paper proposes an earthquake-induced cascaded disaster mitigation-Bayesian decision network model to evaluate pre-disaster mitigation strategies under limited budgets from a systems thinking perspective. Another paper proposes a pre-disaster-in-disaster-post-disaster full-process planning method aimed at improving distribution network resilience, sending instructions to capacitor banks and smart soft switches for regulation to reduce system load shedding costs. To ensure rapid power supply to critical loads in the post-disaster phase, another paper constructs different recovery measures including transmission networks, distribution networks, and offshore wind farms, aiming to minimize total recovery scheduling costs, thereby improving system resilience. Yet another paper incorporates various distributed resources and electric vehicle battery swapping stations into the dispatchable resources for improving load recovery rates in the post-disaster phase of the distribution network, establishing a two-stage stochastic-robust optimization configuration model for electric vehicle battery swapping stations. Some literature has constructed a two-layer planning model for distribution network expansion and energy storage configuration, and considered various methods such as post-disaster equipment repair and power supply to participate in improving distribution network resilience. However, existing research mainly focuses on independent decision-making in the pre-disaster prevention or post-disaster recovery stages, without fully considering the temporal connection and intrinsic relationship between the pre-disaster prevention and post-disaster recovery stages, thus affecting the post-disaster recovery process. In addition, existing research has also neglected the fact that the capacity and travel time of transportation networks are easily affected by disasters, and the modeling methods for mobile resources to participate in emergency supply are too idealistic. Summary of the Invention

[0007] In view of this, in order to solve the problems of limited distributed energy supply capacity and high distribution network reconstruction costs after a sudden disaster, this invention is based on the difficulty of flexible hydrogen resources participating in the emergency supply of the distribution network through multiple channels, taking into full account the susceptibility of transportation network capacity and travel time to disasters. It proposes an emergency supply strategy that uses distributed energy as the basic resource and flexible hydrogen resources participate in the distribution network through multiple channels in multiple stages. On the basis of considering the coupling and synergy of electricity and hydrogen resources and the uncertainty of renewable energy output, it gives full play to the flexibility of hydrogen energy resource dispatch through multiple channels, minimizes load shedding, and reduces dispatch and load shedding costs.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A method for flexibly participating hydrogen resources in emergency power grid dispatching through multiple pathways during sudden disasters includes the following steps: S1: Establish a coupled framework of distribution network-transportation network-hydrogen pipeline network, construct a two-stage hydrogen-electricity conversion operation strategy for distribution network, and a dynamic power flow model that considers the access of various distributed resources in distribution network; S2: Introduce a two-stage robust optimization strategy into the pre-scheduling model to construct a flexible hydrogen resource robust pre-scheduling model; S3: Construct a flexible emergency dispatch model for hydrogen storage tank trucks, and further construct an emergency dispatch model for pipeline hydrogen transportation that considers the pipeline storage effect. S4: Solve the robust pre-scheduling model for flexible hydrogen resources and the emergency scheduling model for pipeline hydrogen transportation to achieve coupled and collaborative optimization of hydrogen and electricity resources and output emergency supply scheduling strategies.

[0009] Furthermore, the coupling framework of the power distribution network-transportation network-hydrogen pipeline network is established as follows: The power distribution network includes hydrogen gas turbines, charging piles, photovoltaic units, multiple ordinary load nodes and multiple important load nodes of the power distribution network; the hydrogen transmission pipeline network includes multiple ordinary load nodes of the power distribution network and multiple nodes coupled with hydrogen gas turbines; the transportation network includes hydrogen-powered generator vehicles and multiple road network nodes corresponding to power distribution network nodes. In the distribution network, distributed resources include photovoltaic units, hydrogen gas turbines, and charging piles. The hydrogen consumed by the hydrogen gas turbines comes from the hydrogen transmission pipeline network and its own hydrogen storage. When the distribution network is not affected by emergencies, the power demand of the distribution network is met by the upstream main grid. When an emergency occurs, the connection between the distribution network and the upstream main grid is interrupted. At this time, distributed resources serve as the foundation, with hydrogen pipelines and emergency hydrogen generator vehicles serving as emergency resources to ensure supply. In the face of disasters, the hydrogen production station and the hydrogen transmission network may also be disconnected. Therefore, in the pre-dispatch phase, the number of hydrogen generator vehicles, the pipeline storage considering dynamic effects, and the hydrogen storage capacity of the hydrogen gas turbines are optimized. In the dynamic dispatch phase, based on distributed energy, the pipeline storage is dynamically dispatched to supply hydrogen to the hydrogen gas turbines, and the emergency hydrogen generator vehicles move between road networks to supply power to the distribution network, so as to minimize the impact on the nodes of the distribution network.

[0010] Furthermore, the two-stage hydrogen-to-electricity conversion operation strategy of the distribution network described in step S1 includes: during the prevention phase, the distribution network is dynamically simulated for power flow timing, and a robust optimization operation model is established based on the uncertain output of photovoltaic units; when the occurrence time and duration of an emergency event are unknown, multiple hydrogen power generation tankers are pre-scheduled to supply power at different distribution network nodes, and the hydrogen pipeline stores sufficient hydrogen in the hydrogen storage tanks to supply hydrogen gas turbine power generation; after the emergency event occurs, by coordinating the output of various distributed resources, the hydrogen storage and power generation tankers, the pipeline's storage capacity, and the hydrogen gas turbine power generation are dynamically scheduled to maximize the power demand of important load nodes and improve the load recovery rate of the distribution network.

[0011] Furthermore, the dynamic power flow model considering the access of multiple distributed resources in the distribution network described in step S1 includes: For radial distribution networks, the DistFlow power flow equation is used to model them: (1) (2) (3) (4) (5) (6) Equations (1) and (2) ensure the balance of active and reactive power at each node in the distribution network. If a node is connected to distributed energy, the power balance is expressed as Equation (1); otherwise, it is expressed as Equation (2). Equation (3) is the relationship between the voltage at both ends of each branch and the power transmitted by the line. The big M method is used to relax Equation (3). Equations (4) and (5) are the voltage constraints of each node in the distribution network and the current constraints of each line, respectively. Equation (6) uses the second-order cone relaxation method to transform the nonlinear relationship between the power, current and node voltage of the distribution network. In the formula, They are located at the nodes respectively i Distributed power supply t Active and reactive power output at all times; They are nodes i exist t The amount of active and reactive power reduction is constantly being measured; They are located at the nodes respectively i The k Mobile hydrogen power generation vehicle t Active and reactive power output at all times; for t Time with nodes i The route with the destination as the destination hi The magnitude of active and reactive power transmitted; For nodes i The set of sending nodes; They are respectively t Time Node i The maximum active and reactive power reduction of the load; for t Timetable hi The square of the magnitude of the transmitted current; The lines are respectively hi The magnitudes of resistance and reactance; They are respectively t Time with nodes i The route starting from ij The magnitude of active and reactive power transmitted; For nodes i The set of receiving nodes; for t Nodes in the distribution network at all times i Square of node voltage; Representing nodes respectively i Minimum and maximum values ​​of the square of the voltage; Indicates the line ij The maximum squared value of the transmission current; 0-1 variables represent t Timetable ij Connectivity state, where a value of 1 indicates connectivity; A set of distributed resource nodes in a power distribution network; N For distribution network nodes; A collection of distribution network lines; M It is an extremely large number; K The set of dispatchable generator cars; These represent the minimum and maximum active power output of the mobile hydrogen generator vehicle, respectively. This is a set of time periods for dynamic scheduling.

[0012] Furthermore, the flexible hydrogen resource robust pre-scheduling model described in step S2 is constructed as follows: The objective function of the pre-scheduling model consists of two parts: the pre-configuration cost of mobile hydrogen storage tankers and the minimum total cost of load reduction at each node in the distribution network. In the two-stage robust optimization model, the decision variables for the first stage are shown in set X. Photovoltaic output is an uncertain variable in robust optimization, and its uncertainty set is as follows: As shown, the decision variables for the second stage are represented by set Y; (7) (8) (9) (10) (11) In the formula, A 0-1 variable, representing the first... k Are the hydrogen storage tank trucks pre-scheduled to the node? j ; Indicates the first k The pre-scheduling cost of each tanker truck; Represents a node j Reduced load per unit cost; A 0-1 variable, representing a pre-scheduled line. ij Switch status; The representative is located at the node j The output of the photovoltaic units; The representative is located at the node j The predicted output of the photovoltaic units; A 0-1 variable, representing a node j The upper and lower limits of photovoltaic unit output; This represents the degree of uncertainty in photovoltaic power output; Represents the set of photovoltaic unit nodes; The first phase constraints include: (12) (13) (14) (15) (16) Equation (12) indicates that each tanker can only be configured at one node of the distribution network during the pre-scheduling process; Equations (13)-(15) consider the cases of island merging and power-free islands, adopt the improved single commodity flow method to ensure that the radial topology requirements are met during the recovery process, and add the number of islands after network reconstruction to the optimization process, and use the big M method to relax the virtual power flow; Equation (16) indicates the state constraint when the line is disconnected; In the formula, It is a 0-1 variable, representing whether this node is a virtual source node; Representative Line ij The virtual flow power flowing through; Representative node i The output virtual power; Indicates the set of disconnected lines; M It is a very large constant; The second phase constraints include: (17) (18) (19) (20) (twenty one) Equations (17) and (18) are respectively the range constraints of the active power reduction of each node in the distribution network and the relationship of the power reduction; Equation (19) is the range of active and reactive power output of each distributed energy node in the distribution network; Equation (20) represents the coupling between the output variables of photovoltaic and hydrogen gas turbine and the output variables of distributed energy; Equation (21) represents the range of reactive and active power output of distributed energy. In the formula, Distribution network nodes i The maximum active and reactive power of the load; They are nodes i Minimum / maximum active power output of distributed resources; They are nodes i Minimum / maximum reactive power output of distributed resources; power factor angle of distributed resources The range of values ​​is , To obtain the tangent value; This indicates the node where the distribution network connects to the upstream power grid; The pre-dispatch phase focuses on determining the initial hydrogen storage capacity of mobile hydrogen tankers during emergency dispatch. The initial location of the tanker truck for emergency dispatch. This is then used as a known quantity and input into real-time emergency dispatch to establish the following model: (twenty two) (twenty three) Since the second phase considers the pre-scheduled distribution of pipeline storage to the hydrogen gas turbine, and needs to meet the pipeline network operation constraints, it is modified as follows: (twenty four) (25) In the formula, Pipes ij Initial inflow and outflow hydrogen flow rates at both ends; Pipes ij Initial pressure at both ends; For pipelines ij Initial storage; During this stage, the power distribution network operation meets power flow constraints: (26).

[0013] Furthermore, the flexible hydrogen storage tanker emergency dispatch model described in step S3 is constructed as follows: In the mobile hydrogen resource emergency dynamic dispatch model, the total time of the power grid node during the occurrence of the emergency is considered. The objective is to minimize the sum of internal load reduction power, as shown below: (27) First, we model the hydrogen storage capacity of hydrogen tank trucks at different power distribution network nodes and the emergency dispatch of the road network: (28) (29) (30) (31) (32) (33) (34) Equation (28) represents the hydrogen storage level relationship of the tanker truck; Equation (29) indicates that the tanker truck must depart from its pre-configured node or continue to stay; Equation (30) imposes a constraint on the uniqueness of the tanker truck's transportation route, that is, the tanker truck can only be located on the forward / reverse transportation route between two points or not carry out transportation; Equation (31) constrains that when the tanker truck starts transportation, its hydrogen storage must be greater than its hydrogen consumption during transportation; Equation (32) indicates that the sum of the distances of all transportation routes of the tanker truck cannot exceed its maximum driving distance; Equation (33) indicates the distance of the tanker truck to the node jDuring emergency dispatch, the travel time must be shorter than the time required to reach the destination. j The maximum time; Equation (34) imposes constraints on the total running time of each tanker truck; In the formula, Indicates the first k Tanker trucks at the node j Hydrogen storage capacity; Indicates the first k Tanker trucks at the node j The original hydrogen storage capacity can be calculated using the pre-scheduling model; A 0-1 variable, representing the first... k Should the tanker trucks choose a route? j → i Transmit; Indicates the first k Tanker trucks on the route i → j Hydrogen consumption during transport; Indicates the first k Tanker trucks on the route i → j The driving distance; For the first k Tanker trucks on the route i → j Travel time on the road; Representing a path j → i With nodes j The set of endpoint nodes that are the starting point; Representing a path i → j With nodes j The set of starting nodes for the endpoint; Represents a set of transportation routes; Indicates the first k Maximum travel distance of a tanker truck; Indicates heading to the node j Maximum required time; Indicates the first k The maximum travel time for a tanker truck; (35) (36) (37) (38) Equation (35) represents the relationship between the active power output of the tank truck and the amount of hydrogen consumed; Equation (36) represents the active and reactive power output constraints of the tank truck; Equation (37) represents the relationship between the emergency hydrogen storage capacity in the tank truck and the amount of hydrogen consumed; Equation (38) represents the emergency hydrogen storage capacity of the tank truck and the range of hydrogen consumed for power generation. In the formula, For the first k Tanker trucks in t At the power grid node j Amount of hydrogen gas released; Given 0-1 variables, determine the first... k tanker trucks t Is the node connected at any time? j A value of 1 indicates access; For the first k Tanker trucks in t At the power grid node j Hydrogen reserves; For the first k Maximum emergency hydrogen storage capacity of each tanker truck; For parameters related to the hydrogen-to-electricity conversion efficiency of emergency hydrogen fuel cell vehicles; The impact of road damage during the emergency phase is reflected in the change of congestion level due to the decrease in traffic capacity, which in turn reduces vehicle speed and changes the equivalent transport distance and travel time. Under this setting, the feasibility of the emergency phase is jointly guaranteed by the uniqueness of the path and the upper bound constraint of the travel time. Furthermore, in the pre-scheduling phase, the accessibility margin of the initial layout can be increased by adjusting the upper bound of the congestion level and the upper bound of the shortest travel time, ensuring that the initial layout is feasible under the model semantics. Considering the impact of mobile hydrogen generator vehicles on road capacity and travel time in emergency situations, the following model establishes the relationship between the actual travel time of mobile hydrogen storage tankers and the equivalent transport distance and actual vehicle speed: (39) (40) (41) Equation (39) represents the actual speed of each tanker at different times; Equation (40) represents the equivalent transportation distance of each tanker on the transportation route at different scheduling times; Equation (41) represents the expression for the actual travel time of the tanker. In the formula, Indicates the first k Tanker trucks in t The actual speed at any given moment; Indicates the first k The ideal speed of the tanker truck is not considered under the influence of emergencies and traffic flow. c Indicates the degree of traffic network congestion caused by unforeseen events, etc. Indicates the first k Tanker trucks in t Path at any given moment i → j The equivalent scheduling distance; Represents the path under ideal conditions i → j The scheduling distance; Indicates the first k Tanker trucks in t Time below path i → j Time required for scheduling.

[0014] Furthermore, the pipeline hydrogen transport emergency dispatch model considering pipeline storage effects described in step S3 is constructed as follows: (42) (43) (44) Equation (42) represents the relationship between the hydrogen storage capacity of each hydrogen refueling station during the emergency dispatch period; Equation (43) represents the relationship between the power generation of the hydrogen gas turbine placed in each hydrogen refueling station and the hydrogen consumption rate; Equation (44) represents the upper and lower limits of the active and reactive power output of the hydrogen gas turbine. In the formula, No. i Within the hydrogen refueling station t Hydrogen storage capacity at any time; For the first i Within the hydrogen refueling station t The amount of hydrogen received from the pipeline at time +1; Indicates the first i Within the hydrogen refueling station t The amount of hydrogen released to the hydrogen gas turbine power plant at time +1; For the first i Within the hydrogen refueling station t The magnitude of active and reactive power output of the hydrogen gas turbine during different time periods; A 0-1 variable, representing the first... i Within the hydrogen refueling station t The operating status of the hydrogen gas turbine during a given period, with 1 indicating operation; The first i Active power and minimum output value of hydrogen gas turbines in each hydrogen refueling station; The first i The reactive power and minimum output value of the hydrogen gas turbine in each hydrogen refueling station; A collection of hydrogen refueling stations; The density of hydrogen gas; (45) (46) (47) (48) (49) Equation (45) represents the relationship between the flow rate at both ends of the hydrogen transmission network pipeline and the pressure at both ends of the pipeline; Equation (46) represents the relationship between the flow rate at both ends of the hydrogen transmission network pipeline and the pipeline storage; Equation (47) represents the relationship between the pressure at both ends of the pipeline and its internal storage. Equation (48) represents the flow balance relationship at the pipeline nodes, without considering the situation where hydrogen is produced at the hydrogen production station and injected into the hydrogen transmission network. During this period, only the pipeline storage is scheduled; Equation (49) represents the gas load demand in the hydrogen transmission network pipeline as the amount of hydrogen injected into the hydrogen storage of each hydrogen refueling station. In the formula, Pipes ij exist t The inflow and outflow of hydrogen at any given time; For pipeline nodes i exist t The pressure of constant time; For pipelines ij At any moment t Inventory; For pipeline nodes i exist t Continuously output hydrogen to the load nodes; For pipelines ij Pipeline constant; A collection of pipeline branches; For pipelines ij Storage constant; This is a set of pipeline nodes.

[0015] Furthermore, the column-constrained C&CG algorithm is used to solve the robust pre-scheduling model for flexible hydrogen resources, and the model in the pre-scheduling stage is constructed into the following compact form: (50) In the formula, X This refers to the set of decision variables for the first stage in the pre-scheduling model. u and Y This is the set of decision variables for the second stage. This represents a set of uncertainties, such as photovoltaic power output. Representing decision variables Y The feasible domain; a , b c and The coefficient vector of the variables; A , B , C , D and It is a constant matrix; n The number of second-order cone constraints; The model is decomposed into a main problem and sub-problems. The main problem is to solve for the pre-layout scheme of mobile hydrogen energy storage and the on / off state of the line under deterministic conditions. The main problem is constructed in the following form: (51) In the formula, Z Indicates auxiliary variables; K Indicates the maximum number of iterations; l This represents the corresponding variable obtained after the current iteration number; Indicates the first l Photovoltaic power output after the next iteration; The subproblem is to determine the worst-case photovoltaic output given the pre-deployment of mobile energy storage. The model is as follows: (52) In the formula, This represents the pre-layout scheme for mobile energy storage obtained from solving the main problem; the min-max bi-level optimization subproblem is transformed into a single-level optimization problem using strong duality theory, and then solved directly using the solver.

[0016] Furthermore, the solution process for the emergency dispatch model of pipeline hydrogen transportation is as follows: The MILP model is established with the objective function of minimizing the load reduction cost during the total period of failure, and the solution is obtained for the tanker truck post-disaster rescue route and pipeline storage dispatch consumption scheme.

[0017] The beneficial effects of this invention are as follows: This invention proposes a multi-pathway strategy for the participation of flexible hydrogen resources in the emergency supply of the power distribution network under sudden disaster scenarios, studies a two-stage robust pre-scheduling method for flexible hydrogen resources, and constructs a pre-scheduling-dynamic emergency scheduling model for the coordinated participation of hydrogen pipelines and mobile hydrogen vehicles in supply guarantee. The main effects are as follows: (1) When an EHES disaster occurs, the emergency dynamic scheduling strategy of flexible hydrogen resources proposed in the invention, together with various distributed energy sources such as hydrogen gas turbines and photovoltaic units, can significantly balance the power supply demand of important loads and normal loads. Combined with the optimization of the spatiotemporal distribution of flexible hydrogen resources by photovoltaic power output fluctuations, the effectiveness of the strategy of prioritizing power supply to important nodes and sending surplus power to external sources is verified.

[0018] (2) The flexible hydrogen resource participation in the distribution network pre-dispatch-dynamic emergency supply guarantee dispatch strategy proposed in the invention can achieve the lowest pre-dispatch cost. Under this pre-dispatch strategy, when the distribution network is subjected to a sudden disaster and emergency dispatch is carried out, it can effectively guarantee the load power demand of important nodes in the distribution network island and further reduce the load reduction of important load nodes.

[0019] (3) By using flexible hydrogen resources to participate in dynamic emergency supply scheduling through multiple pathways, the total cost of load reduction during the dynamic scheduling cycle can be effectively reduced under different emergency disaster scenarios, while ensuring that the active power load recovery rate of important nodes remains high. The load reduction cost of the proposed strategy of using mobile hydrogen resources to participate in dynamic emergency scheduling under emergency disasters in the distribution network through multiple pathways is reduced by 44.81%, 8.8%, and 44.80% compared with single-path and single-schedule strategies 2, 3, and 4, respectively.

[0020] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a structural diagram of the coupled framework of the power distribution network, transportation network, and hydrogen pipeline network. Figure 2 For the various operating states of the power distribution network; Figure 3 The model solution process; Figure 4 A multi-pathway hydrogen transport network architecture; Figure 5 Hydrogen load requirements for each HRS; Figure 6 (a) represents the congestion level of the traffic network during the dynamic scheduling phase, and (b) represents the photovoltaic power output prediction curve of the traffic network during the dynamic scheduling phase. Figure 7 The results of pre-scheduling of mobile hydrogen tankers under different scenarios are shown, where (a) is the pre-scheduling node and output power of mobile hydrogen tankers in scenario 1, and (b) is the pre-scheduling node and output power of mobile hydrogen tankers in scenario 2. Figure 8 The results of pre-scheduling of hydrogen pipeline networks under different scenarios are as follows: (a) includes the pre-scheduling hydrogen supply to each hydrogen refueling station and the HGT output; (b) includes the active load recovery rate and total active load power in each scenario. Figure 9 The results of dynamic emergency dispatch of mobile hydrogen storage tank trucks under different scenarios are shown in (a) for tank truck 1, (b) for tank truck 2, and (c) for tank truck 3. Figure 10 This refers to the recovery status of the load's active power. Figure 11The results show the active power load recovery rate of critical nodes after an emergency event under different strategies in Scenario 1. Figure 12 For different strategies after an emergency event in scenario 1, (a) shows the effect of total active power recovery rate, and (b) shows the effect of active power recovery rate of normal nodes. Detailed Implementation

[0022] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0024] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0025] Example 1: This invention provides a flexible hydrogen resource multi-pathway participation strategy for emergency power supply in power distribution networks under sudden disaster scenarios, including the following steps: Step 1: Coupling Framework of Distribution Network - Transportation Network - Hydrogen Pipeline Network The coupled framework of power distribution network-transportation network-hydrogen pipeline network, such as Figure 1As shown in the diagram, distributed resources in the distribution network mainly consider photovoltaic units, hydrogen gas turbines, and charging piles. The hydrogen consumed by the hydrogen gas turbines primarily comes from the hydrogen pipeline network and their own hydrogen storage. When the distribution network is unaffected by unforeseen events, its load demand is mainly met by the upstream main grid. However, when an unforeseen event occurs, the connection between the distribution network and the upstream main grid is interrupted. In this case, distributed resources serve as the foundation, with hydrogen pipelines and emergency hydrogen generators acting as emergency resources to ensure supply. Furthermore, during disasters, hydrogen production stations may also experience disconnections from the hydrogen transmission network, limiting the continuous hydrogen supply capacity of the pipeline network. Therefore, it is necessary to optimize the number of hydrogen generators, the pipeline storage considering dynamic effects, and the hydrogen storage capacity of the hydrogen gas turbines themselves during the pre-schedule phase. During the dynamic scheduling phase, based on distributed energy resources, the pipeline storage is dynamically scheduled to supply hydrogen to the hydrogen gas turbines, and emergency hydrogen generators move between road networks to supply power to the distribution network, minimizing load shedding at various nodes of the distribution network, especially critical nodes.

[0026] Step 2: Two-stage hydrogen-to-electricity conversion operation strategy for distribution networks Various operating states of the power distribution network, such as Figure 2 As shown. During the prevention phase, the distribution network undergoes dynamic power flow timing simulation, and a robust optimization operation model is established based on the uncertain output of photovoltaic units. When the timing and duration of an emergency event are unknown, multiple hydrogen power generation tankers are pre-scheduled to supply power at different distribution network nodes, and the hydrogen pipeline stores sufficient hydrogen in the storage tanks to power the hydrogen gas turbines. After an emergency event occurs, by coordinating the output of various distributed resources, the hydrogen storage and power generation tankers, pipeline reserves, and hydrogen gas turbine power generation are dynamically scheduled to maximize the power demand of critical load nodes and improve the load recovery rate of the distribution network. The recovery status is as follows. Figure 2 The red shaded area is shown in the middle.

[0027] Step 3: Dynamic power flow model considering multiple distributed resource accesses in the distribution network For a radial distribution network, the DistFlow power flow equation is used to model it. Equations (1)-(2) ensure the balance of active and reactive power at each node in the distribution network. If a node is connected to distributed energy, the power balance is expressed as Equation (1); otherwise, it is expressed as Equation (2). Equation (3) shows the relationship between the voltage at both ends of each branch node and the power transmitted by the line. Since changes in the line opening state will affect the connection of the distribution network lines, the Big M method is used to relax Equation (3). Equations (4) and (5) are the voltage constraints of each node in the distribution network and the current constraints of each line, respectively. Equation (6) uses the second-order cone relaxation method to transform the nonlinear relationship between the power, current and node voltage of the distribution network lines. To avoid the optimistic results caused by relaxation, this embodiment calculates the cone constraint gap. The maximum gap of the second-order cone constraint for each line and each time period is 0.01925, the mean gap is 0.00112, and the 95th percentile gap is 0.00553. For the vast majority of lines and time periods, the cone constraint gap is very small, that is, 95% of the samples do not exceed 0.00553. After relaxation, the model shows good overall relaxation tightness, which can avoid the phenomenon of relaxation leading to overly optimistic conclusions.

[0028] (1) (2) (3) (4) (5) (6) In the formula, They are located at the nodes respectively i Distributed power supply t Active and reactive power output at all times; They are nodes i exist t The amount of active and reactive power reduction is constantly being measured; They are located at the nodes respectively i The k Mobile hydrogen power generation vehicle t Active and reactive power output at all times; for t Time with nodes i The route with the destination as the destination hi The magnitude of active and reactive power transmitted; For nodes i The set of sending nodes; They are respectively t Time Node i The maximum active and reactive power reduction of the load; for t Timetable hi The square of the magnitude of the transmitted current; The lines are respectively hi The magnitudes of resistance and reactance; They are respectively t Time with nodes i The route starting from ij The magnitude of active and reactive power transmitted; For nodes i The set of receiving nodes; for t Nodes in the distribution network at all times i Square of node voltage; Representing nodes respectivelyi Minimum and maximum values ​​of the square of the voltage; Indicates the line ij The maximum squared value of the transmission current; 0-1 variables represent t Timetable ij Connectivity state, where a value of 1 indicates connectivity; A set of distributed resource nodes in a power distribution network; N For distribution network nodes; A collection of distribution network lines; M It is an extremely large number; K The set of dispatchable generator cars; These represent the minimum and maximum active power output of the mobile hydrogen generator vehicle, respectively. This is a set of time periods for dynamic scheduling.

[0029] Step 4: Robust Pre-scheduling Model for Flexible Hydrogen Resources To address the uncertainty in power output from renewable energy sources such as photovoltaics, this embodiment introduces a two-stage robust optimization strategy in the pre-scheduling model to ensure that, even in the worst-case scenario of renewable energy output, the pre-scheduling of various distributed resources can meet the power demand of the electrical load as much as possible.

[0030] The objective function of the pre-scheduling model consists of two parts: the pre-configuration cost of mobile hydrogen storage tankers and minimizing the total cost of load reduction at each node in the distribution network. In the two-stage robust optimization model, the decision variables for the first stage are shown in set X. Photovoltaic output is an uncertain variable in robust optimization, and its uncertainty set is as follows. As shown, the decision variables for the second stage are represented by set Y.

[0031] (7) (8) (9) (10) (11) In the formula, A 0-1 variable, representing the first... k Are the hydrogen storage tank trucks pre-scheduled to the node? j ; Indicates the first k The pre-scheduling cost of each tanker truck; Represents a node j Reduced load per unit cost; A 0-1 variable, representing a pre-scheduled line. ij Switch status; The representative is located at the node j The output of the photovoltaic units; The representative is located at the node j The predicted output of the photovoltaic units; A 0-1 variable, representing a node j The upper and lower limits of photovoltaic unit output; This represents the degree of uncertainty in photovoltaic power output; This represents the set of photovoltaic (PV) generator nodes.

[0032] The first phase constraints include: (12) (13) (14) (15) (16) In the first stage constraint, Equation (12) indicates that each tanker truck can only be configured at one node of the distribution network during the pre-scheduling process. Equations (13)-(15) consider the cases of island merging and islands without power supply, and adopt the improved single commodity flow method to ensure that the radial topology requirements are met during the recovery process. The number of islands after network reconstruction is added to the optimization process, and the Big M method is used to relax the virtual power flow. Equation (16) represents the state constraint when the line is disconnected.

[0033] In the formula, It is a 0-1 variable, representing whether this node is a virtual source node; Representative Line ij The virtual flow power flowing through; Representative node i The output virtual power; Indicates the set of disconnected lines; M It is a very large constant.

[0034] The second phase constraints include: (17) (18) (19) (20) (twenty one) Equations (17) and (18) represent the constraints on the range of active power reduction and the relationship between the power reduction at each node in the distribution network, respectively. Equation (19) represents the range of active and reactive power output at each distributed energy node in the distribution network. Equation (20) represents the coupling between the output variables of photovoltaic and hydrogen gas turbines and the output variables of distributed energy. Equation (21) represents the range of reactive and active power output of distributed energy.

[0035] In the formula, Distribution network nodes i The maximum active and reactive power of the load; They are nodes i Minimum / maximum active power output of distributed resources; They are nodes i Minimum / maximum reactive power output of distributed resources; power factor angle of distributed resources The range of values ​​is , To obtain the tangent value; This indicates the node where the distribution network is connected to the upstream power grid.

[0036] The pre-schedule phase did not incorporate an actual road network model; instead, it focused on determining the initial hydrogen storage capacity of mobile hydrogen tankers during emergency dispatch. The initial location of the tanker truck for emergency dispatch. This is then used as a known quantity and input into real-time emergency dispatch to establish the following model: (twenty two) (twenty three) Since the second phase considers the pre-scheduled distribution of pipeline storage to the hydrogen gas turbine, and needs to meet the pipeline network operation constraints, it is modified as follows: (twenty four) (25) In the formula, Pipes ij Initial inflow and outflow hydrogen flow rates at both ends; Pipes ij Initial pressure at both ends; For pipelines ij Initial storage.

[0037] During this stage, the power distribution network operation still needs to meet power flow constraints: (26) Step 5: Flexible Hydrogen Tank Truck Emergency Dispatch Model In the mobile hydrogen resource emergency dynamic dispatch model, the total time of the power grid node during the occurrence of the emergency is considered. The objective is to minimize the sum of internal load reduction power, as shown below: (27) First, the hydrogen storage capacity of the hydrogen tanker truck under different power distribution network nodes and the emergency dispatch of the road network are modeled. Equation (28) represents the hydrogen storage level relationship of the tanker truck. Equation (29) indicates that the tanker truck must depart from its pre-configured node or continue to stay. Equation (30) imposes a constraint on the uniqueness of the tanker truck's transportation path, that is, the tanker truck can only be located on the forward / reverse transportation path between two points or not carry out transportation. Equation (31) constrains that when the tanker truck starts transportation, the hydrogen storage capacity must be greater than the hydrogen consumption during transportation. Equation (32) indicates that the sum of the distances of all transportation paths of the tanker truck cannot exceed its maximum driving distance. Equation (33) indicates that the distance of the tanker truck to the node j During emergency dispatch, the travel time must be shorter than the time required to reach the destination. j The maximum time. Equation (34) imposes a constraint on the total running time of each tanker truck.

[0038] (28) (29) (30) (31) (32) (33) (34) In the formula, Indicates the first k Tanker trucks at the node j Hydrogen storage capacity; Indicates the first k Tanker trucks at the node j The original hydrogen storage capacity can be calculated using the pre-scheduling model; A 0-1 variable, representing the first... k Should the tanker trucks choose a route? j → i Transmit; Indicates the first k Tanker trucks on the route i → j Hydrogen consumption during transport; Indicates the first k Tanker trucks on the route i → j The driving distance; For the first k Tanker trucks on the routei → j Travel time on the road; Representing a path j → i With nodes j The set of endpoint nodes that are the starting point; Representing a path i → j With nodes j The set of starting nodes for the endpoint; Represents a set of transportation routes; Indicates the first k Maximum travel distance of a tanker truck; Indicates heading to the node j Maximum required time; Indicates the first k The maximum travel time for a tanker truck.

[0039] Equation (35) represents the relationship between the active power output of the tanker truck and the amount of hydrogen consumed. Equation (36) represents the constraints on the active and reactive power output of the tanker truck. Equation (37) represents the relationship between the emergency hydrogen storage capacity inside the tanker truck and the amount of hydrogen consumed. Equation (38) represents the range of emergency hydrogen storage capacity of the tanker truck and the range of hydrogen consumed for power generation.

[0040] (35) (36) (37) (38) In the formula, For the first k Tanker trucks in t At the power grid node j Amount of hydrogen gas released; Given 0-1 variables, determine the first... k tanker trucks t Is the node connected at any time? j A value of 1 indicates access; For the first k Tanker trucks in t At the power grid node j Hydrogen reserves; For the first k Maximum emergency hydrogen storage capacity of each tanker truck; These are parameters related to the hydrogen-to-electricity conversion efficiency of emergency hydrogen fuel cell vehicles.

[0041] This embodiment considers the impact of road damage during the emergency phase primarily through changes in congestion levels caused by reduced traffic capacity, resulting in decreased vehicle speed, changes in equivalent transport distance, and changes in travel time, rather than the discrete inaccessibility problem of complete road blockage. Under this setting, the feasibility of the emergency phase is jointly guaranteed by constraints such as path uniqueness and upper bounds on travel time. Furthermore, during the pre-scheduling phase, the accessibility margin of the initial layout can be increased by adjusting the upper bounds on congestion levels and shortest travel times, ensuring that the initial layout is feasible under the model's semantics.

[0042] Therefore, considering the impact of flexible mobile hydrogen generator vehicles on road capacity and travel time in emergency situations, a relationship model between the actual travel time of mobile hydrogen storage tankers and the equivalent transport distance and actual speed is established as shown below. Equation (39) represents the actual speed of each tanker at different times; Equation (40) represents the equivalent transport distance of each tanker on the transport route at different scheduling times; Equation (41) represents the expression for the actual travel time of the tanker.

[0043] (39) (40) (41) In the formula, Indicates the first k Tanker trucks in t The actual speed at any given moment; Indicates the first k The ideal speed of the tanker truck is not considered under the influence of emergencies and traffic flow. c Indicates the degree of traffic network congestion caused by unforeseen events, etc. Indicates the first k Tanker trucks in t Path at any given moment i → j The equivalent scheduling distance; Represents the path under ideal conditions i → j The scheduling distance; Indicates the first k Tanker trucks in t Time below path i → j Time required for scheduling.

[0044] Step Six: Emergency Dispatch Model for Hydrogen Transportation via Pipeline Considering Pipeline Storage Effect Further, an emergency dispatch model for pipeline hydrogen transportation was established that takes into account the storage effect. This part fully considers the constraints of hydrogen storage at hydrogen refueling stations, gas turbines, and hydrogen pipelines in the modeling.

[0045] Equation (42) represents the relationship between the hydrogen storage capacity of each hydrogen refueling station during the emergency dispatch period. Equation (43) represents the relationship between the power generation of the hydrogen gas turbine placed in each hydrogen refueling station and the hydrogen consumption rate. Equation (44) represents the upper and lower limits of the active and reactive power output of the hydrogen gas turbine.

[0046] (42) (43) (44) In the formula, No. i Within the hydrogen refueling station t Hydrogen storage capacity at any time; For the first i Within the hydrogen refueling station t The amount of hydrogen received from the pipeline at time +1; Indicates the first i Within the hydrogen refueling station t The amount of hydrogen released to the hydrogen gas turbine power plant at time +1; For the first i Within the hydrogen refueling station t The magnitude of active and reactive power output of the hydrogen gas turbine during different time periods; A 0-1 variable, representing the first... i Within the hydrogen refueling station t The operating status of the hydrogen gas turbine during a given period, with 1 indicating operation; The first i Active power and minimum output value of hydrogen gas turbines in each hydrogen refueling station; The first i The reactive power and minimum output value of the hydrogen gas turbine in each hydrogen refueling station; A collection of hydrogen refueling stations; This represents the density of hydrogen gas.

[0047] Equation (45) represents the relationship between the flow rate at both ends of the hydrogen transmission network pipeline and the pressure at both ends of the pipeline. Equation (46) represents the relationship between the flow rate at both ends of the hydrogen transmission network pipeline and the hydrogen storage in the pipeline. Equation (47) represents the relationship between the pressure at both ends of the pipeline and the hydrogen storage in the pipeline. Equation (48) represents the flow balance relationship at the pipeline nodes. Due to the interruption of the distribution network and the external power grid during disasters, the hydrogen production station and the hydrogen transmission network may also be disconnected, which will limit the continuous hydrogen supply capacity of the hydrogen transmission network. Therefore, the situation where the hydrogen production station produces hydrogen and injects it into the hydrogen transmission network is not considered. During this period, only the hydrogen storage in the pipeline is scheduled. Equation (49) represents the gas load demand in the hydrogen transmission network pipeline as the amount of hydrogen injected into the hydrogen storage of each hydrogen refueling station.

[0048] (45) (46) (47) (48) (49) In the formula, Pipes ij exist t The inflow and outflow of hydrogen at any given time; For pipeline nodes i exist t The pressure of constant time; For pipelines ij At any moment t Inventory; For pipeline nodes i exist t Continuously output hydrogen to the load nodes; For pipelines ij Pipeline constant; A collection of pipeline branches; For pipelines ij Storage constant; This is a set of pipeline nodes.

[0049] Step 7: Based on the above modeling, the strategies for leveraging flexible hydrogen energy storage to enhance power system resilience through multiple pathways are mainly divided into pre-scheduling models and emergency dynamic scheduling models. The emergency dynamic scheduling model is a mixed-integer programming problem, which can be directly solved using a commercial solver on the Matlab platform. In the two-stage robust pre-scheduling model for mobile hydrogen energy storage, the column constraint (C&CG) algorithm is used for solving, and the model for the pre-scheduling stage can be constructed into the following compact form: (50) In the formula, X This refers to the set of decision variables for the first stage in the pre-scheduling model. u and Y This is the set of decision variables for the second stage. This represents a set of uncertainties, such as photovoltaic power output. Representing decision variables Y The feasible domain; a , b c and The coefficient vector of the variables; A , B , C , D and It is a constant matrix; n The number of second-order cone constraints.

[0050] The model is decomposed into a main problem and sub-problems. The main problem is to solve for a deterministic mobile hydrogen energy storage pre-layout scheme and the on / off state of the transmission lines. The main problem is constructed in the following form: (51) In the formula, Z Indicates auxiliary variables; K Indicates the maximum number of iterations; l This represents the corresponding variable obtained after the current iteration number; Indicates the first l Photovoltaic output after the next iteration.

[0051] The subproblem is to determine the worst-case photovoltaic output given the pre-deployment of mobile energy storage. The model is as follows: (52) In the formula, This represents the pre-deployment scheme for mobile energy storage obtained from solving the main problem. Using strong duality theory, the min-max bi-level optimization subproblem is transformed into a single-level optimization problem, which can be directly solved using a solver.

[0052] The model solution process is as follows: Figure 3 As shown.

[0053] Example 2: This embodiment provides a simulation analysis of the scheduling method described in Embodiment 1. In this embodiment, all 33 nodes in the distribution network correspond to the corresponding road network nodes. A multi-path hydrogen transportation network is constructed based on a 39-node road network (Transportation System, TS) and a 20-node gas pipeline network, as follows: Figure 4 As shown. Distribution network branch parameters can be found in the literature; the rated voltage is 12.66 kV, and the highest and lowest voltages are 1.1 times and 0.9 times the rated voltage, respectively. The hydrogen gas turbine power generation efficiency is 50%. The maximum hydrogen refueling capacity for a single hydrogen fuel cell vehicle is 5 kg. The demand for each HRS is as follows: Figure 5 As shown.

[0054] The tanker truck has a full hydrogen capacity of 380 kg, with a transportation cost coefficient of 18.05 yuan / km. The mobile hydrogen-powered generator has a hydrogen-to-electricity conversion efficiency of 95%, a hydrogen-to-electricity conversion coefficient of 15.7 kWh / kg, and a maximum output power of 200 kW. The pre-configured cost per vehicle is 500 yuan, and the ideal speed is 25 km / h. The unit cost of renewable energy curtailment is set at 144.4 yuan / (MW·h). The electrolyzer has a rated electrolysis power of 2.5 MW, an efficiency of 60%, and an operating cost coefficient of 14.801 yuan / (MW·h). The maximum capacity of the hydrogen storage equipment is 800 kg. The lower calorific value of hydrogen is 33.3 kW. The relevant factors, such as h / kg, time-of-use electricity price, hydrogen price, relevant pipeline parameters, and initial pipeline stock, are shown in Table 1.

[0055] Table 1

[0056] The congestion level of the traffic network and the photovoltaic power output prediction curve during the dynamic scheduling phase are shown in the figure below. Figure 6 As shown in (a) and (b), the predicted active power demand at different times is shown in Table 2. The unit cost of load reduction at critical nodes and normal nodes is RMB 10 / (kW·h) and RMB 1 / (kW·h), respectively, and the unit cost of electricity purchase is RMB 0.4 / (kW·h). The emergency event occurred at 5:00, with faults occurring on lines 1-2, 3-4, 7-8, 28-29, and 12-22, lasting for 11 hours.

[0057] Since this embodiment takes into account the uncertainty of photovoltaic output, the degree of uncertainty of photovoltaic output is taken as... ,use The obtained scheduling scheme is applicable to any The situation remains robust and feasible, ensuring that the emergency dispatch strategy obtained in this embodiment does not rely on an underestimation of uncertainty, thus demonstrating robustness. The predicted active power demand at different times is shown in Table 2.

[0058] Table 2

[0059] All simulation examples in this embodiment were performed on a computer with an Intel(R) Core(TM) i5-12400F CPU @ 2.90GHz and 8GB of RAM, using the MATLAB R2023b compilation environment and solved using Yalmip+Gurobi.

[0060] To analyze the impact of the proposed multi-technology hydrogen transportation network dynamic characteristics hydrogen supply chain production-storage-transportation collaborative optimization model on the operation of the hydrogen supply chain, the following three scenarios are set up for comparative analysis: Scenario 1: Considering the fluctuation of photovoltaic output, hydrogen storage tank trucks and pipeline hydrogen transportation, which takes into account the storage effect, jointly participate in the two-stage robust pre-scheduling of mobile hydrogen resources. Scenario 2: Considering the fluctuation of photovoltaic output, only hydrogen storage tank trucks participate in the two-stage robust pre-scheduling of mobile hydrogen resources; Scenario 3: Considering the fluctuation of photovoltaic output, the pipeline hydrogen transportation method, which only considers the pipeline storage effect, participates in the two-stage robust pre-schedule of mobile hydrogen resources.

[0061] First, the overall operation results of the system under the three scenarios are analyzed. The pre-scheduling schemes and costs are shown in Table 3. Scenario 3 has the highest pre-configuration cost (including mobile tanker dispatch cost and distribution network active power load reduction cost). Since the mobile emergency hydrogen storage tanker is not considered for connecting to the distribution network for reverse power supply to meet the load power demand of the distribution network nodes, the distributed power types in Scenario 3 are only charging piles, photovoltaics, and hydrogen gas turbines. In emergency situations, the power that can be transmitted to the distribution network is reduced compared to Scenario 1 and 2. Although the tanker pre-scheduling cost is 0 yuan in this scenario, it has to bear a higher load reduction cost of 6286.61 yuan. The main reason for the increased cost of Scenario 2 compared to Scenario 1 is that pipeline dispatch is not included in Scenario 2. Therefore, the hydrogen storage in the hydrogen refueling station cannot meet the hydrogen consumption required for the hydrogen gas turbine to generate electricity at maximum power. The output power of the hydrogen gas turbine is reduced compared to Scenario 1, so the dispatch of mobile emergency hydrogen storage tankers increases, and the pre-scheduling cost increases. From the above analysis, it can be seen that Scenario 1 can achieve the lowest total cost while minimizing the load power reduction.

[0062] Table 3

[0063] Pre-scheduling results of mobile hydrogen storage tankers in different scenarios are as follows: Figure 7 As shown in the figure. The pre-scheduling results of the hydrogen pipeline network under different scenarios are as follows: Figure 8 As shown in (a) and (b).

[0064] Depend on Figure 8 As can be seen, since scenario 2 does not consider pipeline pre-scheduling, the hydrogen storage tanks at each hydrogen refueling station cannot receive hydrogen from the hydrogen transmission network. The hydrogen required by the hydrogen gas turbine can only come from the amount of hydrogen originally stored in the storage tanks, which is insufficient to meet the needs of the hydrogen gas turbine when generating electricity at maximum power. Therefore, the power output of the hydrogen gas turbine is 83kW and 50kW. Since scenarios 1 and 3 both consider pipeline pre-scheduling, the amount of hydrogen pre-scheduled from the hydrogen transmission network received by each hydrogen refueling station is shown in the figure above. The hydrogen storage capacity of each storage tank can meet the consumption of the hydrogen gas turbine when generating electricity at maximum power, with power outputs of 120kW and 80kW respectively, reducing the system load shedding power.

[0065] Although all hydrogen gas turbines output maximum power in Scenario 3, the active load recovery rate after pre-schedule is the lowest. In contrast, the load recovery rates in Scenario 1 and Scenario 2 are close to 100%. The main reason Scenario 2 can achieve a similar load recovery rate to Scenario 1 is that, with other distributed energy sources having the same output, pre-scheduling more emergency hydrogen tankers compensates for the power shortage of the hydrogen gas turbines, thus increasing costs. The reason the active power inventory in Scenario 1 is slightly lower than in Scenario 2 is that both the HGT and other distributed energy sources are outputting at full capacity. After dispatching a certain number of emergency hydrogen tankers, there is still a small amount of load reduction. If more emergency hydrogen tankers are dispatched, the cost exceeds the cost of reducing the load reduction, so no dispatch is performed. In Scenario 2, because the HGT cannot output maximum power, the increased cost of load reduction exceeds the cost of pre-configuring more emergency hydrogen tankers. Therefore, more tankers are dispatched to reduce the load reduction cost. This can also be seen from... Figure 7 It can be seen that, in any scenario, distributed energy output will prioritize meeting the power demands of critical loads during the pre-scheduling process.

[0066] In the emergency dispatch of hydrogen storage power generation tank trucks, the number of tank trucks, the initial location of the tank trucks, and the hydrogen storage capacity of each tank truck are all based on the pre-dispatch results in Scenario 1 of Section 4.2.1 as input for subsequent emergency power supply scheduling. That is, the initial access locations of the three tank trucks are located at nodes 7, 25, and 30 of the distribution network, respectively.

[0067] Figure 9 The diagrams show the dispatch routes of the three tank trucks when they connect to the distribution network nodes during emergency power supply, and output the changes in active power and hydrogen storage at different nodes and dispatch times.

[0068] The nodes that tank truck 1 connects to the power distribution network are as follows: node 7 → node 28 → node 32 → node 4 → node 28; The nodes for connecting tanker truck 2 to the power distribution network are as follows: node 30 → node 24 → node 31 → node 28 → node 24 → node 27; The nodes for connecting tanker truck 3 to the power distribution network are as follows: node 25 → node 7 → node 26 → node 5 → node 4.

[0069] As can be seen from the above connection nodes, the three emergency tanker trucks primarily focus on supplying power to critical load nodes or nodes adjacent to critical load nodes, prioritizing the power needs of critical load nodes. Furthermore, the hydrogen storage in all three tanker trucks was completely consumed after the emergency dispatch cycle ended, maximizing the output of electrical energy to minimize load shedding.

[0070] After the emergency occurred, Tanker Truck 1 initially provided power at Node 7, and then arrived at the critical load node 28 at 9:00 AM to provide continuous power for 2 hours. Simultaneously, the travel time from Node 7 to Node 28 decreased from a peak of 1.48 hours to 0.81 hours, allowing the tanker truck to reach the corresponding node promptly for recharging. After this, Tanker Truck 1's power output was primarily concentrated at the critical load node 4, because the charging station at Node 5 had exhausted its power capacity later in the dispatch period and could not supply power to the adjacent load node 4. Nodes 4-7 and Nodes 26-28 together formed an island.

[0071] Tanker truck 2 arrives at critical load node 24 at 6:00 AM to provide reverse power for two consecutive hours. Afterwards, it arrives at normal load node 31 at 9:00 AM to provide reverse power. At this time, the load demand at this node is 135.4 kW, reaching the maximum load during the emergency dispatch period. Simultaneously, it works with the hydrogen turbine connected to node 29 and the photovoltaic unit at node 33 to meet the load power demand of some nodes. In the final moments, tanker truck 1's output power is relatively low, and tanker truck 3 does not output power. Within the island formed by nodes 4-7 and 26-28, only the hydrogen turbine connected to node 6 can provide stable power output. Therefore, tanker truck 2 arrives at node 27 and operates at maximum power to meet the load power demand of the nodes within the island.

[0072] Tanker 3 primarily outputs power to nodes 26, 5, and 4 within the aforementioned island. As analyzed above, the only distributed energy sources within this island are the electric vehicle charging pile connected to node 5 and the hydrogen turbine at node 6. However, the charging piles have limited capacity. Once their capacity is exhausted, only one hydrogen turbine will be available to supply the power demand of the load nodes within the island. The total load power within the island is at its minimum of 482.9 kW during the period from 8:00 to 15:00. The hydrogen turbine cannot meet this power demand. Furthermore, the number of critical load nodes within this island accounts for 33.3% of the entire system. Therefore, during the scheduling cycle, tanker 3 primarily operates within the island's nodes to provide reverse power supply in order to minimize load shedding.

[0073] Load active power recovery rate and output status of each distributed energy source, such as Figure 10 As shown in the diagram, in this scenario, the recovery rate of critical loads remains high (close to 100%) with minimal fluctuations during the emergency dispatch period, while the recovery rate of normal load active power is already below 10% during the period from 5:00 to 8:00, and gradually increases to only about 40% during the period from 9:00 to 14:00. This reflects the characteristic of prioritizing the supply of critical load nodes under resource constraints and post-disaster constraints.

[0074] During the scheduling period from 9:00 to 14:00, tanker truck 1 mainly outputs power at critical load nodes 4 and 28, tanker truck 2 mainly outputs power at normal load node 31 and critical load node 28, and tanker truck 3 mainly outputs power at critical load node 4 and normal load node 5. From the tanker truck scheduling routes during this period, it can be seen that tanker truck 1 and tanker truck 3 are both located within the aforementioned islanded nodes during this time, and tanker truck 2 also performs continuous discharge operations for 2 hours at critical load node 28 within the island. During this period, the hydrogen gas turbine located within the island can continuously output 720kW of power. Therefore, the load power demand of the critical nodes within the island can be met, thereby reducing the load shedding at the critical load nodes.

[0075] Meanwhile, during this period, the output of each photovoltaic unit gradually increased, from a total output of 556.947kW from 5:00-8:00 to 6551.8kW from 9:00-14:00. After meeting the load demand of important nodes not located on the island together with the hydrogen gas turbine, more surplus active power can be used to meet the power demand of normal load nodes. At the same time, during this period, tanker truck 2 arrived at normal load node 31 at 9:00 and output power of about 200kW. At this time, the load reduction of the node dropped to 0. Tanker truck 3 continuously output power at normal load node 5 for 3 hours. The total load reduction of node 5 was 83kW, which was significantly less than the total load reduction of 183kW during the period from 5:00-8:00.

[0076] To verify the effectiveness of the two-stage emergency dispatch strategy proposed in this embodiment, which involves flexible hydrogen energy storage in multiple ways to enhance the resilience of the power system, and the economic applicability of the proposed strategy under different scenarios, this section sets up four different recovery strategies as shown in Table 4, depending on whether emergency tank trucks and mobile hydrogen energy storage in the hydrogen transmission pipeline network participate in pre-dispatch and the dispatching method during the emergency dynamic dispatch stage. These strategies are then verified in three different scenarios. Strategy 1 is the scheme proposed in this embodiment, and scenario 1 is the scenario used in the aforementioned emergency dispatch simulation.

[0077] Table 4

[0078] Based on different scheduling methods, this embodiment sets up three scenarios: faults occurring at different times (morning, noon, and evening), fault durations, and faults occurring on different lines. Scenario 1: A fault occurs in the power distribution network at 5:00 AM. The faulty lines are 1-2, 3-4, 7-8, 28-29, and 12-22. The fault lasts for 11 hours. Scenario 2: A fault occurs in the power distribution network at 11:00. The faulty lines are 2-3, 12-13, 26-27, and 32-33. The fault lasts for 13 hours. Scenario 3: The power distribution network experiences a fault at 15:00, with no lines affected, and the fault lasts for 6 hours.

[0079] Table 5 shows the costs for each scenario. Under different scenarios, Strategy 1 has the lowest load-cutting cost. In Scenario 1, the load-cutting cost of Strategy 1 is reduced by 44.81%, 8.8%, and 44.80% compared to Strategies 2, 3, and 4, respectively. Strategy 1 has a significant advantage in load-cutting cost across the three scenarios, while Strategy 4 has a higher cost and may not be suitable for scenarios sensitive to load-cutting costs. Strategies 2 and 3 show relatively balanced performance. This verifies the robustness and adaptability of the proposed flexible hydrogen resource multi-pathway participation in multi-stage emergency supply guarantee strategy for the distribution network (Strategy 1) under different fault occurrence times, fault durations, and line fault modes.

[0080] Table 5

[0081] from Figure 11 As can be seen, since Strategy 1 considers the pre-scheduling of tank trucks and their pre-configuration at nodes 7, 25, and 30, each tank truck can be dynamically dispatched to different important load nodes for power supply during emergency dispatch, reducing travel time to the corresponding nodes. At this time, each hydrogen storage tank has already received hydrogen from the hydrogen transmission network during the pre-scheduling phase, and the hydrogen transmission network can also inject hydrogen into each hydrogen storage tank in the later stages of the emergency dispatch period to make up for the hydrogen energy shortage required for hydrogen gas turbine power generation. Under Scenario 1, Strategy 1 maintains an active load recovery rate of approximately 100% at important nodes throughout the entire cycle.

[0082] Compared to Strategy 1, the active power load recovery rate of key nodes decreased significantly under Strategies 2 and 4. Meanwhile, because Strategy 2 considers both pre- and dynamic scheduling of pipeline hydrogen transport, the hydrogen reserves at each hydrogen refueling station are sufficient to meet the hydrogen consumption of the hydrogen gas turbine operating at rated power throughout the emergency period. In contrast, the hydrogen reserves in Strategy 4 are insufficient to meet the operating requirements of the hydrogen gas turbine. Therefore, the overall load recovery rate of key nodes under Strategy 2 is slightly higher than that under Scenario 4.

[0083] A comparison of strategies 2 and 4 shows that without pre-scheduling of mobile hydrogen generators, the collaborative participation of hydrogen pipelines in the recovery of critical loads is not significant. However, a comparison of strategies 3 and 4 shows that pre-scheduling of mobile hydrogen generators significantly improves the recovery rate of critical loads.

[0084] A comparison of strategies 1 and 3 reveals that strategy 3 does not consider pipeline hydrogen supply and relies solely on mobile hydrogen generators as emergency dispatch resources. In this scenario, due to the limited hydrogen storage capacity of hydrogen gas turbines, they cannot output rated power at every moment during the emergency dispatch period. Therefore, it is necessary to distribute power output more evenly at each moment during the emergency dispatch process to ensure that the active load recovery rate of important nodes remains stable, which leads to a decrease or fluctuation in the load recovery rate. The coordinated participation of hydrogen pipelines and mobile hydrogen generators can provide the distribution network with energy through multiple sustainable replenishment channels, solve the problem of limited emergency supply capacity of the distribution network, and thus improve the recovery rate of important loads.

[0085] Combination Figure 12 As shown in (a) and (b), in scenario 1, although the active load recovery rate of normal nodes of strategy 1 is not the highest, it performs best in terms of active load recovery rate of important nodes. At the same time, the total active load recovery rate of strategy 1 is close to that of strategy 2. However, because strategy 1 significantly improves the recovery level of important nodes, the shortage of high-value loads in the system is effectively suppressed, thus making it more advantageous in terms of overall cost: compared with strategy 2, the total load reduction cost of strategy 1 is reduced by 11415.741 yuan.

[0086] Because the unit cost of load shedding at critical nodes is higher than that at normal nodes, distributed energy resources and emergency hydrogen storage tankers prioritize meeting the needs of critical load nodes. Therefore, in Scenario 1, the active power recovery rate curves of critical node loads under the four different strategies do not fluctuate significantly with changes in scheduling time. As the output of photovoltaic units gradually increases, while prioritizing the active power load needs of critical nodes, the load shedding amount at these nodes gradually decreases, and the load recovery rate under each strategy shows an increasing trend. Therefore, the total active power recovery rate also shows an increasing trend.

[0087] In summary, the four strategies proposed in this embodiment not only distinguish between pre-disaster pre-scheduling and post-disaster dynamic scheduling, but also further introduce a comparison of whether to conduct pre-scheduling of mobile hydrogen generators and pipeline hydrogen transportation, as well as multi-path coordination of emergency scheduling. Through numerical examples, Strategy 1 proposed in this embodiment, with its multi-path pre-scheduling of mobile hydrogen generators and pipelines, and post-disaster multi-path dynamic scheduling, has the lowest load-cutting cost in the three fault scenarios. This not only verifies the flexibility of mobile resources but also demonstrates that the multi-path hydrogen-to-electricity conversion, combining dynamic replenishment of the hydrogen pipeline network and coordination with mobile hydrogen generators, can improve the continuous energy supply capacity in the mid-to-late stages of a disaster.

[0088] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for flexibly participating hydrogen resources in emergency power grid dispatching through multiple pathways under sudden disasters, characterized in that: Includes the following steps: S1: Establish a coupled framework of distribution network-transportation network-hydrogen pipeline network, construct a two-stage hydrogen-electricity conversion operation strategy for distribution network, and a dynamic power flow model that considers the access of various distributed resources in distribution network; S2: Introduce a two-stage robust optimization strategy into the pre-scheduling model to construct a flexible hydrogen resource robust pre-scheduling model; S3: Construct a flexible emergency dispatch model for hydrogen storage tank trucks, and further construct an emergency dispatch model for pipeline hydrogen transportation that considers the pipeline storage effect. S4: Solve the robust pre-scheduling model for flexible hydrogen resources and the emergency scheduling model for pipeline hydrogen transportation to achieve coupled and collaborative optimization of hydrogen and electricity resources and output emergency supply scheduling strategies.

2. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The coupling framework of the power distribution network, transportation network, and hydrogen pipeline network is established as follows: The power distribution network includes hydrogen gas turbines, charging piles, photovoltaic units, multiple ordinary load nodes and multiple important load nodes of the power distribution network; the hydrogen transmission pipeline network includes multiple ordinary load nodes of the power distribution network and multiple nodes coupled with hydrogen gas turbines; the transportation network includes hydrogen-powered generator vehicles and multiple road network nodes corresponding to power distribution network nodes. In the distribution network, distributed resources include photovoltaic units, hydrogen gas turbines, and charging piles. The hydrogen consumed by the hydrogen gas turbines comes from the hydrogen transmission pipeline network and its own hydrogen storage. When the distribution network is not affected by emergencies, the power demand of the distribution network is met by the upstream main grid. When an emergency occurs, the connection between the distribution network and the upstream main grid is interrupted. At this time, distributed resources serve as the foundation, with hydrogen pipelines and emergency hydrogen generator vehicles serving as emergency resources to ensure supply. In the face of disasters, the hydrogen production station and the hydrogen transmission network may also be disconnected. Therefore, in the pre-dispatch phase, the number of hydrogen generator vehicles, the pipeline storage considering dynamic effects, and the hydrogen storage capacity of the hydrogen gas turbines are optimized. In the dynamic dispatch phase, based on distributed energy, the pipeline storage is dynamically dispatched to supply hydrogen to the hydrogen gas turbines, and the emergency hydrogen generator vehicles move between road networks to supply power to the distribution network, so as to minimize the impact on the nodes of the distribution network.

3. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The two-stage hydrogen-to-electricity conversion operation strategy of the distribution network described in step S1 includes: during the prevention phase, the distribution network is dynamically simulated for power flow timing, and a robust optimization operation model is established based on the uncertain output of photovoltaic units; when the occurrence time and duration of an emergency event are unknown, multiple hydrogen power generation tankers are pre-scheduled to supply power at different distribution network nodes, and the hydrogen pipeline stores sufficient hydrogen in the hydrogen storage tanks to supply hydrogen gas turbine power generation; after the emergency event occurs, by coordinating the output of various distributed resources, the hydrogen storage and power generation tankers, the pipeline's storage capacity, and the hydrogen gas turbine power generation are dynamically scheduled to maximize the power demand of important load nodes and improve the load recovery rate of the distribution network.

4. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The dynamic power flow model considering the access of multiple distributed resources in the distribution network mentioned in step S1 includes: For radial distribution networks, the DistFlow power flow equation is used to model them: (1) (2) (3) (4) (5) (6) Equations (1) and (2) ensure the balance of active and reactive power at each node in the distribution network. If a node is connected to distributed energy, the power balance is expressed as Equation (1); otherwise, it is expressed as Equation (2). Equation (3) is the relationship between the voltage at both ends of each branch and the power transmitted by the line. The big M method is used to relax Equation (3). Equations (4) and (5) are the voltage constraints of each node in the distribution network and the current constraints of each line, respectively. Equation (6) uses the second-order cone relaxation method to transform the nonlinear relationship between the power, current and node voltage of the distribution network. In the formula, They are located at the nodes respectively i Distributed power supply t Active and reactive power output at all times; They are nodes i exist t The amount of active and reactive power reduction is constantly being measured; They are located at the nodes respectively i The k Mobile hydrogen power generation vehicle t Active and reactive power output at all times; for t Time with nodes i The route with the destination as the destination hi The magnitude of active and reactive power transmitted; For nodes i The set of sending nodes; They are respectively t Time Node i The maximum active and reactive power reduction of the load; for t Timetable hi The square of the magnitude of the transmitted current; The lines are respectively hi The magnitudes of resistance and reactance; They are respectively t Time with nodes i The route starting from ij The magnitude of active and reactive power transmitted; For nodes i The set of receiving nodes; for t Nodes in the distribution network at all times i Square of node voltage; Representing nodes respectively i Minimum and maximum values ​​of the square of the voltage; Indicates the line ij The maximum squared value of the transmission current; 0-1 variables represent t Timetable ij Connectivity state, where a value of 1 indicates connectivity; A set of distributed resource nodes in a power distribution network; N For distribution network nodes; A collection of distribution network lines; M It is an extremely large number; K The set of dispatchable generator cars; These represent the minimum and maximum active power output of the mobile hydrogen generator vehicle, respectively. This is a set of time periods for dynamic scheduling.

5. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The flexible hydrogen resource robust pre-scheduling model described in step S2 is constructed as follows: The objective function of the pre-scheduling model consists of two parts: the pre-configuration cost of mobile hydrogen storage tankers and the minimum total cost of load reduction at each node in the distribution network. In the two-stage robust optimization model, the decision variables for the first stage are shown in set X. Photovoltaic output is an uncertain variable in robust optimization, and its uncertainty set is as follows: As shown, the decision variables for the second stage are represented by set Y; (7) (8) (9) (10) (11) In the formula, A 0-1 variable, representing the first... k Are the hydrogen storage tank trucks pre-scheduled to the node? j ; Indicates the first k The pre-scheduling cost of each tanker truck; Represents a node j Reduced load per unit cost; A 0-1 variable, representing a pre-scheduled line. ij Switch status; The representative is located at the node j The output of the photovoltaic units; The representative is located at the node j The predicted output of the photovoltaic units; A 0-1 variable, representing a node j The upper and lower limits of photovoltaic unit output; This represents the degree of uncertainty in photovoltaic power output; Represents the set of photovoltaic unit nodes; Phase 1 constraints include: (12) (13) (14) (15) (16) Equation (12) indicates that each tanker can only be configured at one node of the distribution network during the pre-scheduling process; Equations (13)-(15) consider the cases of island merging and power-free islands, adopt the improved single commodity flow method to ensure that the radial topology requirements are met during the recovery process, and add the number of islands after network reconstruction to the optimization process, and use the big M method to relax the virtual power flow; Equation (16) indicates the state constraint when the line is disconnected; In the formula, It is a 0-1 variable, representing whether this node is a virtual source node; Representative Line ij The virtual flow power flowing through; Representative node i The output virtual power; Indicates the set of disconnected lines; M It is a very large constant; The second phase constraints include: (17) (18) (19) (20) (21) Equations (17) and (18) are respectively the range constraints of the active power reduction of each node in the distribution network and the relationship of the power reduction; Equation (19) is the range of active and reactive power output of each distributed energy node in the distribution network; Equation (20) represents the coupling between the output variables of photovoltaic and hydrogen gas turbine and the output variables of distributed energy; Equation (21) represents the range of reactive and active power output of distributed energy. In the formula, Distribution network nodes i The maximum active and reactive power of the load; They are nodes i Minimum / maximum active power output of distributed resources; They are nodes i Minimum / maximum reactive power output of distributed resources; power factor angle of distributed resources The range of values ​​is , To obtain the tangent value; This indicates the node where the distribution network connects to the upstream power grid; The pre-dispatch phase focuses on determining the initial hydrogen storage capacity of mobile hydrogen tankers during emergency dispatch. The initial location of the tanker truck for emergency dispatch. This is then used as a known quantity and input into real-time emergency dispatch to establish the following model: (22) (23) Since the second phase considers the pre-scheduled distribution of pipeline storage to the hydrogen gas turbine, and needs to meet the pipeline network operation constraints, it is modified as follows: (24) (25) In the formula, Pipes ij Initial inflow and outflow hydrogen flow rates at both ends; Pipes ij Initial pressure at both ends; For pipelines ij Initial storage; During this stage, the power distribution network operation meets power flow constraints: (26)。 6. The method for flexible hydrogen resource participation in emergency power grid dispatching under sudden disasters according to claim 1, characterized in that: The flexible hydrogen storage tanker emergency dispatch model described in step S3 is constructed as follows: In the mobile hydrogen resource emergency dynamic dispatch model, the total time of the power grid node during the occurrence of the emergency is considered. The objective is to minimize the sum of internal load reduction power, as shown below: (27) First, we model the hydrogen storage capacity of hydrogen tank trucks at different power distribution network nodes and the emergency dispatch of the road network: (28) (29) (30) (31) (32) (33) (34) Equation (28) represents the hydrogen storage level relationship of the tanker truck; Equation (29) indicates that the tanker truck must depart from its pre-configured node or continue to stay; Equation (30) imposes a constraint on the uniqueness of the tanker truck's transportation route, that is, the tanker truck can only be located on the forward / reverse transportation route between two points or not carry out transportation; Equation (31) constrains that when the tanker truck starts transportation, its hydrogen storage must be greater than its hydrogen consumption during transportation; Equation (32) indicates that the sum of the distances of all transportation routes of the tanker truck cannot exceed its maximum driving distance; Equation (33) indicates the distance of the tanker truck to the node j During emergency dispatch, the travel time must be shorter than the time required to reach the destination. j The maximum time; Equation (34) imposes constraints on the total running time of each tanker truck; In the formula, Indicates the first k Tanker trucks at the node j Hydrogen storage capacity; Indicates the first k Tanker trucks at the node j The original hydrogen storage capacity can be calculated using the pre-scheduling model; A 0-1 variable, representing the first... k Should the tanker trucks choose a route? j → i Transmit; Indicates the first k Tanker trucks on the route i → j Hydrogen consumption during transport; Indicates the first k Tanker trucks on the route i → j The driving distance; For the first k Tanker trucks on the route i → j Travel time on the road; Representing a path j → i With nodes j The set of endpoint nodes that are the starting point; Representing a path i → j With nodes j The set of starting nodes for the endpoint; Represents a set of transportation routes; Indicates the first k Maximum travel distance of a tanker truck; Indicates heading to the node j Maximum required time; Indicates the first k The maximum travel time for a tanker truck; (35) (36) (37) (38) Equation (35) represents the relationship between the active power output of the tank truck and the amount of hydrogen consumed; Equation (36) represents the active and reactive power output constraints of the tank truck; Equation (37) represents the relationship between the emergency hydrogen storage capacity in the tank truck and the amount of hydrogen consumed; Equation (38) represents the emergency hydrogen storage capacity of the tank truck and the range of hydrogen consumed for power generation. In the formula, For the first k Tanker trucks in t At the power grid node j Amount of hydrogen gas released; Given 0-1 variables, determine the first... k tanker trucks t Is the node connected at any time? j A value of 1 indicates access; For the first k Tanker trucks in t At the power grid node j Hydrogen reserves; For the first k Maximum emergency hydrogen storage capacity of each tanker truck; For parameters related to the hydrogen-to-electricity conversion efficiency of emergency hydrogen fuel cell vehicles; The impact of road damage during the emergency phase is reflected in the change of congestion level due to the decrease in traffic capacity, which in turn reduces vehicle speed and changes the equivalent transport distance and travel time. Under this setting, the feasibility of the emergency phase is jointly guaranteed by the uniqueness of the path and the upper bound constraint of the travel time. Furthermore, in the pre-scheduling phase, the accessibility margin of the initial layout can be increased by adjusting the upper bound of the congestion level and the upper bound of the shortest travel time, ensuring that the initial layout is feasible under the model semantics. Considering the impact of mobile hydrogen generator vehicles on road capacity and travel time in emergency situations, the following model establishes the relationship between the actual travel time of mobile hydrogen storage tankers and the equivalent transport distance and actual vehicle speed: (39) (40) (41) Equation (39) represents the actual speed of each tanker at different times; Equation (40) represents the equivalent transportation distance of each tanker on the transportation route at different scheduling times; Equation (41) represents the expression for the actual travel time of the tanker. In the formula, Indicates the first k Tanker trucks in t The actual speed at any given moment; Indicates the first k The ideal speed of the tanker truck is not considered under the influence of emergencies and traffic flow. c Indicates the degree of traffic network congestion caused by unforeseen events, etc. Indicates the first k Tanker trucks in t Path at any given moment i → j The equivalent scheduling distance; Represents the path under ideal conditions i → j The scheduling distance; Indicates the first k Tanker trucks in t Time below path i → j Time required for scheduling.

7. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The pipeline hydrogen transport emergency dispatch model considering pipeline storage effects described in step S3 is constructed as follows: (42) (43) (44) Equation (42) represents the relationship between the hydrogen storage capacity of each hydrogen refueling station during the emergency dispatch period; Equation (43) represents the relationship between the power generation of the hydrogen gas turbine placed in each hydrogen refueling station and the hydrogen consumption rate; Equation (44) represents the upper and lower limits of the active and reactive power output of the hydrogen gas turbine. In the formula, No. i Within the hydrogen refueling station t Hydrogen storage capacity at any time; For the first i Within the hydrogen refueling station t The amount of hydrogen received from the pipeline at time +1; Indicates the first i Within the hydrogen refueling station t The amount of hydrogen released to the hydrogen gas turbine power plant at time +1; For the first i Within the hydrogen refueling station t The magnitude of active and reactive power output of the hydrogen gas turbine during different time periods; A 0-1 variable, representing the first... i Within the hydrogen refueling station t The operating status of the hydrogen gas turbine during a given period, with 1 indicating operation; The first i Active power and minimum output value of hydrogen gas turbines in each hydrogen refueling station; The first i The reactive power and minimum output value of the hydrogen gas turbine in each hydrogen refueling station; A collection of hydrogen refueling stations; The density of hydrogen gas; (45) (46) (47) (48) (49) Equation (45) represents the relationship between the flow rate at both ends of the hydrogen transmission network pipeline and the pressure at both ends of the pipeline; Equation (46) represents the relationship between the flow rate at both ends of the hydrogen transmission network pipeline and the pipeline storage; Equation (47) represents the relationship between the pressure at both ends of the pipeline and the internal storage; Equation (48) represents the flow balance relationship at the pipeline nodes, without considering the situation where hydrogen is produced by the hydrogen production station and injected into the hydrogen transmission network. During this period, only the storage in the pipeline network is scheduled; Equation (49) represents the gas load demand in the hydrogen transmission network pipeline as the amount of hydrogen injected into the hydrogen storage of each hydrogen refueling station. In the formula, Pipes ij exist t The inflow and outflow of hydrogen at any given time; For pipeline nodes i exist t The pressure of constant time; For pipelines ij At any moment t Inventory; For pipeline nodes i exist t Continuously output hydrogen to the load nodes; For pipelines ij Pipeline constant; A collection of pipeline branches; For pipelines ij Storage constant; This is a set of pipeline nodes.

8. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The column-constrained C&CG algorithm is used to solve the robust pre-scheduling model for flexible hydrogen resources, and the model in the pre-scheduling stage is constructed into the following compact form: (50) In the formula, X This refers to the set of decision variables for the first stage in the pre-scheduling model. u and Y This is the set of decision variables for the second stage. This represents a set of uncertainties, such as photovoltaic power output. Representing decision variables Y The feasible domain; a , b c and The coefficient vector of the variables; A , B , C , D and It is a constant matrix; n The number of second-order cone constraints; The model is decomposed into a main problem and sub-problems. The main problem is to solve for the pre-layout scheme of mobile hydrogen energy storage and the on / off state of the line under deterministic conditions. The main problem is constructed in the following form: (51) In the formula, Z Indicates auxiliary variables; K Indicates the maximum number of iterations; l This represents the corresponding variable obtained after the current iteration number; Indicates the first l Photovoltaic power output after the next iteration; The subproblem is to determine the worst-case photovoltaic output given the pre-deployment of mobile energy storage. The model is as follows: (52) In the formula, This represents the pre-layout scheme for mobile energy storage obtained from solving the main problem; the min-max bi-level optimization subproblem is transformed into a single-level optimization problem using strong duality theory, and then solved directly using the solver.

9. The method for flexible hydrogen resource participation in emergency power grid supply scheduling under sudden disasters according to claim 1, characterized in that: The solution process for the emergency dispatch model of pipeline hydrogen transportation is as follows: The MILP model is established with the objective function of minimizing the load reduction cost during the total period of failure, and the solution is obtained for the tanker truck post-disaster rescue route and pipeline storage dispatch consumption scheme.

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