Optimized scheduling method for electric hydrogen vehicle system for service power supply guarantee

Abstract

The invention discloses an electric hydrogen vehicle system optimization scheduling method for service electric power supply guarantee, and the method comprises the steps: S1, building an interaction network of a power grid system-hydrogen energy system-hydrogen energy vehicle system based on system dynamics, and carrying out the quantitative analysis of a dynamic feedback mechanism of electric power supply and demand fluctuation, hydrogen energy supply demands and a traffic behavior mode; the power grid system comprises a power grid module, the hydrogen energy system comprises a hydrogen energy module, and the hydrogen energy vehicle system comprises a hydrogen energy vehicle module; s2, embedding the power grid module into the upper-layer time model, embedding the hydrogen energy module into the lower-layer space model, and constructing a space-time double-layer scheduling model comprising the upper-layer time model and the lower-layer space model; and S3, coupling the power grid module with the traffic network, operating the space-time double-layer scheduling model, and coordinating operation among the power grid system, the hydrogen energy system and the hydrogen energy vehicle system through multiple times of loop iteration of the upper-layer time model and the lower-layer space model. According to the invention, under the extreme high temperature scene, the electric hydrogen energy flow can be optimized to improve the power supply protection capability and the toughness of the power distribution network.

Description

Technical Field

[0001] The present invention relates to the technical field of power system optimal dispatching, and in particular to an optimal dispatching method for an electric-hydrogen vehicle system serving power supply guarantee. Background Art

[0002] With the intensification of global climate change, the frequent occurrence of extreme high-temperature weather events has posed severe challenges to the stability of the distribution network and power supply. Under high-temperature weather, the power load will increase significantly. At the same time, high temperature will reduce the efficiency of transmission equipment such as lines and transformers, increasing the risk of power grid failures. To address this challenge and enhance the resilience of the distribution network in extreme high-temperature scenarios and ensure the continuous and stable power supply has become an important issue in the current energy field.

[0003] Traditional power supply guarantee strategies mostly rely on load management means, such as peak shaving and valley filling, load transfer, etc., to adjust demand to cope with power load fluctuations. Enhancing the resilience of the distribution network in extreme high-temperature scenarios and ensuring rapid restoration of operation are key issues in the current energy field. Some studies have proposed methods based on demand response, load regulation, energy storage optimization, etc. to alleviate the impact of high temperature on the power grid. For example, optimizing the load distribution through peak shaving and valley filling strategies, using energy storage systems to enhance power supply capacity, or adopting microgrid and distributed energy optimal dispatching strategies to improve the flexibility of the power grid. However, in extreme high-temperature situations, it is difficult to meet the high reliability requirements of power supply solely relying on these strategies. Therefore, developing a comprehensive power supply guarantee plan and combining advanced energy management technologies has become the key to solving this problem.

[0004] The electric-hydrogen coupled energy system is one of the important directions for future energy structure adjustment and the realization of carbon neutrality goals. As a clean and efficient secondary energy source, hydrogen energy is gradually applied to multiple fields such as power, transportation, and industry, becoming an effective means to address power supply problems under extreme climate conditions. The electric-hydrogen coupled system uses renewable energy to produce hydrogen, and converts hydrogen energy into electrical energy through hydrogen fuel cell vehicles, providing regulation capacity for the power grid and enhancing the flexibility and stability of the energy system. In recent years, various countries have formulated hydrogen energy development strategies to promote the application of hydrogen energy in scenarios such as renewable energy consumption, power peak shaving, and distributed energy supply. Among them, the electric-hydrogen coupled system uses renewable energy to produce hydrogen, and converts hydrogen energy into electrical energy through hydrogen fuel cell vehicles, providing regulation capacity for the power grid and enhancing the flexibility and stability of the energy system.

[0005] In addition, due to its high efficiency, low carbon, and fast response characteristics, hydrogen fuel cells are regarded as potential supporting technologies for power systems. Some studies have explored the emergency power supply applications of hydrogen energy systems under extreme weather conditions, such as using hydrogen fuel cell stacks to provide backup power or playing a regulating role in microgrids. The electric-hydrogen vehicle system can build a diversified energy supply system, using hydrogen energy storage and hydrogen vehicles as backup energy sources to ensure power supply to critical facilities in extreme high-temperature scenarios.

[0006] Currently, the optimal scheduling of the electric-hydrogen coupling system has become an important research direction in the energy system, covering aspects such as the coordinated operation of the power grid and the hydrogen energy system, energy management, equipment configuration optimization, and market mechanism design. The core technologies of the electric-hydrogen coupling system include electrolytic water hydrogen production, hydrogen storage and transportation, fuel cell power generation, and energy management of hydrogen fuel cell vehicles. Among them, electrolytic water hydrogen production technology is the key link connecting the power system and the hydrogen energy system. Current research mainly focuses on using technologies such as alkaline electrolyzers and proton exchange membrane electrolyzers (PEM) for efficient hydrogen production, and combining the optimal scheduling of renewable energy to reduce hydrogen production costs and improve hydrogen production efficiency. In terms of hydrogen energy storage, technical solutions include high-pressure hydrogen storage, liquid hydrogen storage, and solid-state hydrogen storage, which can meet different application requirements, such as long-term energy storage, long-distance hydrogen transportation, and on-vehicle hydrogen storage. Fuel cell power generation technologies mainly include proton exchange membrane fuel cells (PEMFC) and solid oxide fuel cells (SOFC), which can be used in distributed energy systems, backup power supplies, and grid peak shaving scenarios to improve the resilience and reliability of the power grid.

[0007] In terms of the modeling and optimization of the electric-hydrogen system, existing research mostly adopts mixed integer programming, stochastic optimization, and scheduling methods based on game theory, aiming to develop optimal equipment configuration and scheduling strategies for a specific operation cycle. Some studies have constructed an integrated electric-hydrogen energy system to achieve the complementarity of the electric-hydrogen system by coordinating renewable energy generation, hydrogen production, and storage, thereby improving energy utilization efficiency. At the same time, the optimization of the hydrogen supply chain is also an important research direction, including the location and scale optimization of hydrogen production plants, hydrogen storage tanks, and hydrogen refueling stations to reduce the overall system cost and improve the reliability of supply. Regarding the coordinated operation of the electric-hydrogen system, some studies have explored the role of hydrogen fuel cell vehicles (HV) in the electric-hydrogen system. HV is not only the main consumer of hydrogen but also a flexible resource capable of energy storage and power generation. When the power grid load fluctuates, HV can provide regulation capabilities for the power grid. Existing research has deeply explored the charge-discharge scheduling strategy of HV, the optimization of the layout of hydrogen refueling stations, and the hydrogen energy market pricing mechanism, further enhancing the economy and flexibility of the electric-hydrogen system.

[0008] Although the electric-hydrogen coupling system has seen its advantages during development, there are still some problems: in the face of the problem of power supply-demand imbalance in the distribution network under extreme high-temperature scenarios, there are scheduling models that do not fully consider the complex dynamic interaction relationships among the power grid, the hydrogen energy storage system, and hydrogen vehicles (HV), resulting in the inability to accurately reflect the synergy effects of each entity under extreme high-temperature conditions. The optimal scheduling problem of the electric-hydrogen vehicle system is to optimize the electric-hydrogen energy flow while satisfying the operation constraints of the power grid, considering the impact of extreme high temperature on renewable energy output, hydrogen energy storage, and load demand, so as to enhance the power supply guarantee ability and the resilience of the distribution network. However, the existing scheduling models do not fully characterize the dynamic interaction characteristics of the electric-hydrogen vehicle system, and the spatio-temporal optimization method has a high computational complexity, making it difficult to balance scheduling accuracy and solution efficiency. Summary of the Invention

[0009] The object of the present invention is to provide an optimal scheduling method for an electric-hydrogen vehicle system for power supply guarantee, which can optimize the electric-hydrogen energy flow while satisfying the operation constraints of the power grid, considering the impact of extreme high temperature on renewable energy output, hydrogen energy storage, and load demand, so as to enhance the power supply guarantee ability and the resilience of the distribution network.

[0010] To achieve the above object, the present invention provides an optimal scheduling method for an electric-hydrogen vehicle system for power supply guarantee, including:

[0011] Step S1, establish an interaction relationship network among the power grid system - hydrogen energy system - hydrogen vehicle system based on system dynamics, and quantitatively analyze the dynamic feedback mechanisms of power supply-demand fluctuations, hydrogen energy replenishment requirements, and traffic behavior patterns; the power grid system includes a power grid module, the hydrogen energy system includes a hydrogen energy module, and the hydrogen vehicle system includes a hydrogen vehicle module;

[0012] Step S2, embed the power grid module into the upper-layer time model, and embed the hydrogen energy module into the lower-layer space model to construct a spatio-temporal two-layer scheduling model including the upper-layer time model and the lower-layer space model;

[0013] Step S3, couple the power grid module with the transportation network, and run the spatio-temporal two-layer scheduling model. By repeatedly iterating the upper-layer time model and the lower-layer space model, coordinate the operations among the power grid system, the hydrogen energy system, and the hydrogen vehicle system.

[0014] Optionally, the step S1 includes:

[0015] S1.1, construct a power grid module, a hydrogen energy module, a hydrogen vehicle module, and a fusion module through system dynamics, and simulate to form the interaction relationships among the modules;

[0016] S1.2. Quantitatively analyze the dynamic feedback mechanism of power supply and demand fluctuations, hydrogen energy replenishment requirements, and traffic behavior patterns by simulating the flowcharts of the power grid module and the hydrogen energy module through system dynamics.

[0017] Optionally, the power grid module feeds back the supply and demand fluctuations within the power grid module through the fluctuations of the electricity consumption price and the power generation price, and then makes power grid operation decisions; the hydrogen energy module is electrically connected to the power grid module, uses the power generated by the power grid module to produce hydrogen, and affects the operation of the power grid module through the generation and consumption of hydrogen; the hydrogen energy vehicle module is electrically connected to the hydrogen energy module, drives the hydrogen energy vehicle module through the hydrogen produced by the hydrogen energy module, and feeds back the consumption and surplus of hydrogen by the hydrogen energy vehicle module to the hydrogen energy module; the fusion module is respectively in communication connection with the power grid module, the hydrogen energy vehicle module, and the hydrogen energy module to coordinate the flow of electricity, hydrogen, and benefits.

[0018] Optionally, the step S2 includes:

[0019] S2.1. Embed the power grid module into the upper-layer time model to provide real-time power demand prediction and scheduling data;

[0020] S2.2. Embed the hydrogen energy module into the lower-layer space model to affect the generation and storage of hydrogen in the hydrogen energy module through the power demand prediction and scheduling data generated by the upper-layer time model; and feedback to the upper-layer time model according to the hydrogen demand and storage volume, thereby affecting the power load scheduling of the power grid module.

[0021] Optionally, the upper-layer time model simulates the operation of the power grid module through system dynamics to obtain real-time electricity prices, power generation compensation prices, and voltage and power data of each node of the power grid module; the lower-layer space model simulates the operation of the hydrogen energy module through system dynamics to obtain hydrogen prices, the number and spatial distribution of HVs participating in the response, and real-time hydrogen storage results.

[0022] Optionally, the step S3 includes:

[0023] S3.1. Dynamically couple the power grid module with the transportation network to obtain the intraday load, new energy output, and temperature curves by integrating the road network topology and the operation data of the power grid module;

[0024] S3.2. Input the given intraday load, new energy output, and temperature curves into the upper-layer time model, and output the electricity consumption price, power generation compensation price, and voltage and power data of each node of the power grid module;

[0025] S3.3. Input the output data of the upper-layer time model into the lower-layer space model and run the lower-layer space model to obtain the calculation results including the hydrogen price, the number and location distribution of HV users participating in the response, and the hydrogen storage volume;

[0026] S3.4. Feed back the calculation results of the lower-layer space model to the upper-layer time model, perform multiple iterative solutions to finally correct the electricity price signal, and finally obtain the operating cost of the electric-hydrogen vehicle system, the new energy consumption situation, and the user satisfaction situation.

[0027] Optionally, the objective function min z of the upper-layer time model y is:

[0028]

[0029] In the formula, y is the number of iterations, α up is the weight coefficient of the upper-layer objective function, β i is the node load importance coefficient, is the load reduction amount per unit time interval, is the net revenue of the power grid per unit time interval; B is the set of power grid module nodes.

[0030] Optionally, the boundary conditions of the upper-layer time model include: load classification management constraints; hydrogen energy facilities and HV constraints; power grid system operation constraints;

[0031] Among them, the load classification management constraints include:

[0032] represents the load reduction amount generated by centralized control of each node of the power grid module

[0033]

[0034] In the formula, is the aggregated power change amount of the temperature-controlled load, ΔT set is the change amount of the air conditioner set temperature, T c is the time set of centralized control of the temperature-controlled load;

[0035] represents the upper and lower limit constraints of the target temperature set by the temperature-controlled load

[0036]

[0037] In the formula: is the lowest target temperature set by the air conditioner, is the highest target temperature set by the air conditioner;

[0038] represents the willingness of users to interrupt the load and the willingness to transfer the load:

[0039]

[0040] In the formula, is the proportionality coefficient of the load that general load users can interrupt at time t, is the maximum interruptible load proportionality coefficient, λ int is the willingness coefficient of general load users to interrupt the load, and this value changes with the influence of temperature, is the unit power compensation electricity price for interrupting the load at time t, c int is the expected compensation electricity price for users to interrupt the load; is the proportionality coefficient of the load that general load users can transfer at time t, is the maximum transferable load proportionality coefficient, λ shi is the willingness coefficient of general load users to transfer the load, and this value changes with the influence of temperature, is the unit power compensation electricity price for transferring the load at time t, c shi is the expected compensation electricity price for users to transfer the load;

[0041] represents the power of the transferable load, which consists of the transferred-out power and the transferred-in power. There are balance and time constraints for load transfer:

[0042]

[0043]

[0044] In the formula, is the power of the transferable load at node i at time t, is the load power transferred out from node i at time t, is the load power transferred into node i at time t;

[0045] represents the maximum and minimum values of the interruptible load and the transferable load:

[0046]

[0047] In the formula, is the maximum transferable-in load proportionality coefficient, is the active power demand at the t-time node;

[0048] Among them, the hydrogen energy facilities and HV constraints include:

[0049] It means that the electrolyzers and fuel cells connected to each node of the power grid module should meet the power upper and lower limit constraints:

[0050]

[0051] In the formula, is the switch state of the electrolyzer at time t, is the minimum power of the electrolyzer at node i, is the power of the electrolyzer at node i at time t, is the maximum power of the electrolyzer at node i, is the switch state of the fuel cell at time t, is the minimum power of the fuel cell at node i, is the maximum power of the fuel cell at node i, is the power of the fuel cell at node i at time t; represents the electrolysis power; represents the power generation of the fuel cell;

[0052] represents the power injection constraints injected by each V2G site (vehicle-grid interaction site) into each node of the power grid module:

[0053]

[0054] In the formula, is the power injected by the V2G site under node i at time t, is the number of HVs connected to the V2G site under node i at time t, is the output power of the k-th HV at time t, σ v2g is the response willingness of HFCV users, is the sensitivity of the HFCV at time t, N v is the total number of HFCVs in this area, T V is the time set for HVs to participate in the response;

[0055] represents the HV access quantity constraints for each V2G node:

[0056]

[0057] In the formula, represents the number of HVs connected to the V2G site under node i; is the maximum number of HVs connected to the V2G site under node i;

[0058] Among them, the operation constraints of the power grid system include:

[0059] The upper-layer time model adopts the optimal power flow model for power grid module reconstruction with second-order cone constraints, and the power constraints are:

[0060]

[0061] In the formula: P i,t and Q i,t respectively represent the injected active power and reactive power of node i in the t period; and respectively represent the active power and reactive power of the generator at node i during period t; and respectively represent the active power demand and reactive power demand at node i during period t; δ(i) is the set of nodes connected to node i by branches; P ij,t and Q ij,t respectively represent the active power and reactive power flowing from node i to node j during period t; r ij and x ij respectively represent the resistance and reactance of branch (i, j); is the square of the modulus of the transmission current of branch (i, j) during period t;

[0062] The voltage and current constraints are expressed as:

[0063]

[0064] In the formula: and V i respectively represent the upper limit and lower limit of the voltage modulus at node i; V i,t is the voltage modulus at node i during period t, V j,t is the voltage modulus at node j during period t; p ij,t is the active power flow between nodes i and j at time t, q ij,t is the reactive power flow between nodes i and j at time t; z ij,t is a 0-1 variable, reflecting the power flow direction of the branch. Taking 1 means the positive power flow direction is from node i to node j; K is a positive real number; E is the set of branches in the power grid module;

[0065]

[0066] In the formula: and I ij respectively represent the maximum value and minimum value of the transmission current amplitude of branch (i, j);

[0067] The radial constraint is expressed as:

[0068]

[0069] The second-order cone constraint is expressed as:

[0070]

[0071] In the formula, P ij,t represents the active power flow between nodes i and j at time t, Q ij,t represents the reactive power flow between nodes i and j at time t; z ji，t represents a 0-1 variable.

[0072] Optionally, the objective function max g of the lower spatial model y for:

[0073] max g y =α low R hsop,y +(1-α low )R v,y

[0074] Where: y is the number of iterations, α low is the weight coefficient of the objective function of the lower spatial model, R hsop,y is the net income of the hydrogen energy storage operator, R v,y Net profit for HFCV users.

[0075] Optionally, the boundary conditions of the lower spatial model include: hydrogen production, transportation, storage, and utilization constraints; HV travel behavior constraints;

[0076] The constraints on hydrogen production, transportation, storage and utilization include:

[0077] Indicates the hydrogen production of the electrolyzer of each node of the hydrogen energy module and fuel cell hydrogen consumption

[0078]

[0079] In the formula, is the power of the electrolyzer at node i at time t; is the power of the fuel cell at node i at time t; η elt represents the efficiency of the electrolyzer, η fc Indicates the efficiency of the fuel cell.

[0080] Indicates the change in hydrogen in the hydrogen storage tank of each node of the hydrogen energy module and the maximum value of reserves The relationship between the minimum value 0:

[0081]

[0082] Express the transport constraints of HT:

[0083]

[0084] Where: Represents the real-time status information of HT at time t; is a 0-1 variable representing the position state of the kth HT at time t, (r,r') represents the position state from node r to node r'; is the remaining hydrogen storage of the kth HT at time t, m k,maxis the maximum hydrogen storage of the k-th HT; is the HRS object supplied by the k-th HT at time t; is the equivalent time for the k-th HT to travel in the transportation network, s f is the road length, v k is the average driving speed of the k-th HT is the remaining driving time of the k-th HT on section f at time t is the remaining stay time of the k-th HT at node r at time t, T r k The planned stay time of the k-th HT at node r is a 0-1 variable representing the arrival or departure state of the k-th HT at node r at time t is the HRS hydrogen demand at node r at time t

[0085] Among them, the HV travel behavior constraint includes:

[0086] represents the travel willingness σ of HV users tra :

[0087] σ tra = exp(-λ tra PPD)

[0088] In the formula, λ tra is the travel chain correction coefficient, and PPD is the predicted dissatisfaction ratio index in the human comfort index standard;

[0089] represents the input parameters for the interaction between the V2G station and the power grid module:

[0090]

[0091] In the formula: represents the real-time status information of the x-th HV at time t; is the driving state, that is, it represents the position of the x-th HV in the transportation network at time t; is the hydrogen storage of the x-th HV at time t; is the V2G node number closest to the x-th HV at time t.

[0092] In summary, compared with the prior art, the present invention has the following beneficial effects:

[0093] 1. The present invention first proposes that the power grid guides the joint participation of hydrogen energy storage and hydrogen energy vehicles in optimizing power supply in extreme high-temperature scenarios. Compared with traditional load management methods, the synergy of the electric-hydrogen vehicle system effectively avoids the risk of load shedding in the distribution network. Based on the dynamic price signal and revenue feedback mechanism, a benefit-sharing model among the power grid, hydrogen energy operators, and HV users is constructed. The interaction laws revealed by the system dynamics model drive the efficient matching of the three-party resources: the power grid reduces the regulation cost through peak shaving and valley filling of HV, hydrogen energy operators optimize the storage and transportation paths to improve operation efficiency, and HV users obtain additional benefits through response incentives, forming a virtuous cycle energy ecosystem.

[0094] 2. An optimized dispatching method for the electric-hydrogen vehicle system serving power supply guarantee provided by the present invention, in which the hybrid system dynamics and spatio-temporal double-layer optimization model breaks through the limitations of single-dimensional dispatching: the upper-layer model focuses on the minute-level dynamic balance of power supply and demand, and the lower-layer model coordinates the spatial optimal allocation of hydrogen energy production, storage, transportation, and consumption, realizing the double coordination of "time response - space replenishment". This method significantly improves the hydrogen energy replenishment efficiency in extreme high-temperature regions, enhances the cross-network coordination ability between the transportation network and the distribution network, and constructs a more resilient energy supply system.

[0095] 3. An optimized dispatching method for the electric-hydrogen vehicle system serving power supply guarantee provided by the present invention. In extreme high-temperature scenarios, by integrating the system dynamics and spatio-temporal double-layer optimization methods, it accurately coordinates the energy storage characteristics of the electric-hydrogen vehicle (HV) cluster and the dynamic replenishment ability of the hydrogen energy network. During the load peak period caused by high temperature, it quickly activates the flexible support role of HV as a mobile energy storage unit, significantly reducing the risk of load shedding in the distribution network, ensuring the continuous power supply of critical loads, and breaking the excessive dependence of the traditional power supply guarantee mode on fixed energy storage. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] Figure 1 is the flow chart of the spatio-temporal double-layer dispatching model of the present invention;

[0097] Figure 2 is the interactive causal loop diagram of the electric-hydrogen vehicle of the present invention;

[0098] Figure 3 is the flow diagram of the power grid module of the present invention;

[0099] Figure 4 is the flow diagram of the hydrogen energy module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0100] The following will combine the attached Figures 1 to 4 , and through preferred embodiments, the technical content, structural features, achieved objectives, and effects of the present invention will be described in detail.

[0101] It should be noted that the attached drawings are in a very simplified form and use non-precise scales, only for the purpose of conveniently and clearly assisting in explaining the embodiments of the present invention, rather than being used to limit the limiting conditions for the implementation of the present invention. Therefore, they do not have technical substantial significance. Any modification of the structure, change in the proportional relationship, or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope covered by the technical content disclosed in the present invention.

[0102] In the description of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. is based on the orientation or positional relationship shown in the attached drawings, only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation to the present invention. In addition, the terms "first", "second", "third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0103] In the description of the present invention, it should be noted that unless otherwise clearly specified and limited, the terms "installed", "connected", "coupled" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection; it can be directly connected, or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0104] The present invention provides an optimized scheduling method for an electric-hydrogen vehicle system for power supply guarantee, which realizes the optimized scheduling of the electric-hydrogen vehicle system through a system dynamics and spatio-temporal double-layer scheduling model. The optimized scheduling method includes:

[0105] Step S1, based on system dynamics, establish an interaction relationship network among the power grid system - hydrogen energy system (HES) - hydrogen energy vehicle (HV) system, and quantitatively analyze the dynamic feedback mechanism of power supply and demand fluctuations, hydrogen energy replenishment requirements, and traffic behavior patterns; the power grid system includes a power grid module, the hydrogen energy system includes a hydrogen energy module, and the hydrogen energy vehicle system includes a hydrogen energy vehicle module;

[0106] Step S2, embed the power grid module into the upper-layer time model, embed the hydrogen energy module into the lower-layer space model, and construct a spatio-temporal double-layer scheduling model including the upper-layer time model and the lower-layer space model;

[0107] Step S3: Couple the power grid module with the transportation network and run the spatio-temporal two-layer scheduling model. Through multiple cyclic iterations of the upper-layer time model and the lower-layer space model, coordinate the operations among the power grid system, the hydrogen energy system, and the hydrogen energy vehicle system.

[0108] Specifically, as Figure 2 shown, step S1 includes:

[0109] S1.1: Construct a power grid module, a hydrogen energy module, a hydrogen energy vehicle module, and a fusion module through system dynamics, and simulate to form the interaction relationships among the modules;

[0110] Among them, the power grid module feeds back the supply-demand fluctuations within the power grid module through the fluctuations of the electricity consumption price and the power generation price, and then makes power grid operation decisions; the hydrogen energy module, which is electrically connected to the power grid module, uses the electricity generated by the power grid module to produce hydrogen, and affects the operation of the power grid module through the production and consumption of hydrogen; the hydrogen energy vehicle module, which is electrically connected to the hydrogen energy module, drives the hydrogen energy vehicle module through the hydrogen produced by the hydrogen energy module, and feeds back the consumption and surplus of hydrogen by the hydrogen energy vehicle module to the hydrogen energy module; the fusion module is respectively communicatively connected to the power grid module, the hydrogen energy vehicle module, and the hydrogen energy module to coordinate the flow of electricity, hydrogen, and benefits.

[0111] Furthermore, an electrolyzer is provided within the hydrogen energy module. The electrolyzer is electrically connected to the power grid module and can use the electricity generated by the power grid module to produce hydrogen, which is then supplied to the hydrogen energy vehicle module.

[0112] S1.2: Simulate the flow diagram of the power grid module and the flow diagram of the hydrogen energy module through system dynamics to quantitatively analyze the dynamic feedback mechanism of power supply-demand fluctuations, hydrogen energy replenishment requirements, and traffic behavior patterns.

[0113] Among them, the flow diagram of the power grid module is as Figure 3 shown. In the operation of this power grid module, it can be seen that the load fluctuation directly drives the change in the power demand of the power grid, forcing the power plant to adjust the output fluctuation to maintain the supply-demand balance. The dynamic correlation between the power demand and the electricity consumption price affects the electricity consumption behavior of users through the electrolysis price sensitivity; the electrolysis power is generated by the electrolyzer. The electrolyzer uses the electric energy in the power grid to produce hydrogen through the electrolysis of water. In this process, the power demand of the electrolyzer is closely related to the power generation power of the power grid. The operation of the electrolyzer will consume the electric energy of the power grid and be converted into hydrogen energy through the hydrogen production process for subsequent hydrogen energy storage or use by hydrogen energy vehicles. Flexible resources such as fuel cells and HV regulate the power generation power and power generation volume through the economy of the cost electricity price and the power generation price; the electrolysis power participates in energy storage or demand response based on the electrolysis cost electricity price to further suppress the load fluctuation and achieve the maximization of the stable operation and economic benefits of the power grid module.

[0114] In the flow diagram of the power grid module, the operating rules involved are as follows:

[0115] The power demand of the power grid is affected by both the power generation end and the load fluctuation, resulting in power surplus or power shortage. The calculation formula for the power demand of the power grid is as follows:

[0116]

[0117] In formula (1): is the power demand. When its value is positive, it represents a power shortage, and when it is negative, it represents a power surplus. is the load fluctuation. is the power output fluctuation of the power plant, and T represents the set of time periods within a day.

[0118] The dynamic correlation between power demand and electricity price affects users' electricity consumption behavior through the electrolysis price sensitivity. Among them, the electrolysis price sensitivity The calculation formula is:

[0119]

[0120] In formula (2): λ elt is the electrolysis price sensitivity coefficient, and E elt is the cost electricity price of the electrolyzer. is the electricity price of the electrolyzer at time t.

[0121] Furthermore, the electrolysis power is generated by the electrolyzer. The electrolyzer utilizes the electrical energy in the power grid module and generates hydrogen through the electrolysis of water. During this process, the power demand of the electrolyzer is closely related to the power generation power of the power grid module. The operation of the electrolyzer consumes the electrical energy of the power grid and is converted into hydrogen energy through the hydrogen production process for subsequent hydrogen energy storage or hydrogen-powered vehicle use. Among them, the electrolysis power The calculation formula is:

[0122]

[0123] In formula (3): P elt·max is the maximum power of the electrolyzer.

[0124] Furthermore, the electrolysis electricity consumption is determined by the electrolysis power and the electricity consumption time. The calculation formula for the electrolysis electricity consumption is expressed as:

[0125]

[0126] Furthermore, the power consumption revenue generated by the power grid module through electricity price guidance is obtained The calculation formula is:

[0127]

[0128] When the power grid module has insufficient power, the fuel cell in the hydrogen energy module can generate electricity by consuming hydrogen in the hydrogen storage tank to ensure that the hydrogen energy system can provide sufficient hydrogen supply to HV when needed, and the generated electricity can also be supplied to the power grid module. HV user price sensitivity and fuel cell price sensitivity are calculated by the following formulas:

[0129]

[0130] In the formula: λ v is the HV user price sensitivity coefficient, E v is the cost electricity price of HV users, is the electricity price for power generation, λ fc is the fuel cell price sensitivity coefficient, E fc is the cost electricity price of the fuel cell.

[0131] Furthermore, the total HV power generation and the fuel cell power generation are calculated by the following formulas respectively:

[0132]

[0133] In the formula: is the total HV power generation, α v is the willingness of HV to participate in response, is the HV power generation, is the fuel cell power generation, P fc,max is the maximum power generation of the fuel cell, N v is the total number of HVs in the region.

[0134] Furthermore, the HV power generation and the fuel cell power generation are calculated by the following formulas respectively:

[0135]

[0136] Furthermore, the power compensation generated by the power grid module through electricity price guidance is calculated by the following formula:

[0137]

[0138] In formula (12): E loss is the power shortage cost per unit of electricity.

[0139] Furthermore, the net income of the power grid within a unit time interval The calculation formula is as follows:

[0140]

[0141] In formula (13): C inv is the annual investment and construction cost newly added by the power grid to achieve interaction with the hydrogen energy module and HV, is the valley filling income.

[0142] Furthermore, the change rate of the electricity consumption price is jointly affected by the power demand, the electrolysis power, and the net income of the power grid, and its calculation formula is:

[0143]

[0144] In formula (14): is the change rate of the electricity consumption price, n is the unit simulation time step, χ u is the electricity demand determination flag, ξ u is the adjustment factor of the electricity consumption price change rate, is the power demand at time t.

[0145] Furthermore, define the electricity consumption price Its calculation formula is:

[0146]

[0147] In formula (15): f delay1 (x,y) is the time delay function in system dynamics, where x represents the variable to be delayed, y represents the delay value, n u is the time step of the electricity consumption price adjustment, is the reference electricity consumption price.

[0148] Furthermore, define the power generation compensation price and give the calculation formula:

[0149]

[0150] In formulas (16)-(17): is the power generation compensation price, is the change rate of the power generation compensation price, χ g is the power generation demand determination flag, ξ g is the adjustment factor of the power generation compensation price change rate, n g is the time step of the power generation compensation price adjustment, is the reference power generation compensation price.

[0151] Among them, the flow graph of the hydrogen energy module is as Figure 4As shown. In this hydrogen energy module, the hydrogen addition price is regulated by the benchmark hydrogen price and the hydrogen addition price sensitivity. When the hydrogen price rises, the willingness of users to respond decreases, resulting in a reduction in hydrogen addition demand, which in turn affects the decision-making of hydrogen addition volume; conversely, a decrease in the hydrogen price will stimulate demand growth. The electrolyzer set in the hydrogen energy module produces hydrogen through electrolysis of water to provide hydrogen for the hydrogen energy module; while the fuel cell set in the hydrogen energy module consumes hydrogen energy. The electrolysis efficiency of the electrolyzer and the electrolysis power consumption jointly determine the hydrogen production volume by electrolysis, combined with the hydrogen production cost and the hydrogen transportation cost, affecting the operating cost of the hydrogen refueling station and the final hydrogen sales revenue. The hydrogen storage volume indirectly affects the system economy through the hydrogen storage cost and the equipment depreciation cost, while the correction rate dynamically adjusts the hydrogen price and the hydrogen production strategy according to market demand and cost fluctuations, forming a closed-loop feedback.

[0152] In the flow diagram of the hydrogen energy module, the operating rules involved are as follows:

[0153] Hydrogen production volume m of the electrolyzer elt and hydrogen consumption volume m of the fuel cell fc are calculated through formulas (18)-(19) to obtain the production and consumption volumes of hydrogen:

[0154]

[0155] In the formula: m elt is the hydrogen production volume of the electrolyzer, T elt is the operating period set of the electrolyzer, η elt is the electrolyzer efficiency, m fc is the hydrogen consumption volume of the fuel cell, T fc is the operating period set of the fuel cell, η fc is the fuel cell efficiency.

[0156] For HV users with different hydrogen consumption volumes, their sensitivities to the hydrogen addition price are different. Formula (20) gives the calculation formula for the hydrogen addition price sensitivity ω ref :

[0157]

[0158] In the formula: ω ref is the hydrogen addition price sensitivity, E h is the hydrogen addition price, is the benchmark hydrogen price, δ ref is the hydrogen addition price sensitivity coefficient.

[0159] Furthermore, the hydrogen addition volume decision is jointly affected by the hydrogen price and the remaining hydrogen of HV. Formula (21) gives the probability of HV users adding hydrogen in a day, that is, the calculation formula for the hydrogen addition volume decision D ref :

[0160] D ref = γref (1 + ω ref ) (19)

[0161] where: D ref is the hydrogenation amount decision, representing the probability of a user hydrogenating within one day, and γ ref is the decision coefficient.

[0162] The power demand of the HV response grid module. Power is supplied to the grid module at the interaction node and benefits are obtained. Equations (22 - 24) provide the formula for the net benefit R v of the HV user within a unit time interval:

[0163] R v = R v,elec - C v,h - C v,time (20)

[0164]

[0165] where: R v,elec is the power generation benefit of the HV user, C v,h is the hydrogenation cost of the HV user, C v,time is the time cost of the HV user within a unit time interval, and η v is the efficiency of the hydrogen fuel cell vehicle (HFCV).

[0166] Furthermore, the decision on the hydrogenation amount will cause a change in the hydrogen storage of the HV user, thereby affecting its willingness to respond. At the same time, the change in the benefit of the HV user will also affect its willingness to respond. Equation (25) defines the willingness to respond σ v2g of the HV user, and the calculation formula is:

[0167]

[0168] where: λ v2g is the benchmark coefficient for participating in the HFCV, and x v is the hydrogen storage percentage of the HFCV.

[0169] At the same time, through the estimation of the user's hydrogenation amount decision, the hydrogenation demand m ref within one day is obtained from the calculation formula of Equation (26):

[0170] m ref = N v ∫D ref (1 - x v )f(x v )dx v (24)

[0171] Equation (27) represents the situation of calculating m hyt for the change in the hydrogen amount in the hydrogen storage tank on the current day:

[0172]

[0173] Wherein: is the initial hydrogen amount in the hydrogen storage tank, m tra is the hydrogen amount transported to the hydrogen refueling station.

[0174] In the operation process of the hydrogen energy storage operator, its revenue is affected by various revenue and cost factors. Equation (29 - 35) defines the net revenue of the daily hydrogen energy storage operator:

[0175]

[0176] R h,sale = m ref E h (27)

[0177]

[0178] C h,tra = c tra m tra (29)

[0179]

[0180] C h,store = c store m hyt (31)

[0181] C fc,elec = c fc m fc (32)

[0182] C h,stat = c stat m ref (33)

[0183] Wherein: R hsop is the net revenue of the hydrogen energy storage operator, R h,sale is the revenue from selling hydrogen, R fc,elec is the revenue from fuel cell power generation, C h，tra is the hydrogen transportation cost, c tra is the unit mass hydrogen transportation cost, C h,prod is the electrolyzer operation cost, c elt is the raw material, labor and operation and maintenance cost for the electrolyzer to produce unit mass hydrogen, C h,store is the hydrogen storage cost, m hyg the hydrogen amount in the storage tank, c store is the unit mass hydrogen storage cost, C fc,elec is the fuel cell operation cost, c fcLet \(C\) be the operating cost of the fuel cell per unit mass of hydrogen consumed. h,stat Let \(c\) be the operating cost of the hydrogen refueling station. stat Let \(c\) be the operating cost of the hydrogen refueling station for selling hydrogen per unit mass.

[0184] The hydrogen price change rate \(\Delta E\) h is jointly affected by the hydrogen refueling demand and the revenue of the hydrogen energy storage operator. Its calculation formula is:

[0185]

[0186] In the formula: \(\Delta E\) h is the hydrogen price change rate, \(\xi\) h is the adjustment factor of the hydrogen price change rate, \(\chi\) hsop is the determination flag of the revenue change rate of the hydrogen energy storage operator.

[0187] Furthermore, the hydrogen price \(E\) of hydrogen refueling is obtained h The calculation formula is:

[0188]

[0189] In the formula: \(E\) h is the hydrogen price of hydrogen refueling, \(n\) h is the time step of hydrogen price adjustment, is the benchmark hydrogen price.

[0190] Specifically, the step S2 includes:

[0191] S2.1. Embed the power grid module into the upper - layer time model to provide real - time power demand prediction and scheduling data;

[0192] In the model architecture design, the upper - layer time model focuses on multi - period dynamic response. Combining the power load fluctuation characteristics caused by extreme high temperature and the time - varying characteristics of the hydrogen energy module, a running constraint framework aiming to minimize the system load shedding is constructed to optimize the power grid power balance strategy.

[0193] Specifically, in the upper - layer time model, the upper - layer objective is to obtain the minimum power deficit (load shedding) or the maximum revenue of the power grid operator. The upper - layer boundary conditions include the number, location distribution of hydrogen - powered vehicles (HV) and hydrogen storage data; the upper - layer time model simulates the operation of the power grid module through system dynamics and optimizes the dispatching strategy of the distribution network, and finally outputs data such as real - time electricity price, power generation compensation price, and voltage and power of each node.

[0194] S2.2. Embed the hydrogen energy module into the lower-layer space model to affect the generation and storage of hydrogen in the hydrogen energy module through the power demand prediction and scheduling data generated by the upper-layer time model; and feedback to the upper-layer time model according to the hydrogen demand and storage volume, thereby affecting the power load scheduling of the power grid module.

[0195] In terms of model architecture design, after the hydrogen energy module is embedded in the lower-layer space model, the lower-layer space model coordinates the layout of hydrogen production facilities, the planning of storage and transportation paths, and the dynamic replenishment strategy of hydrogen refueling stations to systematically solve the problem of spatio-temporal mismatch of hydrogen energy, thereby ensuring the efficient allocation of resources.

[0196] Specifically, in the lower-layer space model, the lower-layer objective is to maximize the benefits of hydrogen energy storage operators or hydrogen vehicle users, and the lower-layer boundary conditions are the hydrogen production and consumption amounts provided by the upper-layer space model; the lower-layer model simulates the production, storage, and consumption processes of hydrogen in the hydrogen energy module through system dynamics, and conducts optimization of hydrogen transportation and simulation of HV travel chains to plan the transportation paths and distribution amounts of hydrogen, and at the same time predict the hydrogen refueling demand and driving paths of hydrogen vehicles, and output results such as the hydrogen market price, the number and spatial distribution of HVs participating in the response, and the real-time hydrogen storage amounts of mother and subsidiary stations.

[0197] Specifically, as Figure 1 shown, step S3 includes:

[0198] S3.1. Dynamically couple the power grid module with the transportation network, and obtain the intraday load, new energy output, and temperature curve by integrating the road network topology and the operation data of the power grid module;

[0199] S3.2. Input the given intraday load, new energy output, and temperature curve into the upper-layer time model, and output the electricity price, power generation compensation price, and voltage power data of each node of the power grid module;

[0200] S3.3. Input the output data of the upper-layer time model into the lower-layer space model, and run the lower-layer space model to obtain the operation results including the hydrogen price, the number and location distribution of HV users participating in the response, and the hydrogen storage amount;

[0201] In this step, the hydrogen production and consumption amounts need to be used as boundary conditions.

[0202] S3.4. Feed back the operation results of the lower-layer space model to the upper-layer time model, and perform multiple iterative solutions to finally correct the electricity price signal, and finally obtain the operation cost of the electric-hydrogen vehicle system, the new energy consumption situation, and the user satisfaction situation.

[0203] After accessing the transportation network, the spatio-temporal two-layer scheduling model, aiming at the heterogeneous characteristics of the distributed electric-hydrogen vehicle system, establishes a multi-agent revenue maximization model based on the traffic network topology and the spatial difference between hydrogen energy supply and demand. Specifically, the spatio-temporal two-layer scheduling model focuses on the dynamic coupling system of the power grid module - hydrogen energy vehicle module, and constructs an analysis framework for the interaction behavior between the electric-hydrogen vehicle (HV) cluster and the energy infrastructure under extreme high temperature scenarios. By characterizing the spatio-temporal distribution characteristics of the HV mobile load and the hydrogen energy replenishment demand, and combining the node voltage constraints of the hydrogen energy vehicle module and the power grid module, a two-way energy flow balance model of the electric-hydrogen coupling system is established to achieve the coordinated optimization of traffic energy demand and power grid regulation capacity, and improve the overall resilience of the urban energy system under extreme climates.

[0204] Specifically, the operation of the upper-layer time model is as follows:

[0205] The upper-layer time model constructs a two-objective optimization framework to achieve the optimal balance between the system load shedding and the economic benefits of the power grid operator through a dynamic weight factor. Equation (38) is the objective function min z of the upper-layer time model:

[0206]

[0207] In the formula: y is the number of iterations, α up is the weight coefficient of the upper-layer objective function, β i is the node load importance coefficient, is the load reduction amount per unit time interval, is the net revenue of the power grid per unit time interval; B is the set of nodes in the power grid module.

[0208] The boundary conditions of the upper-layer time model are:

[0209] a. Load classification management constraint

[0210] There is a strong interdependence between the electricity consumption demand of users in the power grid module and meteorological factors such as ambient temperature, and different types of loads show their own characteristics. Equation (39) classifies the node loads in the power grid module into three categories: important loads, centralized control loads, and general loads.

[0211] B = B1 ∪ B2 ∪ B3 (37)

[0212] In the formula, B is the set of nodes in the power grid module, B1 is the set of important load nodes, B2 is the set of centralized control load nodes, and B3 is the set of general load nodes.

[0213] Facing the challenge of load peaks in extreme high-temperature scenarios, it is necessary to build a hierarchical power supply guarantee mechanism to ensure the continuous power supply of important loads and avoid power outages. First, ensure the continuous power supply of important loads (loads whose interruption of power supply will cause personal injury or death, major equipment damage, political or economic losses). At this time, it is necessary to establish a real-time monitoring and rapid fault isolation mechanism to ensure zero power outage risk; second, for the centralized control loads (loads regulated by the centralized control system), especially the air-conditioning temperature control loads with a significant proportion in high-temperature scenarios, deploy a set temperature adjustment plan based on the group control system; finally, for general loads (loads with less impact on power supply interruption), build a demand-side response system, and comprehensively use the load interruption compensation mechanism and time-of-use electricity price guidance strategy to promote the optimal allocation of transferable loads to off-peak periods.

[0214] By increasing the target temperature set by the air conditioner, the aggregated power of the temperature control load can be reduced. Equation (40) represents the load reduction amount generated by each node of the power grid module through centralized control.

[0215]

[0216] Wherein, is the change in the aggregated power of the temperature control load, and ΔT set is the change in the set temperature of the air conditioner, and T c is the time set for centralized control of the temperature control load.

[0217] Equation (41) represents the upper and lower limit constraints of the target temperature set by the temperature control load.

[0218]

[0219] Wherein: is the lowest target temperature set by the air conditioner, is the highest target temperature set by the air conditioner.

[0220] For general load nodes in the power grid module, implement a demand-side response incentive strategy during peak load periods.

[0221] Equations (42)-(43) define the willingness to interrupt the load and the willingness to transfer the load of users:

[0222]

[0223] Wherein, is the proportionality coefficient of the general load user's ability to interrupt the load at time t, is the maximum interruptible load proportionality coefficient, and λ int is the willingness coefficient of the general load user to interrupt the load, and this value changes with the influence of temperature, The unit power compensation electricity price for interrupted load at time t, c int is the expected compensation electricity price for the user's interrupted load. is the proportionality coefficient of the load that general load users can transfer at time t, is the maximum transferable load proportionality coefficient, λ shi is the willingness coefficient of general load users to transfer load, and this value changes with temperature, The unit power compensation electricity price for transferred load at time t, c shi is the expected compensation electricity price for the user's transferred load.

[0224] Formulas (44)-(45) represent the power of the transferable load, which consists of the transferred-out power and the transferred-in power. There are balance and time constraints for load transfer:

[0225]

[0226] Wherein, is the power of the transferable load at node i at time t, is the load power transferred out from node i at time t, is the load power transferred into node i at time t.

[0227] Formulas (46)-(48) represent the maximum and minimum values of the interruptible load and the transferable load:

[0228]

[0229] Wherein, is the maximum transferable-in load proportionality coefficient, is the active power demand at the time node t.

[0230] b. Hydrogen energy facilities and HV constraints

[0231] Formulas (49)-(52) represent that the electrolyzers and fuel cells connected to each node of the power grid module should meet the power upper and lower limit constraints:

[0232]

[0233] Wherein, is the switch state of the electrolyzer at time t, is the minimum power of the electrolyzer at node i, is the power of the electrolyzer at node i at time t, is the maximum power of the electrolyzer at node i, is the switch state of the fuel cell at time t, is the minimum power of the fuel cell at node i, is the maximum power of the fuel cell at node i, is the power of the fuel cell at node i at time t; represents the electrolysis power; represents the power generation of the fuel cell.

[0234] Equations (53)-(54) are the power injection constraints for each V2G site (vehicle-to-grid interaction site) into each node of the power grid module. A V2G site refers to the two-way interaction point between the hydrogen vehicle module and the power grid module. At this site, the hydrogen vehicle can not only charge but also feed back the stored electrical energy to the power grid module to address the issue of power supply-demand imbalance.

[0235]

[0236] In the formula, is the power injected by the V2G site at node i at time t, is the number of HVs connected to the electric vehicle grid connection (V2G) station at node i at time t, is the output power of the x-th hydrogen vehicle (HV) at time t, σ v2g is the response willingness of HFCV users, is the sensitivity of the HFCV at time t, N v is the total number of HFCVs in this area, T e is the time set for HVs to participate in the response.

[0237] Equation (55) is the constraint on the number of HVs connected to each V2G node:

[0238]

[0239] In the formula, represents the number of HVs connected to the V2G station under node i; is the maximum number of HVs connected to the V2G station under node i.

[0240] c. Power grid system operation constraints

[0241] The upper-layer time model adopts the optimal power flow model for power grid module reconstruction with second-order cone constraints. Equations (56)-(58) are the power constraints:

[0242]

[0243] In the formula: P i,t and Q i,t respectively represent the active power and reactive power injected at node i during time period t; and respectively represent the active power and reactive power of the generator at node i during time period t; and respectively represent the active power demand and reactive power demand of node i at time t; δ(i) is the set of nodes connected to node i by branches; P ij,t and Q ij,t respectively represent the active power and reactive power flowing from node i to node j at time t; r ij and x ij respectively represent the resistance and reactance of branch (i, j); is the square of the modulus of the transmission current of branch (i, j) at time t.

[0244] Equations (59)-(61) are voltage and current constraints:

[0245]

[0246] In the formula: and V i respectively represent the upper limit and lower limit of the voltage modulus of node i; V i,t is the voltage modulus of node i at time t, V j,t is the voltage modulus of node j at time t; p ij,t is the active power flow between nodes i and j at time t, q ij,t is the reactive power flow between nodes i and j at time t; z ij,t is a 0-1 variable, reflecting the power flow direction of the branch. Taking 1 means the positive power flow direction is from node i to node j; K is a positive real number; E is the set of branches in the power grid module.

[0247]

[0248] In the formula: and I ij respectively represent the maximum and minimum values of the transmission current amplitude of branch (i, j).

[0249] Equation (62) is the radial constraint:

[0250]

[0251] Equation (63) is the second-order cone constraint:

[0252]

[0253] In the formula, P ij,t represents the active power flow between nodes i and j at time t, Q ij,t represents the reactive power flow between nodes i and j at time t; z ji，t represents a 0-1 variable.

[0254] Specifically, the construction and optimization of the lower-layer space model are as follows:

[0255] Equation (64) shows the topological structure of the transportation network, including the set of transportation nodes and roads:

[0256] G = (N, S), H ∈ N (62)

[0257] Where: G is an undirected graph depicting the topological structure of the transportation network; N is the set of transportation network nodes; S is the set of connection lines between transportation network nodes; H is the set of hydrogen refueling stations.

[0258] While the V2G stations act as nodes in the transportation network, they also have electrical characteristics, that is, HV can transmit electrical energy to the power grid module at the V2G stations on the transportation network nodes. Equations (65)-(66) construct the coupling relationship between the power grid module and the transportation network nodes.

[0259] ξ = {L cou ∈ B × N} (63)

[0260]

[0261] Where: ξ is the set of each interaction node connecting the power grid module and the transportation network; L cou is an element in the set ξ; A 0-1 variable used to represent whether the transportation network node and the power grid module node are coupled. If then it means that electrical energy can be transmitted to the power grid module through the transportation network node at this place.

[0262] The lower-level space model aims to optimize the economic benefits of the hydrogen energy storage operator and the energy consumption cost of HV users. Equation (57) is the objective function of the lower-level space model max g y :

[0263] max g y = α low R hsop,y +(1 - α low )R v,y (65)

[0264] Where: y is the number of iterations, α low is the weight coefficient of the objective function of the lower-level space model, R hsop,y is the net income of the hydrogen energy storage operator, R v,y is the net income of HFCV users.

[0265] The boundary conditions of this lower-level space model are:

[0266] a. Hydrogen production, transportation, storage, and utilization constraints

[0267] Equations (68)-(69) are the hydrogen production amounts of the electrolyzers at each node of the hydrogen energy module and the hydrogen consumption of the fuel cell

[0268]

[0269] wherein, is the power of the electrolyzer at node i at time t; is the power of the fuel cell at node i at time t; η elt represents the efficiency of the electrolyzer, η fc represents the efficiency of the fuel cell.

[0270] Furthermore, Equations (70)-(72) represent the change in the hydrogen storage of the hydrogen storage tanks at each node of the hydrogen energy module as well as the relationship between the maximum value of the storage capacity and the minimum value of 0:

[0271]

[0272] The hydrogen supply from the main station to the sub-station is transported by hydrogen tube trailers. Equation (73) introduces the hydrogen tube trailer (HT), and the driving state set is used to record the real-time state of each HT at each time t. Equations (74)-(80) are the HT transportation constraints:

[0273]

[0274] wherein: represents the real-time state information of the HT at time t; is a 0-1 variable of the position state of the k-th HT at time t, and (r, r') represents the position state from node r to node r'; is the remaining hydrogen storage of the k-th HT at time t, m k,max is the maximum hydrogen storage of the k-th HT; is the HRS object supplied by the k-th HT at time t. is the equivalent time for the k-th HT to travel in the transportation network, s f is the road length, v k is the average driving speed of the k-th HT, is the remaining driving time of the k-th HT on section f at time t, is the remaining stay time of the k-th HT at node r at time t, the planned stay time of the k-th HT at node r, is a 0-1 variable of the arrival or departure state of the k-th HT at node r at time t, is the hydrogen demand of the hydrogen refueling station (HRS) at node r at time t.

[0275] b. HV travel behavior constraints

[0276] Under extreme high - temperature weather, the travel willingness of HV users is affected by temperature changes and adjusted according to the human comfort index standard. Equation (81) defines the travel willingness σ of HV users tra as follows:

[0277] σ tra = exp(-λ tra PPD) (79)

[0278] where λ tra is the travel chain correction coefficient, and PPD is the predicted percentage dissatisfied index in the human comfort index standard, and the value of this index varies with temperature.

[0279] Equation (82) introduces a driving state set to record the real - time state of each HV at each moment t, as an input parameter for the interaction between the V2G station and the power grid module.

[0280]

[0281] where: represents the real - time state information of the x - th HV at time t; is the driving state, which characterizes the position of the x - th HV in the traffic network at time t; is the hydrogen storage of the x - th HV at time t; is the number of the V2G node closest to the x - th HV at time t.

[0282] To sum up, an optimized scheduling method for an electric - hydrogen vehicle system for power supply guarantee provided by the present invention aims to solve the optimization problem of the power distribution network in the scheduling of the electric - hydrogen vehicle system under extreme high - temperature scenarios, especially in complex environments such as power load fluctuations caused by high temperature, unstable energy supply of the hydrogen energy storage system, and increased power grid supply pressure. How to effectively improve the power supply guarantee and the resilience of the power distribution network. The present invention constructs a power grid module through system dynamics, describes the complex feedback relationships among the power grid, the hydrogen energy storage system, and hydrogen - energy vehicles, reveals the interaction among the electric energy flow, hydrogen energy flow, and benefit flow, and provides theoretical support for optimized scheduling. The spatio - temporal double - layer optimization model conducts scheduling from two dimensions of time and space, considers the power demand fluctuations and hydrogen energy supply uncertainties under extreme high - temperature conditions, coordinates the operation strategies and spatial configurations of electric - hydrogen resources, optimizes the operation modes of electrolyzers, fuel cells, and hydrogen - energy vehicles, and at the same time reasonably distributes the energy flow in combination with vehicle travel behaviors to ensure the resilience of the power distribution network and the reliability of energy supply.

[0283] Although the content of the present invention has been described in detail through the above preferred embodiments, it should be recognized that the above description should not be construed as a limitation of the invention. After those skilled in the art have read the above content, various modifications and alternatives to the present invention will be obvious. Therefore, the scope of protection of the present invention shall be defined by the appended claims.

Claims

1. An optimized scheduling method for an electric-hydrogen vehicle system for power supply guarantee, characterized in that The method includes the following steps: Step S1: Based on system dynamics, establish an interaction relationship network among the power grid system, the hydrogen energy system, and the hydrogen energy vehicle system, and quantitatively analyze the dynamic feedback mechanism of power supply and demand fluctuations, hydrogen energy replenishment demand, and traffic behavior patterns. The power grid system includes a power grid module, the hydrogen energy system includes a hydrogen energy module, and the hydrogen energy vehicle system includes a hydrogen energy vehicle module. Step S2: Embed the power grid module into the upper-layer time model and embed the hydrogen energy module into the lower-layer space model to construct a spatio-temporal double-layer scheduling model including the upper-layer time model and the lower-layer space model. Step S3: Couple the power grid module with the transportation network and run the spatio-temporal double-layer scheduling model. By iteratively running the upper-layer time model and the lower-layer space model multiple times, coordinate the operation among the power grid system, the hydrogen energy system, and the hydrogen energy vehicle system.

2. The optimized scheduling method for the electric hydrogen vehicle system according to claim 1, wherein, The above step S1 includes: S1.1: Construct a power grid module, a hydrogen energy module, a hydrogen energy vehicle module, and a fusion module through system dynamics, and simulate to form the interaction relationships among the modules. S1.2: Simulate the flow diagram of the power grid module and the flow diagram of the hydrogen energy module through system dynamics to quantitatively analyze the dynamic feedback mechanism of power supply and demand fluctuations, hydrogen energy replenishment demand, and traffic behavior patterns.

3. The optimized scheduling method for the electric-hydrogen vehicle system according to claim 2, characterized in that, The power grid module feeds back the supply and demand fluctuations in the power grid module through the fluctuations of the electricity consumption price and the power generation price, and then makes power grid operation decisions. The hydrogen energy module is electrically connected to the power grid module, uses the electricity generated by the power grid module to produce hydrogen, and affects the operation of the power grid module through the generation and consumption of hydrogen. The hydrogen energy vehicle module is electrically connected to the hydrogen energy module, drives the hydrogen energy vehicle module through the hydrogen generated by the hydrogen energy module, and feeds back the consumption and surplus of hydrogen by the hydrogen energy vehicle module to the hydrogen energy module. The fusion module is communicatively connected to the power grid module, the hydrogen energy vehicle module, and the hydrogen energy module respectively to coordinate the flow of electricity, hydrogen, and benefits.

4. The optimized scheduling method for the electric hydrogen vehicle system according to claim 1, characterized in that, The above step S2 includes: S2.1: Embed the power grid module into the upper-layer time model to provide real-time power demand prediction and scheduling data. S2.2: Embed the hydrogen energy module into the lower-layer space model to affect the generation and storage of hydrogen in the hydrogen energy module through the power demand prediction and scheduling data generated by the upper-layer time model; and According to the hydrogen demand and storage volume, feedback to the upper-layer time model, and then affect the power load scheduling of the power grid module.

5. The optimized scheduling method for the electric hydrogen vehicle system according to claim 4, wherein, The upper-layer time model simulates the operation of the power grid module through system dynamics to obtain real-time electricity prices, power generation compensation prices, and voltage and power data of each node of the power grid module. The lower-layer space model simulates the operation of the hydrogen energy module through system dynamics to obtain hydrogen prices, the number and spatial distribution of HVs participating in the response, and real-time hydrogen storage results.

6. The optimized scheduling method for the electric hydrogen vehicle system according to claim 1, characterized in that, The above step S3 includes: S3.1: Dynamically couple the power grid module with the transportation network. By integrating the road network topology and the operation data of the power grid module, obtain the intraday load, new energy output, and temperature curves. S3.2, input the given intra-day load, new energy output, and temperature curve into the upper-layer time model, and output the electricity price, power generation compensation price, and voltage-power data of each node of the grid module; S3.3, input the output data of the upper-layer time model into the lower-layer space model, and run the lower-layer space model to obtain the calculation results including hydrogen price, the number and location distribution of HV users participating in the response, and hydrogen storage; S3.4, feedback the calculation results of the lower-layer space model to the upper-layer time model, perform multiple iterative solutions to finally correct the electricity price signal, and finally obtain the operating cost of the electric-hydrogen vehicle system, the new energy consumption situation, and the user satisfaction situation.

7. The optimized scheduling method for the electric hydrogen vehicle system according to claim 6, characterized in that The objective function minz of the upper-layer time model y is as follows: where y is the number of iterations, α up is the weight coefficient of the upper-level objective function, β i is the importance coefficient of node load, is the load reduction amount within a unit time interval, is the net grid income within a unit time interval; B is the set of grid module nodes.

8. The optimized scheduling method for the electric hydrogen vehicle system according to claim 7, characterized in that, The boundary conditions of the upper-layer time model include: load classification management constraints; hydrogen energy facilities and HV constraints; grid system operation constraints; Among them, the load classification management constraints include: Indicates the load reduction generated by each node of the power grid module through centralized control In the formula, is the aggregated power change of the temperature-controlled load, and ΔT set is the change in the air-conditioning set temperature, and T c is the time set for centralized control of the temperature-controlled load; Indicates the upper and lower limit constraints of the target temperature for the temperature control load setting T set,min ≤T t set ≤T set,max Wherein: is the lowest target temperature set for the air conditioner, is the highest target temperature set for the air conditioner; indicating the willingness of users to interrupt the load and transfer the load: In the formula, is the proportion coefficient of the interruptible load for general load users at time t, is the maximum interruptible load proportion coefficient, λ int is the willingness coefficient of general load users to interrupt the load, and this value changes with the influence of temperature. is the compensation electricity price per unit power for interrupting the load at time t, c int is the expected compensation electricity price for users to interrupt the load; is the proportion coefficient of the shiftable load for general load users at time t, is the maximum shiftable load proportion coefficient, λ shi is the willingness coefficient of general load users to shift the load, and this value changes with the influence of temperature. is the compensation electricity price per unit power for shifting the load at time t, c shi is the expected compensation electricity price for users to shift the load; indicating the power of the transferable load, which consists of the transferred-out power and the transferred-in power, and the load transfer has balance and time constraints: Wherein, is the power of the transferable load of node i at time t, is the load power transferred out from node i at time t, is the load power transferred into node i at time t; indicating the maximum and minimum values of the interruptible load and the transferable load: In the formula, is the maximum transferable load ratio coefficient, is the active power demand at time node t; Among them, the hydrogen energy facilities and HV constraints include: indicating that the electrolyzers and fuel cells connected to each node of the grid module should meet the upper and lower power limits: Wherein, is the switch state of the electrolyzer at time t, and P i elt,min is the minimum power of the electrolyzer at node i, is the power of the electrolyzer at node i at time t, and P i elt,max is the maximum power of the electrolyzer at node i, is the switch state of the fuel cell at time t, and P i fc,min is the minimum power of the fuel cell at node i, and P i fc,max is the maximum power of the fuel cell at node i, is the power of the fuel cell at node i at time t; and P t elt represents the electrolysis power; and P t fc represents the power generation of the fuel cell; indicating the power injection constraints of each V2G station (vehicle-grid interaction station) into each node of the grid module: In the formula, is the power injected into the V2G station at node i at time t, is the number of HVs connected to the V2G station at node i at time t, is the output power of the k-th HV at time t, σ v2g is the response willingness of HFCV users, is the sensitivity of HFCV at time t, N v is the total number of HFCVs in this area, T V is the time set for HVs to participate in the response; indicating the HV access quantity constraints of each V2G node: In the formula, represents the number of HVs connected to the V2G station under node i; is the maximum number of HVs connected to the V2G station under node i; Among them, the grid system operation constraints include: The upper-layer time model adopts a second-order cone constraint-based optimal power flow model for grid module reconstruction, and the power constraints are: Where: P i,t and Q i,t respectively represent the active power injection and reactive power injection of node i at time t; and respectively represent the active power and reactive power of the generator at node i at time t; and respectively represent the active power demand and reactive power demand of node i at time t; δ(i) is the set of nodes connected to node i by branches; P ij,t and Q ij,t respectively represent the active power and reactive power flowing from node i to node j at time t; r ij and x ij respectively represent the resistance and reactance of branch (i, j); is the square of the modulus of the transmission current of branch (i, j) at time t; The voltage and current constraints are expressed as: Where: and V i respectively represent the upper and lower limits of the voltage magnitude of node i; V i,t is the voltage magnitude of node i at time t, V j,t is the voltage magnitude of node j at time t; p ij,t is the active power flow between nodes i and j at time t, q ij,t is the reactive power flow between nodes i and j at time t; z ij,t is a 0-1 variable that reflects the power flow direction of the branch. Taking 1 indicates that the positive direction of the power flow is from node i to node j; K is a positive real number; E is the set of branches in the power grid module; Wherein: and I ij respectively represent the maximum and minimum values of the magnitude of the transmission current of branch (i, j); The radial constraints are expressed as: The second-order cone constraints are expressed as: where P ij,t represents the active power flow between nodes i and j at time t, and Q ij,t represents the reactive power flow between nodes i and j at time t; z ji，t represents a 0-1 variable.

9. The optimized scheduling method for the electric hydrogen vehicle system according to claim 8, wherein The objective function of the lower-layer space model is max g y is as follows: max g y = α low R hsop,y +(1 - α low )R v,y where: y is the number of iterations, and α low is the objective function weight coefficient of the lower-layer space model, and R hsop,y is the net income of the hydrogen energy storage operator, and R v,y is the net income of HFCV users.

10. The optimized scheduling method for the electric hydrogen vehicle system according to claim 9, characterized in that, The boundary conditions of the lower-layer space model include: hydrogen production, transportation, storage, and utilization constraints; HV travel behavior constraints; Among them, the hydrogen production, transportation, storage, and utilization constraints include: Indicates the hydrogen production of the electrolyzer at each node of the hydrogen energy module and the hydrogen consumption of the fuel cell wherein, is the power of the electrolyzer at node i at time t; is the power of the fuel cell at node i at time t; η elt is the electrolyzer efficiency; η fc is the fuel cell efficiency; Indicates the change in the amount of hydrogen in the hydrogen storage tanks at each node of the hydrogen energy module and the maximum value of the storage capacity and the relationship between the minimum value of 0: indicating the transportation constraints of HT: In the formula: represents the real-time status information of HT at time t; is a 0-1 variable of the position status of the k-th HT at time t, and (r, r') represents the position status from node r to node r’; is the remaining hydrogen storage of the k-th HT at time t, m k,max is the maximum hydrogen storage of the k-th HT; is the HRS object supplied by the k-th HT at time t; is the equivalent time for the k-th HT to travel in the transportation network, s f is the road length, v k is the average driving speed of the k-th HT; is the remaining driving time of the k-th HT on section f at time t; is the remaining stay time of the k-th HT at node r at time t; is the planned stay time of the k-th HT at node r; is a 0-1 variable of the arrival or departure status of the k-th HT at node r at time t; is the HRS hydrogen demand at node r at time t; Among them, the HV travel behavior constraints include: Indicates the travel willingness σ of HV users tra : σ tra = exp(-λ tra PPD) where λ tra is the travel chain correction coefficient, and PPD is the predicted dissatisfied proportion index in the human comfort index standard; indicating the input parameters of the interaction between the V2G station and the grid module: Where: V t x represents the real-time status information of the x-th HV at time t; is the driving state, which characterizes the position of the x-th HV in the transportation network at time t; is the hydrogen storage of the x-th HV at time t; is the V2G node number closest to the x-th HV at time t.

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