A net-zero carbon optimization scheduling method for isolated integrated energy system

Abstract

The present application relates to the field of comprehensive new energy technology and the field of optimal scheduling, in particular to a kind of isolated comprehensive energy system net zero carbon optimal scheduling method, including isolated comprehensive energy system, satisfy the energy hub of the heterogeneous energy balance constraint of energy hub model, satisfy the power network system of power network flow constraint of power network system model, satisfy the natural gas system of natural gas pipeline energy flow constraint of natural gas system model and satisfy the equipment operation characteristic constraint of each type of equipment model;With the minimum operation cost as the optimization goal, each system model and equipment model as the constraint condition, the minimum operation cost minimization model is established;The minimum operation cost minimization model is converted into mixed integer linear model by linearization strategy;Input data and solve, and each equipment is optimally scheduled.The technical problem that the present lack of isolated comprehensive energy system for the purpose of net zero carbon optimal scheduling scheme is solved.

Description

Technical Field

[0001] This invention relates to the fields of integrated new energy technology and optimized scheduling, specifically a net-zero carbon optimized scheduling method for isolated integrated energy systems. Background Technology

[0002] Considering the feasibility and economics of energy supply, building isolated energy supply systems is more suitable for remote, sparsely populated areas where energy imports and grid connection costs are high. Existing isolated power systems generate high fuel costs, significant pollution, and greenhouse gas emissions, which are detrimental to local green development.

[0003] Therefore, isolated energy systems need to reduce their reliance on external energy supplies and enhance their low-carbon operating capabilities. Integrated energy systems are integrated energy production, supply, and sales systems that organically coordinate and optimize all aspects of energy production and consumption, possessing excellent new energy absorption capacity and low-carbon characteristics. Driven by electricity, hydrogen and carbon can theoretically achieve an equal cycle; therefore, exploring the material-energy balance relationship in the hydrogen-carbon cycle is of significant value for achieving high self-sufficiency and further reducing carbon emissions in isolated integrated energy systems. Currently, there are few optimization scheduling methods for isolated integrated energy systems aimed at net-zero carbon emissions. Summary of the Invention

[0004] The purpose of this invention is to provide a net-zero carbon optimization scheduling method for isolated integrated energy systems, in order to solve the technical problem of the lack of optimization scheduling schemes for isolated integrated energy systems with net-zero carbon as the goal.

[0005] The present invention provides a net-zero carbon optimization scheduling method for isolated integrated energy systems, which includes energy hubs, power systems, and natural gas systems.

[0006] The energy hub includes an electricity-to-hydrogen (E2G) unit, a hydrogen-to-gas (H2G) unit, a gas turbine unit (GT), a post-combustion carbon capture (PCC) unit, a direct atmospheric carbon capture (DAC) unit, a hydrogen storage tank (HS), and a carbon storage tank (CS).

[0007] The power network system includes a power network, wind power stations (WT), photovoltaic power stations (PV), and battery energy storage stations (ESS).

[0008] The natural gas system includes natural gas pipelines and a natural gas storage station GS;

[0009] The method mainly includes the following steps:

[0010] Step 1: Establish an energy hub model in the isolated integrated energy system that satisfies the heterogeneous energy balance constraint of the energy hub;

[0011] Step 2: Establish a power network system model that satisfies the power network flow constraints of the power network system in the isolated integrated energy system.

[0012] Step 3: Establish a natural gas system model that satisfies the energy flow constraints of the natural gas pipeline in the isolated integrated energy system.

[0013] Step 4: Establish a device model in the isolated integrated energy system that meets the operating characteristic constraints of various devices;

[0014] Step 5: Based on the above model, with the goal of minimizing operating costs and with the system models and equipment models as constraints, establish the operating cost minimization model of the isolated integrated energy system.

[0015] Step 6 takes minimizing operating cost as the optimization objective and transforms the operating cost minimization model into a mixed-integer linearized model through a linearization strategy;

[0016] Step 7: Input the required data and solve the mixed integer linearization model. Based on the obtained optimal output scheme, optimize the output of each device in each time period within the scheduling cycle.

[0017] Furthermore, the heterogeneous energy balance constraints mentioned in step 1 include electricity balance, natural gas balance, hydrogen balance, and carbon dioxide balance constraints.

[0018] The equations for the power balance constraints are as follows:

[0019]

[0020] In the formula: the subscript m represents the energy hub number, and the subscript i represents the power system node number corresponding to the energy hub; P EH,i (t), Q EH,i (t) represents the active and reactive power injected by the energy hub to node i of the power system; P GT,m (t), Q GT,m (t) represents the active and reactive power generation of GT at time t; Let be the input electrical power of E2H at time t; Let be the input electrical power of H2G at time t; P represents the input power of the PCC at time t. DAC,m (t) represents the input power of the DAC at time t;

[0021] The equations for the natural gas balance constraint are as follows:

[0022]

[0023] In the formula: the subscript m represents the energy hub number, and the subscript u represents the natural gas system node number corresponding to the energy hub; f EH,u This refers to the injection gas flow from the energy hub to the natural gas node u. f is the methane flow rate output from H2G. GT,m (t) represents the natural gas intake flow rate of GT; The hydrogen flow rate directly injected into the natural gas system is affected by the hydrogen blending ratio of the natural gas. The constraints are:

[0024]

[0025] Where: Ω I2G Ω H2G Ω GS These represent the set of natural gas nodes where H2 is injected, the set of energy hub numbers where H2G is located, and the set of natural gas nodes where the natural gas storage station is located, respectively. The amount of natural gas purchased for the gas storage station at node u on that day;

[0026] The equation for the hydrogen balance constraint is as follows:

[0027]

[0028] In the formula: the subscript m represents the energy hub number; Let H2 be the output flow rate of E2H at time t; Inject traffic into H2G at time t; If H2 flow is injected into HS, Then H2 is injected into HS at time t; otherwise, HS releases H2. Let t be the flow rate of H2 consumed by the hydrogen refueling station;

[0029] The equations for the carbon dioxide balance constraint are shown below:

[0030] f PCC，m (t)+f DAC，m (t)=f CS，m (t)+f H2G，CO2，m (t),

[0031] In the formula: the subscript m is the energy hub number, f PCC,m (t) represents the CO2 flow rate of the PCC at time t; f DAC,m (t) represents the CO2 flow rate of the DAC at time t; f CS,m (t) represents the CO2 injection flow rate of CS, if f CS,m If (t)≥0, then CO2 is injected into CS; otherwise, CS releases CO2. H2G.CO2,m (t) represents the CO2 flow rate injected into H2G;

[0032] Furthermore, the power flow constraints in step 2 include node power balance constraints, node voltage constraints, and branch current magnitude constraints.

[0033] The node power balance constraints are as follows:

[0034]

[0035] In the formula: subscripts i and j are the power system node numbers; U i (t), U j (t) represents the node voltage magnitude, I ij (t) represents the current amplitude of line ij, θ ij (t) represents the voltage phase angle difference between node i and node j; G ij B ij P represents the real and imaginary parts of the node admittance matrix corresponding to i and j; L,i (t), Q L,i (t) represents the predicted active and reactive load at node i, P EES,i (t) represents the ESS charging power at node i; P PV,i (t), P WT,i (t), Q PV,i (t), Q WT,i (t) represents the PV of node i and the active or passive power output of WT, respectively;

[0036] The node voltage constraint is a node voltage magnitude constraint, and its equation is as follows:

[0037]

[0038] In the formula: Indicates the upper and lower limits of the node voltage amplitude;

[0039] The branch current constraint is a branch current amplitude constraint, and its equation is as follows:

[0040]

[0041] In the formula: Indicates the upper limit of the branch current amplitude;

[0042] Furthermore, the energy flow constraints of the natural gas pipeline mentioned in step 3 include node gas flow balance constraints, natural gas pipeline gas flow constraints, node gas pressure constraints, and operating characteristic constraints of the gas turbine unit.

[0043] The equations for the nodal gas flow balance constraints are shown below:

[0044]

[0045] In the formula: fGS,u (t) represents the gas inlet flow rate of the natural gas storage station located at natural gas node u. If f GS,u If (t)≥0, then natural gas is injected into the gas storage station through the pipeline. GS,u If (t) < 0, the gas storage station injects natural gas into the pipeline; GL,u (t) represents the predicted gas load flow rate at natural gas node u.

[0046] The natural gas pipeline gas flow constraint is a steady-state model of the natural gas system's pipeline gas flow, and its equations are shown below:

[0047]

[0048] In the formula: f uv (t) represents the gas flow rate in the pipeline from node u to node v; K gas,uv p is the pipeline transmission coefficient. u (t), p v (t) represents the air pressure at nodes u and v;

[0049] The nodal pressure constraints are the upper and lower limits of the nodal pressures in the natural gas system, as shown below:

[0050]

[0051] In the formula: and The upper and lower limits of the air pressure at node u.

[0052] Furthermore, the equipment operation characteristic models and constraints for each device in step 4 include the upper and lower limits of output constraints for wind power stations and photovoltaic power stations, the operation characteristic models and constraints for electricity-to-hydrogen and hydrogen-to-gas equipment, the operation characteristic models and constraints for gas turbine units, the operation characteristic models and constraints for after-combustion carbon capture units, the operation characteristic models and constraints for direct atmospheric carbon capture units, the operation characteristic models and constraints for battery energy storage power stations, the operation characteristic constraints for natural gas storage stations, and the operation characteristic models and constraints for hydrogen storage tanks.

[0053] The upper and lower limits of the output of the wind power station and the photovoltaic power station are as follows:

[0054]

[0055] In the formula: For WT and PV, there are limitations on the presence or absence of active power output. Used as start / stop markers for WT and PV;

[0056] The operating characteristic models and constraints of the electro-hydrogen conversion equipment and the hydrogen-to-gas conversion equipment include:

[0057] The relationship between H2 flow rate and E2H power is shown in the equation below:

[0058]

[0059] The E2H gradeability limit can be expressed as follows:

[0060]

[0061] The upper and lower limits of the input power of E2H are expressed as follows:

[0062]

[0063] In the formula: η E2H The conversion factor of E2H, The upper limit of the gradeability of E2H. The rated power of E2H is denoted by Δt, and the scheduling time step is Δt.

[0064] The operational characteristic models and constraints of the electro-hydrogen conversion equipment and the hydrogen-to-gas conversion equipment include,

[0065] The relationship between H2 flow rate and H2G power is shown in the equation below:

[0066] f H2G.H2,m (t)=η H2G P H2G,m (t),

[0067] The ramp rate limit for H2G is expressed as follows:

[0068]

[0069] The upper and lower limits of H2G input power are expressed as follows:

[0070]

[0071] The equations relating the flow rates of H2 and CO2 injected into H2G to the flow rates of CH4 output from H2G are shown below:

[0072]

[0073] In the formula, η H2G The conversion factor for H2G. The rated power of H2G for the m-th energy hub, The upper limit of the ramp rate for H2G;

[0074] The operating characteristic model and constraints of the gas turbine unit include:

[0075] The power output characteristics of GT are shown in the following equation:

[0076] PGT,m (t)=f GT,m (t)η GT,m (t)·H V,gas ,

[0077] The calorific value of hydrogen-blended natural gas is given by the following equation:

[0078] H V,gas =(1-β) H2.CH4 )H V,ng +β H2.CH4 H V,h ,

[0079] The upper and lower limits of reactive power for GT are expressed as follows:

[0080]

[0081] In the formula: s GT,m (t) represents the start / stop flag of GT during time period t, where 1 indicates start and 0 indicates stop. η GT,m For the electrical efficiency of GT, H V,gas The calorific value of hydrogen-mixed natural gas; H V,ng H represents the calorific value of natural gas. V,h The calorific value of H2; This represents the maximum output of GT during time period t, after considering reserve capacity. Indicates minimum output; θ GT The maximum power factor angle of GT;

[0082] The operational characteristic model and constraints of the post-combustion carbon capture unit include:

[0083] The relationship between PCC power and CO2 capture flow rate is shown in the following equation:

[0084]

[0085] The upper and lower limits of the input electrical power of the PCC are expressed as follows:

[0086]

[0087] The limitation of CO2 emissions from the GT on the CO2 capture flow rate of the PCC is expressed as follows:

[0088]

[0089] In the formula, η PCC The power consumption coefficient of PCC; This is the minimum power of the PCC; This refers to the rated power of the PCC.

[0090] For the maximum capture efficiency of PCC, fGT,m (t) represents the intake airflow of GT;

[0091] Direct carbon capture (DCC) units consist of several DAC adsorption-desorption units. The operating characteristic model and constraints of DCC units include:

[0092] The relationship between the DAC's CO2 capture flow rate, input power, and device status can be expressed as follows:

[0093]

[0094] The operating time constraints of a single DAC adsorption-desorption unit can be expressed as follows:

[0095]

[0096]

[0097] In the formula: n represents the number of the DAC adsorption-desorption device. The power consumption rating of the DAC adsorption-desorption device. This is a start / stop indicator for the DAC adsorption-desorption equipment. 0 indicates the equipment is in adsorption or off state, and 1 indicates it is in desorption state. Ω DAC,m This is the set of DAC adsorption-desorption devices for the m-th EH; The CO2 flow rate at the rated power of the nth device; This is the adsorption start period. This is the period when desorption begins. This is the start time of the desorption process from the previous cycle. This refers to the adsorption time during the cycle. This refers to the desorption time;

[0098] The operating characteristic model and constraints of the battery energy storage power station include,

[0099] The relationship between the charge capacity of an ESS and its charging / discharging power can be expressed as follows:

[0100]

[0101] The constraints on the charging and discharging power of the ESS can be expressed as follows:

[0102]

[0103] The upper and lower limits of charge capacity constraints for an ESS can be expressed as follows:

[0104]

[0105] The constraint that the charge level of the ESS is equal at the beginning and end of the scheduling period can be expressed as follows:

[0106] S EES,i (t)| t＝T =S EES,i (t)| t＝0 ,

[0107] In the formula: The charging and discharging power of the ESS at node i during time period t. For maximum charging and discharging power, η is a 0-1 variable representing the charging and discharging state of the ESS at node i during time period t; EES To improve ESS work efficiency, S EES,i (t) represents the state of charge of ESS during time period t. The maximum and minimum charge constraints allowed by the ESS;

[0108] The operating characteristic model and constraints of the natural gas storage station include,

[0109] The relationship between natural gas reserves and the injection and release rates at gas storage stations can be expressed as follows:

[0110]

[0111] The natural gas reserves and the upper limit of natural gas injection and release flow at the gas storage station can be expressed as follows:

[0112]

[0113] The upper and lower limits of natural gas reserves and natural gas reserves in gas storage stations can be expressed as follows:

[0114]

[0115] The natural gas reserves at the beginning and end of the gas storage station's scheduling cycle are the same, which can be represented as follows:

[0116] S GS,u (t)| t＝T =S GS,u (t)| t＝0 ,

[0117] In the formula: This is the gas storage volume of the u-node gas storage station during time period t; This refers to the upper limit of the gas filling and discharging flow rate of the gas storage station. These are the upper and lower limits of the gas storage station's capacity, respectively.

[0118] The operating characteristic model and constraints of the hydrogen storage tank include, as follows:

[0119] The temporal relationship between HS injection flow rate and H2 reserves.

[0120] S HS,m (t+Δt)=S HS,m (t)+f HS,m (t)Δt,

[0121] The upper and lower limits of the HS injection flow constraint can be expressed as follows:

[0122]

[0123] The upper and lower limits of HS capacity constraints can be expressed as follows:

[0124]

[0125] The H2 reserves are the same at the start and end of the HS scheduling period, which can be represented as follows:

[0126] S HS,m (t) t＝T =S HS,m (t) t＝0 ,

[0127] In the formula: S sets the upper and lower limits for the flow rates of H2 injected into and released from HS. HS,m (t) represents the reserves of HS in time period t. Set the upper limit for HS reserves;

[0128] The operating characteristic model and constraints of the carbon storage tank include,

[0129] The temporal relationship between CS-injected CO2 flow rate and CO2 reserves can be expressed as follows:

[0130]

[0131] The upper and lower limits of the CO2 injection flow constraint in CS can be expressed as follows:

[0132]

[0133] The upper and lower limits of the CO2 reserve constraint for CS can be expressed as follows:

[0134]

[0135] The CO2 reserves are the same at the beginning and end of the scheduling cycle, which can be represented as follows:

[0136] S CS,m (t) t＝T =S CS,m (t) t＝0 ,

[0137] In the formula: The amount of carbon feedstock purchased by the m-th energy hub on that day, t Tp This refers to the transportation period for that day; The upper limit of CO2 flow rate for injecting and releasing CO2 into CS; S CS,m (t) represents the reserves of CS during time period t. Set the upper limit for CS reserves.

[0138] Furthermore, the objective function of the operating cost minimization model described in step 5 can be expressed as follows:

[0139]

[0140] In the formula: F is the total daily operating cost of the system, F OM F Buy , These are the system's daily operation and maintenance costs, material purchase costs, and carbon emission penalty costs, respectively.

[0141] The daily system operation and maintenance cost refers to the maintenance expenses incurred by various devices during operation, which can be expressed as follows:

[0142]

[0143] In the formula: T is the scheduling period. The daily maintenance cost coefficient for equipment of type k. The input electrical power of a single device during time period t, in Ω. DEV Ω is a collection of adjustable devices. DEV ={GT,E2H,H2G,PCC,DAC}, Ω k For the set of device numbers of class k, Ω represents the injection flow rate of a single storage device during time period t. ES For a collection of storage devices, Ω ES ={HS,CS}; Ω represents the daily maintenance cost coefficient of ESS. EES The set of power system nodes where the ESS is located; Ω represents the daily operation and maintenance cost coefficient for natural gas storage stations. GS The set of nodes in the natural gas system where the gas storage station is located;

[0144] The cost of purchasing the materials refers to the expenses incurred in purchasing natural gas and CO2 from external sources. Assuming the system can purchase natural gas and CO2 from external sources via transportation at a certain time each day, then:

[0145]

[0146] In the formula: C gas C CO2 For natural gas and CO2 prices; αTp The transportation penalty coefficient represents the additional costs incurred due to transportation when purchasing natural gas and CO2.

[0147] The carbon emission cost is the carbon emission penalty corresponding to the CO2 ultimately emitted into the atmosphere by the system within one scheduling cycle, which is:

[0148]

[0149] In the formula: This is the carbon emission penalty coefficient.

[0150] Furthermore, the specific steps in step 6 to transform the operating cost minimization model into a mixed-integer linearized model using a linearization strategy are as follows:

[0151] Replace the node power balance constraint with the following form:

[0152]

[0153] In the formula: The values ​​of constants a, b, c1, c2, c3, and c4 are shown below:

[0154]

[0155] In the formula: θ ij.0 U is the initial value of the voltage phase angle difference of line ij. i,0 U j,0 Let be the initial voltage amplitude values ​​at nodes i and j;

[0156] Replace the aforementioned steady-state model of pipeline airflow with the following formula:

[0157]

[0158] In the formula: f ij (t)|f ij (t)| represents the nonlinear part, which is linearized using the following piecewise linearization method:

[0159]

[0160]

[0161]

[0162] 0≤γ l (t)≤u l (t),

[0163] Where: N L -1 represents the number of segments within the constraint range, which contains a total of N segments.L Known discrete points f ij,l Define an auxiliary variable γ for each segment. l (t), a 0-1 binary variable u l (t), l={1,2…,N L};

[0164] The daily maintenance cost F of the system OM Replace it with the following form:

[0165]

[0166] in, The meaning is shown in the following formula:

[0167]

[0168] In the formula: These are 0-1 variables, representing the input and output states of the natural gas storage station; It is a 0-1 variable that represents the input and output states of device k.

[0169] This invention provides a net-zero carbon optimization scheduling method for isolated integrated energy systems. Based on a hydrogen-carbon coupling mechanism, this method establishes an isolated integrated energy system and explores the material-energy balance relationship within the hydrogen-carbon cycle. Through multi-energy conversion of hydrogen and full capture and utilization of carbon, it effectively absorbs renewable energy, avoids the cost of purchasing external energy, and achieves net-zero carbon operation, thus improving the system's economic efficiency and environmental friendliness. Through energy complementarity and storage in the hydrogen-carbon cycle, it effectively addresses the intermittent fluctuations of renewable energy, fully absorbs renewable energy, alleviates the energy supply-demand imbalance in isolated systems, and fully captures carbon dioxide generated by the system, converting it into reusable energy to reduce carbon emissions.

[0170] The model established in this invention takes into account the equal amounts of hydrogen and carbon elements in the cycle process, and can achieve 100% self-sufficiency and net zero carbon operation of the system.

[0171] The method proposed in this invention can not only enable the system to achieve 100% self-sufficiency and net-zero carbon operation, but also minimize operating costs, which is conducive to the economic development of isolated systems. Attached Figure Description

[0172] Figure 1 This is a schematic diagram of an isolated integrated energy system and its hydrogen-carbon coupled net-zero carbon operation mechanism in an embodiment of the present invention.

[0173] Figure 2This is a schematic diagram of the material balance relationship of the hydrogen-carbon cycle in an isolated integrated energy system according to an embodiment of the present invention.

[0174] Figure 3 This is a flowchart of the net-zero carbon optimization scheduling method for an isolated integrated energy system in an embodiment of the present invention.

[0175] Figure 4 This is a schematic diagram of the day-ahead forecast curve for renewable energy used in the experimental examples of this invention.

[0176] Figure 5 This is a schematic diagram of the day-ahead prediction curve of the electricity-gas-hydrogen load used in the experimental examples of this invention.

[0177] Figure 6 This is a schematic diagram of the curves showing the change of renewable energy consumption over time in experimental schemes 1 to 4 of the present invention.

[0178] Figure 7 This is a schematic diagram showing the curves of GT and P2G power changing with time in experimental schemes 1-2 of this invention.

[0179] Figure 8 This is a schematic diagram showing the change in the storage capacity of the storage device over time in experimental examples 1 to 4 of this invention.

[0180] Figure 9 This is a schematic diagram showing the changes in H2G natural gas production and CCS CO2 capture flow rate over time for experimental schemes 1-2 in this embodiment of the invention.

[0181] Figure 10 This is a schematic diagram showing the curves of GT, H2G, and CCS flow rates changing over time in experimental examples 1 and 3 of this invention.

[0182] Figure 11 This is a schematic diagram showing the changes in CO2 flow rate of GT, PCC, and DAC over time in experimental schemes 1 and 4 of this invention. Detailed Implementation

[0183] The present invention will be further described below with reference to embodiments, but it should not be construed that the scope of the present invention is limited to the following embodiments. Various substitutions and modifications made based on ordinary technical knowledge and common practices in the art without departing from the above-described technical concept of the present invention should be included within the scope of protection of the present invention.

[0184] The isolated integrated energy system and its hydrogen-carbon coupled net-zero carbon operation mechanism in this embodiment are basically as follows: Figure 1As shown, the system includes a wind power station (WT), a photovoltaic power station (PV), an electricity-to-hydrogen (E2H) system, a hydrogen-to-gas (H2G) system, a gas turbine (GT), a post-combustion carbon capture (PCC) system, a direct atmospheric carbon capture (DAC) system, an energy storage power station (ESS), a hydrogen storage tank (HS), a carbon storage tank (CS), and a natural gas storage station (GS). The E2H, H2G, gas turbine, and post-combustion and direct atmospheric carbon capture systems form a hydrogen-carbon cycle within the energy hub, which can enhance the complementarity of heterogeneous energy sources while meeting the needs of multiple energy supplies and minimizing carbon emissions. The hydrogen and carbon storage devices can decouple the E2H, H2G, gas turbine, and post-combustion and direct atmospheric carbon capture systems, avoiding mutual restrictions on the dispatchability of various devices due to their different operating characteristics. Figure 2 As shown, the material balance of the hydrogen-carbon cycle in an isolated integrated energy system is based on the law of conservation of elements in chemical reactions, with the total amount of hydrogen and carbon elements remaining constant during the cycle of the system.

[0185] The net-zero carbon optimization scheduling method for isolated integrated energy systems in this embodiment is basically as follows: Figure 3 As shown, the main steps include the following:

[0186] 1) Establish an energy hub model in the system that satisfies the heterogeneous energy balance constraints of the energy hub, including electricity balance, natural gas balance, hydrogen (H2) balance, and carbon dioxide (CO2) balance. The heterogeneous energy balance constraints of the energy hub are as follows:

[0187] 1.1) Power Balance. The power balance equation for the energy hub is shown below:

[0188]

[0189] In the formula: the subscript m represents the energy hub number, and the subscript i represents the power system node number corresponding to the energy hub; P EH,i (t), Q EH,i (t) represents the active and reactive power injected by the energy hub to node i of the power system; P GT,m (t), Q GT,m (t) represents the active and reactive power generation of GT at time t; Let be the input electrical power of E2H at time t; Let be the input electrical power of H2G at time t; P represents the input power of the PCC at time t. DAC,m (t) represents the input power of the DAC at time t.

[0190] 1.2) Natural Gas Balance. The natural gas balance equation for the energy hub is shown below:

[0191]

[0192] In the formula: the subscript m represents the energy hub number, and the subscript u represents the natural gas system node number corresponding to the energy hub; f EH,u This refers to the injection gas flow from the energy hub to the natural gas node u. f is the methane flow rate output from H2G. GT,m (t) represents the natural gas intake flow rate of GT; The hydrogen flow rate directly injected into the natural gas system is affected by the hydrogen blending ratio of the natural gas. constraint:

[0193]

[0194] Where: Ω I2G Ω H2G Ω GS These represent the set of natural gas nodes where H2 is injected, the set of energy hub numbers where H2G is located, and the set of natural gas nodes where the natural gas storage station is located, respectively. The amount of natural gas purchased for the gas storage station at node u on that day.

[0195] 1.3) Hydrogen balance: The hydrogen balance equation for the energy hub is shown below:

[0196]

[0197] In the formula: the subscript m represents the energy hub number; Let H2 be the output flow rate of E2H at time t; Inject traffic into H2G at time t; If H2 flow is injected into HS, Then H2 is injected into HS at time t; otherwise, HS releases H2. Let t be the flow rate of H2 consumed by the hydrogen refueling station.

[0198] 1.4) Carbon dioxide balance: The carbon dioxide balance equation for the energy hub is shown below:

[0199] f PCC,m (t)+f DAC,m (t)=f CS,m (t)+f H2G.CO2,m (t)(5)

[0200] In the formula: the subscript m is the energy hub number, f PCC,m (t) represents the CO2 flow rate of the PCC at time t; f DAC,m (t) represents the CO2 flow rate of the DAC at time t; f CS,m (t) represents the CO2 injection flow rate of CS, if f CS,mIf (t)≥0, then CO2 is injected into CS; otherwise, CS releases CO2. H2G.CO2,m (t) represents the CO2 flow rate injected into the H2G.

[0201] 2) Establish a power system model that satisfies the power network flow constraints of the power system, including node power balance equations, node voltage constraints, and branch current constraints. The power network flow constraints of the power system are as follows:

[0202] 2.1) Nodal power balance equations: The nodal power balance equations of the power system are shown below:

[0203]

[0204] In the formula: subscripts i and j are the power system node numbers; U i (t), U j (t) represents the node voltage magnitude, I ij (t) represents the current amplitude of line ij, θ ij (t) represents the voltage phase angle difference between node i and node j; G ij B ij P represents the real and imaginary parts of the node admittance matrix corresponding to i and j; L,i (t), Q L,i (t) represents the predicted active and reactive load at node i, P EES,i (t) represents the ESS charging power at node i; P PV,i (t), P WT,i (t), Q PV,i (t), Q WT,i (t) represents the PV of node i and the active or passive power output of WT.

[0205] 2.2) Node voltage constraints: The node voltage magnitude constraints of the power system are as follows:

[0206]

[0207] In the formula: Indicates the upper and lower limits of the node voltage amplitude.

[0208] 2.3) Branch current constraints: The branch current amplitude constraints of the power system are as follows.

[0209]

[0210] In the formula: This indicates the upper limit of the branch current amplitude.

[0211] 3) Establish a natural gas system model that satisfies the energy flow constraints of the natural gas pipelines within the system, including node gas flow balance constraints, natural gas pipeline gas flow constraints, and node gas pressure constraints. The energy flow constraints of the natural gas pipelines in the natural gas system are as follows:

[0212] 3.1) Node gas flow balance constraints: The node gas flow balance equations for the natural gas system are shown below:

[0213]

[0214] In the formula: f GS,u (t) represents the gas inlet flow rate of the natural gas storage station located at natural gas node u. If f GS,u If (t)≥0, then natural gas is injected into the gas storage station through the pipeline. GS,u If (t) < 0, the gas storage station injects natural gas into the pipeline; GL,u (t) represents the predicted gas load flow rate at natural gas node u.

[0215] 3.2) Natural gas pipeline gas flow constraints: The steady-state model of the natural gas system's pipeline gas flow is shown below:

[0216]

[0217] In the formula: f uv (t) represents the gas flow rate in the pipeline from node u to node v; K gas,uv p is the pipeline transmission coefficient. u (t), p v (t) represents the air pressure at nodes u and v.

[0218] 3.3) Node pressure constraints: The upper and lower limits of the node pressure in the natural gas system are shown below:

[0219]

[0220] In the formula: and The upper and lower limits of the air pressure at node u.

[0221] 4) Establish equipment models in the system that satisfy the operational characteristic constraints of various types of equipment, including wind power plants, photovoltaic power plants, electricity-to-hydrogen equipment, hydrogen-to-gas equipment, gas turbines, post-combustion carbon capture equipment, direct atmospheric carbon capture equipment, battery energy storage power plants, hydrogen storage tanks, carbon storage tanks, and natural gas storage stations. The operational characteristic models of each piece of equipment are shown below.

[0222] 4.1) The upper and lower limits of output for wind power stations and photovoltaic power stations are shown in the following formula:

[0223]

[0224] In the formula: For WT and PV, there are limitations on the presence or absence of active power output. Used as start / stop markers for WT and PV.

[0225] 4.2) The operating characteristics of the electro-hydrogen conversion equipment and the hydrogen-to-gas conversion equipment are shown below. Equation (13) gives the relationship between H2 flow rate and E2H power, Equation (14) is the E2H ramp rate limit, and Equation (15) is the upper and lower limits of the input electrical power of E2H:

[0226]

[0227]

[0228]

[0229] In the formula: η E2H The conversion factor of E2H, The upper limit of the gradeability of E2H. Let be the rated power of E2H, and Δt be the scheduling time step.

[0230] 4.3) The operating characteristics of the electro-hydrogen conversion equipment and the hydrogen-to-gas conversion equipment are shown below. Equation (16) gives the relationship between H2 flow rate and H2G power. Equation (17) is the ramp rate limit of H2G. Equation (18) is the upper and lower limits of the input electrical power of H2G. Equation (19) is the relationship between the flow rates of H2 and CO2 injected into H2G and CH4 output by H2G:

[0231]

[0232]

[0233]

[0234]

[0235] In the formula: η H2G The conversion factor for H2G. The rated power of H2G for the m-th energy hub, This represents the upper limit of the ramp rate for H2G.

[0236] 4.4) The operating characteristics of the gas turbine unit are shown below. Equation (20) gives the power output characteristics of GT as shown. Equation (21) represents the calorific value of hydrogen-blended natural gas. Equation (22) is the upper and lower limits of the active and reactive power of GT.

[0237] P GT,m (t)=f GT,m (t)η GT,m (t)·H V,gas(20)

[0238] H V,gas =(1-β) H2.CH4 )H V,ng +β H2.CH4 H V,h (twenty one)

[0239]

[0240] In the formula: s GT,m (t) represents the start / stop flag of GT during time period t, where 1 indicates start and 0 indicates stop. η GT,m For the electrical efficiency of GT, H V,gas The calorific value of hydrogen-mixed natural gas; H V,ng H represents the calorific value of natural gas. V,h The calorific value of H2;

[0241] This represents the maximum output of GT during time period t, after considering reserve capacity. Indicates minimum output; θ GT θ is the maximum power factor angle of GT.

[0242] 4.5) The operating characteristics of the post-combustion carbon capture unit (PCC) are shown below. Equation (23) gives the relationship between the power of the PCC and the CO2 capture flow rate. Equation (24) represents the upper and lower limits of the input electrical power of the PCC. Equation (25) represents the limitation of the CO2 flow rate emitted by the GT on the CO2 capture flow rate of the PCC.

[0243]

[0244]

[0245]

[0246] In the formula: η PCC The power consumption coefficient of PCC; This is the minimum power of the PCC; This refers to the rated power of the PCC.

[0247] For the maximum capture efficiency of PCC, f GT,m (t) represents the intake airflow of GT.

[0248] 4.6) The operating characteristics of the direct carbon capture unit (DAC) are shown below. The DAC consists of several DAC adsorption-desorption units. Equation (26) represents the relationship between the CO2 capture flow rate, input electrical power and equipment status of the DAC; Equations (27) to (28) represent the time constraints of the operating status of a single DAC adsorption-desorption unit.

[0249]

[0250]

[0251]

[0252] In the formula: n represents the number of the DAC adsorption-desorption device. The power consumption rating of the DAC adsorption-desorption device. This is a start / stop indicator for the DAC adsorption-desorption equipment. 0 indicates the equipment is in adsorption or off state, and 1 indicates it is in desorption state. Ω DAC,m This is the set of DAC adsorption-desorption devices for the m-th EH; The CO2 flow rate at the rated power of the nth device; This is the adsorption start period. This is the period when desorption begins. This is the start time of the desorption process from the previous cycle. This refers to the adsorption time during the cycle. This refers to the desorption time.

[0253] 4.7) The operating characteristics of the battery energy storage power station are shown below. Equation (29) gives the relationship between the ESS charge and the charging and discharging power; Equation (30) is the constraint condition for the ESS charging and discharging power; Equation (31) is the upper and lower limit constraint of the ESS charge; Equation (32) indicates that the ESS charge is equal at the beginning and end of the scheduling cycle.

[0254]

[0255]

[0256]

[0257] S EES,i (t)| t＝T =S EES,i (t)| t＝0 (32)

[0258] In the formula: The charging and discharging power of the ESS at node i during time period t. For maximum charging and discharging power, η is a 0-1 variable representing the charging and discharging state of the ESS at node i during time period t; EES To improve ESS work efficiency, S EES,i (t) represents the state of charge of ESS during time period t. The maximum and minimum charge constraints allowed by the ESS.

[0259] 4.8) The operating characteristics of the natural gas storage station are shown below. Equation (33) gives the relationship between the natural gas storage and the injection and release flow of the storage station. Equations (34) and (35) are the upper limit of the natural gas injection and release flow and the upper and lower limits of the natural gas storage, respectively. Equation (36) stipulates that the natural gas storage at the beginning of the scheduling cycle is the same as the natural gas storage at the end of the cycle.

[0260]

[0261]

[0262]

[0263] S GS,u (t)| t＝T =S GS,u (t)| t＝0 (36)

[0264] In the formula: S GS,u (t) represents the gas storage capacity of the u-node gas storage station during time period t; This refers to the upper limit of the gas filling and discharging flow rate of the gas storage station. These represent the upper and lower limits of the gas storage station's capacity, respectively.

[0265] 4.9) The operating characteristics of the hydrogen storage tank are shown below. Equation (37) gives the time relationship between the injection flow rate of HS and the H2 storage. Equations (38) to (39) are the injection flow rate of HS and the upper and lower limits of the HS capacity constraint, respectively. Equation (40) stipulates that the H2 storage is the same at the beginning and end of the scheduling period.

[0266] S HS,m (t+Δt)=S HS,m (t)+f HS,m (t)Δt (37)

[0267]

[0268]

[0269] S HS,m (t)| t＝T =S HS,m (t)| t＝0 (40)

[0270] In the formula: S sets the upper and lower limits for the flow rates of H2 injected into and released from HS. HS,m (t) represents the reserves of HS in time period t. This sets the upper limit for HS reserves.

[0271] 4.10) The operating characteristics of the carbon storage tank are shown below. Equation (41) gives the time relationship between the CO2 injection flow rate and the CO2 storage. Equations (42) to (43) are the upper and lower limits of the CO2 injection flow rate and CO2 storage constraints of the CS. Equation (44) stipulates that the CO2 storage is the same at the beginning and end of the scheduling cycle.

[0272]

[0273]

[0274]

[0275] S CS,m (t) t＝T =S CS,m (t) t＝0 (44)

[0276] In the formula: The amount of carbon feedstock purchased by the m-th energy hub on that day, t Tp This refers to the transportation period for that day; The upper limit of CO2 flow rate for injecting and releasing CO2 into CS; S CS,m (t) represents the reserves of CS during time period t. Set the upper limit for CS reserves.

[0277] 5) Based on the above model, with the minimization of operating cost as the optimization objective and the above system model and equipment model as constraints, a model for minimizing the operating cost of the system is established. The constraints of this model have been stated above, and its objective function is as follows:

[0278] The objective function of this model includes equipment operation and maintenance costs, material purchase costs, and carbon emission penalty costs, as shown below:

[0279]

[0280] In the formula: F is the total daily operating cost of the system, F OM F Buy , These are the system's daily operation and maintenance costs, material purchase costs, and carbon emission penalty costs. The details are as follows:

[0281] 5.1) Daily system operation and maintenance costs

[0282] The daily system maintenance cost is the maintenance expense incurred by various devices during operation.

[0283]

[0284] In the formula: T is the scheduling period. The daily maintenance cost coefficient for equipment of type k. The input electrical power of a single device during time period t, in Ω. DEV Ω is a collection of adjustable devices. DEV ={GT,E2H,H2G,PCC,DAC}, Ω k For the set of device numbers of class k, Ω represents the injection flow rate of a single storage device during time period t. ES For a collection of storage devices, Ω ES ={HS,CS}; Ω represents the daily maintenance cost coefficient of ESS. EES The set of power system nodes where the ESS is located; Ω represents the daily operation and maintenance cost coefficient for natural gas storage stations. GS This refers to the set of nodes in the natural gas system where the gas storage station is located.

[0285] 5.2) Material purchase cost

[0286] The cost of purchasing materials is the expense incurred in purchasing natural gas and CO2 from external sources. It is assumed that the system can purchase natural gas and CO2 from external sources through transportation at a certain time each day.

[0287]

[0288] In the formula: C gas , For natural gas and CO2 prices; α Tp The transportation penalty coefficient represents the additional costs incurred due to transportation when purchasing natural gas and CO2.

[0289] 5.3) Carbon emission penalty costs

[0290] Carbon emission cost is the carbon penalty for CO2 emitted into the atmosphere by the system within a scheduling cycle. Let the carbon emission cost of the system be in the following form:

[0291]

[0292] In the formula: This is the carbon emission penalty coefficient.

[0293] 6) Based on the aforementioned operating cost minimization model, with operating cost minimization as the optimization objective, the model is transformed into a mixed-integer linearized model using a linearization strategy. The specific steps are as follows:

[0294] 6.1) Replace the power system node power balance equation, i.e., equation (6), with the following form:

[0295]

[0296] In the formula: The values ​​of constants a, b, c1, c2, c3, and c4 are shown below:

[0297]

[0298] In the formula: θ ij.0 U is the initial value of the voltage phase angle difference of line ij. i,0 U j,0 Let be the initial voltage amplitude values ​​for nodes i and j.

[0299] 6.2) Replace the steady-state model of the natural gas system pipeline, i.e., equation (10), with the following formula:

[0300]

[0301] In the formula: f ij (t)|f ij (t)| represents the nonlinear part, which is linearized using the following piecewise linearization method:

[0302]

[0303]

[0304]

[0305] 0≤γ l (t)≤u l (t) (55)

[0306] Where: N L -1 represents the number of segments within the constraint range, which contains a total of N segments. L Known discrete points f ij,l Define an auxiliary variable γ for each segment. l (t), a 0-1 binary variable u l (t), l={1,2…,N L The expression indicates that there is one and only one segment in the startup process.

[0307] 6.3) Replace the daily system maintenance cost, i.e., equation (46), with the following form:

[0308]

[0309] in, The meaning is shown in the following formula:

[0310]

[0311] In the formula: These are 0-1 variables, representing the input and output states of the natural gas storage station; It is a 0-1 variable that represents the input and output states of device k.

[0312] 7) Read system and equipment parameters, and use commercial software CPLEX to solve for the optimal scheduling scheme that minimizes system operating costs. This scheme includes the optimal output data of the equipment in each time period within the scheduling cycle.

[0313] In this embodiment, to verify the effectiveness of the method, four control experimental schemes were set up, the details of which are as follows:

[0314] Option 1: The net-zero carbon optimization scheduling method proposed in this example;

[0315] Option 2: Ignore the net zero carbon operation mechanism based on hydrogen-carbon coupling, specifically by removing the hydrogen-to-gas equipment model from the equipment model;

[0316] Option 3: Ignore the hydrogen-carbon storage equipment in the net-zero carbon operation mechanism based on hydrogen-carbon coupling, specifically by removing the hydrogen storage tank and carbon storage tank models from the equipment model;

[0317] Option 4: Ignore the hydrogen-carbon storage device in the net-zero carbon operation mechanism based on hydrogen-carbon coupling, specifically consider only the PCC device model in the device model and remove the DAC device model.

[0318] The specific data read in each scheme is shown below:

[0319] 1) Power system data

[0320] The power system adopts the IEEE 39-node network system, including node data, line data, and load data. The generator sets at nodes 30-39 are replaced with the other equipment described in Implementation Case 2. The energy hub is located at nodes 31 and 36, photovoltaic generator sets at nodes 32, 33, and 34, wind turbine generator sets at nodes 30, 35, and 39, and battery energy storage power stations at nodes 37 and 38. Node 31 is designated as the balancing node, with upper and lower limits for node voltage amplitude. The data is shown in Table 1:

[0321] Table 1 IEEE 39-node system data

[0322]

[0323]

[0324] 2) Natural gas system data

[0325] The natural gas system selected is the Belgian NGS20 node network system, including node data, pipeline data, and natural gas load data. Specifically, energy hubs are located at nodes 1 and 8, natural gas storage stations at nodes 4 and 11, the balancing node is node 1, and the node gas pressure upper and lower limits are [not specified]. The data is shown in Table 2:

[0326] Table 2 NGS20 Node System Data

[0327]

[0328]

[0329] 3) Renewable energy and load forecast curves

[0330] The system provides exemplary day-ahead wind and solar power output forecast data, such as... Figure 4 As shown.

[0331] Exemplary day-ahead electricity, natural gas, and hydrogen load forecast data from the system are as follows: Figure 5 As shown.

[0332] 4) Equipment parameters

[0333] The system's equipment parameters are shown in Table 3, and all similar equipment has the same parameters.

[0334] Table 3 Equipment Parameters

[0335]

[0336]

[0337]

[0338] 5) Economic parameters

[0339] The economic parameters of the system are shown in Table 4. All similar equipment has the same parameters.

[0340] Table 4 System Economic Coefficients

[0341]

[0342] The comparison data of the simulation results are shown below:

[0343] 1) Economic viability and renewable energy consumption

[0344] The simulation results for the four schemes are shown in Table 5: the economic data include the total daily operating cost F and the daily maintenance cost F. OM Material purchase cost F buy Carbon emission cost F CO2Compared to Scheme 1, Scheme 2 has lower daily operation and maintenance costs, but lacks the functions of producing natural gas and utilizing CO2, resulting in insufficient equipment utilization and high material purchase and carbon emission costs, increasing the total daily operating cost of Scheme 2 by RMB 1,021,029. Scheme 3 has slightly higher daily operation and maintenance costs than Scheme 1 because it lacks regulation of hydrogen-carbon storage equipment, leading to increased utilization of batteries and gas storage stations. Scheme 4 has lower operating costs than Scheme 1 due to the savings in DAC units, but in order to replenish carbon feedstock lost in the carbon cycle and offset carbon emission costs, a certain amount of CO2 purchase costs are incurred, increasing the total cost by RMB 91,890 compared to Scheme 1.

[0345] Table 5. Economic Feasibility of Schemes 1-4

[0346]

[0347] The renewable energy consumption situation of Schemes 1 to 4 is as follows: Figure 6 As shown, renewable energy sources in Schemes 1, 3, and 4 can be fully utilized in all time periods, while the utilization rate of renewable energy in Scheme 2 is significantly insufficient.

[0348] 2) Comparison of Scheme 1 and Scheme 2

[0349] Figure 7 A comparison of the daily power curves of the GT and P2G (P2G, referring to both E2H and H2G) units in Schemes 1 and 2 was made: the P2G input power (P2G power is the sum of E2H and H2G power) in Scheme 2 remained at a lower level compared to Scheme 1. Combined with... Figure 1 It is evident that Scheme 2 lacks an H2G stage, resulting in a lack of utilization pathways for H2. This means that E2H only needs to operate at low power to meet a small amount of hydrogen load demand. Therefore, Scheme 2 cannot absorb excess renewable energy through the P2G process, and GT also remains at a low output level due to fuel cost constraints. Figure 7 and Figure 8 As shown in (a), to maintain a balance between power supply and demand, the frequency of battery state-of-charge changes in Scheme 2 is higher than that in Scheme 1. Analysis Figure 8 (b) It can be seen that since there is no H2G as an internal natural gas source, Scheme 2 needs to purchase natural gas from outside to meet the gas load and GT demand. On the one hand, GT will be kept at a low level of output due to lack of fuel. On the other hand, it further reduces the renewable energy utilization rate of Scheme 2 in terms of overall energy supply and demand, resulting in a natural gas purchase cost of RMB 762,056, which significantly reduces the economic efficiency of the system.

[0350] Figure 9A comparison was made between the H2G natural gas production and CCS (a collective term for PCC and DAC) CO2 capture flow rates of Scheme 1 and Scheme 2: Although Scheme 2 was equipped with CCS equipment, its CO2 capture rate remained at 0. Analysis Figure 1 As can be seen, H2G is the main pathway for CO2 utilization, and Scheme 2 cannot meet the carbon balance requirements of the system, therefore the CCS cannot operate normally. In Scheme 2, CO2 generated by GT and distributed carbon sources is emitted into the atmosphere, resulting in a carbon emission penalty cost of 246,411 yuan.

[0351] 3) Comparison of Scheme 1 and Scheme 3

[0352] Figure 10 The natural gas flow rates and CCS capture rates of GT and H2G under Scheme 1 and Scheme 3 were compared: the flow rate curves of GT, E2H, H2G, and PCC and DAC in Scheme 3 showed consistency. Analysis Figure 1 It can be seen that due to the lack of hydrogen-carbon storage devices, coupling has occurred between E2H, H2G, and CCS. Figure 2 This indicates that the carbon capture capacity of the PCC is limited by the GT output. Therefore, the hydrogen-carbon coupling in Scheme 3 has actually degenerated into a GT-limited P2G process, weakening the system's regulation capability. Furthermore, as... Figure 8 As shown, the fluctuation range of the battery and gas storage station curves in Scheme 3 is greater than that in Scheme 1. The battery operation and maintenance cost increases from RMB 376.04 in Scheme 1 to RMB 1639.24, and the gas storage station operation and maintenance cost increases from RMB 328.67 in Scheme 1 to RMB 4466.51, while the cost savings for hydrogen and carbon storage equipment is only RMB 28.98.

[0353] 4) Comparison of Scheme 1 and Scheme 4

[0354] Figure 11 Comparing the CO2 flow curves of GT, PCC, and DAC in Scheme 1 and Scheme 4: Scheme 4 and Scheme 1 have basically the same GT scheduling scheme, while Scheme 4 maintains the maximum capture flow in PCC. Figure 2 The specific reasons for the cost of purchasing carbon feedstock in Scheme 4 are explained as follows: Due to the isolation of the system, to achieve 100% self-sufficiency, carbon needs to be maintained in an equal cycle within the system. However, Scheme 4 lacks a DAC (Diverterless Catalytic Converter), and the PCC (Potentially Catalyst Controlled Carbon) can only capture a portion of the CO2 emitted by the GT (Gas Torque). Therefore, the carbon within the system will gradually be lost into the atmosphere during operation, resulting in a corresponding amount of carbon emissions. Thus, Scheme 4 must supplement its carbon feedstock by purchasing CO2. In this simulation, Scheme 4 purchased 113 tons of CO2 as the carbon feedstock for that day.

[0355] The results show that:

[0356] 1) The net-zero carbon optimization scheduling method for an isolated integrated energy system proposed in this invention takes the multi-energy conversion of hydrogen as the core, makes full use of renewable energy to realize multi-energy supply within the system, avoids dependence on external energy supply, and reduces the operating cost of the system.

[0357] 2) The net-zero carbon optimization scheduling method for an isolated integrated energy system proposed in this invention uses storage devices in conjunction with multi-energy conversion to flexibly schedule the system, overcoming the intermittency and counter-demand characteristics of renewable energy.

[0358] 3) The net-zero carbon optimization scheduling method for an isolated integrated energy system proposed in this invention achieves full capture and utilization of carbon elements by establishing a hydrogen-carbon element material and energy balance model, thus maintaining the net-zero carbon emission characteristics of the system.

[0359] The above are merely embodiments of the present invention. The invention is not limited to the fields covered by these embodiments. Commonly known structures and characteristics in the solutions are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are able to access all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A net-zero carbon optimization scheduling method for an isolated integrated energy system, the isolated integrated energy system comprising an energy hub, a power system, and a natural gas system, characterized in that: The energy hub includes an electricity-to-hydrogen (E2G) unit, a hydrogen-to-gas (H2G) unit, a gas turbine unit (GT), a post-combustion carbon capture (PCC) unit, a direct atmospheric carbon capture (DAC) unit, a hydrogen storage tank (HS), and a carbon storage tank (CS). The power system includes a power grid, wind power stations (WT), photovoltaic power stations (PV), and battery energy storage stations (ESS). The natural gas system includes natural gas pipelines and a natural gas storage station GS; The method mainly includes the following steps: Step 1: Establish an energy hub model in the isolated integrated energy system that satisfies the heterogeneous energy balance constraint of the energy hub; The heterogeneous energy balance constraints include electricity balance, natural gas balance, hydrogen balance, and carbon dioxide balance constraints. The equations for the power balance constraints are as follows: ， In the formula: the subscript m represents the energy hub number, and the subscript i represents the power system node number corresponding to the energy hub; , The active and reactive power injected by the energy hub into node i of the power system; , Let GT be the active and reactive power generation of GT at time t; Let be the input electrical power of E2H at time t; Let be the input electrical power of H2G at time t; Let t be the input power of the PCC. Let be the input power of the DAC at time t; The equations for the natural gas balance constraint are as follows: ， In the formula: the subscript m represents the energy hub number, and the subscript u represents the natural gas system node number corresponding to the energy hub; The injection gas flow from the energy hub to natural gas node u; The methane flow rate output from the H2G; GT's natural gas intake flow rate; The hydrogen flow rate directly injected into the natural gas system is affected by the hydrogen blending ratio of the natural gas. The constraints are: ， In the formula: These represent the set of natural gas nodes where H2 is injected, the set of energy hub numbers where H2G is located, and the set of natural gas nodes where the natural gas storage station is located, respectively. The amount of natural gas purchased for the gas storage station at node u on that day; The equation for the hydrogen balance constraint is as follows: ， In the formula: the subscript m represents the energy hub number; Let H2 be the output flow rate of E2H at time t; Inject traffic into H2G at time t; If H2 flow is injected into HS, If H2 is injected into HS at time t, then HS releases H2. Let t be the flow rate of H2 consumed by the hydrogen refueling station; The equations for the carbon dioxide balance constraint are shown below: ， In the formula: the subscript m is the energy hub number, Let be the CO2 flow rate of PCC at time t; Let be the CO2 flow rate of the DAC at time t; This indicates the CO2 injection flow rate of CS. If CO2 is injected into CS, then CO2 is injected into CS; otherwise, CS releases CO2. CO2 flow rate for injecting H2G; Step 2: Establish a power system model that satisfies the power network flow constraints of the power system in the isolated integrated energy system. Step 3: Establish a natural gas system model that satisfies the energy flow constraints of the natural gas pipeline in the isolated integrated energy system. Step 4: Establish a device model in the isolated integrated energy system that meets the operating characteristic constraints of various devices; Step 5: Based on the above model, with the goal of minimizing operating costs and with the system models and equipment models as constraints, establish the operating cost minimization model of the isolated integrated energy system. Step 6 takes minimizing operating cost as the optimization objective and transforms the operating cost minimization model into a mixed-integer linearized model through a linearization strategy; Step 7: Input the required data and solve the mixed integer linearization model. Based on the obtained optimal output scheme, optimize the output of each device in each time period within the scheduling cycle.

2. The method according to claim 1, characterized in that, The power flow constraints in step 2 include node power balance constraints, node voltage constraints, and branch current magnitude constraints. The node power balance constraints are as follows: ， In the formula: subscripts i and j are the node numbers of the power system; , Indicates the node voltage amplitude. Let be the current amplitude of line ij. This represents the voltage phase angle difference between node i and node j; Let i be the real part and j be the imaginary part of the node admittance matrix. For the predicted active and reactive loads of node i, The ESS charging power for node i; Let PV be the PV of node i, and WT be the presence or absence of WT and the power output, respectively. The node voltage constraint is a node voltage magnitude constraint, and its equation is as follows: ， In the formula: Indicates the upper and lower limits of the node voltage amplitude; The branch current constraint is a branch current amplitude constraint, and its equation is as follows: ， In the formula: This indicates the upper limit of the branch current amplitude.

3. The method according to claim 2, characterized in that, The energy flow constraints of the natural gas pipeline mentioned in step 3 include node gas flow balance constraints, natural gas pipeline gas flow constraints, node gas pressure constraints, and operating characteristic constraints of the gas turbine unit. The equations for the nodal gas flow balance constraints are shown below: In the formula: This represents the inlet gas flow rate of the natural gas storage station located at natural gas node u. Then the pipeline injects natural gas into the gas storage station, if Then the gas storage station injects natural gas into the pipeline; The predicted gas load flow rate for natural gas node u; The natural gas pipeline gas flow constraint is a steady-state model of the natural gas system's pipeline gas flow, and its equations are shown below: In the formula: Let be the gas flow rate in the pipeline from node u to node v; For pipeline transmission coefficient, Let u and v be the air pressures at nodes u and v. The nodal pressure constraints are the upper and lower limits of the nodal pressures in the natural gas system, as shown below: In the formula: The upper and lower limits of the air pressure at node u.

4. The method according to claim 3, characterized in that, The equipment operation characteristic models and constraints for each device in step 4 include the upper and lower limits of output constraints for wind power stations and photovoltaic power stations, the operation characteristic models and constraints for electricity-to-hydrogen and hydrogen-to-gas equipment, the operation characteristic models and constraints for gas turbine units, the operation characteristic models and constraints for after-combustion carbon capture units, the operation characteristic models and constraints for direct atmospheric carbon capture units, the operation characteristic models and constraints for battery energy storage power stations, the operation characteristic constraints for natural gas storage stations, and the operation characteristic models and constraints for hydrogen storage tanks. The upper and lower limits of the output of the wind power station and the photovoltaic power station are as follows: In the formula: For WT and PV, there are limitations on the presence or absence of active power output. Used as start / stop markers for WT and PV; The operating characteristic models and constraints of the electro-hydrogen conversion equipment and the hydrogen-to-gas conversion equipment include: The relationship between H2 flow rate and E2H power is shown in the equation below: ， The E2H gradeability limit can be expressed as follows: ， The upper and lower limits of the input power of E2H are expressed as follows: ， In the formula: The conversion factor of E2H, , The upper limit of the gradeability of E2H. For the rated power of E2H, The scheduling time step; The operational characteristic models and constraints of the electro-hydrogen conversion equipment and the hydrogen-to-gas conversion equipment include, The relationship between H2 flow rate and H2G power is shown in the equation below: ， The ramp rate limit for H2G is expressed as follows: ， The upper and lower limits of H2G input power are expressed as follows: ， The equations relating the flow rates of H2 and CO2 injected into H2G to the flow rates of CH4 output from H2G are shown below: ， In the formula, The conversion factor for H2G. The rated power of H2G for the m-th energy hub, , The upper limit of the ramp rate for H2G; The operating characteristic model and constraints of the gas turbine unit include: The power output characteristics of GT are shown in the following equation: ， The calorific value of hydrogen-blended natural gas is given by the following equation: ， The upper and lower limits of reactive power for GT are expressed as follows: ， In the formula: This is the start / stop flag for GT during time period t, where 1 indicates start and 0 indicates stop. For the GT's electrical efficiency, The calorific value of the mixed hydrogen natural gas; The calorific value of natural gas. The calorific value of H2; This represents the maximum output of GT during time period t, after considering reserve capacity. Indicates minimum output; The maximum power factor angle of GT; The operational characteristic model and constraints of the post-combustion carbon capture unit include: The relationship between PCC power and CO2 capture flow rate is shown in the following equation: ， The upper and lower limits of the input electrical power of the PCC are expressed as follows: ， The limitation of CO2 emissions from the GT on the CO2 capture flow rate of the PCC is expressed as follows: ， In the formula, The power consumption coefficient of PCC; This is the minimum power of the PCC; This refers to the rated power of the PCC. This represents the maximum capture efficiency of PCC. This refers to the intake airflow of the GT. Direct carbon capture (DCC) units consist of several DAC adsorption-desorption units. The operating characteristic model and constraints of DCC units include: The relationship between the DAC's CO2 capture flow rate, input power, and device status can be expressed as follows: , The operating time constraints of a single DAC adsorption-desorption unit can be expressed as follows: In the formula: n represents the number of the DAC adsorption-desorption device. The power consumption rating of the DAC adsorption-desorption device. This is the start / stop indicator for the DAC adsorption-desorption equipment. 0 indicates that the DAC adsorption-desorption equipment is in adsorption or off state, and 1 indicates that it is in desorption state. This is the set of DAC adsorption-desorption devices for the m-th EH; The CO2 flow rate at the rated power of the nth device; This is the adsorption start period. This is the period when desorption begins. This is the start time of the desorption process from the previous cycle. This refers to the adsorption time during the cycle. This refers to the desorption time; The operating characteristic model and constraints of the battery energy storage power station include, The relationship between the charge capacity of an ESS and its charging / discharging power can be expressed as follows: ， The constraints on the charging and discharging power of the ESS can be expressed as follows: ， The upper and lower limits of charge capacity constraints for an ESS can be expressed as follows: ， The constraint that the charge level of the ESS is equal at the beginning and end of the scheduling period can be expressed as follows: ， In the formula: The charging and discharging power of the ESS at node i during time period t. For maximum charging and discharging power, , which are 0-1 variables, representing the charging and discharging state of ESS at node i during time period t; To improve ESS work efficiency, Let ESS be the state of charge of ESS during time period t. The maximum and minimum charge constraints allowed by the ESS; The operating characteristic model and constraints of the natural gas storage station include, The relationship between natural gas reserves and the injection and release rates at gas storage stations can be expressed as follows: ， The natural gas reserves and the upper limit of natural gas injection and release flow at the gas storage station can be expressed as follows: ， The upper and lower limits of natural gas reserves and natural gas reserves in gas storage stations can be expressed as follows: Natural gas reserves and storage stations The natural gas reserves at the beginning and end of the scheduling cycle are the same, which can be represented as follows: ， In the formula: This is the gas storage volume of the u-node gas storage station during time period t; This refers to the upper limit of the gas filling and discharging flow rate of the gas storage station. These are the upper and lower limits of the gas storage station's capacity, respectively. The operating characteristic model and constraints of the hydrogen storage tank include, as follows: The temporal relationship between HS injection flow rate and H2 reserves. ， The upper and lower limits of the HS injection flow constraint can be expressed as follows: ， The upper and lower limits of HS capacity constraints can be expressed as follows: ， The H2 reserves are the same at the start and end of the HS scheduling period, which can be represented as follows: ， In the formula: Set the upper and lower limits for the flow rate of H2 injected into and released from HS. Let represent the reserves of HS during time period t. Set the upper limit for HS reserves; The operating characteristic model and constraints of the carbon storage tank include, The temporal relationship between CS-injected CO2 flow rate and CO2 reserves can be expressed as follows: ， The upper and lower limits of the CO2 injection flow constraint in CS can be expressed as follows: ， The upper and lower limits of the CO2 reserve constraint for CS can be expressed as follows: ， The CO2 reserves are the same at the beginning and end of the scheduling cycle, which can be represented as follows: ， In the formula: The amount of carbon feedstock purchased by the mth energy hub on that day. This refers to the transportation period for that day; The upper limit of CO2 flow is injected into and released to CS; Let be the reserves of CS during time period t. Set the upper limit for CS reserves.

5. The method according to claim 4, characterized in that, The objective function of the operating cost minimization model described above can be expressed as follows: ， In the formula: F is the total daily operating cost of the system. These are the system's daily operation and maintenance costs, material purchase costs, and carbon emission penalty costs, respectively. The daily maintenance cost, which refers to the maintenance expenses incurred by various types of equipment during operation, can be expressed as follows: ， In the formula: T is the scheduling period. The daily maintenance cost coefficient for equipment of type k. The input electrical power of a single device during time period t. A collection of adjustable devices. , For the set of device numbers of class k, The injection flow rate for a single storage device during time period t. A collection of storage devices, ; This is the daily maintenance cost coefficient for ESS. The set of power system nodes where the ESS is located; This represents the daily operation and maintenance cost coefficient for natural gas storage stations. The set of nodes in the natural gas system where the gas storage station is located; The cost of purchasing the materials refers to the expenses incurred in purchasing natural gas and CO2 from external sources. Assuming the system can purchase natural gas and CO2 from external sources via transportation at a certain time each day, then: ， In the formula: For natural gas and CO2 prices; The transportation penalty coefficient represents the additional costs incurred due to transportation when purchasing natural gas and CO2. Carbon emission cost is the carbon emission penalty corresponding to the CO2 ultimately emitted into the atmosphere by the system within a scheduling cycle, which is: In the formula: This is the carbon emission penalty coefficient.

6. The method according to claim 5, characterized in that, The specific steps in step 6 to transform the running cost minimization model into a mixed-integer linearized model using a linearization strategy are as follows: Replace the node power balance constraint with the following form: ， In the formula: , The values ​​of constants a, b, c1, c2, c3, and c4 are shown below: ， In the formula: Let be the initial value of the voltage phase angle difference of line ij. Let be the initial voltage amplitude values ​​at nodes i and j; Replace the steady-state model of the pipeline airflow with the following formula: In the formula: , For the nonlinear part, the following piecewise linearization method is used to linearize it: ， In the formula: The number of segments within the constraint range, comprising a total of Known discrete points Define an auxiliary variable for each segment. A 0-1 binary variable ; The daily operation and maintenance cost of the system Replace it with the following form: ， in, The meaning is shown in the following formula: ， In the formula: These are 0-1 variables, representing the input and output states of the natural gas storage station; It is a 0-1 variable that represents the input and output states of device k.

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