A space-time coordinated optimization scheduling method for gas-electricity coupled system with p2h and heterogeneous energy storage

Abstract

The application discloses a kind of considering P2H and heterogeneous energy storage gas-electric coupling system space-time collaborative optimization scheduling method, comprising the following steps: S1: build considering electric hydrogen, hydrogen is mixed into pipe network and the physical model of gas-electric coupling system of heterogeneous energy storage, including network side model and coupling hub and heterogeneous energy storage model;S2: on the basis of the physical model of gas-electric coupling system, build the integrated scheduling model with global operation cost optimization as the guide;S3: the integrated scheduling model is solved using sparrow search algorithm, and the gas-electric coupling system is carried out space-time collaborative optimization scheduling according to the solution result.The application can realize the unified modeling of electric-gas-hydrogen multi-energy flow and collaborative description of operation constraint, and the sparrow search algorithm can improve the global search capability and obtain more accurate optimal solution.

Description

Technical Field

[0001] This invention relates to the field of energy dispatching technology, and in particular to a spatiotemporal collaborative optimization dispatching method for a gas-electric coupling system considering P2H and heterogeneous energy storage. Background Technology

[0002] In the process of global energy structure transformation towards low-carbonization, building a new power system with a high proportion of renewable energy has become an inevitable trend. However, the spatiotemporal mismatch between the strong volatility of wind and solar resources and the rigidity of energy demand on the load side has led to a significant narrowing of the system's flexibility adjustment margin. The large-scale phenomenon of "wind and solar curtailment" has become an engineering bottleneck restricting the efficient consumption of clean energy.

[0003] To break down the barriers of isolated operation of single energy networks, integrated gas-electric coupled energy systems (IEGS) provide an effective path to improve system flexibility through cross-medium energy exchange. With the deepening physical interaction between the two networks, the core orientation of system scheduling has expanded from a single economic indicator to multi-dimensional goals such as improving absorption capacity and reducing carbon emissions. This drives the evolution of the scheduling paradigm from early unidirectional energy supply to closed-loop bidirectional coupling.

[0004] In bidirectional coupling architectures, early power-to-gas (P2G) conversion was often used as the hub, but its engineering economics faced significant challenges due to high carbon capture costs and secondary conversion losses. In recent years, power-to-hydrogen (P2H) combined with hydrogen-blended natural gas (HBNG) technology has become a research hotspot because it eliminates the methanation step, improves conversion efficiency, and enables wide-area energy transport via pipeline networks. Empirical studies show that with a hydrogen blending volume ratio below 20%, existing pipeline networks can achieve safe blending without large-scale modifications.

[0005] While considering the significant potential of the gas-electric coupling architecture of P2H (Power-to-Hydrogen) systems, its underlying physical modeling and optimization remain extremely challenging. Steady-state modeling of power systems is relatively mature, while the flow rate of natural gas pipelines is affected by fluid compressibility and is typically described by the nonlinear Weymouth equations. Early research relied heavily on traditional heuristic algorithms (such as Particle Swarm Optimization, PSO) to handle these nonlinear constraints, but these are prone to getting trapped in local optima in high-dimensional optimization spaces and have limited solution speed. To pursue higher computational accuracy, Zlotnik et al. employed the Legendre-Gauss-Lobatto (LGL) pseudospectral collocation method to transform the spatiotemporally coupled dynamic control problem into a large-scale nonlinear programming (NLP) problem that can be directly solved using commercial solvers. Zhao proposed an alternating optimization algorithm that approximates the exact optimal solution of the system by iteratively solving between the power network and the natural gas network. Wang proposed using a second-order cone programming (SOCP) model to allow the problem to be solved efficiently using mature analytical solvers. However, when the model introduces a large number of discrete energy storage state variables and complex physical hydrogen doping limits, conventional analytical algorithms often face the curse of dimensionality and truncation error, making it difficult to balance solution efficiency and global optimality.

[0006] Pazouki, Cheng, and others have laid a solid foundation for low-carbon dispatching and renewable energy consumption in gas-electric coupling systems by introducing source-load dual-side response, microgrid equipment planning, and carbon trading mechanisms. However, it is undeniable that the focus of macroeconomic dispatching leads it to treat natural gas pipeline networks as an ideal "infinite energy consumption pool," stripping away complex fluid dynamics characteristics. Furthermore, few studies in current optimization systems can accurately quantify the substantial physical inverse constraint that the upper limit of dynamic hydrogen doping in pipeline networks poses on source-side wind power consumption.

[0007] Liu, Gao, Saedi, and others have made significant contributions to the low-carbon scheduling architecture of gas-electric coupling systems by introducing equipment operating cost analysis, carbon capture linkage architecture, and cutting-edge pipeline hydrogen blending mechanisms. However, these studies treat P2G and P2H devices as directly connected energy transmission channels without simultaneously configuring associated gas or hydrogen storage facilities. This unbuffered system assumption forces multiple energy flows into a rigid "produce-as-you-go" operation, limiting the potential for renewable energy consumption on the source side to the instantaneous transmission extremes of the gas network, and significantly weakening the system's cross-temporal and spatial peak-shaving flexibility.

[0008] Gao, Li, Belderbos, and others successfully constructed a highly flexible gas-electricity joint scheduling model by deeply integrating conventional energy storage, generalized energy storage, and dynamic pipeline storage, fully demonstrating the global superiority of multi-flexible element synergy. However, existing multi-energy coordinated scheduling research mostly optimizes each component as a whole, and rarely uses the means of controlling variables to accurately isolate and compare the independent driving effects of cross-grid hydrogen blending channels, natural gas storage facilities, and associated hydrogen storage tanks on the global operating boundary. Summary of the Invention

[0009] To address the aforementioned issues, this invention aims to provide a spatiotemporal collaborative optimization scheduling method for gas-electric coupling systems that consider P2H and heterogeneous energy storage.

[0010] The technical solution of the present invention is as follows: A spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage includes the following steps: S1: Construct a physical model of a gas-electric coupling system that considers electricity-to-hydrogen conversion, hydrogen doping in the pipeline network, and heterogeneous energy storage, including a network-side model and a coupling hub and heterogeneous energy storage model; S2: Based on the physical model of the gas-electric coupling system, construct a comprehensive scheduling model oriented towards optimizing global operating costs; S3: The sparrow search algorithm is used to solve the integrated scheduling model, and the spatiotemporal coordinated optimization scheduling of the gas-electric coupling system is performed based on the solution results.

[0011] Preferably, in step S1, the network-side model includes a power system model, a thermodynamic and physical property characterization of mixed gas, and a steady-state flow model of hydrogen-doped natural gas pipeline network. In the power system model, the active power of branch mn during time period t is calculated using the following formula: (1) In the formula: Let mn be the active power of branch mn during time period t, in MW; , Let X be the voltage phase angles of nodes m and n during time interval t, in rad; mn The line reactance is expressed in Ω. For any node m in a power system, its nodal equilibrium equations are as follows: (2) In the formula: P G,i,t For the active power output of thermal power unit i, MW; P GT,j,t P represents the power generation capacity of gas turbine unit j, in MW. W,m,t P PV,m,t The actual power absorbed by wind and solar power is expressed in MW; P. P2H,m,tP represents the power consumption of the P2H device at node m, in MW; EC,m,t P represents the power consumption of the electric compressor at node m, in MW. L,m,t For conventional electrical loads, MW; and These are the sets of generator sets and the set of gas turbines connected to node m, respectively. Let m be the set of network nodes adjacent to node m. The dynamic evolution equation of the energy state of batteries configured in a power system is as follows: (3) In the formula: E e,t E e,t-1 These represent the battery's stored energy in time periods t and t-1, respectively, in MWh. This is the self-loss coefficient; , These are the charging power and discharging power, respectively, in W; , These are the charging and discharging efficiencies, respectively. h is the scheduling time step; When performing thermodynamic and physical property characterization of gas mixtures, the compressibility factor is calculated using the following formula: (4) In the formula: z mix It is the compression factor; , , Tc represents the critical temperature of the mixed gas, natural gas, and hydrogen, respectively, in K0; T0 represents the actual operating temperature of the gas in the pipeline network, in Kc. Let mn be the average pressure at node m, in MPa; , , These are the critical pressures (MPa) of the mixed gas, natural gas, and hydrogen, respectively; p m The pressure at node m is in MPa; p n The pressure at node n, in MPa; This refers to the hydrogen volume blending ratio; The equivalent density and dynamic viscosity of the gas mixture are calculated using the following formulas: (5) (6) In the formula: The equivalent density of the gas mixture is expressed in kg / cm³. 3 ; , The densities of hydrogen and natural gas are respectively, in kg / cm³. 3 ; The dynamic viscosity of the gas mixture is Pa. s; , The dynamic viscosity of hydrogen and natural gas, respectively, in Pa. s; , Here are the molar masses of hydrogen and natural gas, respectively, in kg / mol. In the steady-state flow model of the hydrogen-blended natural gas pipeline network, the nonlinear mapping relationship between nodal pressure gradients and pipeline flow rates is described by the following equation: (7) (8) In the formula: Q mn For the airflow rate in the pipeline, m 3 / h;C mn To comprehensively consider the gas mixture temperature, pipeline geometry parameters, and compressibility factor, the physical constants of the pipeline network; D mn λ is the inner diameter of the pipe, in meters (m); mn Z is the pipeline resistance coefficient; z is the compressibility factor of hydrogen-blended natural gas; T is the gas temperature, K; L mn δ is the pipeline length, in meters; δ is the relative density of natural gas, in kilograms per cubic meter of water. 3 ; The law of conservation of volumetric flow rate at any natural gas node m is: (9) In the formula: , These represent the natural gas supply and conventional load demand at node m at time t, respectively. 3 / h; The equivalent amount of hydrogen injected into the P2H pipeline network, m 3 / h; For gas turbine cross-grid gas consumption, m 3 / h; Let Q be the set of natural gas nodes connected to node m; mn,t Let m be the airflow rate in the pipe during time period t. 3 / h; The power consumption of the electric compressor and the gas consumption of the gas-driven compressor are as follows: (10) In the formula: The power consumption of the electric compressor is measured in MW. For the gas consumption of the gas-driven compressor, m 3 / h; The power consumption of the electric compressor is measured in MW. , , These are the compressor hydraulic efficiency, motor drive efficiency, and gas turbine thermal efficiency, respectively. The low calorific value of natural gas, MWh / m 3 .

[0012] As a preferred option, the actual power absorption capacity of wind power and photovoltaic power is calculated using the following formulas: (11) (12) In the formula: Let t be the actual output active power of the wind turbine, in MW; The rated output power of the wind turbine is expressed in MW. , , , Let be the wind speed at time t, the cut-in wind speed, the cut-out wind speed, and the rated wind speed, respectively, in m / s; Let t be the actual output active power of the photovoltaic array, in MW; The rated output power of the photovoltaic array under standard test conditions is expressed in MW. Let be the actual solar irradiance at time t, in W / m². The reference irradiance under standard test conditions, in W / m² 2 k is the power-temperature correlation coefficient of the photovoltaic module; Let t be the operating temperature of the photovoltaic cell, in °C. The reference temperature under standard test conditions is ℃.

[0013] Preferably, in step S1, the coupling hub and heterogeneous energy storage model include an electro-hydrogen conversion model, a gas turbine model, and a heterogeneous energy storage model. The electro-hydrogen conversion model is as follows: (13) In the formula: The volumetric flow rate of hydrogen produced by the electrolyzer, in Nm³. 3 / h; The energy conversion efficiency of electro-hydrogen conversion; The active power consumed by the P2H device, in MW; The lower heating value of hydrogen, NWh / Nm³. 3 ; The gas turbine model is as follows: (14) In the formula: Q NG The equivalent gas consumption of the gas turbine is m. 3 / h; a, b, and c are all fixed physical parameters characterizing the heat engine's conversion efficiency and operating characteristics; P NGThe active power output of the gas turbine is expressed in MW. The heterogeneous energy storage model is as follows: (15) (16) In the formula: , The hydrogen storage state quantities at the dispatch section during time periods t and t-1 are respectively, m 3 ; , These are the actual volumetric flow rates of hydrogen charge and discharge, respectively, in m. 3 / h; , These represent the unit energy consumption for the gas injection and gas extraction processes, respectively, in MWh / m³. 3 ; , These represent hourly gas extraction and injection volumes, in m³. 3 / h; a1 and a2 are the energy consumption ratio coefficients for the gas injection and gas extraction processes, respectively; b1 and b2 are the corresponding nonlinear exponential constants.

[0014] Preferably, in step S2, the integrated scheduling model guided by the optimization of global operating cost includes an integrated operating objective function and multi-energy flow operating constraints; The comprehensive operation objective function aims to minimize the comprehensive operation cost within a single daily scheduling cycle. The multi-energy flow operation constraints include grid security and unit operation constraints, hydrogen-blended gas network transmission and distribution and hydrogen blending security constraints, and cross-grid hub and heterogeneous energy storage periodic constraints. The constraints on grid security and unit operation include unit output and ramping constraints, and line transmission capacity constraints. The constraints on hydrogen blending network distribution and hydrogen blending security include gas supply and node pressure constraints, compressor operating condition constraints, and pipeline hydrogen blending safety ratio boundaries. The constraints on cross-grid hubs and heterogeneous energy storage periodicity include P2H device and gas turbine constraints, energy storage capacity and charge / discharge / gas constraints, and scheduling cycle beginning and end state consistency constraints.

[0015] Preferably, the comprehensive operational objective function is: (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) (twenty four) Where: G oc For overall operating costs; C n C is the cost of purchasing natural gas. e The cost of power generation for conventional coal-fired units; C h Cost of hydrogen storage tank; C maint For the operation and maintenance costs of P2H devices; C q C. Penalty costs for wind and solar power curtailment; c For environmental carbon emission costs; C z For policy subsidies received for full grid connection of green electricity; N T h represents the scheduling range; For the set of natural gas source nodes; c gas Price per unit volume of natural gas, in yuan / m³ 3 ; Let m be the natural gas source flow rate of node m during time period t. 3 ; h is the scheduling time step; A collection of coal-fired power units; a i b i c i All are power generation cost coefficients for coal-fired units; P G,i,t The active power output of thermal power unit i is expressed in MW and N. H2 The number of hydrogen storage tanks configured; C inv,H2 The initial investment per tank is in yuan; r is the discount rate; L h For the engineering life; c op,H2 With c P2H This refers to the corresponding unit operation and maintenance cost coefficient; and The instantaneous hydrogen charge and discharge volumetric flow rates, in m, are respectively for time period t. 3 / h; The active power consumed by the P2H device, in MW; The unit is the penalty coefficient for wind and solar power curtailment; , These represent the predicted extreme values ​​of day-ahead power output for wind turbines and photovoltaic arrays, respectively, in MW. , The actual active power dispatched to the grid is expressed in MW. The carbon trading price is expressed in yuan / kg; e i e gas These are the unit carbon emission intensity factors for coal and natural gas, respectively; The unit price for the new energy vehicle grid connection subsidy is yuan.

[0016] Preferably, the unit output and ramping constraint are as follows: (25) (26) In the formula: subscripts min and max represent the minimum and maximum thresholds of the corresponding parameters, respectively; R u,i R d,i Limits the unit's upward and downward ramp rates; The line transmission capacity constraint is: (27) In the formula: Let m be the minimum transmission power from line m to line n, in MW. The gas supply and node pressure constraints are as follows: (28) (29) In the formula: Q m,t Let m be the natural gas flow rate at node m at time t. 3 / h;p m,t Let be the pressure at node m at time t, in MPa; The operating conditions of the compressor are constrained as follows: (30) In the formula: Let be the compressor power at node m at time t, in kW; The safe hydrogen doping ratio boundary for the pipeline network is: (31) In the formula: This refers to the hydrogen volume blending ratio; The constraints between the P2H device and the gas turbine are as follows: (32) (33) In the formula: P represents the active power consumed by the P2H device during time period t, in MW; NG,t Let t be the active power output of the gas turbine during time period t, in MW; The energy storage capacity and charge / discharge / gas constraints are as follows: (34) (35) (36) In the formula: Let m be the hydrogen storage state quantity at the dispatch section during time period t. 3 ; , Let be the charging and discharging power of the energy storage device during time period t, in MV; , These are 0-1 discrete Boolean variables representing the energy storage charging / discharging states, respectively. The consistency constraint between the beginning and end of the scheduling cycle is: (37) In the formula: Let x be the initial energy stored at the beginning of a single scheduling cycle, in MWh; Let MWh be the energy stored by energy storage device x at the end of a single scheduling cycle.

[0017] Preferably, in step S3, when using the sparrow search algorithm to solve the comprehensive scheduling model, the individual dimensionality reduction encoding is as follows: (38) In the formula: P is the position vector of the individual; G,t The active power output of thermal power units is measured in MW. P represents the active power consumed by the P2H device during time period t, in MW; NG,t Let t be the active power output of the gas turbine during time period t, in MW; , These are the actual volumetric flow rates of hydrogen charge and discharge, respectively, in m. 3 / h;N T Where h is the scheduling period; N is the total number of dimensions of the control variables. By introducing a dynamic penalty function method, the model with complex constraints is equivalently transformed into an unconstrained fitness evaluation mechanism. The constructed fitness function is as follows: (39) In the formula: F fit The fitness function; The total operating cost of the system is [amount in yuan]. The maximum penalty factor is dynamically increased with the number of iterations; K is the total number of physical constraints contained in the system. This measures various physical violations.

[0018] Preferably, the following steps are also included: S4: Conduct multi-scenario comparisons and sensitivity analyses to quantitatively assess the impact of key parameters on the system's economy and low-carbon performance, and identify the optimal configuration range for key parameters; the key parameters include the hydrogen doping ratio and hydrogen storage capacity.

[0019] Preferably, in step S4, when constructing multiple scenarios, four progressive scenarios are constructed, including: Scenario 1: Only activate the P2H and associated hydrogen storage modules, forcibly shut down the pipeline hydrogen blending and gas storage channels, and build a decoupled benchmark for independent operation of the gas-electric network; Scenario 2: Activate the pipeline hydrogen-blended and gas turbine modules, and strip away all heterogeneous energy storage buffers; Scenario 3: Activate P2H, gas turbine module and gas storage, and keep hydrogen blending off; Scenario 4: Simultaneous activation of P2H, heterogeneous energy storage array and pipeline hydrogen doping channel.

[0020] The beneficial effects of this invention are: This invention constructs a spatiotemporal collaborative optimization model for multi-energy flow considering electro-hydrogen conversion (P2H), heterogeneous energy storage, and dynamic hydrogen doping boundaries. It also introduces the Sparrow Search Algorithm (SSA) to achieve efficient solution of high-dimensional mixed integer nonlinear programming problems, thereby enabling this invention to perform spatiotemporal collaborative optimization scheduling of gas-electric coupling systems more accurately. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the overall technical framework of the spatiotemporal collaborative optimization scheduling method for gas-electric coupling systems considering P2H and heterogeneous energy storage in this invention. Figure 2 This is a schematic diagram of a gas-electric coupling system considering hydrogen doping and heterogeneous energy storage in a specific embodiment. Figure 3 A schematic diagram of a gas-electric coupling integrated energy system considering P2H in a specific embodiment; Figure 4 This is a schematic diagram of the predicted timing of the base load and fluctuating power output on a typical day in a specific embodiment. Figure 5 This is a schematic diagram illustrating the cost structure and marginal benefit assessment of wind power integrated consumption under multi-energy synergistic configuration in a specific embodiment; Figure 6 This is a schematic diagram of the time-series evolution of the system wind power utilization rate under different scenarios in a specific embodiment; Figure 7 This is a schematic diagram of the spatiotemporal response characteristics of the supply and demand balance of a gas-electric system under multi-energy flow cooperative evolution in a specific embodiment; Figure 8This is a schematic diagram of the timing of heterogeneous energy storage and multi-energy flow cross-medium coordinated scheduling under a comprehensive configuration scenario in a specific embodiment; wherein, (a) is a schematic diagram of the timing buffer of P2H hydrogen production and hydrogen storage on the source side; and (b) is a schematic diagram of the peak-shaving response of the grid-side pipeline hydrogen incorporation and gas storage. Detailed Implementation

[0023] The present invention will be further described below with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and technical features described in this application can be combined with each other. It should also be pointed out that, unless otherwise indicated, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terms "comprising" or "including" and similar words used in this invention refer to elements or objects preceding the word that encompass the elements or objects listed following the word and their equivalents, without excluding other elements or objects.

[0024] like Figure 1 As shown, this invention provides a spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage, comprising the following steps: S1: Construct a physical model of a gas-electric coupling system that considers electricity-to-hydrogen conversion, hydrogen doping in the pipeline network, and heterogeneous energy storage, including a network-side model and a coupling hub and heterogeneous energy storage model.

[0025] In one specific embodiment, the physical model of the gas-electric coupling system is as follows: Figure 2 As shown, when the output of renewable energy on the source side exceeds the limit, the P2H facility will convert the surplus active power into hydrogen energy and perform pipeline injection or on-site storage according to the system operating status; when the system faces a peak-shaving gap, the gas turbine will achieve cross-medium energy feedback by consuming hydrogen-blended natural gas.

[0026] In one specific embodiment, the power network of the network-side model adopts a linearized DC power flow model, while the natural gas network of the network-side model considers the evolution of physical properties after hydrogen doping and steady-state flow constraints. Specifically, the network-side model includes: (1) Power system model In this embodiment, the power network is linearized based on a DC power flow model. The active power of branch mn during time period t is... Depends on the voltage phase angle difference between the two nodes: (1) In the formula: Let mn be the active power of branch mn during time period t, in MW; , Let X be the voltage phase angles of nodes m and n during time interval t, in rad; mn The line reactance is expressed in Ω. For any node m in a power system, its active power injection and outflow follow Kirchhoff's laws, and the node balance equation is characterized as follows: (2) In the formula: P G,i,t For the active power output of thermal power unit i, MW; P GT,j,t P represents the power generation capacity of gas turbine unit j, in MW. W,m,t P PV,m,t The actual power absorbed by wind and solar power is expressed in MW; P. P2H,m,t P represents the power consumption of the P2H device at node m, in MW; EC,m,t P represents the power consumption of the electric compressor at node m, in MW. L,m,t For conventional electrical loads, MW; and These are the sets of generator sets and the set of gas turbines connected to node m, respectively. Let m be the set of network nodes adjacent to node m. Optionally, the actual power absorption capacity of wind power and photovoltaic power can be calculated using the following formulas: (11) (12) In the formula: Let t be the actual output active power of the wind turbine, in MW; The rated output power of the wind turbine is expressed in MW. , , , Let be the wind speed at time t, the cut-in wind speed, the cut-out wind speed, and the rated wind speed, respectively, in m / s; Let t be the actual output active power of the photovoltaic array, in MW; The rated output power of the photovoltaic array under standard test conditions is expressed in MW. Let be the actual solar irradiance at time t, in W / m². The reference irradiance under standard test conditions, in W / m² 2 k is the power-temperature correlation coefficient of the photovoltaic module; Let t be the operating temperature of the photovoltaic cell, in °C. The reference temperature under standard test conditions is ℃.

[0027] Battery energy storage (BESS) configured in a power system has the characteristics of continuous time-series operation, and its dynamic evolution equation of energy state is as follows: (3) In the formula: E e,t E e,t-1These represent the battery's stored energy in time periods t and t-1, respectively, in MWh. This is the self-loss coefficient; , These are the charging power and discharging power, respectively, in W; , These are the charging and discharging efficiencies, respectively. h represents the scheduling time step.

[0028] (2) Thermodynamics and property characterization of mixed gases The injection of hydrogen gas alters the hydrodynamic characteristics of the pipeline network. This embodiment uses Kay's mixing rule to calculate the pseudo-critical parameters of the mixed gas and combines this with the corresponding state principle to solve for the compressibility factor. (4) In the formula: z mix It is the compression factor; , , Tc represents the critical temperature of the mixed gas, natural gas, and hydrogen, respectively, in K0; T0 represents the actual operating temperature of the gas in the pipeline network, in Kc. Let mn be the average pressure at node m, in MPa; , , These are the critical pressures (MPa) of the mixed gas, natural gas, and hydrogen, respectively; p m The pressure at node m is in MPa; p n The pressure at node n, in MPa; This refers to the hydrogen volume blending ratio; Meanwhile, the equivalent density of the mixed gas With dynamic viscosity This is key to analyzing the operating pressure of the gas network; both are calculated using the following formulas: (5) (6) In the formula: The equivalent density of the gas mixture is expressed in kg / cm³. 3 ; , The densities of hydrogen and natural gas are respectively, in kg / cm³. 3 ; The dynamic viscosity of the gas mixture is Pa. s; , The dynamic viscosity of hydrogen and natural gas, respectively, in Pa. s; , , respectively, are the molar masses of hydrogen and natural gas, in kg / mol.

[0029] (3) Steady-state flow model of hydrogen-blended natural gas pipeline network The nonlinear mapping relationship between nodal pressure gradient and pipeline flow rate is described by the following equation, which can analyze the gas flow capacity occupancy and pressure redistribution mechanism caused by hydrogen injection: (7) (8) In the formula: Q mn For the airflow rate in the pipeline, m 3 / h;C mn To comprehensively consider the gas mixture temperature, pipeline geometry parameters, and compressibility factor, the physical constants of the pipeline network; D mn λ is the inner diameter of the pipe, in meters (m); mn Z is the pipeline resistance coefficient; z is the compressibility factor of hydrogen-blended natural gas; T is the gas temperature, K; L mn δ is the pipeline length, in meters; δ is the relative density of natural gas, in kilograms per cubic meter of water. 3 ; The law of conservation of volumetric flow rate at any natural gas node m is: (9) In the formula: , These represent the natural gas supply and conventional load demand at node m at time t, respectively. 3 / h; The equivalent amount of hydrogen injected into the P2H pipeline network, m 3 / h; For gas turbine cross-grid gas consumption, m 3 / h; Let Q be the set of natural gas nodes connected to node m; mn,t Let m be the airflow rate in the pipe during time period t. 3 / h; In the gas network, the compressor station consumes active power to pressurize the gas. The power consumption of the electric compressor and the gas consumption of the gas-driven compressor are as follows: (10) In the formula: The power consumption of the electric compressor is measured in MW. For the gas consumption of the gas-driven compressor, m 3 / h; The power consumption of the electric compressor is measured in MW. , , These are the compressor hydraulic efficiency, motor drive efficiency, and gas turbine thermal efficiency, respectively. The low calorific value of natural gas, MWh / m 3 .

[0030] In the physical model of the gas-electric coupling system of this invention, the cross-medium flow and spatiotemporal smoothing of energy mainly rely on two types of core flexibility resources: one is the energy conversion hub that breaks down physical barriers (P2H device and gas turbine); the other is the heterogeneous energy storage unit (hydrogen storage tank and gas storage tank) that provides temporal buffering capabilities. In a specific embodiment, the coupling hub and heterogeneous energy storage model include: (1) Electro-to-hydrogen (P2H) model In one specific embodiment, the hydrogen electrolysis equipment employs a proton exchange membrane (PEM) electrolyzer, which is characterized by its strong adaptability to fluctuating power supplies and high electrolysis efficiency. The active power consumed and the volumetric flow rate of the produced hydrogen satisfy the following relationship: (13) In the formula: The volumetric flow rate of hydrogen produced by the electrolyzer, in Nm³. 3 / h; The energy conversion efficiency of electro-hydrogen conversion; The active power consumed by the P2H device, in MW; The lower heating value of hydrogen, NWh / Nm³. 3 ; (2) Gas turbine model The gas turbine executes rapid response peak-shaving commands within the system. Its output active power P NG This drives the demand for mixed gas consumption on the gas grid side, and this physical mapping exhibits nonlinear quadratic function characteristics: (14) In the formula: Q NG The equivalent gas consumption of the gas turbine is m. 3 / h; a, b, and c are all fixed physical parameters characterizing the heat engine's conversion efficiency and operating characteristics; P NG The active power output of the gas turbine is expressed in MW. (3) Heterogeneous energy storage model Hydrogen storage tanks effectively mitigate the timing misalignment between continuous P2H hydrogen production commands and the physical hydrogen doping limits of the pipeline network through stock throughput. Their capacity status exhibits temporal continuity, and the evolution equation is as follows: (15) In the formula: , The hydrogen storage state quantities at the dispatch section during time periods t and t-1 are respectively, m 3 ; , These are the actual volumetric flow rates of hydrogen charge and discharge, respectively, in m. 3 / h; The natural gas storage facility (UGS), serving as a buffer unit on the gas grid side, has the following injection and production energy consumption model: (16) In the formula: , These represent the unit energy consumption for the gas injection and gas extraction processes, respectively, in MWh / m³. 3 ; , These represent hourly gas extraction and injection volumes, in m³. 3 / h; a1 and a2 are the energy consumption ratio coefficients for the gas injection and gas extraction processes, respectively; b1 and b2 are the corresponding nonlinear exponential constants.

[0031] S2: Based on the physical model of the gas-electric coupling system, construct a comprehensive scheduling model oriented towards optimizing global operating costs.

[0032] In a specific embodiment, the integrated scheduling model guided by the optimization of global operating cost includes an integrated operating objective function and multi-energy flow operating constraints.

[0033] In a specific embodiment, the comprehensive operation objective function aims to minimize the comprehensive operation cost within a single daily scheduling cycle. This objective function is decoupled into three main modules: energy interaction cost, flexible resource depreciation and operation and maintenance cost, and low-carbon consumption penalty / benefit. Its mathematical expression is as follows: (17) Where: G oc For overall operating costs; C n C is the cost of purchasing natural gas. e The cost of power generation for conventional coal-fired units; C h Cost of hydrogen storage tank; C maint For the operation and maintenance costs of P2H devices; C q C. Penalty costs for wind and solar power curtailment; c For environmental carbon emission costs; C z The policy subsidies for the full grid connection of green electricity.

[0034] (1) Basic fossil energy supply cost. The external input of fossil energy directly represents the scale of carbon-based energy consumption at the bottom of the system, including the cost of purchasing natural gas C. n Compared with the power generation cost of conventional coal-fired units C e : (18) (19) Where: N T h represents the scheduling range; For the set of natural gas source nodes; c gas Price per unit volume of natural gas, in yuan / m³ 3 ; Let m be the natural gas source flow rate of node m during time period t. 3 ; h is the scheduling time step; A collection of coal-fired power units; a i b i c i All are power generation cost coefficients for coal-fired units; P G,i,t The active power output of thermal power unit i is MW; (2) Calculation and maintenance costs of flexibility resources. System flexibility resources (including hydrogen storage tank C) h Operation and maintenance of P2H equipment C maint Cost allocation and accounting: (20) (twenty one) Where: N H2 The number of hydrogen storage tanks configured; C inv,H2 The initial investment per tank is in yuan; r is the discount rate; L h For the engineering life; c op,H2 With c P2H This refers to the corresponding unit operation and maintenance cost coefficient; and The instantaneous hydrogen charge and discharge volumetric flow rates, in m, are respectively for time period t. 3 / h; The active power consumed by the P2H device, in MW; (3) Penalties for New Energy Consumption and Environmental Costs. To drive the system to prioritize the use of cross-medium flexible resources to promote low-carbon evolution, the objective function internalizes the wind and solar curtailment penalty C. q Environmental carbon emission costs C c And the policy subsidies C for full grid connection of green electricity z : (twenty two) (twenty three) (twenty four) In the formula: The unit is the penalty coefficient for wind and solar power curtailment; , These represent the predicted extreme values ​​of day-ahead power output for wind turbines and photovoltaic arrays, respectively, in MW. , The actual active power dispatched to the grid is expressed in MW. The carbon trading price is expressed in yuan / kg; e i e gas These are the unit carbon emission intensity factors for coal and natural gas, respectively; The unit price for the new energy vehicle grid connection subsidy is yuan.

[0035] To ensure the safe, stable, and sustainable operation of the gas-electric coupling system during the day-ahead scheduling cycle, the objective function solution process must strictly adhere to the physical boundaries of the multi-energy network and the operating conditions of the equipment. In addition to satisfying physical equation constraints such as node power / flow balance, the system must also meet the following multi-energy flow operation constraints: (1) Power grid security and unit operation constraints Unit output and ramp-up constraints: (25) (26) In the formula: subscripts min and max represent the minimum and maximum thresholds of the corresponding parameters, respectively; R u,i R d,i Limits the unit's upward and downward ramp rates; Line transmission capacity constraints: (27) In the formula: Let m be the minimum transmission power from line m to line n, in MW. (2) Safety constraints of hydrogen blending network distribution and hydrogen mixing Gas supply and node pressure constraints: (28) (29) In the formula: Q m,t Let m be the natural gas flow rate at node m at time t. 3 / h;p m,t Let be the pressure at node m at time t, in MPa; Compressor operating condition constraints: (30) In the formula: Let be the compressor power at node m at time t, in kW; Safety limits for hydrogen doping ratio in pipeline networks: (31) In the formula: This refers to the hydrogen volume blending ratio; (3) Periodic constraints of cross-network hubs and heterogeneous energy storage P2H unit and gas turbine constraints: (32) (33) In the formula: P represents the active power consumed by the P2H device during time period t, in MW; NG,t Let t be the active power output of the gas turbine during time period t, in MW; Energy storage capacity and charge / discharge / gas constraints: The real-time state (SOC / SoHC) of batteries and hydrogen / gas storage tanks must meet physical capacity boundaries, and charging and discharging behaviors must be mutually exclusive within the same scheduling period. (34) (35) (36) In the formula: Let m be the hydrogen storage state quantity at the dispatch section during time period t. 3 ; , Let be the charging and discharging power of the energy storage device during time period t, in MV; , These are 0-1 discrete Boolean variables representing the energy storage charging / discharging states, respectively. The consistency constraint between the beginning and end of the scheduling cycle is required to ensure that heterogeneous energy storage devices have the same regulation capability on the next day. This requires that the remaining energy state at the end of a single scheduling cycle T must be consistent with the initial state, thereby ensuring the sustainability and operational resilience of the system under continuous scheduling over multiple days. (37) In the formula: Let x be the initial energy stored at the beginning of a single scheduling cycle, in MWh; Let MWh be the energy stored by energy storage device x at the end of a single scheduling cycle.

[0036] S3: The sparrow search algorithm is used to solve the integrated scheduling model, and the spatiotemporal coordinated optimization scheduling of the gas-electric coupling system is performed based on the solution results.

[0037] In this invention, the integrated scheduling model internally couples discrete energy storage state variables with continuous unit output / pipeline flow variables, and embeds the nonlinear Weymouth equation, which is mathematically a high-dimensional mixed-integer nonlinear programming (MINLP) problem. Conventional mathematical analytical algorithms are prone to getting stuck in local optima or encountering the "curse of dimensionality" when dealing with such strongly coupled and inseparable variables.

[0038] In one specific embodiment, this invention selected five cutting-edge metaheuristic algorithms, including the Slime Mold Algorithm (SMA), the Sparrow Search Algorithm (SSA), and the Seagull Algorithm (SOA), and conducted rigorous cross-sectional evaluations on nine standard benchmark functions covering single-modal / multi-modal and separable / non-separable features. Test data confirmed that, when faced with multimodal non-separable functions (such as F5 and F12) that highly simulate the features of the model in this invention, the SSA algorithm, with its underlying explorer-follower mechanism and danger warning exit strategy, exhibited overwhelming global search capability (the average fitness and standard deviation converged to 0.00 under complex surfaces). Therefore, considering convergence accuracy, anti-premature convergence capability, and adaptability to multidimensional problems, this invention ultimately selected the Sparrow Search Algorithm (SSA) as the core solver.

[0039] To achieve a deep mapping between the optimization algorithm and the physical model, the closed-loop solution framework based on SSA is set up as follows: (1) Individual dimensionality reduction coding strategy Each individual "sparrow" in the algorithm represents a potential trajectory of the system within the scheduling period T. The position vector of each individual encompasses all independent control variables of the system, and its matrix structure is designed as follows: (38) In the formula: P is the position vector of the individual; G,t The active power output of thermal power units is measured in MW. P represents the active power consumed by the P2H device during time period t, in MW; NG,t Let t be the active power output of the gas turbine during time period t, in MW; , These are the actual volumetric flow rates of hydrogen charge and discharge, respectively, in m. 3 / h;N T Where h is the scheduling period; N is the total number of dimensions of the control variables.

[0040] (2) Constraint handling and fitness penalty To ensure that the optimization process strictly adheres to the electro-physical boundaries, this invention introduces a dynamic penalty function method. This method effectively transforms the optimization model with complex constraints into an unconstrained fitness evaluation mechanism, where the fitness function F... fit The structure is as follows: (39) In the formula: F fit The fitness function; The total operating cost of the system is [amount in yuan]. The maximum penalty factor is dynamically increased with the number of iterations; K is the total number of physical constraints contained in the system. This measures various physical violations.

[0041] The SSA algorithm iteratively updates the positions of discoverers and joiners, continuously approaching the global theoretical lower bound of the overall system operating cost.

[0042] In a specific embodiment, the spatiotemporal coordinated optimization scheduling method for gas-electric coupling systems considering P2H and heterogeneous energy storage according to the present invention further includes the following steps: S4: Conduct multi-scenario comparisons and sensitivity analyses to quantitatively assess the impact of key parameters on the system's economy and low-carbon performance, and identify the optimal configuration range for key parameters; the key parameters include the hydrogen doping ratio and hydrogen storage capacity.

[0043] In one specific embodiment, when constructing multiple scenarios, four progressive scenarios are constructed, including: Scenario 1: Only activate the P2H and associated hydrogen storage modules, forcibly shut down the pipeline hydrogen blending and gas storage channels, and construct a decoupled benchmark for the independent operation of the gas-electric network to analyze the isolated buffer extreme value of source-side energy storage. Scenario 2: Activate the pipeline network hydrogen-infused and gas turbine modules, and strip away all heterogeneous energy storage buffers; the aim is to analyze the rigid reverse constraint that the physical pipeline network imposes on cross-network energy flow when there is a lack of time-series regulation capabilities. Scenario 3: Activate P2H, gas turbine module and gas storage, and keep hydrogen blending off; focus on evaluating the independent regulation efficiency of multiple types of energy storage resources under network decoupling conditions; Scenario 4: Simultaneously activate P2H, heterogeneous energy storage array and pipeline hydrogen doping channel; realize the full-element coordination of "cross-medium conversion-time buffer-spatial transmission" and reveal the optimal operating boundary of the system.

[0044] In a specific embodiment, taking a gas-electric coupling system as an example, the spatiotemporal coordinated optimization scheduling method for gas-electric coupling systems considering P2H and heterogeneous energy storage described in this invention is used for its spatiotemporal coordinated optimization scheduling. In this embodiment, a gas-electric coupling system is constructed, consisting of a 33-node IEEE distribution network and a 20-node natural gas network. Its topology and coupling relationship are as follows: Figure 3 As shown.

[0045] The system's time-series scheduling cycle is set to 24 hours, with a step size of 1 hour. The power side is connected to wind power, photovoltaic, and coal-fired power units, while the natural gas side is supplied by a gas source. The power-gas system achieves cross-medium energy conversion with the gas turbine through a P2H device, thus forming a bidirectional coupling structure. To mitigate high-frequency fluctuations generated during cross-grid energy exchange, the system synchronously configures a natural gas storage facility and associated hydrogen storage tanks at the core physical hub (hydrogen not injected into the pipeline network is considered as temporary storage across time periods or supplied to the local stable hydrogen load). Relevant energy prices and penalty cost parameters are shown in Table 1; the capacity, operating constraints, and investment parameters of the hydrogen storage unit are shown in Table 2; the typical daily gas-power load and wind / solar output curves are shown in Table 2. Figure 4 As shown.

[0046] Table 1. Basic Price Parameter Settings for Day-ahead Cooperative Optimization Scheduling

[0047] Table 2. Operational Constraints and Engineering Economic Parameters of Associated Hydrogen Storage Tanks

[0048] To analyze the impact of different flexibility resources on system operation, four progressive scenarios were constructed under a unified baseline condition (upper limit of hydrogen doping ratio of 8% and 20 hydrogen storage tanks) to separate the independent and synergistic effects of each factor. The configuration relationship of the elements in each scenario is shown in Table 3. Table 3. Control Variable Setting Matrix for Progressive Gas-Electric Co-operation Scenario

[0049] Note: In Table 3, “√” indicates that the module or channel is activated in the current simulation scenario, and “×” indicates that the module is deactivated or the channel is closed.

[0050] Based on the four progressive configuration scenarios mentioned above, comparative analysis is used to quantitatively evaluate the marginal contribution of different flexible resources to the performance of the gas-electric coupling system from a macroscopic perspective. By "stripping away variables," the underlying synergistic mechanism between pipeline hydrogen doping in the spatial dimension and heterogeneous energy storage in the temporal dimension in the economic efficiency and decarbonization process of the driving system is revealed. Detailed comparisons of cost boundaries and key indicators are shown in Table 4 and... Figure 5 .

[0051] Table 4. Multidimensional operational cost boundary assessment of the system in a progressive configuration scenario.

[0052] From Table 4 and Figure 5It can be seen that the total daily operating cost of the system exhibits a clear non-linear decreasing trend as flexibility elements are gradually integrated. From scenario one to scenario four, the total cost is optimized from 8.9598 million yuan to 8.1282 million yuan, a reduction of 9.28%. The underlying economic logic is that the introduction of flexibility resources essentially completes a "replacement of sunk costs with high-value assets".

[0053] In the baseline scenario, the system, lacking cross-medium regulation capabilities, incurs high penalties for load shedding and wind curtailment costs. However, with the activation of the all-factor coordination mechanism, although the system increases P2H operating expenses and energy storage infrastructure depreciation, these deterministic equipment investments effectively offset the high penalties caused by random energy curtailment. Therefore, the combined configuration of heterogeneous energy storage and hydrogen-doped channels is not a simple functional addition, but rather achieves deep optimization of marginal costs by altering the distribution characteristics of the system's energy flow.

[0054] The core issue in controlling carbon emissions in the system lies in the ratio of coal-fired power load to fossil gas consumption. Data shows that when only "time-dimensional" adjustments are available (such as in scenario three), the carbon emission cost (714,100 yuan) is almost no better than in the baseline scenario. The underlying physical reason is that without a "spatial-dimensional" physical hydrogen-incorporation channel, hydrogen energy can only function as an isolated energy cycle and cannot achieve a substantial replacement of fossil gas sources.

[0055] Only in the full-element scenario four, when the temporal scheduling of hydrogen storage units is deeply integrated with the spatial transmission of hydrogen blended into the pipeline network, does the system's carbon emission cost show a significant downward trend (down to 705,100 yuan). This indicates that hydrogen blending is a physical prerequisite for activating the low-carbon benefits of energy storage resources, and the coupling of the two is a key hub driving the system to overcome the carbon reduction bottleneck.

[0056] Comparing the cost structure of wind power consumption in different scenarios reveals a counterintuitive physical phenomenon: in Scenario 2, where all energy storage buffers are removed, the cost of wind curtailment penalty (369,800 yuan) is even higher than in Scenario 1, where no hydrogen blending technology is applied (260,100 yuan). The mechanism behind this phenomenon lies in the "injection blockage" effect: after the forced removal of hydrogen / gas storage facilities, the P2H unit and the natural gas pipeline network are in a state of "strong rigid coupling". Limited by the pipeline pressure constraint during the nighttime natural gas load trough and the physical hydrogen blending limit of 8%, the real-time hydrogen production of P2H is locked by the instantaneous absorption limit of the pipeline network, causing the source-side wind power to be forced to be reduced on a large scale because it has "nowhere to go".

[0057] This discovery provides important engineering insights: simply building cross-medium connections without buffer capacity will lead to the "cross-network transfer" of operational bottlenecks and strengthen system rigidity. Only by configuring heterogeneous energy storage to decouple the "on-demand production and transmission" constraint can the potential for hydrogen absorption in pipeline networks be truly released.

[0058] Figure 6 The simulation revealed the temporal evolution characteristics of wind power utilization under different configuration scenarios. Simulation results show that the effect of single-pipeline hydrogen blending (Scenario 2) on improving wind power absorption is extremely limited. Its underlying bottleneck lies in the fact that during nighttime off-peak load periods, the natural gas pipeline itself is already operating under high pressure, and the remaining physical space is insufficient to cover the large-scale hydrogen production demand, leading to a "space-constrained" stalemate. In Scenario 4, with full-element synergy, the minimum point of nighttime wind power utilization significantly increased from 70% to 85.9%. This leap forward stems from a deep decoupling of the "time-space" dual dimensions: the hydrogen storage tank receives hydrogen energy that cannot be injected in real-time due to pipeline blockage on the source side, releasing it during the daytime peak gas consumption period. This synergistic mode breaks the rigid "produce and transmit as needed" operation of P2H devices, freeing wind power absorption from the instantaneous physical extremes of the gas network.

[0059] Figure 7 The paper demonstrates the dynamic response of power balance between the electricity and gas grids under four scenarios. Comparative analysis reveals that in deeply coupled energy systems, improper allocation of flexible resources can easily lead to the "cross-grid transfer" of operational bottlenecks. Taking Scenario 2 as an example, although the intervention of gas turbines compensated for the approximately 100MW power shortage during the evening peak (20:00), the lack of buffer regulation from gas storage facilities caused a surge in natural gas demand during peak gas turbine operation, forcing gas source nodes to reach their output limits and triggering a more severe gas grid load shedding. Scenario 4, through the integration of heterogeneous energy storage and hydrogen blending technologies, reconstructed the spatial distribution of energy flow. The active power response of P2H at night reduced the surplus electricity, while the synergistic energy release of the gas storage facility and the hydrogen blending pipeline network during the day precisely filled the peak gaps in both gas and electricity, achieving a complete zeroing of the dual-grid load shedding throughout the entire cycle. This powerfully demonstrates that only by achieving global synergy among multiple flexible elements can the disorderly spread of system risks between heterogeneous networks be avoided.

[0060] Figure 8 The study delves into the micro-scheduling logic of each core asset in scenario four, revealing the closed-loop path of "electricity-hydrogen-gas" energy flow. During the peak wind power generation period in the early morning (01:00-05:00), the P2H unit maintains full-power operation to capture the green electricity dividend. At this time, due to the gas grid injection space reaching its limit, the hydrogen storage unit initiates continuous gas injection operations, undertaking the core function of a "time-sequence conversion station," converting hydrogen energy that does not meet the conditions for instantaneous grid connection into high-quality time-shifted assets. Entering the daytime peak gas consumption period, the scheduling strategy shifts to "space release." The hydrogen temporarily stored overnight is released centrally and connected to the pipeline network ( Figure 8The surge in the dark gold curve (b) not only effectively reduces the supply pressure of fossil natural gas on the source side, but also significantly smooths out the peak injection and production of gas storage facilities, reducing the operational burden on the gas network. The essence of this synergistic mechanism is that the hydrogen storage tank, through flexible adjustment over a time scale, provides a stable output environment for P2H, thereby replacing the peak-shaving pressure of traditional energy sources on a spatial scale through hydrogen blending into the pipeline network, achieving optimal configuration of overall system flexibility.

[0061] In summary, this invention enables unified modeling and coordinated operation of multi-energy flows involving electricity, gas, and hydrogen. The pipeline example results show that, under a fully coordinated scenario, the total system operating cost is reduced by approximately 9.28%, the cost of wind and solar curtailment decreases by approximately 43.8%, and the carbon emission cost decreases by approximately 1.26%, indicating that the proposed model has a significant effect on improving system economics and renewable energy absorption capacity. Mechanism analysis shows that hydrogen blending into the pipeline network expands the energy carrying capacity of the gas network, while heterogeneous energy storage provides time-series regulation capabilities. The synergistic effect of these two factors effectively alleviates the operational rigidity of the gas-electric coupling system, thereby improving the overall system flexibility. Sensitivity analysis shows that the upper limit of the hydrogen blending ratio and the hydrogen storage capacity have a significant impact on system performance, and the system possesses an optimal configuration range that balances economic efficiency and low carbon emissions. These conclusions can provide a reference for the planning, design, and operational optimization of gas-electric coupled integrated energy systems.

[0062] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage, characterized in that, Includes the following steps: S1: Construct a physical model of a gas-electric coupling system that considers electricity-to-hydrogen conversion, hydrogen doping in the pipeline network, and heterogeneous energy storage, including a network-side model and a coupling hub and heterogeneous energy storage model; S2: Based on the physical model of the gas-electric coupling system, construct a comprehensive scheduling model oriented towards optimizing global operating costs; S3: The sparrow search algorithm is used to solve the integrated scheduling model, and the spatiotemporal coordinated optimization scheduling of the gas-electric coupling system is performed based on the solution results.

2. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 1, characterized in that, In step S1, the network-side model includes a power system model, a thermodynamic and physical property characterization of mixed gas, and a steady-state flow model of hydrogen-doped natural gas pipeline network; In the power system model, the active power of branch mn during time period t is calculated using the following formula: （1） In the formula: Let mn be the active power of branch mn during time period t, in MW; , Let X be the voltage phase angles of nodes m and n during time interval t, in rad; mn The line reactance is expressed in Ω. For any node m in a power system, its nodal equilibrium equations are as follows: （2） In the formula: P G,i,t For the active power output of thermal power unit i, MW; P GT,j,t P represents the power generation capacity of gas turbine unit j, in MW. W,m,t P PV,m,t The actual power absorbed by wind and solar power is expressed in MW; P. P2H,m,t P represents the power consumption of the P2H device at node m, in MW; EC,m,t P represents the power consumption of the electric compressor at node m, in MW. L,m,t For conventional electrical load, MW; and These are the sets of generator sets and the set of gas turbines connected to node m, respectively. Let m be the set of network nodes adjacent to node m. The dynamic evolution equation of the energy state of batteries configured in a power system is as follows: （3） In the formula: E e,t E e,t-1 These represent the battery's stored energy in time periods t and t-1, respectively, in MWh. This is the self-loss coefficient; , These are the charging power and discharging power, respectively, in W; , These are the charging and discharging efficiencies, respectively. h is the scheduling time step; When performing thermodynamic and physical property characterization of gas mixtures, the compressibility factor is calculated using the following formula: （4） In the formula: z mix It is the compression factor; , , , respectively, are the critical temperatures (K) of the mixed gas, natural gas, and hydrogen; T0 is the actual operating temperature of the gas in the pipeline network, in K; Let mn be the average pressure at node m, in MPa; , , These are the critical pressures (MPa) of the mixed gas, natural gas, and hydrogen, respectively; p m The pressure at node m is in MPa; p n The pressure at node n, in MPa; This refers to the hydrogen volume blending ratio; The equivalent density and dynamic viscosity of the gas mixture are calculated using the following formulas: （5） （6） In the formula: The equivalent density of the gas mixture is expressed in kg / cm³. 3 ; , The densities of hydrogen and natural gas are shown in kg / cm³. 3 ; The dynamic viscosity of the gas mixture is Pa. s; , The dynamic viscosity of hydrogen and natural gas, respectively, in Pa. s; , Here are the molar masses of hydrogen and natural gas, respectively, in kg / mol. In the steady-state flow model of the hydrogen-blended natural gas pipeline network, the nonlinear mapping relationship between nodal pressure gradients and pipeline flow rates is described by the following equation: （7） （8） In the formula: Q mn For the airflow rate in the pipeline, m 3 / h;C mn To comprehensively consider the temperature of the mixed gas, pipeline geometric parameters, and compressibility factor, the physical constants of the pipeline network; D mn λ is the inner diameter of the pipe, in meters (m); mn L is the pipeline resistance coefficient; z is the compressibility factor of hydrogen-blended natural gas; T is the gas temperature, K; L mn δ is the pipeline length, in meters; δ is the relative density of natural gas, in kilograms per cubic meter of water. 3 ; The law of conservation of volumetric flow rate at any natural gas node m is: （9） In the formula: , These represent the natural gas supply and conventional load demand at node m at time t, respectively. 3 / h; The equivalent amount of hydrogen injected into the P2H pipeline network, m 3 / h; For gas turbine cross-grid gas consumption, m 3 / h; Let Q be the set of natural gas nodes connected to node m; mn,t Let m be the airflow rate in the pipe during time period t. 3 / h; The power consumption of the electric compressor and the gas consumption of the gas-driven compressor are as follows: （10） In the formula: The power consumption of the electric compressor is measured in MW. For the gas consumption of the gas-driven compressor, m 3 / h; The power consumption of the electric compressor is measured in MW. , , These are the compressor hydraulic efficiency, motor drive efficiency, and gas turbine thermal efficiency, respectively. The low calorific value of natural gas, MWh / m 3 .

3. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 2, characterized in that, The actual power absorption capacity of wind power and photovoltaic power is calculated using the following formulas: （11） （12） In the formula: Let t be the actual output active power of the wind turbine, in MW; The rated output power of the wind turbine is expressed in MW. , , , Let be the wind speed at time t, the cut-in wind speed, the cut-out wind speed, and the rated wind speed, respectively, in m / s; Let t be the actual output active power of the photovoltaic array, in MW; The rated output power of the photovoltaic array under standard test conditions is expressed in MW. Let be the actual solar irradiance at time t, in W / m². The reference irradiance under standard test conditions, in W / m² 2 k is the power-temperature correlation coefficient of the photovoltaic module; Let t be the operating temperature of the photovoltaic cell, in °C. The reference temperature under standard test conditions is ℃.

4. The spatiotemporal coordinated optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 1, characterized in that, In step S1, the coupling hub and heterogeneous energy storage model include an electro-hydrogen conversion model, a gas turbine model, and a heterogeneous energy storage model; The electro-hydrogen conversion model is as follows: （13） In the formula: The volumetric flow rate of hydrogen produced by the electrolyzer, in Nm³. 3 / h; The energy conversion efficiency of electro-hydrogen conversion; The active power consumed by the P2H device, in MW; The lower heating value of hydrogen is NWh / Nm³. 3 ; The gas turbine model is as follows: （14） In the formula: Q NG The equivalent gas consumption of the gas turbine is m. 3 / h; a, b, and c are all fixed physical parameters characterizing the heat engine's conversion efficiency and operating characteristics; P NG The active power output of the gas turbine is expressed in MW. The heterogeneous energy storage model is as follows: （15） （16） In the formula: , The hydrogen storage state quantities at the dispatch section during time periods t and t-1 are respectively, m 3 ; , These are the actual volumetric flow rates of hydrogen charge and discharge, respectively, in m. 3 / h; , These represent the unit energy consumption for the gas injection and gas extraction processes, respectively, in MWh / m³. 3 ; , These represent hourly gas extraction and injection volumes, in m³. 3 / h; a1 and a2 are the energy consumption ratio coefficients for the gas injection and gas extraction processes, respectively; b1 and b2 are the corresponding nonlinear exponential constants.

5. The spatiotemporal coordinated optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 1, characterized in that, In step S2, the integrated scheduling model guided by the optimization of global operating cost includes an integrated operating objective function and multi-energy flow operating constraints; The comprehensive operation objective function aims to minimize the comprehensive operation cost within a single daily scheduling cycle. The multi-energy flow operation constraints include grid security and unit operation constraints, hydrogen-blended gas network transmission and distribution and hydrogen blending security constraints, and cross-grid hub and heterogeneous energy storage periodic constraints. The constraints on grid security and unit operation include unit output and ramping constraints, and line transmission capacity constraints. The constraints on hydrogen blending network transmission and distribution and hydrogen blending security include gas supply and node pressure constraints, compressor operating condition constraints, and pipeline hydrogen blending safety ratio boundaries. The constraints on cross-grid hubs and heterogeneous energy storage periodicity include P2H device and gas turbine constraints, energy storage capacity and charge / discharge / gas constraints, and scheduling cycle beginning and end state consistency constraints.

6. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 5, characterized in that, The comprehensive operational objective function is: （17） （18） （19） （20） （21） （22） （23） （24） Where: G oc For overall operating costs; C n C is the cost of purchasing natural gas. e The cost of power generation for conventional coal-fired units; C h Cost of hydrogen storage tank; C maint For the operation and maintenance costs of P2H devices; C q C. Penalty costs for wind and solar power curtailment; c For environmental carbon emission costs; C z For policy subsidies received for full grid connection of green electricity; N T h represents the scheduling range. For the set of natural gas source nodes; c gas Price per unit volume of natural gas, in yuan / m³ 3 ; Let m be the natural gas source flow rate of node m during time period t. 3 ; h is the scheduling time step; A collection of coal-fired power units; a i b i c i All are power generation cost coefficients for coal-fired units; P G,i,t The active power output of thermal power unit i is expressed in MW and N. H2 The number of hydrogen storage tanks configured; C inv,H2 The initial investment per tank is in yuan; r is the discount rate; L h For the engineering life; c op,H2 With c P2H This refers to the corresponding unit operation and maintenance cost coefficient; and The instantaneous hydrogen charge and discharge volumetric flow rates, in m, are respectively for time period t. 3 / h; The active power consumed by the P2H device, in MW; The unit is the penalty coefficient for wind and solar power curtailment; , These represent the predicted extreme values ​​of day-ahead power output for wind turbines and photovoltaic arrays, respectively, in MW. , The actual active power dispatched to the grid is expressed in MW. The carbon trading price is expressed in yuan / kg; e i e gas These are the unit carbon emission intensity factors for coal and natural gas, respectively; The unit price for the new energy vehicle grid connection subsidy is yuan.

7. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 6, characterized in that, The unit output and ramping constraints are as follows: （25） （26） In the formula: subscripts min and max represent the minimum and maximum thresholds of the corresponding parameters, respectively; R u,i R d,i Limits the unit's upward and downward ramp rates; The line transmission capacity constraint is: （27） In the formula: Let m be the minimum transmission power from line m to line n, in MW. The gas supply and node pressure constraints are as follows: （28） （29） In the formula: Q m,t Let m be the natural gas flow rate at node m at time t. 3 / h;p m,t Let be the pressure at node m at time t, in MPa; The operating conditions of the compressor are constrained as follows: （30） In the formula: Let be the compressor power at node m at time t, in kW; The safe hydrogen doping ratio boundary for the pipeline network is: （31） In the formula: This refers to the hydrogen volume blending ratio; The constraints between the P2H device and the gas turbine are as follows: （32） （33） In the formula: P represents the active power consumed by the P2H device during time period t, in MW; NG,t Let t be the active power output of the gas turbine during time period t, in MW; The energy storage capacity and charge / discharge / gas constraints are as follows: （34） （35） （36） In the formula: Let m be the hydrogen storage state quantity at the dispatch section during time period t. 3 ; , Let be the charging and discharging power of the energy storage device during time period t, in MV; , These are 0-1 discrete Boolean variables representing the energy storage charging / discharging states, respectively. The consistency constraint between the beginning and end of the scheduling cycle is: （37） In the formula: Let x be the initial energy stored at the beginning of a single scheduling cycle, in MWh; Let MWh be the energy stored by energy storage device x at the end of a single scheduling cycle.

8. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 1, characterized in that, In step S3, when solving the comprehensive scheduling model using the sparrow search algorithm, the individual dimensionality reduction encoding is as follows: （38） In the formula: P is the position vector of the individual; G,t The active power output of thermal power units is measured in MW. P represents the active power consumed by the P2H device during time period t, in MW; NG,t Let t be the active power output of the gas turbine during time period t, in MW; , These are the actual volumetric flow rates of hydrogen charge and discharge, respectively, in m. 3 / h;N T Where h is the scheduling period; N is the total number of dimensions of the control variables. By introducing a dynamic penalty function method, the model with complex constraints is equivalently transformed into an unconstrained fitness evaluation mechanism. The constructed fitness function is as follows: （39） In the formula: F fit The fitness function; The total operating cost of the system is [amount in yuan]. The maximum penalty factor is dynamically increased with the number of iterations; K is the total number of physical constraints contained in the system. This measures various physical violations.

9. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage according to any one of claims 1-8, characterized in that, It also includes the following steps: S4: Conduct multi-scenario comparisons and sensitivity analyses to quantitatively assess the impact of key parameters on the system's economy and low-carbon characteristics, and identify the optimal configuration range for key parameters; the key parameters include the hydrogen doping ratio and hydrogen storage capacity.

10. The spatiotemporal collaborative optimization scheduling method for a gas-electric coupling system considering P2H and heterogeneous energy storage as described in claim 9, characterized in that, In step S4, when constructing multiple scenes, four progressive scenes are constructed, including: Scenario 1: Only activate the P2H and associated hydrogen storage modules, forcibly shut down the pipeline hydrogen blending and gas storage channels, and build a decoupled benchmark for independent operation of the gas-electric network; Scenario 2: Activate the pipeline hydrogen-blended and gas turbine modules, and strip away all heterogeneous energy storage buffers; Scenario 3: Activate P2H, gas turbine module and gas storage, and keep hydrogen blending off; Scenario 4: Simultaneous activation of P2H, heterogeneous energy storage array and pipeline hydrogen doping channel.

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