A hydrogen energy storage and transportation integrated supply chain optimization control method

Abstract

The present application belongs to the technical field of hydrogen energy supply chain network optimization, and is especially a hydrogen energy storage and transportation integrated supply chain optimization control method. The method comprises the following steps: S1: multi-technology modular modeling of each link of HSC; S2: constructing a double-layer optimization framework with the optimal economy as the target; S3: iterative solution of the double-layer model and scheme output. The present application plans facilities investment, operation and maintenance cost through the double-layer optimization framework, significantly reduces hydrogen energy transportation cost, and greatly improves the annual operation profit of the system. The present application uses hydrogen energy as a large-scale and long-period energy storage medium, effectively suppresses the fluctuation of energy output through the flexible adjustment of electric-hydrogen hybrid energy storage, and significantly reduces the abandoned electricity phenomenon caused by the time and space dislocation of source and load, and realizes the full use of surplus wind and light resources.

Description

Technical Field

[0001] This invention relates to the field of hydrogen energy supply chain network optimization technology, specifically to an integrated hydrogen energy storage and transportation supply chain optimization and control method. Background Technology

[0002] Driven by the current global energy transition and the "dual carbon" goal, hydrogen energy, as a key secondary energy source for achieving high-proportion renewable energy consumption and deep decarbonization, is increasingly highlighting its strategic importance. Traditional single-technology planning or simple superposition methods are insufficient to coordinate the operational characteristics and economic benefits of each link at the global optimal level, and there is also a lack of a unified standard system and mutual recognition mechanism to support cross-regional hydrogen energy trade. Therefore, exploring a control strategy that can effectively coordinate electric-hydrogen hybrid energy storage and optimize the configuration and operation of the integrated hydrogen energy storage and transportation supply chain is of great practical significance for improving the economy and reliability of the entire energy system.

[0003] While existing research has made some progress in hydrogen energy supply chain modeling and optimization, current methods still tend to optimize hydrogen production, storage, and transportation as independent units step by step. They lack a global characterization of the dynamic interactions and cross-spatial collaborative matching mechanisms between these stages in the context of multi-energy integration (electricity-hydrogen-gas), making it difficult for the system to achieve overall economic optimization and effectively addressing the bottlenecks caused by uneven spatiotemporal distribution of sources and loads and the reliance on a single technological approach. Therefore, this invention proposes an integrated hydrogen energy storage and transportation supply chain optimization and control method considering hybrid electric-hydrogen energy storage. Summary of the Invention

[0004] To address the aforementioned technical problems, according to one aspect of the present invention, the present invention provides the following technical solution: A method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain includes the following steps: S1: Multi-technology modular modeling of each HSC link: Based on the HSC architecture with "power generation-energy storage-transmission" as the core, establish a multi-technology planning and operation model for each HSC process; S1.1: Operation and Planning Model of P2H Unit: Based on the three electrolyte types of P2H, the relationship between the input power and hydrogen production of P2H unit is established, and the "maximum sequential programming method" is proposed to realize the coordinated planning of multiple P2H technologies; S1.2: Operation and planning model of hydrogen energy storage facility based on multiple time scales: Using high-pressure gaseous and cryogenic liquid energy storage as seasonal energy storage methods, an operation model of hydrogen energy storage facility is established to achieve multi-technology collaborative planning; S1.3: Operation and planning model of hydrogen energy transmission network based on operational characteristics: Establish cross-regional transportation model of H2 trucks and pipelines to realize the transportation process between hydrogen source and load demand area; S1.4: Operation and Planning Model of Hydrogen Energy Conversion Facility: Establish an operation model for the conversion facility to achieve collaborative planning of conversion technologies; S2: Construct a two-layer optimization framework with the goal of economic optimization: minimize the operating costs of EGIS and the annualized investment and maintenance costs of HSC facilities under seasonal division, including the operating costs of the power system, the natural gas system, the hydrogen system, and the investment and maintenance costs of HSC components; S3: Iterative solution and solution output of the two-layer model: Establish a two-layer optimization configuration model of HSC under EGIS that considers planning-operation coordination. The upper-layer model optimizes the operation with the objective function of minimizing the total operating cost (COP) of EGIS and HSC, while the lower-layer model aims to minimize the investment and maintenance costs of HSC facilities.

[0005] As a preferred embodiment of the integrated hydrogen energy storage and transportation supply chain optimization control method described in this invention, in step S1.1, the relationship between the input power of the P2H device and the hydrogen production is expressed as follows: ; In the formula, The conversion efficiency of P2H; It has the low calorific value of hydrogen. Input power to the P2H facility in time period t at grid node n; The collaborative planning method for multiple P2H technologies is as follows: Set the upper limit for the module planning of each node n pairs of M-type P2H technologies to 1. Introducing 0-1 programming variables This indicates whether the k-th M-type module at node n has been planned, ordered by module number from 1 to... Plan sequentially until the k-th module Up to a value of 0, the total P2H configuration capacity of node n is: ; In the formula, The total planned capacity of P2H facilities in grid node n; Capacity of P2H modules using M-type technology; Planning upper limit constraints: ; Sequential constraints: ; The start-up and shutdown variables related to module operation should be constrained by the module's planning (λ): .

[0006] In a preferred embodiment of the present invention, in step S1.2, the method for establishing the hydrogen energy storage facility operation model is as follows: The operating principles of various energy storage facilities are similar, and the time-series dynamic representation of hydrogen is: ; In the formula, Let n be the amount of hydrogen stored at node n in time period t; when =1 indicates hydrogen charging in the current time period; when Hydrogen is released for the current period, and there are additional constraints to prevent both from being 1 at the same time; and To inject and release hydrogen gas during period t at node n; The multi-technology collaborative planning method is as follows: based on the hydrogen load of section n at time t. Define surplus / deficient hydrogen energy: ; In the formula, The hydrogen produced at grid node n at time t; The surplus can be used for energy storage or inter-regional transportation: ; Calculate the hourly maximum energy storage demand of node n for each time period: ; In the formula, The planned capacity of the X-type hydrogen storage technology at node n; Daily continuous energy storage demand is calculated by accumulating over consecutive hours: ; And take the maximum value to obtain the maximum continuous demand within the day: ; Maximum daily demand is the maximum of the hourly and intraday continuous demand: ; Seasonal demand is: ; Take the maximum value for each season as the total configuration requirement for node n: ; In the formula, Let n be the total configured capacity of the hydrogen storage facility at node n; After determining the total configuration capacity, introduce 0-1 variables. This represents the planning state of X-type energy storage technology at node n, thereby enabling the coordinated configuration of multiple technologies: ; In the formula, This represents the 0-1 planning state of X-type storage technology on node n.

[0007] As a preferred embodiment of the present invention, the specific method of S1.3 is as follows: Hydrogen tanker truck operation model transportation process: The truck is filled with hydrogen at the source point, travels to a designated unloading point to release the hydrogen, and then returns to the source point. Assuming a constant travel speed, the time required for a single transportation is as follows: ; ; ; In the formula, For loading / unloading time, The distance from the source point to the load point. For average driving speed, to meet load requirements The source point needs to dispatch a vehicle at time t, and the required number of vehicles is rounded up. The time required for a single delivery process; ; In the formula, The amount of hydrogen that a truck can carry; Let be the number of trucks that perform the delivery process at time t and node n; The number of returned vehicles meets the following requirements: ; In the formula, The number of trucks that return to the source point within time t at node n; The fleet inventory status is as follows: ; The transport volumes of HTT and LHTT at time t are respectively: ; ; In the formula, and Hydrogen is injected and released at node n during period t; and The amount of hydrogen that can be loaded into high-temperature and low-temperature tanks; and Let n be the quantity of HTT and LHTT in the inventory at time period t; The total number of vehicles required for the plan is: ; Pipeline transportation operation model: Pipeline transportation includes hydrogen pipelines and hydrogen-rich compressed natural gas injected into the natural gas pipeline network. ; In the formula, This represents the volumetric flow rate of the pipe segment between node i and node j under standard operating conditions. Indicates air density; and These represent pressure and temperature under standard operating conditions, respectively. and These represent the inlet and outlet pressures of the pipe section, respectively. Indicates the pipe diameter; This represents the hydraulic friction coefficient of the pipeline; The compressibility factor of the gas mixture; This refers to the length of the pipe. For pipe temperature; Indicates the component ratio of a gas mixture; Under maximum pressure differential conditions, the maximum transport capacity of the pipeline is obtained; the hydrogen transport capacities of HPL and HCNG are respectively: ; ; In the formula, ϕ is the hydrogen doping ratio of HCNG; and The maximum volumetric flow rate of hydrogen that can be transported via HPL and HCNG methods; the corresponding transport limit for each phase is: ; In the formula, Hydrogen gas transported by adding hydrogen at room temperature; The minimum diameter of the HPL is determined by the required cross-sectional area for current flow: ; In the formula, For flow rate, The cross-sectional area; Hydrogen transported via high-pressure liquefaction and hydrogenation; The density of hydrogen gas; Collaborative planning model for hydrogen transportation technology: based on inter-regional hydrogen demand Compute the transmission capacity requirement of node n And introduce 0-1 variables This indicates the planning status of the Y-type conveyor technology, achieving the following collaborative configuration: .

[0008] In a preferred embodiment of the present invention, the method for establishing the conversion facility operation model in S1.4 is as follows: power consumption is uniformly used to characterize the conversion process. ; In the formula, To convert the power consumption of the facility, The power required for one unit of hydrogen conversion. The amount of hydrogen converted; The method for collaborative planning of conversion technologies is as follows: based on the hydrogen production, storage, and consumption of node n in each time period, calculate the power demand of the Z-type conversion equipment in each process and each time period, and plan the capacity based on its maximum value in all time periods. ; In the formula, The power consumption of the Z-type method at point n at time t; Let n be the planned capacity of the Z-type conversion facility at node n.

[0009] In a preferred embodiment of the present invention, the overall objective function in S2 is expressed as: ; In the formula, , and These are system operating costs, HSC investment costs, and maintenance costs, respectively. , and These represent the operating costs of the electricity, natural gas, and hydrogen systems, respectively. , , and These represent the investment costs for hydrogen production, hydrogen storage, hydrogen conversion, and conversion equipment, respectively. , , and These represent the maintenance costs of hydrogen production, storage, transportation, and conversion equipment, respectively; s∈SEA represents the seasonal components. The corresponding seasonal weights.

[0010] In a preferred embodiment of the present invention, in step S2, the power system operating cost is: ; ; ; ; ; ; In the formula, , and These represent the costs of the gas turbine, nuclear power unit, and external power transmission, respectively. and These represent the penalty costs for abandoning photovoltaic / solar thermal power plants and for power load shortages, respectively; n represents the grid node, n=1,2,…,N e; , and Onshore / offshore wind and solar power generation capacity; , and The costs include the single start-up and shutdown cost, fixed operating cost, and marginal electricity cost of the gas turbine. , , , , and At time t at node n, the grid-connected power from gas turbines, nuclear generator sets, external transmission, onshore wind farms, offshore wind farms, and photovoltaic power plants; , and The cost of curtailment for onshore wind power, offshore wind power, and photovoltaic power generation; and Penalty costs for shortfalls in electricity and gas load demand; Natural gas system operating costs: ; ; ; ; ; In the formula, , , and These represent the natural gas production cost, natural gas storage cost, external natural gas transportation cost, and penalty cost incurred due to a shortage of natural gas demand, respectively; m represents a natural gas network node, m=1,2,…,N g ; and For node m, the gas source natural gas volumetric flow rate and the gas storage injection and release volume during time period t; Let m be the external gas supply to node m during time period t; Hydrogen system operating costs: ; In the formula, For the cost of hydrogen production; Cost of hydrogen transport trucks; External hydrogen purchase cost; ; ; In the formula, Let P2H equipment start-up and shutdown cost be denoted as n at grid node n during time period t. The amount of water required to produce one unit of hydrogen; Unit water cost; and The single start-up cost and downtime cost of M-type P2H technology; It consists of fuel costs and driver wages: ; ; ; ; In the formula, and These represent fuel costs and driver wages, respectively. This indicates the fuel cost for a single transport trip; Fuel consumption per 100 kilometers for trucks; Let k be the transport frequency of the kth truck at power grid node n. ; In the formula, w represents the quantity of each region, w = 1, 2, ..., N h ; Cost per unit of hydrogen purchased; The amount of external hydrogen source procured by region w during time period t; Investment and maintenance costs of HSC components: Investment and maintenance costs are calculated based on a full lifecycle approach. ; ; ; In the formula, This indicates the annual investment coefficient of the equipment; n represents the weighted investment coefficient; year The depreciation period of the equipment; For equipment costs; and These represent the annual investment and maintenance costs of the equipment, respectively. The annual maintenance factor for the equipment; The device scaling function is used to calculate the total investment cost of the equipment. ; In the formula, Cost per unit of equipment invested in; For the construction capacity of the equipment; This represents the minimum unit capacity of the equipment. This is the scaling factor for the equipment size; When hydrogen is transported through hydrogen pipelines The minimum diameter of the hydrogen pipeline is related to the following formula: ; ; In the formula, The corrosion rate of the hydrogen pipeline; The design diameter of the hydrogen pipeline; The cost per meter of installation is calculated using the following formula: ; In the formula, , and This refers to the construction cost parameters required per unit length of hydrogen pipelines.

[0011] As a preferred embodiment of the present invention, the specific method of step S3 is as follows: Step 1: Obtain photovoltaic power output data, as well as electricity load, gas load, and hydrogen load data, based on the source-load scenario reduction method; Step 2: Initialize the iteration parameters, setting the initial values ​​of the conversion equipment power and the hydrogen system operating cost to zero, i.e., setting... and =0; Step 3: Substitute the above parameters into the optimal operation model of the upper-level EGIS system to solve and obtain the unconsumed renewable energy. Step 4: Based on the system operating costs and unabsorbed renewable energy obtained from the upper-level model, determine the required capacity of various types of equipment for each HSC process in the lower-level model; Step 5: Calculate and compare the difference in planned capacity for each type of equipment before and after each iteration with the preset convergence threshold; if the difference is greater than the convergence threshold, then... , Update back to step 3 and continue iterating; if the difference is less than the convergence threshold, the iteration ends and the optimal combination planning configuration scheme and optimal running result of HSC are output.

[0012] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing an integrated collaborative model covering the entire process of "production-storage-transportation-sales," it breaks through the limitations of isolated optimization of each link, achieving system-level economic optimization. This method, through a two-layer optimization framework, coordinates facility investment, operation, and maintenance costs, significantly reducing hydrogen transportation costs and substantially increasing the system's annualized operating profit. This invention utilizes hydrogen energy as a large-scale, long-term energy storage medium, and through the flexible adjustment of electricity-hydrogen hybrid energy storage, effectively mitigates fluctuations in energy output. Simulation results show that this method can significantly reduce power curtailment caused by source-load spatiotemporal misalignment, achieving full utilization of surplus wind and solar resources. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and detailed embodiments. Wherein: Figure 1 This is a flowchart of the present invention; Figure 2 This is a framework diagram of the integrated hydrogen energy storage and transportation supply chain system of the present invention; Figure 3 The diagram shows the input power efficiency of the three P2H technologies of this invention. Figure 4 This is a diagram of the hydrogen energy supply chain architecture of the present invention; Figure 5 This is a flowchart of the solution process for the two-layer optimization configuration model of the present invention; Figure 6 The example analysis of this invention includes the EGIS topology diagrams: (a) power system topology diagram, (b) natural gas system topology diagram, and (c) spatial distribution of source and load. Figure 7 This is a graph showing the results of photovoltaic output and load demand reduction in the example analysis of this invention; Figure 8 This is a graph showing the renewable energy consumption and output on a typical day of each season in the example analysis of this invention; Figure 9 This is a surplus RES map for each region on a typical day in the numerical analysis of this invention; Figure 10 This is a source-load time distribution diagram for a typical day in the example analysis of this invention; Figure 11 This is a comparison chart of regional hydrogen load and hydrogen production from unconsumed renewable energy sources in the example analysis of this invention; Figure 12 This is the HSC result diagram under Case 1 of the present invention; Figure 13 This is the HSC result diagram under Case 2 of the present invention; Figure 14 This is a diagram showing the results of HSC planning in Case 3 and extended device configuration in Case 2. Figure 15 This is a diagram showing the hydrogen load supply situation in Examples 3 and 2 of the present invention; Figure 16 This is a diagram showing renewable energy consumption under different systems of the present invention; Figure 17 This is a diagram showing the consumption of surplus renewable energy in Examples 3 and 2 of the present invention. Detailed Implementation

[0014] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0015] This invention addresses challenges such as the mismatch between source and load distribution in time and space, poor adaptability of technological routes, and difficulties in cross-stage coordination by constructing a collaborative model of the entire hydrogen energy supply chain under an interconnected electricity-gas-hydrogen system. Firstly, based on a core architecture of "power generation-storage-transmission," a multi-technology planning and operation model is established. In the hydrogen production stage, a "maximum sequential programming method" is used to introduce 0-1 variables to achieve modular collaborative configuration of electro-hydrogen conversion technologies such as alkaline electrolyzers, proton exchange membrane electrolyzers, and solid oxide electrolyzers, optimizing the hydrogen production capacity and flexibility of each node. In the hydrogen storage stage, multi-timescale demand analysis is used to calculate the energy storage capacity requirements of each node, and 0-1 variables are introduced to coordinate high-pressure gaseous hydrogen storage, liquid hydrogen storage, and cavernous hydrogen storage technologies. In the hydrogen transportation stage, combined with hydrogen tanker truck and pipeline transportation models, the path and capacity are dynamically planned based on cross-regional hydrogen load. In the conversion stage, the configuration of liquefaction and evaporation facilities is optimized based on power consumption requirements. The optimization framework employs a two-layer model: the upper layer optimizes the system operation strategy with the goal of minimizing the operating cost of the electro-gas interconnection system, while the lower layer determines the optimal capacity configuration with the goal of minimizing the investment and maintenance costs of hydrogen energy supply chain facilities. An iterative algorithm gradually converges to the global optimum. The objective function minimizes the total cost, while imposing constraints on power balance, hydrogen system balance, and equipment operation to ensure system economy and reliability. This method significantly improves the renewable energy absorption capacity and reduces the cost of hydrogen energy applications through multi-technology collaboration and spatiotemporal adjustment. The entire process is as follows: Figure 1 As shown.

[0016] This invention proposes an energy allocation optimization control method for an integrated hydrogen energy storage and transportation supply chain that considers hybrid electric-hydrogen energy storage. The implementation process of this technical solution strictly follows three logically progressive core steps. Step 1 establishes a multi-technology modular model based on the "production-storage-transportation-transfer" full-chain architecture. During this process, the "maximum sequential programming method" and a multi-timescale balancing mechanism (see Equations 1 to 32) are used to collaboratively determine the technology selection and capacity boundaries of hydrogen production, storage, and inter-regional hydrogen transportation facilities. Step 2 constructs a two-layer energy allocation optimization control framework covering electricity, natural gas, and hydrogen energy systems. The upper layer focuses on minimizing the operating cost of the electric-gas interconnection system (see Equations 33 to 52), while the lower layer focuses on optimizing the investment and maintenance costs throughout the entire lifecycle of the hydrogen energy supply chain facilities (see Equations 53 to 59). Finally, in Step 3, equipment power and cost parameters are initialized and a convergence threshold is set. The closed-loop iterative calculation of the two-layer model (e.g., ...) is then performed. Figure 5 (As shown in the process), until the planned capacity difference meets the convergence criterion, thereby outputting the globally optimal integrated energy distribution scheme for the hydrogen energy supply chain.

[0017] The implementation scheme is as follows: A method for optimizing and controlling the integrated hydrogen energy storage and transportation supply chain, comprising the following steps: Step 1: Multi-technology modular modeling of each link in HSC: Hydrogen energy supply chain in the framework of electric-gas interconnection system, such as Figure 2 As shown, based on the HSC architecture centered on "power generation-energy storage-transmission", a multi-technology planning and operation model for each HSC process is established, providing a model foundation for the optimal configuration of HSC under EGIS.

[0018] Step 1.1 Operation and Planning Model of P2H Unit: (1) P2H Device Operation Model: P2H technology can be classified according to electrolyte type into: alkaline electrolyzers, proton exchange membrane electrolyzers, and solid oxide electrolyzers; each has its advantages in terms of cost, flexibility, and efficiency. The conversion efficiency curves of the three technologies as a function of input power are shown in the figure. Figure 3 The relationship between the input power of a P2H unit and the hydrogen production can be expressed as: (1); In the formula, The conversion efficiency of P2H; It has the low calorific value of hydrogen. The input power for the P2H facility in time period t at grid node n.

[0019] (2) P2H Multi-Technology Collaborative Planning Model: To achieve collaborative planning of multiple P2H technologies, a "maximum sequential planning method" is proposed. The upper limit of the module planning for each node n pairs of M-type P2H technologies is set to... Introducing 0-1 programming variables This indicates whether the k-th M-type module at node n has been planned, ordered by module number from 1 to... Plan sequentially until the k-th module The total P2H configuration capacity of node n is up to 0. (2); In the formula, The total planned capacity of P2H facilities in grid node n; Capacity of P2H modules using M-type technology; Planning upper limit constraints: (3); Sequential constraints: (4); The start-up and shutdown variables related to module operation should be constrained by the module's planning (λ): (5); Step 1.2 Operation and Planning Model of Hydrogen Energy Storage Facility Based on Multiple Time Scales: HSC needs to be equipped with energy storage to smooth out fluctuations in production and demand across multiple time scales. This paper uses high-pressure gaseous and cryogenic liquid energy storage as seasonal energy storage methods. Common gaseous energy storage includes high-pressure hydrogen tanks and cavernous hydrogen storage, while liquid hydrogen mainly uses liquid hydrogen tanks.

[0020] (1) Operation model of hydrogen energy storage facility: The operating principles of various energy storage facilities are similar, and the time-series dynamics of hydrogen can be expressed as: (6); In the formula, Let n be the amount of hydrogen stored at node n in time period t; when =1 indicates hydrogen charging in the current time period; when Hydrogen is released for the current period, and there are additional constraints to prevent both from being 1 at the same time; and To inject and release hydrogen gas during period t at node n.

[0021] (2) Multi-technology collaborative planning: First, based on the hydrogen load of section n at time t Define surplus / deficient hydrogen energy: (7); In the formula, Let be the hydrogen produced at grid node n at time t.

[0022] The surplus can be used for energy storage or inter-regional transportation. (8); Calculate the hourly maximum energy storage demand of node n for each time period: (9); In the formula, The planned capacity of the X-type hydrogen storage technology at node n.

[0023] Daily continuous energy storage demand is calculated by accumulating over consecutive hours: (10); And take the maximum value to obtain the maximum continuous demand within the day: (11); Maximum daily demand is the maximum of the hourly and intraday continuous demand: (12); Seasonal demand is: (13); Take the maximum value for each season as the total configuration requirement for node n: (14); In the formula, Let n be the total configured capacity of the hydrogen storage facility at node n.

[0024] After determining the total configuration capacity, introduce 0-1 variables. This represents the planning state of X-type energy storage technology at node n, thereby enabling the coordinated configuration of multiple technologies: (15); In the formula, This represents the 0-1 planning state of X-type storage technology on node n.

[0025] Step 1.3 Operation and Planning Model of Hydrogen Transportation Network Based on Operational Characteristics: P2H and storage facilities can eliminate the time difference between hydrogen sources and load demand in each region. However, a hydrogen transportation network is still needed to address the spatial differences between hydrogen production and demand across multiple regions. Therefore, an H2 truck and pipeline cross-regional transportation model is established to realize the transportation process between hydrogen sources and regions with load demand.

[0026] 1) Hydrogen tanker truck operation model transportation process: The truck is loaded with hydrogen at the source point, travels to a designated unloading point to release the hydrogen, and then returns to the source point. Assuming a constant travel speed, the time required for a single transport is as follows: (16); (17); (18); In the formula, For loading / unloading time, The distance from the source point to the load point. This represents the average driving speed. It is to meet the load requirements. The source point needs to dispatch a vehicle at time t, and the required number of vehicles is (rounded up). The time required for a single delivery process.

[0027] (19); In the formula, The amount of hydrogen that a truck can carry; Let be the number of trucks that perform the delivery process at time t and node n.

[0028] The number of returned vehicles meets the following requirements: (20); In the formula, The number of trucks that return to the source point within time t at node n; The fleet inventory status is as follows: (twenty one); The transport volumes of HTT and LHTT at time t are respectively: (twenty two); (twenty three); In the formula, and Hydrogen is injected and released at node n during period t; and The amount of hydrogen that can be loaded into high-temperature and low-temperature tanks; and Let represent the quantity of HTT and LHTT in the inventory of node n during time period t.

[0029] The total number of vehicles required for the plan is: (twenty four); 2) Pipeline transportation operation model: Pipeline transportation includes hydrogen pipelines and hydrogen-rich compressed natural gas injected into the natural gas pipeline network.

[0030] (25); In the formula, This represents the volumetric flow rate of the pipe segment between node i and node j under standard operating conditions. Indicates air density; and These represent pressure and temperature under standard operating conditions, respectively. and These represent the inlet and outlet pressures of the pipe section, respectively. Indicates the pipe diameter; This represents the hydraulic friction coefficient of the pipeline; The compressibility factor of the gas mixture; This refers to the length of the pipe. For pipe temperature; This indicates the proportion of components in a gas mixture.

[0031] Under maximum pressure differential conditions, the maximum transport capacity of the pipeline can be obtained; the hydrogen transport capacities of HPL and HCNG are respectively: (26); (27); In the formula, ϕ is the hydrogen doping ratio of HCNG; and The maximum volumetric flow rate of hydrogen that can be transported via HPL and HCNG methods; the corresponding transport limit for each phase is: (28); In the formula, This refers to hydrogen transported by adding hydrogen gas at room temperature.

[0032] The minimum diameter of the HPL is determined by the required cross-sectional area for current flow: (29); In the formula, For flow rate, The cross-sectional area; Hydrogen transported via high-pressure liquefaction and hydrogenation; This represents the density of hydrogen gas.

[0033] 3) Collaborative planning model for hydrogen transportation technology: based on inter-regional hydrogen demand. Compute the transmission capacity requirement of node n And introduce 0-1 variables This indicates the planning status of the Y-type conveyor technology, achieving the following collaborative configuration: (30); Step 1.4 Operation and planning model of hydrogen energy conversion facility: The power required for the liquefaction plant to liquefy a unit amount of hydrogen is approximately 6.78 kW; the evaporator consumes approximately 0.6 kW / kg to convert liquid hydrogen into gas; the expander consumes approximately 0.29 kW / kg to expand hydrogen from 200 bar to 70 bar; the liquid hydrogen pump consumes approximately 0.1 kW per kilogram of liquid hydrogen; the gas separation plant adopts the separation technology described in the literature.

[0034] 1) Conversion facility operation model: The conversion process is uniformly characterized by power consumption. (31); In the formula, To convert the power consumption of the facility, The power required for one unit of hydrogen conversion. The amount of hydrogen converted.

[0035] 2) Coordinated planning of conversion technologies: Based on the hydrogen production, storage, and consumption of node n in each time period, calculate the power demand of the Z-type conversion equipment in each process and each time period, and plan the capacity based on its maximum value in all time periods: (32); In the formula, The power consumption of the Z-type method at point n at time t; Let n be the planned capacity of the Z-type conversion facility at node n.

[0036] The entire green hydrogen supply chain, from production to consumption, such as Figure 4 As shown.

[0037] Step 2: Construct a two-layer optimization framework with the goal of achieving optimal economic efficiency: The overall objective is to minimize the operating cost of EGIS and the annualized investment and maintenance costs of HSC facilities under seasonal divisions. The overall objective function is expressed as: (33); In the formula, , and These are system operating costs, HSC investment costs, and maintenance costs, respectively. , and These represent the operating costs of the electricity, natural gas, and hydrogen systems, respectively. , , and These represent the investment costs for hydrogen production, hydrogen storage, hydrogen conversion, and conversion equipment, respectively. , , and These represent the maintenance costs of hydrogen production, storage, transportation, and conversion equipment, respectively; s∈SEA represents the seasonal components. The corresponding seasonal weights.

[0038] (1) Power system operating costs: (34); (35); (36); (37); (38); (39); In the formula, , and These represent the costs of the gas turbine, nuclear power unit, and external power transmission, respectively. and These represent the penalty costs for abandoning a photovoltaic / solar thermal power plant and for power load shortages, respectively. n represents a grid node, n=1,2,…,N e ; , and Onshore / offshore wind and solar power generation capacity; , and The costs include the single start-up and shutdown cost, fixed operating cost, and marginal electricity cost of the gas turbine. , , , , and At time t at node n, the grid-connected power from gas turbines, nuclear generator sets, external transmission, onshore wind farms, offshore wind farms, and photovoltaic power plants; , and The cost of curtailment for onshore wind power, offshore wind power, and photovoltaic power generation; and The penalty cost for the shortfall in electricity and gas load demand.

[0039] (2) Operating costs of the natural gas system: (40); (41); (42); (43); (44); In the formula, , , and These represent the natural gas production cost, natural gas storage cost, external natural gas transportation cost, and penalty cost incurred due to a shortage of natural gas demand, respectively; m represents a natural gas network node, m=1,2,…,N g ; and For node m, the gas source natural gas volumetric flow rate and the gas storage injection and release volume during time period t; Let m be the external gas supply to node m during time period t.

[0040] (3) Operating costs of the hydrogen system: (45); In the formula, For the cost of hydrogen production; Cost of hydrogen transport trucks; Cost of purchasing external hydrogen.

[0041] (46); (47); In the formula, Let P2H equipment start-up and shutdown cost be denoted as n at grid node n during time period t. The amount of water required to produce one unit of hydrogen; Unit water cost; and The single start-up cost and downtime cost of M-type P2H technology; It consists of fuel costs and driver wages: (48); (49); (50); (51); In the formula, and These represent fuel costs and driver wages, respectively. This indicates the fuel cost for a single transport trip; Fuel consumption per 100 kilometers for trucks; Let n be the transport frequency of the kth truck at power grid node n.

[0042] (52); In the formula, w represents the quantity of each region, w = 1, 2, ..., N h ; Cost per unit of hydrogen purchased; This represents the amount of external hydrogen source procured by region w during time period t.

[0043] (4) Investment and maintenance costs of HSC components: Investment and maintenance costs are calculated based on the entire lifecycle method. (53); (54); (55); In the formula, This indicates the annual investment coefficient of the equipment; n represents the weighted investment coefficient; year The depreciation period of the equipment; For equipment costs; and These represent the annual investment and maintenance costs of the equipment, respectively. This is the annual maintenance factor for the equipment.

[0044] The device scaling function is used to calculate the total investment cost of the equipment. (56); In the formula, Cost per unit of equipment invested in; For the construction capacity of the equipment; This represents the minimum unit capacity of the equipment. This is the scaling factor for the equipment size.

[0045] When hydrogen is transported through hydrogen pipelines The minimum diameter of the hydrogen pipeline is related to the following formula: (57); (58); In the formula, The corrosion rate of the hydrogen pipeline; This is the design diameter of the hydrogen pipeline.

[0046] The cost per meter of installation can be calculated using the following formula: (59); In the formula, , and This refers to the construction cost parameters required per unit length of hydrogen pipelines.

[0047] Constraints (1) Constraints on conventional power system units: Minimum start-up and shutdown time limits are: (60); (61); In the formula, Let be a binary variable representing the switching state at time t; if If it is 1, it means that the unit is in the on state; An integer variable with a periodicity; and These represent the shortest times required for the unit to turn on and off, respectively.

[0048] The unit switching state constraint is: (62); In the formula, For the unit in the on state during period t, if This indicates that the unit is in the startup state; If the unit is in the closed state of the cycle, A value of 1 indicates that the unit is in a closed state.

[0049] The unit operation power constraint is: (63); In the formula, The unit output power during period t; and These represent the minimum and maximum operating power per unit, respectively.

[0050] (2) Power Constraints: The active power of the transmission network is calculated using a DC power flow model. (64); (3) Gas source output constraints: (65); In the formula, The flow rate of natural gas supplied by the natural gas source at time t; and These represent the maximum amount of natural gas supplied by the natural gas source in each time period and each day, respectively.

[0051] (4) Constraints of gas storage equipment:

[0052] (66); (67); (68); In the formula, This refers to the flow rate of the natural gas pipeline. Pressure at natural gas network nodes; and These represent the lower and upper limits of the pipeline pressure.

[0053] (5) Power balance constraint: The power consumption of P2H comes from the unconsumed RES in EGIS. The total power consumption at grid node n... It should be less than the abandoned wind and solar power. .

[0054] (69); In the formula, Let n be the power consumed at node n by the Kth model of the M-type P2H technology; Let n be the amount of wind and solar energy abandoned at node n.

[0055] The power balance constraints for each power system node are shown in (70).

[0056] (70); In the formula, This is a node-branch association matrix; and This is a correlation matrix between traditional / renewable energy units and nodes; Power of the transmission line; and Power of traditional / renewable energy units; Power shortage due to power load; For load power; The power of the HSC compressor; The power of the HSC conversion equipment.

[0057] (6) Equilibrium constraints of the hydrogen system: (71); In the formula, This is the correlation matrix between hydrogen load nodes and hydrogen transport equipment; The amount of hydrogen output by the transportation equipment; Hydrogen purchased from external sources; To meet hydrogen loading requirements; and This represents the interaction matrix between hydrogen production / storage facilities and hydrogen loading points. The quality of the output from the hydrogen storage device; This is the quantity output from the P2H device.

[0058] Step 3: Iterative solution and solution output of the two-layer model: Please refer to [link / reference]. Figure 5 A two-layer optimal configuration model for HSC under EGIS, considering planning-operation coordination, was established. The upper-layer model optimizes operation with the objective function of minimizing the total operating cost (COP) of EGIS and HSC, while the lower-layer model aims to minimize the investment and maintenance costs of HSC facilities. The specific steps are as follows.

[0059] Step 1: Obtain photovoltaic power output data, as well as electricity load, gas load, and hydrogen load data, based on the source-load scenario reduction method; Step 2: Initialize the iteration parameters, setting the initial values ​​of the conversion equipment power and the hydrogen system operating cost to zero, i.e., setting... and =0; Step 3: Substitute the above parameters into the optimal operation model of the upper-level EGIS system to solve and obtain the unconsumed renewable energy. Step 4: Based on the system operating costs and unabsorbed renewable energy obtained from the upper-level model, determine the required capacity of various types of equipment for each HSC process in the lower-level model; Step 5: Calculate and compare the difference in planned capacity for each type of equipment before and after each iteration with the preset convergence threshold; if the difference is greater than the convergence threshold, then... , Update back to Step 3 and continue iterating; if the difference is less than the convergence threshold, the iteration ends and the optimal combination planning configuration scheme and optimal running result of HSC are output.

[0060] Case Study: A simplified real power system is simulated. Based on electricity demand, it can be divided into 16 regions. This simplified real power system consists of 29 nodes, 50 branches (single-circuit transmission capacity of 750MW, double-circuit of 1500MW), 18 gas turbines, 7 nuclear power units, and 6 transmission lines. Figure 6 As shown in (a), the simplified real natural gas system consists of 46 nodes, 58 branches, 9 gas sources, 9 large-capacity natural gas storage facilities, and 1 set of external natural gas transmission equipment, as follows: Figure 6 As shown in (b), the datasets used for the simulation, including wind speed, light intensity, electricity and gas loads, and traffic flow, were obtained from energy information websites. Figure 6 (c) illustrates the spatial distribution of the aforementioned source loads. The results of photovoltaic output and load reduction are shown in the figure. The technical parameters of the equipment are listed in Tables 1-4. The results of photovoltaic output and load demand reduction are as follows: Figure 7 As shown. The simulation calculations were performed using the Gurobi solver called by Matlab, on a computer with an Intel i7-13700 processor and 32GB of RAM.

[0061] Table 1 P2H Technical Parameters

[0062] Table 2 Hydrogen Energy Storage Technology Parameters

[0063] Table 3 Hydrogen transport parameters

[0064] Table 4 HPL Parameters

[0065] Based on the optimal operation model of EGIS, Figure 8 The data presents the renewable energy consumption patterns on typical days of each season. As shown in the figure, photovoltaic (PV) power output is largely absorbed within the system; however, onshore and offshore wind power output is higher on typical days in spring and winter, and cannot be fully absorbed due to source-load fluctuations or grid congestion. The peak-to-valley difference and fluctuation range of unabsorbed renewable energy at different grid nodes on typical days are significant, exhibiting marked temporal and spatial variations. Figure 9 It can also be seen that the peak-valley difference and fluctuation range of unabsorbed renewable energy at different grid nodes under typical days are large, with significant spatiotemporal differences.

[0066] (1) Source-charge time characteristics of hydrogen: Figure 10 The hydrogen load demand on typical days in each season was compared, and the amount of hydrogen that could be converted from unused RES was theoretically calculated (theoretical conversion: 1kW of electrical energy can control 25.2g of H2). From Figure 10It is evident that the hydrogen load ranking for typical days of the season is winter > autumn > summer > spring. However, the unused renewable energy output used for hydrogen production is mainly concentrated in spring and winter, indicating a significant mismatch between hydrogen sources and hydrogen load in time, which restricts the further development of hydrogen energy systems.

[0067] (2) Source-charge space characteristics of hydrogen: Figure 11 This study compares the hydrogen load demand in regions Z1–Z16 during a typical spring season with the theoretically available hydrogen production from surplus renewable energy sources. Except for Z3, regions Z1–Z6 have abundant wind power resources, but due to transmission line capacity limitations, surplus renewable energy output cannot be fully transported to other regions for consumption. As shown in the figure, the amount of hydrogen that can be produced from surplus renewable energy in these regions far exceeds their own hydrogen load demand. However, in regions Z7–Z16, since renewable energy can be consumed locally without long-distance transmission, the available surplus renewable energy is limited, and the corresponding hydrogen production is insufficient to meet the hydrogen load of these regions. The upper half of the region is more suitable for using renewable energy sources (RES) to produce hydrogen to meet the regional hydrogen load, but some of the produced hydrogen cannot be consumed. The hydrogen load in the lower half of the region is more concentrated, but the spatial distribution of renewable energy and load, as well as inter-regional transmission capacity, make large-scale deployment of green hydrogen energy systems extremely difficult.

[0068] (3) Economic analysis of building a green hydrogen energy supply chain based on EGIS: Table 5 shows the amount of hydrogen that can be produced by the surplus RES theory and the amount of hydrogen supply that can be utilized after considering the source-load distribution.

[0069] Table 5. Cost Comparison of Typical Sunlight Hydrogen Energy Systems in Four Seasons

[0070] As shown in the table, the typical annual hydrogen energy load of this system is 179,697.6 tons / year. Based on a hydrogen purchase cost of 45.96 yuan / kg, the annual hydrogen purchase cost for this system reaches 8.26 * 10^6 tons / year. 9 Theoretically, surplus renewable energy can produce up to 150,533.1 tons of hydrogen per year. If the spatial and temporal differences between hydrogen source and load are disregarded and all theoretically produced hydrogen is used for load, 83.77% of the supply and demand can be met, and hydrogen purchase costs can be reduced by approximately 6.92 * 10^6 tons. 9 However, due to the spatial and temporal differences in the distribution of hydrogen sources and loads, the surplus hydrogen production that can actually be directly utilized accounts for only 6.26% of the total demand, or 11,257.2 tons / year. Therefore, it is urgent to conduct research on the rational planning and allocation of the hydrogen energy supply chain to mitigate the impact of the spatial and temporal differences between sources and loads, improve the capacity for renewable energy absorption, and reduce the cost of hydrogen energy applications.

[0071] Design Scenario: A hydrogen energy supply chain is planned for areas with surplus renewable energy (Z1, Z2, Z4, Z5, Z6, Z8, Z9, Z11, Z14, Z15) to achieve the application of green hydrogen energy and the consumption of renewable energy. To verify the superiority of the proposed planning model, three comparative cases are designed: Case 1: At each grid node and gas node, only a single technology is used for planning and configuration of each HSC process; Case 2: Within each grid node and gas grid, the multi-technology collaborative planning method proposed in this paper is used for overall planning and configuration of each HSC process; Case 3: Within each grid node and gas grid, only the multi-technology collaborative planning method proposed in this paper is used for hydrogen production and storage, while transportation and conversion are still planned and configured using a single technology.

[0072] As shown in Table 6, when using HTT and HPL transportation methods, Case 2 utilizes existing natural gas pipelines to reduce the length of newly constructed pipelines, resulting in a lower hydrogen transportation planning cost compared to Case 1. In Case 1's hydrogen transportation scheme, LHTT and HCNG require additional conversion equipment, with conversion costs (9.49 * 10) 8 Yuan, 7.68*10 8 The conversion cost (in yuan) is significantly higher than that in scenario 2 (6.01 * 10). 8 (RMB). The annualized operating profit for scenario 2 is 1.61 * 10. 9 Yuan, higher than several corresponding cases of case 1 (4.22*10) 8 Yuan, 1.21*10 9 Yuan, 3.17*10 8 Yuan, 1.43*10 9 The revenue from HSC in Scenario 2 increased by approximately 282%, 33.9%, 408%, and 12.68%, respectively. In Scenario 3, the investment cost is lower due to the smaller P2H capacity, hydrogen storage capacity, and conversion process scale. However, Scenario 2 considers inter-regional hydrogen energy transmission, allowing the generated hydrogen to be transported to multiple regions to increase revenue. Compared to Scenario 3, the total HSC revenue in Scenario 2 increased significantly, from 1.25 * 10^6 yuan. 9 The amount increased to 4.07 * 10. 9 Yuan; its operating profit is 1.61 * 10 9 The value is also significantly higher than 2.77*10 in scenario 3. 8The above analysis shows that the multi-technology collaborative planning method can effectively incorporate the operational economics of different technologies, thereby obtaining a better planning scheme. While relying solely on "power generation-storage" to absorb RES (Renewable Energy Storage) can absorb surplus renewable energy to some extent, the RES absorption capacity of the HSC (High-Speed ​​Central Storage) remains limited due to the spatial mismatch between source and load, resulting in a large amount of surplus RES within the EGIS (Environmentally Secured Geological Survey). Further considering inter-regional transmission to construct a complete HSC can significantly improve the system-level absorption capacity of RES.

[0073] Table 6 System scheduling results for scenarios 1, 2, and 3

[0074] Implementation Case 1: By Figure 12 As can be seen, P2H devices were configured at grid nodes E1–E4, E7, E9–E10, E20, and E26–E27. Although there is a surplus of renewable energy at grid nodes E5–E6, E8, E11–E12, E19, and E28, these nodes were not configured because planning under a single technology solution is not economical. In this case, P2H selected two types of electrolytic hydrogen production technologies: AEC and SOEC. The specific reasons for the selection are as follows: the surplus renewable energy at nodes E3 and E4 is sufficient to meet the hydrogen load of region Z4, and inter-regional transportation is not economically viable. Therefore, AEC, which has lower cost but lower efficiency, was prioritized at these nodes. At nodes where the load cannot be met locally (such as E7, E20, E26, and E27) or where inter-regional transportation is required (such as E1, E2, and E9), SOEC, which has higher electro-hydrogen conversion efficiency, was selected. In the case of transport via HCNG, node E1 cannot be connected due to its distance from the natural gas pipeline network. Therefore, AEC was still selected for E1, and its planned capacity is smaller than the configuration capacity under other transport methods.

[0075] Energy storage and P2H devices are mostly configured at the same nodes, and three hydrogen storage technologies have been selected: HHT, LHT, and CHS. Based on the hydrogen storage needs of different regions, the nodes can be divided into two categories: seasonal hydrogen storage nodes (E1–E4, E7, E9, E10) and intraday hydrogen storage nodes (E20, E26, E27). The first category of nodes has a larger planned hydrogen storage capacity due to the concentrated hydrogen production demand in the spring. HHT has higher storage costs, while CHS and LHT have lower unit hydrogen storage costs once a certain scale is reached; moreover, CHS does not require additional conversion facilities such as liquefaction, thus it has a higher priority in large-scale hydrogen storage scenarios. Therefore, CHS was selected for E1–E3, E7, and E9, while only LHT was configured for E4 and E10. The second type of node only needs to meet the intraday fluctuating hydrogen storage demand on a typical day, so it only adopts the daily storage form; under the low capacity hydrogen storage demand, although HHT is more expensive, it is competitive for small-scale hydrogen storage, so E20, E26 and E27 adopted HHT.

[0076] When using the HPL transportation method, a total of 13 hydrogen pipelines were newly built, with a total length of 1395.4 km and a diameter range of 412–1223 mm. Among them, pipeline (4) is responsible for the transportation of hydrogen from nodes 1, 2 and 9, and is the branch with the largest flow in the entire hydrogen pipeline network system. By constructing HPL, the excess hydrogen generated in the upper half (E1, E2, E9) is transported to the lower half to meet the hydrogen load center point of the Z7–Z16 region. In the HTT and LHTT transportation schemes, the transportation paths are the same. However, due to the significant difference in the carrying capacity of a single vehicle: the unit carrying capacity of HTT is much smaller than that of LHTT. Under the same P2H and hydrogen storage configuration, the required number of vehicles is HTT=950 and LHTT=111, respectively. In the HCNG transportation scheme, some source-load points are too far from the natural gas pipeline network to be connected to the natural gas pipeline, and therefore cannot be transported using the HCNG method.

[0077] Implementation Case 2: For example Figure 12 and Figure 13 As shown, compared to Case 1, Case 2 replaced some P2H devices at nodes E1, E2, E7, E9, E20, E26, and E27. This adjustment was mainly due to significant source-load timing fluctuations at these nodes, necessitating greater operational flexibility. Compared to other P2H technologies, PEMEC is superior to SOEC in terms of flexibility. Therefore, replacing some SOEC devices with PEMEC at certain nodes helps to address the lack of flexibility in SOEC technology.

[0078] The selection of hydrogen storage technology depends on the scale of hydrogen storage and techno-economic feasibility; therefore, there is an optimal hydrogen storage technology for different scales of hydrogen storage demand. The selection of transportation technology can fully leverage the flexibility of the HPL (High-Performance Network) planning layout, allowing for inter-regional hydrogen transportation through the collaboration of HCNG (High-Performance Gas Generation Network) and HPL. In the upper part of the region, hydrogen from nodes E1, E2, and E9 is transported to gas node G13 via the gas network, and then transported by HPL to the hydrogen load center in the lower part. Throughout this process, only gas separation equipment needs to be installed at gas nodes G5, G8, and G13, significantly reducing the conversion costs required by the HCNG. Compared to Scenario 1, the total length of the hydrogen pipeline in Case 2 is 988.5 km, approximately 29.2% shorter than in Case 1.

[0079] Implementation Case 3: Figure 14 The HSC planning results for Case 3 are presented. The right-hand subplot compares the differences in the configuration of newly added equipment between Case 2 and Case 3. Grid nodes E1, E2, and E9 in regions Z1, Z2, and Z6 respectively increased P2H capacity; the previously configured AEC units were replaced by more efficient SOEC and PEMEC units at several nodes. A large-scale seasonal hydrogen storage unit, CHS, was also added to grid node E2. This is because renewable energy resources are relatively abundant in Z1, Z2, and Z6; to reduce costs, Case 3 still tends to choose the more economical AEC technology in certain local situations. Considering inter-regional hydrogen energy transmission, Case 2 expanded hydrogen production and storage capacity to provide intraday and seasonal hydrogen loads to other regions and improved the economics of HSC by increasing the P2H capacity of SOEC and PEMEC. Figure 17 It is evident that although node E4 has a surplus of renewable energy at certain times, the new energy output of this node only occurs during two periods in the spring, and the planned transmission equipment is not economically advantageous. Therefore, the P2H device configuration was not expanded at this node.

[0080] Analysis of RES consumption and hydrogen load supply in Cases 2 and 3: Based on the above multi-technology collaborative planning scheme, the system's RES absorption capacity and hydrogen energy supply composition were analyzed. The results are shown in [Table missing]. Figure 15 –17. From Figure 15 a) It can be seen that in Case 3, the majority of the hydrogen load is met by purchased hydrogen, accounting for 67.1%. The self-sufficiency share of HSC (hydrogen-storage) through the "power generation-storage" link is only 32.9%, of which P2H real-time supply accounts for 10.6% and hydrogen storage equipment supply accounts for 22.3%. Compared with Case 3, Case 2 considers the inter-regional transport of hydrogen energy in the model, thus significantly improving the spatiotemporal coverage of hydrogen load. Figure 15(b) It can be seen that the hydrogen source of the system consists of four parts: real-time P2H supply (10.6%), hydrogen storage supply (22.3%), transportation supply (30.8%), and external supply (36.3%). After the introduction of inter-regional transportation, the HSC's self-sufficiency rate for hydrogen load increased from 32.9% to 63.7%.

[0081] Depend on Figure 16 and Figure 17 a) It is evident that relying solely on "power generation-storage" to absorb RES (Renewable Energy) can, to some extent, absorb excess renewable energy output. However, due to the spatial mismatch between source and load, the RES absorption capacity of the HSC (High-Speed ​​Storage Center) remains limited, and a large amount of surplus RES still exists within the EGIS (Environmentally, Geologically Integral Gas System). Further consideration of inter-regional transmission to construct a complete HSC can significantly enhance the system-level absorption capacity of RES. Figure 17 b) shows that, after considering hydrogen transportation, most of the surplus RES has been absorbed; only in a few areas, due to the highly intermittent nature of renewable energy output, the surplus electricity cannot be effectively utilized.

[0082] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain, characterized in that, Includes the following steps: S1: Multi-technology modular modeling of each HSC link: Based on the HSC architecture with "power generation-energy storage-transmission" as the core, establish a multi-technology planning and operation model for each HSC process; S1.1: Operation and Planning Model of P2H Unit: Based on the three electrolyte types of P2H, the relationship between the input power and hydrogen production of P2H unit is established, and the "maximum sequential programming method" is proposed to realize the coordinated planning of multiple P2H technologies; S1.2: Operation and planning model of hydrogen energy storage facility based on multiple time scales: Using high-pressure gaseous and cryogenic liquid energy storage as seasonal energy storage methods, an operation model of hydrogen energy storage facility is established to achieve multi-technology collaborative planning; S1.3: Operation and planning model of hydrogen energy transmission network based on operational characteristics: Establish cross-regional transportation model of H2 trucks and pipelines to realize the transportation process between hydrogen source and load demand area; S1.4: Operation and Planning Model of Hydrogen Energy Conversion Facility: Establish an operation model for the conversion facility to achieve collaborative planning of conversion technologies; S2: Construct a two-layer optimization framework with the goal of economic optimization: minimize the operating costs of EGIS and the annualized investment and maintenance costs of HSC facilities under seasonal division, including the operating costs of the power system, the natural gas system, the hydrogen system, and the investment and maintenance costs of HSC components; S3: Iterative solution and solution output of the two-layer model: Establish a two-layer optimization configuration model of HSC under EGIS that considers planning-operation coordination. The upper-layer model optimizes the operation with the objective function of minimizing the total operating cost (COP) of EGIS and HSC, while the lower-layer model aims to minimize the investment and maintenance costs of HSC facilities.

2. The hydrogen energy storage and transportation integrated supply chain optimization control method according to claim 1, characterized in that, In S1.1, the relationship between the input power of the P2H device and the hydrogen production is expressed as follows: ； In the formula, The conversion efficiency of P2H; It has the low calorific value of hydrogen. Input power to the P2H facility in time period t at grid node n; The collaborative planning method for multiple P2H technologies is as follows: Set the upper limit for the module planning of each node n pairs of M-type P2H technologies to 1. Introducing 0-1 programming variables This indicates whether the k-th M-type module at node n has been planned, ordered by module number from 1 to... Plan sequentially until the k-th module Up to a value of 0, the total P2H configuration capacity of node n is: ； In the formula, The total planned capacity of P2H facilities in grid node n; Capacity of P2H modules using M-type technology; Planning upper limit constraints: ； Sequential constraints: ； The start-up and shutdown variables related to module operation should be constrained by the module's planning (λ): 。 3. The method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain according to claim 1, characterized in that, In S1.2, the method for establishing the hydrogen energy storage facility operation model is as follows: The operating principles of various energy storage facilities are similar, and the time-series dynamic representation of hydrogen is: ； In the formula, Let n be the amount of hydrogen stored at node n in time period t; when =1 indicates hydrogen charging in the current time period; when Hydrogen is released for the current period, and there are additional constraints to prevent both from being 1 at the same time; and To inject and release hydrogen gas during period t at node n; The multi-technology collaborative planning method is as follows: based on the hydrogen load of section n at time t. Define surplus / deficient hydrogen energy: ； In the formula, The hydrogen produced at grid node n at time t; The surplus can be used for energy storage or inter-regional transportation: ； Calculate the hourly maximum energy storage demand of node n for each time period: ； In the formula, The planned capacity of the X-type hydrogen storage technology at node n; Daily continuous energy storage demand is calculated by accumulating over consecutive hours: ； And take the maximum value to obtain the maximum continuous demand within the day: ； Maximum daily demand is the maximum of the hourly and intraday continuous demand: ； Seasonal demand is: ； Take the maximum value for each season as the total configuration requirement for node n: ； In the formula, Let n be the total configured capacity of the hydrogen storage facility at node n; After determining the total configuration capacity, introduce 0-1 variables. This represents the planning state of X-type energy storage technology at node n, thereby enabling the coordinated configuration of multiple technologies: ； In the formula, This represents the 0-1 planning state of X-type storage technology on node n.

4. The hydrogen energy storage and transportation integrated supply chain optimization control method according to claim 1, characterized in that, The specific method of S1.3 is as follows: Hydrogen tanker truck operation model transportation process: fill with hydrogen at the source point, drive to the designated unloading point to release hydrogen, and then return to the source point. Assuming the driving speed is constant, the time required for a single transportation is as follows: ； ； ； In the formula, For loading / unloading time, The distance from the source point to the load point. For average driving speed, to meet load requirements The source point needs to dispatch a vehicle at time t, and the required number of vehicles is rounded up. The time required for a single delivery process; ； In the formula, The amount of hydrogen that a truck can carry; Let be the number of trucks that perform the delivery process at time t and node n; The number of returned vehicles meets the following requirements: ； In the formula, The number of trucks that return to the source point within time t at node n; The fleet inventory status is as follows: ； The transport volumes of HTT and LHTT at time t are respectively: ； ； In the formula, and Hydrogen is injected and released at node n during period t; and The amount of hydrogen that can be loaded into high-temperature and low-temperature tanks; and Let n be the quantity of HTT and LHTT in the inventory at time period t; The total number of vehicles required for the plan is: ； Pipeline transportation operation model: Pipeline transportation includes hydrogen pipelines and hydrogen-rich compressed natural gas injected into the natural gas pipeline network. ； In the formula, This represents the volumetric flow rate of the pipe segment between node i and node j under standard operating conditions. Indicates air density; and These represent pressure and temperature under standard operating conditions, respectively. and These represent the inlet and outlet pressures of the pipe section, respectively. Indicates the pipe diameter; This represents the hydraulic friction coefficient of the pipeline; The compressibility factor of the gas mixture; This refers to the length of the pipe. For pipe temperature; Indicates the component ratio of a gas mixture; Under maximum pressure differential conditions, the maximum transport capacity of the pipeline is obtained; the hydrogen transport capacities of HPL and HCNG are respectively: ； ； In the formula, ϕ is the hydrogen doping ratio of HCNG; and The maximum volumetric flow rate of hydrogen that can be transported via HPL and HCNG methods; the corresponding transport limit for each phase is: ； In the formula, Hydrogen gas transported by adding hydrogen at room temperature; The minimum diameter of the HPL is determined by the required cross-sectional area for current flow: ； In the formula, For flow rate, The cross-sectional area; Hydrogen transported via high-pressure liquefaction and hydrogenation; The density of hydrogen gas; Collaborative planning model for hydrogen transportation technology: based on inter-regional hydrogen demand Compute the transmission capacity requirement of node n And introduce 0-1 variables This indicates the planning status of the Y-type conveyor technology, achieving the following collaborative configuration: 。 5. The method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain according to claim 1, characterized in that, The method for establishing the conversion facility operation model in S1.4 is as follows: power consumption is used to uniformly characterize the conversion process. ； In the formula, To convert the power consumption of the facility, The power required for one unit of hydrogen conversion. The amount of hydrogen converted; The method for collaborative planning of conversion technologies is as follows: based on the hydrogen production, storage, and consumption of node n in each time period, calculate the power demand of the Z-type conversion equipment in each process and each time period, and plan the capacity based on its maximum value in all time periods. ； In the formula, The power consumption of the Z-type method at point n at time t; Let n be the planned capacity of the Z-type conversion facility at node n.

6. The method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain according to claim 1, characterized in that, The overall objective function in S2 is expressed as: ； In the formula, , and These are system operating costs, HSC investment costs, and maintenance costs, respectively. , and These represent the operating costs of the electricity, natural gas, and hydrogen systems, respectively. , , and These represent the investment costs for hydrogen production, hydrogen storage, hydrogen conversion, and conversion equipment, respectively. , , and These represent the maintenance costs of hydrogen production, storage, transportation, and conversion equipment, respectively; s∈SEA represents the seasonal components. The corresponding seasonal weights.

7. The method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain according to claim 1, characterized in that, In S2, the power system operating cost is: ； ； ； ； ； ； In the formula, , and These represent the costs of the gas turbine, nuclear power unit, and external power transmission, respectively. and These represent the penalty costs for abandoning photovoltaic / solar thermal power plants and for power load shortages, respectively; n represents the grid node, n=1,2,…,N e ; , and Onshore / offshore wind and solar power generation capacity; , and The costs include the single start-up and shutdown cost, fixed operating cost, and marginal electricity cost of the gas turbine. , , , , and At time t at node n, the grid-connected power from gas turbines, nuclear generator sets, external transmission, onshore wind farms, offshore wind farms, and photovoltaic power plants; , and The cost of curtailment for onshore wind power, offshore wind power, and photovoltaic power generation; and Penalty costs for shortfalls in electricity and gas load demand; Natural gas system operating costs: ； ； ； ； ； In the formula, , , and These represent the natural gas production cost, natural gas storage cost, external natural gas transportation cost, and penalty cost incurred due to a shortage of natural gas demand, respectively; m represents a natural gas network node, m=1,2,…,N g ; and For node m, the gas source natural gas volumetric flow rate and the gas storage injection and release volume during time period t; Let m be the external gas supply to node m during time period t; Hydrogen system operating costs: ； In the formula, For the cost of hydrogen production; Cost of hydrogen transport trucks; External hydrogen purchase cost; ； ； In the formula, Let P2H equipment start-up and shutdown cost be denoted as n at grid node n during time period t. The amount of water required to produce one unit of hydrogen; Unit water cost; and The single start-up cost and downtime cost of M-type P2H technology; It consists of fuel costs and driver wages: ； ； ； ； In the formula, and These represent fuel costs and driver wages, respectively. This indicates the fuel cost for a single transport trip; Fuel consumption per 100 kilometers for trucks; Let k be the transport frequency of the kth truck at power grid node n. ； In the formula, w represents the quantity of each region, w = 1, 2, ..., N h ; Cost per unit of hydrogen purchased; The amount of external hydrogen source procured by region w during time period t; Investment and maintenance costs of HSC components: Investment and maintenance costs are calculated using a life-cycle approach: ； ； ； In the formula, This indicates the annual investment coefficient of the equipment; n represents the weighted investment coefficient; year The depreciation period of the equipment; For equipment costs; and These represent the annual investment and maintenance costs of the equipment, respectively. The annual maintenance factor for the equipment; The device scaling function is used to calculate the total investment cost of the equipment. ； In the formula, Cost per unit of equipment invested in; For the construction capacity of the equipment; This represents the minimum unit capacity of the equipment. This is the scaling factor for the equipment size; When hydrogen is transported through hydrogen pipelines The minimum diameter of the hydrogen pipeline is related to the following formula: ； ； In the formula, The corrosion rate of the hydrogen pipeline; The design diameter of the hydrogen pipeline; The cost per meter of installation is calculated using the following formula: ； In the formula, , and This refers to the construction cost parameters required per unit length of hydrogen pipelines.

8. The method for optimizing and controlling an integrated hydrogen energy storage and transportation supply chain according to claim 1, characterized in that, The specific method of S3 is as follows: Step 1: Obtain photovoltaic power output data, as well as electricity load, gas load, and hydrogen load data, based on the source-load scenario reduction method; Step 2: Initialize the iteration parameters, setting the initial values ​​of the conversion equipment power and the hydrogen system operating cost to zero, i.e., setting... and =0; Step 3: Substitute the above parameters into the optimal operation model of the upper-level EGIS system to solve and obtain the unconsumed renewable energy. Step 4: Based on the system operating costs and unabsorbed renewable energy obtained from the upper-level model, determine the required capacity of various types of equipment for each HSC process in the lower-level model; Step 5: Calculate and compare the difference in planned capacity for each type of equipment before and after each iteration with the preset convergence threshold; if the difference is greater than the convergence threshold, then... , Update back to step 3 and continue iterating; If the difference is less than the convergence threshold, the iteration ends, and the optimal combination planning configuration scheme and optimal running result of HSC are output.

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