An improved optimization method for integrated energy system based on two-stage life cycle carbon emission and hydrogen-to-methanol

Abstract

The application discloses an improved comprehensive energy system optimization configuration method based on two-stage life cycle carbon emission and hydrogen-to-methanol, step 1, an improved comprehensive energy system containing a hydrogen-to-methanol unit, a carbon capture unit, new energy power generation and an organic Rankine cycle (ORC) is established; step 2, a carbon emission mathematical model corresponding to the two-stage life cycle is established for the equipment in step one; step 3, a target function and corresponding equipment constraint conditions are established for the system under a low-carbon planning operation strategy; and step 4, a renewable energy price tracking (PFR) strategy is used for the energy storage equipment in the system. The application considers the operation configuration of the hydrogen-to-methanol unit and the ORC on the basis of the traditional comprehensive energy system, and further improves the recycling of carbon dioxide and waste heat. The application considers the life carbon emission of each equipment in the system, can better realize zero carbon emission, improves the economy of the system and consumes a large amount of renewable energy.

Description

Technical Field

[0001] This invention belongs to the field of electrical engineering, and specifically relates to an optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol production. Background Technology

[0002] With global warming becoming increasingly severe, low-carbon development has become a global consensus. As the world's largest energy consumer, my country's total carbon emissions continue to rise. Integrated energy systems, which utilize multiple energy sources to meet energy demand and aim for economic and environmental sustainability, have enormous potential to achieve near-zero or even negative carbon emissions. Against this backdrop, low-carbon planning and operation strategies based on life-cycle carbon emissions are particularly important.

[0003] Lifecycle carbon emissions is a comprehensive and systematic approach to carbon emission control, integrating carbon emission considerations into all stages of equipment design, production, transportation, and operation. Current research largely focuses on the economic capacity configuration of integrated energy systems, but the timeframe for capacity configuration is insufficient, artificially reducing the weight of operating costs. This may result in locally optimal solutions that fail to reflect the value of renewable energy sources such as solar and wind power. Two-stage lifecycle carbon emissions low-carbon planning and operation further refines the lifecycle carbon emissions into two stages: the equipment configuration stage and the optimal scheduling stage, aiming to minimize carbon emission indices and achieve more stable and economical system operation, respectively.

[0004] The integrated energy application of hydrogen-to-methanol synthesis in comprehensive energy systems is of great significance for achieving low-carbon goals. Methanol, as an economical and clean energy source, solves the difficulties of hydrogen storage and transportation. Its production process's ability to absorb carbon dioxide is crucial for the low-carbon operation of the entire energy system. ORC (Organic Energy Recovery) equipment recovers heat from the methanol synthesis unit, improving energy utilization efficiency and strengthening the system's energy coupling. Summary of the Invention

[0005] To address the above problems, this invention provides an optimized configuration method for an improved integrated energy system based on two-stage lifecycle carbon emissions and hydrogen-to-methanol (HPM) production. This method is applied to integrated energy systems that incorporate HPM production, employing a two-stage lifecycle carbon emission analysis approach for low-carbon planning and optimization. Building upon traditional integrated energy systems, it incorporates an HPM unit and an ORC (Organic Rankine cycle) operation configuration to further improve carbon dioxide and waste heat recovery and utilization. This invention considers the lifetime carbon emissions of each system component, enabling better achievement of zero carbon emissions. It enhances the system's economic efficiency while absorbing a significant amount of renewable energy, thus contributing to solving renewable energy absorption issues and alleviating grid regulation pressure.

[0006] This invention proposes an optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol production. The specific design scheme is as follows:

[0007] Step 1: Establish an improved integrated energy system that includes equipment such as a hydrogen-to-methanol unit, a carbon capture unit, new energy power generation, and an organic Rankine cycle (ORC).

[0008] Step 2: Establish a mathematical model for carbon emissions corresponding to the two-stage carbon dioxide life cycle of the equipment in Step 1.

[0009] Step 3: Establish the objective function and corresponding equipment constraints for the system under the low-carbon planning and optimization operation strategy.

[0010] Step 4: Apply the PFR (Price Following Renewable) strategy to the energy storage devices in the system.

[0011] Furthermore, in step 1, an improved integrated energy system is established, comprising a hydrogen-to-methanol unit, a carbon capture unit, wind and solar power generation, a two-stage P2G system, a gas turbine unit, a methanol storage tank, energy storage equipment, a fuel cell, and an Organic Rankine Cycle (ORC) system. The specific steps are as follows:

[0012] Because a hydrogen-to-methanol unit is incorporated, this improved integrated energy system exhibits multiple energy flow patterns. The mathematical model and constraints of the hydrogen-to-methanol unit in this invention are as follows:

[0013] Carbon dioxide captured by the carbon capture unit and hydrogen from the two-stage P2G decomposition react with a catalyst to produce methanol. The expression is as follows:

[0014]

[0015] In the formula: m represents the hydrogen produced in the first stage of P2G during time period t. 3 ; η represents the amount of methanol synthesized during time period t, in kg; P2H , The conversion efficiencies are λ for the first stage of P2G synthesis and for methanol synthesis, respectively. c The catalyst reaction rate; These represent the electrical energy input to the first stage of P2G and the amount of hydrogen input to the methanol synthesis system during time period t, respectively.

[0016] The heat generated in the hydrogen-to-methanol reaction is recovered by the ORC unit from low- to medium-grade heat energy and converted into high-grade electrical energy, thereby improving the energy efficiency of the hydrogen-to-methanol system. This portion of heat Q MPU (t) can be estimated as follows:

[0017]

[0018] In the formula: η MPU,Q The thermal conversion rate of the hydrogen-to-methanol unit; carbon dioxide, as one of the raw materials for methanol production, needs to be captured and transported to the hydrogen-to-methanol unit through a carbon capture unit, and the amount of carbon dioxide consumed in this process. The estimate is as follows:

[0019]

[0020] In the formula: The carbon dioxide consumption rate during methanol production; the heat Q generated during methanol synthesis. MPU (t), this portion of heat can be recovered by the ORC device to generate electricity, as expressed below:

[0021]

[0022] in For the power generation of ORC, η P,ORC The thermal-to-electric efficiency coefficient of the ORC device;

[0023] The mathematical model of the energy storage device is as follows:

[0024]

[0025] In the formula, SOC(t) represents the remaining energy state of energy storage device i at time t, and η ES_ch and η ES_dis For ES efficiency, η HS_ch and η HS_dis For HS efficiency, y ES and y HS For ES and HS capacity, kWh, footnote dis indicates release and footnote ch indicates charging.

[0026] Furthermore, the specific steps of step 2 are as follows:

[0027] In the first phase, targets are set to establish equipment capacity; in this phase, carbon emissions throughout the system's lifecycle are optimized; the capacity of each piece of equipment is determined by minimizing lifecycle carbon emissions and installation investment indices; the targets for this phase consider equipment configuration investment costs, equipment production carbon emissions, equipment operation carbon emissions, and decommissioning carbon emissions; the objective mathematical model is shown below:

[0028] Configuration cost is the cost related to the lining capacity of each piece of equipment:

[0029] C CI =∑ i C I,i y i

[0030] Where C I,i It is the investment in equipment i, y i It is the capacity of device i;

[0031] Carbon emissions from equipment production:

[0032] C PCE =∑ i C CE,i y i

[0033] Where C CE,i It is the carbon emissions from the production of equipment i;

[0034] Carbon emissions from operation:

[0035]

[0036] Where C OE,i It is the carbon emissions of device i during operation over time t. Let i be the electrical power of device i at time t;

[0037] Stop carbon emissions:

[0038] C DC =∑ i C D,i y i

[0039] Where C D,i It is the carbon emissions from the retirement of equipment i;

[0040] The second phase addresses system operation issues. Having minimized carbon emissions in the first phase, making zero-carbon and negative-carbon emissions possible, the second phase considers practical realities, taking into account user load uncertainty and economic considerations. The optimization model for the second phase is a maximum-minimum robust model, aiming to maximize load uncertainty and minimize operating costs. The maximum-minimum problem is transformed into a maximum problem using the Karush-Kuhn-Tucker (KKT) conditions, allowing the original problem to be solved using linear optimization theory.

[0041] In this invention, the box uncertainty set is used to describe the uncertainty of user loading.

[0042]

[0043] Where D e (t), D h (t) and D g (t) represents the demand for electricity, heat, and natural gas, respectively, and d represents the demand for natural gas. e (t), d h (t) and d g (t) represent the uncertainties related to electrical energy, thermal energy, and natural gas, respectively. and Let Δd represent the predicted power values ​​of electrical energy, thermal energy, and natural gas at time t, respectively. e (t), Δd h (t) and Δd g (t) represent the power fluctuation values ​​of electrical energy, thermal energy, and natural gas at time t, respectively, and g t For the maximum user load uncertainty at time t, the annual cumulative uncertainty of the model set is less than or equal to Γ, i.e., ∑ g ≤Γ, where Γ is a constant;

[0044] To enhance the system's robustness, a robust optimization model was introduced in the second stage. Based on the derivation process of KKT, the matrix form of the second-stage objective function, energy balance, and storage constraints in the second-stage robust model is as follows:

[0045]

[0046] Ax + By = D

[0047] Where d is the uncertainty, S D Let be the set of values ​​for d. For runtime variables, This indicates that given d and the planning variable x, The feasible region is defined by x, which represents the output decision variables of all units in the system; y, which represents the start-stop decision variables of all units in the system; m, which represents the coefficient matrix of the decision variables for the objective function in the second stage; and A, B, and D, which represent the coefficient matrices corresponding to the energy balance and storage operation constraints.

[0048] The power upper limit constraint matrix is ​​in the following form:

[0049] Cx + Ey ≤ 0

[0050] Where C and E are the coefficient matrices of the power upper limit constraint;

[0051] The Lagrange relaxation function is constructed as follows:

[0052] L(x,π,θ,ρ)=m T x + (Ax + By - D)π + (Cx - Ey)θ - x T ρ

[0053] πfree, θ≥0, ρ≥0

[0054] in π, θ, and ρ are the dual variables corresponding to each constraint.

[0055] Based on the KKT conditions, the derivation process is as follows:

[0056]

[0057] Complementary relaxation conditions:

[0058] (Cx-Ey)θ=0

[0059] x T ρ = 0

[0060] Furthermore, the specific steps of step 3 are as follows:

[0061] Under a low-carbon planning and operation strategy, the performance of an improved integrated energy system is primarily measured by two-stage objective functions. Based on the carbon emission and present value of cost (PVC) method used in step 2, the objective function for the first stage is as follows:

[0062]

[0063] Where μ carbon This is the carbon price, CNY / kg, r is the interest rate, i is the inflation rate, and T is the operating year.

[0064] The objective function for the second stage is as follows:

[0065]

[0066] Where m iLet i be the operating cost of device i.

[0067] Equipment capacity constraints:

[0068] D i,min ≤E i,max ≤D i,max

[0069] In the formula: E i,max For the capacity of each device; D i,max D i,min These are the upper and lower limits of capacity constraints for each device.

[0070] The operation of an improved integrated energy system (IIES) should meet the requirements of electrical balance, thermal balance, and gas balance, as follows:

[0071]

[0072] in and These represent the electrical power of each device in the system. and These represent the thermal power of each device in the system. These represent the natural gas power of each device in the system;

[0073] Constraints of the hydrogen-to-methanol unit:

[0074]

[0075] In the formula: These are the upper and lower limits of the electrical energy input to P2H, respectively; Capacity of methanol synthesis equipment; These represent the upper and lower limits of methanol synthesis equipment output, respectively. These are the upper and lower limits for the hydrogen input into the methanol synthesis system, respectively.

[0076] Furthermore, the specific steps of step 4 are as follows:

[0077] The low-carbon planning operation strategy has a long scheduling interval (1 day). In order to improve the absorption of renewable energy, a renewable energy price tracking (PFR) strategy is proposed for energy storage devices:

[0078] In the PFR strategy, the base electricity price is the normal electricity price when the electricity price is used. When the photovoltaic radiation reaches the annual average radiation or the wind speed is greater than the cut-out wind speed, the electricity price of the coal-fired power plant will be increased; the electricity price is multiplied by K1 (K1∈[1.5,2]), and ES switches to charging mode; if SOC HS (t) <SOC HS_maxHS switches to release mode; when renewable energy generation is insufficient, the electricity price will be reduced; the electricity price is multiplied by K2 (K2∈[0,0.5]), and the energy storage device strategy is the opposite of the above.

[0079] The present invention, by adopting the above technical solution, achieves the following beneficial effects:

[0080] (1) The improved integrated energy system model for hydrogen-to-methanol and the low-carbon planning and operation strategy for two-stage life cycle carbon emissions proposed in this invention are significantly different from the long-term carbon emission performance of traditional IES. First, the system under this operation strategy can achieve zero carbon emissions or even negative carbon emissions during the operation cycle, thus improving the environmental friendliness of the system.

[0081] (2) The improved integrated energy system, due to the addition of the hydrogen-to-methanol unit, enhances the system's ability to absorb clean energy and carbon dioxide, converting hydrogen into methanol with low storage and transportation costs and high safety. Combined with ORC equipment, it recovers the heat from the methanol synthesis reaction, improving energy utilization efficiency. Attached Figure Description

[0082] Figure 1 This is a structural diagram of the improved integrated energy system in this invention;

[0083] Figure 2 This is a flowchart of the renewable energy price tracking strategy in this invention;

[0084] Figure 3 This is a comparison chart of different energy storage strategies and carbon emissions. Detailed Implementation

[0085] The present invention will be further illustrated below with specific embodiments. To better illustrate the present invention, the proposed method will be verified using Matlab numerical simulation.

[0086] An optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol conversion, comprising the following steps:

[0087] Step 1: Establish an improved integrated energy system comprising a hydrogen-to-methanol unit, a carbon capture unit, wind and solar power generation, a two-stage P2G system, a gas turbine unit, methanol storage tanks, energy storage equipment, fuel cells, and an Organic Rankine Cycle (ORC) system. The specific steps are as follows:

[0088] Because of the addition of a hydrogen-to-methanol unit, this improved integrated energy system has multiple energy flow modes, as detailed in the appendix. Figure 1 As shown. The mathematical model and constraints of the hydrogen-to-methanol unit in this invention are as follows:

[0089] Carbon dioxide captured by the carbon capture unit and hydrogen from the two-stage P2G decomposition react with a catalyst to produce methanol. The expression is as follows:

[0090]

[0091] In the formula: m represents the hydrogen produced in the first stage of P2G during time period t. 3 ; η represents the amount of methanol synthesized during time period t, in kg; P2H , The conversion efficiencies are λ for the first stage of P2G synthesis and for methanol synthesis, respectively. c The catalyst reaction rate; These represent the electrical energy input to the first stage of P2G and the amount of hydrogen input to the methanol synthesis system during time period t, respectively.

[0092] The heat generated in the hydrogen-to-methanol reaction is recovered by the ORC unit from low- to medium-grade heat energy and converted into high-grade electrical energy, thereby improving the energy efficiency of the hydrogen-to-methanol system. This portion of heat Q MPU (t) can be estimated as follows:

[0093]

[0094] In the formula: η MPU,Q This represents the heat conversion rate of the hydrogen-to-methanol unit. Carbon dioxide, as one of the raw materials for methanol production, needs to be captured and transported to the hydrogen-to-methanol unit via a carbon capture unit. The amount of carbon dioxide consumed in this process is... The estimate is as follows:

[0095]

[0096] In the formula: The carbon dioxide consumption rate during methanol production. The heat generated during methanol synthesis, Q. MPU (t), this portion of heat can be recovered by the ORC device to generate electricity, as expressed below:

[0097]

[0098] in For the power generation of ORC, η P_ORC This represents the thermal-to-electric efficiency coefficient of the ORC device.

[0099] The mathematical model of the energy storage device is as follows:

[0100]

[0101] In the formula, SOC(t) represents the remaining energy state of energy storage device i at time t, and η ES_ch and ηES_dis For ES efficiency, η HS_ch and η HS_dis For HS efficiency, y ES and y HS For ES and HS capacities, in kWh. The footnote "dis" indicates release, and "ch" indicates charging.

[0102] Step 2: Establish a mathematical model for carbon emissions corresponding to the two-stage carbon dioxide life cycle of the equipment in Step 1. The specific steps are as follows:

[0103] In the first phase, a complex objective was set to establish equipment capacity. During this phase, carbon emissions throughout the system's lifecycle were optimized. The capacity of each unit was determined by minimizing lifecycle carbon emissions and the installation investment index. The objectives for this phase considered equipment configuration investment costs, carbon emissions from equipment production, carbon emissions from equipment operation, and carbon emissions from equipment decommissioning. The objective mathematical model is shown below:

[0104] Configuration cost is the cost related to the lining capacity of each piece of equipment:

[0105] C CI =∑ i C I,i y i

[0106] Where C I,i It is the investment in equipment i, y i It is the capacity of device i.

[0107] Carbon emissions from equipment production:

[0108] C PCE =∑ i C CE,i y i

[0109] Where C CE,i It is the carbon emissions from the production of equipment i.

[0110] Carbon emissions from operation:

[0111]

[0112] Where C OE,i It is the carbon emissions of device i during operation over time t. Let i be the electrical power of device i at time t;

[0113] Stop carbon emissions:

[0114] C DC =∑ i C D,i y i

[0115] Where C D,i It is the carbon emissions from the retirement of equipment i.

[0116] The second phase addresses system operation issues. Having minimized carbon emissions in the first phase, making zero-carbon and negative-carbon emissions possible, the second phase considers practical realities, taking into account user load uncertainty and economic considerations. The optimization model for the second phase is a maximum-minimum robust model, aiming to maximize load uncertainty and minimize operating costs. The maximum-minimum problem is transformed into a maximum problem using the Karush-Kuhn-Tucker (KKT) conditions, allowing the original problem to be solved using linear optimization theory.

[0117] In this invention, the box uncertainty set is used to describe the uncertainty of user loading.

[0118]

[0119] Where D e (t), D h (t) and D g (t) represents the demand for electricity, heat, and natural gas, respectively, and d represents the demand for natural gas. e (t), d h (t) and d g (t) represent the uncertainties related to electrical energy, thermal energy, and natural gas, respectively. and Let Δd represent the predicted power values ​​of electrical energy, thermal energy, and natural gas at time t, respectively. e (t), Δd h (t) and Δd g (t) represent the power fluctuation values ​​of electrical energy, thermal energy, and natural gas at time t, respectively, and g t For the maximum user load uncertainty at time t, the annual cumulative uncertainty of the model set is less than or equal to Γ, i.e., ∑ g ≤Γ, where Γ is a constant;

[0120] To enhance the system's robustness, a robust optimization model was introduced in the second stage. Based on the derivation process of KKT, the matrix form of the second-stage objective function, energy balance, and storage constraints in the second-stage robust model is as follows:

[0121]

[0122] Ax + By = D

[0123] Where d is the uncertainty, S D Let be the set of possible values ​​for d, and let χ be the runtime variable. This indicates that given d and the planning variable x, The feasible region is defined by x, which represents the output decision variables of all units in the system; y, which represents the start-stop decision variables of all units in the system; m, which represents the coefficient matrix of the decision variables for the objective function in the second stage; and A, B, and D, which represent the coefficient matrices corresponding to the energy balance and storage operation constraints.

[0124] The power upper limit constraint matrix is ​​in the following form:

[0125] Cx + Ey ≤ 0

[0126] Where C and E are the coefficient matrices of the power upper limit constraint;

[0127] The Lagrange relaxation function is constructed as follows:

[0128] L(X,π,θ,ρ)=m T x + (Ax + By - D)π + (Cx - Ey)θ - x T ρ

[0129] πfree, θ≥0, ρ≥0

[0130] in π, θ, and ρ are the dual variables corresponding to each constraint.

[0131] Based on the KKT conditions, the derivation process is as follows:

[0132]

[0133] Complementary relaxation conditions:

[0134] (Cx-Ey)θ=0

[0135] x T ρ = 0

[0136] Step 3: Establish the objective function and corresponding device constraints for the system under the optimized operation strategy. The specific steps are as follows:

[0137] Under a low-carbon planning and operation strategy, the performance of an improved integrated energy system is primarily measured by two-stage objective functions. Based on the carbon emission and present value of cost (PVC) method used in step 2, the objective function for the first stage is as follows:

[0138]

[0139] Where μ carbon This is the carbon price, CNY / kg, r is the interest rate, i is the inflation rate, and T is the operating year.

[0140] The objective function for the second stage is as follows:

[0141]

[0142] Where m i Let i be the operating cost of device i.

[0143] Equipment capacity constraints:

[0144] D i,min ≤E i,max ≤D i,max

[0145] In the formula: E i,max For the capacity of each device; D i,max D i,min These are the upper and lower limits of capacity constraints for each device.

[0146] The Improved Integrated Energy System (IIES) should meet the following energy balance constraints during operation:

[0147]

[0148] in and These represent the electrical power of each device in the system. and These represent the thermal power of each device in the system. These represent the natural gas power of each device in the system;

[0149] Constraints of the hydrogen-to-methanol unit:

[0150]

[0151] In the formula: These are the upper and lower limits of the electrical energy input to P2H, respectively; Capacity of methanol synthesis equipment; These represent the upper and lower limits of methanol synthesis equipment output, respectively. These are the upper and lower limits for the hydrogen input into the methanol synthesis system, respectively.

[0152] Step 4: Apply the PFR (Price Following Renewable) strategy to the energy storage devices in the system. The specific steps are as follows:

[0153] Propose a renewable energy price tracking (PFR) strategy for energy storage devices, such as Figure 2 As shown:

[0154] In the PFR strategy, the base electricity price is the normal electricity price when the electricity price is used. When the photovoltaic radiation reaches the annual average radiation or the wind speed is greater than the cut-out wind speed, the electricity price of the coal-fired power plant will be increased; the electricity price is multiplied by K1 (K1∈[1.5,2]), and ES switches to charging mode; if SOCHS (t) <SOC HS_max HS switches to release mode; when renewable energy generation is insufficient, the electricity price will be reduced; the electricity price is multiplied by K2 (K2∈[0,0.5]), and the energy storage device strategy is the opposite of the above. A comparison of different energy storage strategies and carbon emissions is as follows: Figure 3 As shown, PFR has the lowest average annual carbon emissions, which is lower than the next highest electricity load (FEL) and the next highest heat load (FHL). This demonstrates that PFR can reduce carbon emissions during operation.

[0155] The above embodiments are used to explain the present invention, but not to limit the present invention. Any modifications and changes made to the present invention within the spirit and scope of the claims shall fall within the protection scope of the present invention.

Claims

1. An optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol, characterized in that, Includes the following steps: Step 1: Establish an improved integrated energy system that includes a hydrogen-to-methanol unit, a carbon capture unit, new energy power generation, and an Organic Rankine Cycle (ORC). Step 2: Establish a mathematical model for carbon emissions corresponding to the two-stage life cycle of the equipment in Step 1; Step 3: Establish the objective function and corresponding equipment constraints for the system under the low-carbon planning and operation strategy; Step 4: Apply the renewable energy price tracking (PFR) strategy to the energy storage devices in the system.

2. The optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol as described in claim 1, characterized in that, In step 1, an improved integrated energy system is established, comprising a hydrogen-to-methanol unit, a carbon capture unit, wind and solar power generation, a two-stage P2G system, a gas turbine unit, a methanol storage tank, energy storage equipment, a fuel cell, and an Organic Rankine Cycle (ORC) system. The specific steps are as follows: The mathematical model and constraints for the hydrogen-to-methanol unit are as follows: Carbon dioxide captured by the carbon capture unit and hydrogen from the two-stage P2G decomposition react with a catalyst to produce methanol, as shown in the following expression: In the formula: m represents the hydrogen produced in the first stage of P2G during time period t. 3 ; The amount of methanol synthesized during time period t, in kg; η P2H , The conversion efficiencies are λ for the first stage of P2G synthesis and for methanol synthesis, respectively. c The catalyst reaction rate; These represent the electrical energy input to the first stage of P2G and the amount of hydrogen input to the methanol synthesis system during time period t, respectively. The heat generated in the hydrogen-to-methanol reaction is recovered by the ORC unit from low- to medium-grade heat energy and converted into high-grade electrical energy, thereby improving the energy efficiency of the hydrogen-to-methanol system. This portion of heat Q MPU The estimate for (t) is as follows: In the formula: η MPU,Q The thermal conversion rate of the hydrogen-to-methanol unit; carbon dioxide, as one of the raw materials for methanol production, needs to be captured and transported to the hydrogen-to-methanol unit through a carbon capture unit, and the amount of carbon dioxide consumed in this process. The estimate is as follows: In the formula: The carbon dioxide consumption rate during methanol production; the heat Q generated during methanol synthesis. MPU (t), this portion of heat is recovered by the ORC device to generate electricity, as expressed below: in For the power generation of ORC, η P , ORC For the thermal-to-electric efficiency of ORC devices Rate coefficient; The mathematical model of the energy storage device is as follows: In the formula, SOC(t) represents the remaining energy state of energy storage device i at time t, and η ES_ch and η ES_dis For ES efficiency, η HS_ch and η HS_dis For HS efficiency, y ES and y HS For ES and HS capacity, kWh, footnote dis indicates release and footnote ch indicates charging.

3. The optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol as described in claim 2, characterized in that, The specific steps of step 2 are as follows: In the first phase, targets are set to establish equipment capacity; in this phase, carbon emissions throughout the system's lifecycle are optimized; the capacity of each piece of equipment is determined by minimizing lifecycle carbon emissions and installation investment indices; the targets for this phase consider equipment configuration investment costs, equipment production carbon emissions, equipment operation carbon emissions, and decommissioning carbon emissions; the objective mathematical model is shown below: Configuration cost is the cost related to the lining capacity of each piece of equipment: C CI =∑ i C I,i y i Where C I,i It is the investment in equipment i, y i It is the capacity of device i; Carbon emissions from equipment production: C PCE =∑ i C CE,i y i Where C CE,i It is the carbon emissions from the production of equipment i; Carbon emissions from operation: Where C OE,i It is the carbon emissions of device i during operation over time t. Let i be the electrical power of device i at time t; Stop carbon emissions: C DC =∑ i C D,i y i Where C D,i It is the carbon emissions from the retirement of equipment i; The second stage addresses the system operation problem. The optimization model for the second stage is the max-min robust model, which aims to maximize the load uncertainty and minimize the operating cost. The max-min problem is transformed into a max problem using KKT conditions, and the original problem is solved using linear optimization theory. The box uncertainty set is used to describe the uncertainty of user loading; Where D e (t), D h (t) and D g (t) represents the demand for electricity, heat, and natural gas, respectively, and d represents the demand for natural gas. e (t), d h (t) and d g (t) represent the uncertainties related to electrical energy, thermal energy, and natural gas, respectively. and Let Δd represent the predicted power values ​​of electrical energy, thermal energy, and natural gas at time t, respectively. e (t), Δd h (t) and Δd g (t) represent the power fluctuation values ​​of electrical energy, thermal energy, and natural gas at time t, respectively, and g t For the maximum user load uncertainty at time t, the annual cumulative uncertainty of the model set is less than or equal to Γ, i.e., ∑ g ≤Γ, where Γ is a constant; In the second stage, a robust optimization model is introduced. Based on the derivation process of KKT, the matrix form of the objective function, energy balance, and storage constraints in the second-stage robust model is as follows: Ax + By = D Where d is the uncertainty, S D Let be the set of values ​​for d. For runtime variables, This indicates that given d and the planning variable x, The feasible region is defined by x, which represents the output decision variables of all units in the system; y, which represents the start-stop decision variables of all units in the system; m, which represents the coefficient matrix of the decision variables for the objective function in the second stage; and A, B, and D, which represent the coefficient matrices corresponding to the energy balance and storage operation constraints. The power upper limit constraint matrix is ​​in the following form: Cx + Ey ≤ 0 Where C and E are the coefficient matrices of the power upper limit constraint; The Lagrange relaxation function is constructed as follows: πfree, θ≥0, ρ≥0 in π, θ, and ρ are the dual variables corresponding to each constraint; Based on the KKT conditions, the derivation process is as follows: Complementary relaxation conditions: (Cx-Ey)θ=0 x T p = 0.

4. The optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol as described in claim 3, characterized in that, The specific steps of step 3 are as follows: Under a low-carbon planning and operation strategy, the performance of an improved integrated energy system is measured by a two-stage objective function. Based on the carbon emissions and present value of costs (PVC) method for each stage in step 2, the objective function for the first stage is as follows: Where μ carbon This is the carbon price, CNY / kg, r is the interest rate, i is the inflation rate, and T is the operating year; The objective function for the second stage is as follows: Where m i The operating cost of device i; Equipment capacity constraints: D i,min ≤E i,max ≤D i,max In the formula: E i,max For the capacity of each device; D i,max D i,min These are the upper and lower limits of capacity constraints for each device; The operation of an improved integrated energy system (IIES) should meet the requirements of electrical balance, thermal balance, and gas balance, as follows: in and These represent the electrical power of each device in the system. and These represent the thermal power of each device in the system. These represent the natural gas power of each device in the system; Constraints of the hydrogen-to-methanol unit: In the formula: These are the upper and lower limits of the electrical energy input to P2H, respectively; Capacity of methanol synthesis equipment; These represent the upper and lower limits of methanol synthesis equipment output, respectively. These are the upper and lower limits for the hydrogen input into the methanol synthesis system, respectively.

5. The optimized configuration method for an improved integrated energy system based on two-stage life cycle carbon emissions and hydrogen-to-methanol as described in claim 4, characterized in that, The specific steps of step 4 are as follows: For energy storage devices using a renewable energy price tracking (PFR) strategy: In the PFR strategy, the base electricity price is the normal electricity price when the electricity price is used. When the photovoltaic radiation reaches the annual average radiation or the wind speed is greater than the cut-out wind speed, the electricity price of the coal-fired power plant will be increased; the electricity price is multiplied by K1 (K1∈[1.5,2]), and ES switches to charging mode; if SOC HS (t) <SOC HS_max HS switches to release mode; when renewable energy generation is insufficient, the electricity price will be reduced; the electricity price is multiplied by K2 (K2∈[0,0.5]), and the energy storage device strategy is the opposite of the above.

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