Green electricity-hydrogen-methanol integrated comprehensive energy system and optimal scheduling method thereof

Abstract

The invention relates to a green electricity-hydrogen-methanol integrated comprehensive energy system and an optimal scheduling method thereof, and belongs to the technical field of comprehensive energy system optimization. Wind power / photovoltaic power serves as primary energy, green power-green hydrogen bidirectional conversion is achieved through a hydrogen energy storage unit (comprising an electrolytic bath, a compressor, a hydrogen storage tank and a hydrogen fuel cell), and a carbon closed loop path is formed in cooperation with an electric methanol preparation unit (coupling a carbon capture device and a methanol synthesis reactor); designing a day-ahead scheduling optimization model based on multi-market interaction, and integrating equipment operation constraint, multi-energy flow balance and market transaction mechanisms with the purpose of minimizing the total operation cost; according to the optimal scheduling method of the green electricity-hydrogen-methanol integrated comprehensive energy system provided by the invention, a day-ahead scheduling decision for minimizing the operation cost is provided, and dynamic collaborative optimization of electricity-hydrogen-methanol multi-energy flow and efficient cyclic utilization of carbon resources are realized.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system optimization technology, and in particular relates to a green electricity-to-methanol integrated energy system that integrates wind power, photovoltaic, hydrogen energy storage and carbon capture, and a day-ahead scheduling optimization method for minimizing its operating costs. Background Technology

[0002] In the global energy transition, the large-scale application of renewable energy sources such as wind power and solar power has become a key path. However, their inherent intermittency and volatility lead to significant curtailment of wind and solar power, severely restricting energy utilization efficiency and economics. To mitigate these fluctuations and improve energy absorption capacity, the academic community has proposed a "green electricity-green hydrogen" conversion pathway: converting surplus electricity into hydrogen through water electrolysis, achieving energy transfer across time and space. Although hydrogen energy possesses zero-carbon characteristics, its direct application is limited by high storage and transportation costs and insufficient infrastructure. Therefore, extending downstream to the synthesis of green liquid fuels has become an important direction for overcoming bottlenecks, with green electricity-to-methanol attracting considerable attention due to its combined technological feasibility and commercial potential.

[0003] Methanol, a basic chemical raw material and potential clean fuel with a global demand exceeding 100 million tons, is traditionally heavily reliant on fossil fuels for production. However, green electricity-based methanol production, by coupling renewable energy, electrolytic hydrogen production, and carbon dioxide capture, can achieve a closed-loop cycle of carbon-hydrogen-oxygen elements: on the one hand, captured industrial carbon dioxide serves as a synthesis feedstock, turning greenhouse gases into resources; on the other hand, green hydrogen replaces fossil syngas, eliminating process carbon emissions at the source. This pathway not only facilitates deep decarbonization in the chemical industry but also produces high-value-added "green methanol," meeting the needs of multiple scenarios such as transportation fuel, hydrogen carrier, and chemical feedstock.

[0004] However, existing research often focuses on single aspects (such as hydrogen production efficiency or methanol synthesis catalyst development), lacking a global optimization of the dynamic coupling mechanism of the entire "electricity-hydrogen-methanol" chain. At the system's coordinated operation level, key equipment such as electrolyzers, hydrogen storage tanks, and methanol reactors need to dynamically respond to fluctuations in wind and solar power output, time-of-use electricity pricing signals, and diverse market demands. Traditional rigid scheduling strategies based on "electricity-driven hydrogen" or "hydrogen-driven methanol" are insufficient to meet the system's flexibility requirements across multiple time scales. At the economic level, the system needs to consider operation and maintenance costs, market revenue (electricity / hydrogen / methanol / carbon trading), and penalty costs (wind and solar curtailment, load shedding). Existing strategies have not fully explored the potential for improving economic efficiency through the multi-directional conversion capability of hydrogen energy storage ("electricity-gas-fuel"). At the carbon cycle level, the dynamic matching mechanism between carbon dioxide capture and methanol synthesis is still unclear, and carbon source utilization efficiency is easily affected by wind and solar power fluctuations.

[0005] Therefore, it is urgent to build a collaborative scheduling framework for integrated energy systems that produce methanol from green electricity. This framework should be developed through precise modeling of multi-energy flow coupling constraints and deep integration with market-based interaction mechanisms to collaboratively optimize the goals of minimizing system operating costs and efficiently utilizing carbon resources. Summary of the Invention

[0006] The purpose of this invention is to propose an integrated green electricity-hydrogen-methanol energy system and its optimized scheduling method to solve the problems of insufficient synergistic optimization of wind-solar-hydrogen-methanol multi-energy flow, the need to improve carbon cycle efficiency, and poor operating economy in the existing technology. This invention breaks through the bottleneck of multi-energy conversion by dynamically coupling hydrogen energy storage charging and discharging capacity with electric methanol production, and achieves the dual goals of minimizing system operating costs and closed-loop utilization of carbon resources.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] The integrated green electricity-hydrogen-methanol energy system includes a renewable energy power generation unit, a hydrogen energy storage unit, and an electric methanol production unit, and achieves multi-dimensional energy interaction through the external power grid, power load, hydrogen energy market, and methanol market;

[0009] The green electricity generated by the renewable energy power generation unit is fed into the power hub and then distributed through multiple paths, specifically including:

[0010] Some green electricity drives the electrolyzer in the hydrogen energy storage unit to electrolyze water to produce hydrogen. The produced hydrogen is compressed and stored in a hydrogen storage tank with charging and discharging capabilities. The stored hydrogen serves two purposes: first, it provides raw materials for hydrogen fuel cells to generate electricity and feed it back to the grid; second, it is directly transported to the hydrogen energy market.

[0011] Another portion of the green electricity supplies the methanol electrochemical unit, which, combined with the carbon source obtained from the carbon dioxide capture device, catalytically converts the carbon into methanol in the methanol synthesis reactor. The product is then stored in a methanol storage tank that also supports bidirectional flow before being output to the methanol market to ensure the system's internal power load demand.

[0012] Preferably, an integrated optimal scheduling model for green electricity-hydrogen-methanol is constructed based on the system, and the function representation of the model is as follows:

[0013]

[0014] In the formula, C total C0 represents the total daily operating cost of the integrated green electricity-hydrogen-methanol energy system; C0 represents the sum of all operating costs of the system; R0 represents the sum of all operating benefits of the system; c EL and These represent the unit output operation and maintenance cost of the electrolytic cell and the electrolytic cell power at time t; c HC and c represents the unit output operation and maintenance cost of the hydrogen compressor and the electrical power of the hydrogen compressor at time t, respectively; HFC and c represents the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at time t, respectively; HS c MS These represent the unit cost of filling / discharging hydrogen / methanol into the hydrogen storage tank / methanol storage tank, respectively. These represent the hydrogen / methanol charging power of the hydrogen storage tank / methanol storage tank at time t, respectively. c represents the hydrogen / methanol release power of the hydrogen storage tank / methanol storage tank at time t; cut , c represents the unit penalty cost for load shedding and the load shedding at time t, respectively; M c c c W c P These represent the unit output operation and maintenance costs of methanol reactors, carbon capture, wind power generation, and photovoltaic power generation, respectively. Let represent the carbon capture, methanol production, wind power generation, and photovoltaic power generation at time t, respectively. and ρ represents the system's electricity sales / purchase price, electricity sales volume, and electricity purchase volume at time t; H ρ M These represent the prices for hydrogen and methanol, respectively. and R represents the amount of hydrogen and methanol sold at time t, respectively; C This indicates the returns from the carbon market.

[0015] Preferably, the constraints of the integrated green electricity-hydrogen-methanol scheduling model include the following:

[0016] 1) Constraints of Hydrogen Energy Storage

[0017] 1.1) Electrolytic cell

[0018]

[0019] In the formula, Indicates hydrogen production; Indicates the electrical power input of the electrolytic cell; P EL,max Indicates the rated power of the electrolytic cell; η EL Indicates the hydrogen production coefficient of the electrolyzer;

[0020] 1.2) Hydrogen compressor

[0021]

[0022] In the formula, P represents the electrical power input for hydrogen compression; HC,maxIndicates the planned capacity of the hydrogen compressor; η HC This indicates the hydrogen compression coefficient of the hydrogen compressor;

[0023] 1.3) Hydrogen fuel cells

[0024]

[0025] In the formula, Indicates hydrogen consumption; Indicates the amount of electricity generated; η represents the amount of hydrogen consumed at time t; HFC This indicates the power generation efficiency of a hydrogen-oxygen fuel cell; This indicates the rated electrical power of the hydrogen fuel cell; and These represent the downward and upward ramp coefficients, respectively.

[0026] 1.4) Hydrogen storage tank

[0027]

[0028] In the formula, Ψ t This indicates the amount of hydrogen stored; Ψ0 indicates the initial amount of hydrogen stored. Indicates the amount of hydrogen charged; Indicates the amount of hydrogen released; Indicates the rated hydrogen charge / discharge power; Ψ T Indicates the amount of hydrogen stored at the end of time period T; Ψ max Indicates the planned capacity of the hydrogen storage tank;

[0029] 2) Constraints on the main body of renewable energy power generation

[0030]

[0031] In the formula, Indicates the amount of light discarded; Indicates the amount of wind curtailed; Indicates photovoltaic power output; Indicates the output of the wind turbine unit;

[0032] 3) Main constraints of electro-methanol production

[0033] 3.1) Carbon capture

[0034]

[0035] In the formula, Indicates carbon capture efficiency; Indicates CO2 capture rate; λ C μ C These represent the capture efficiency and the conversion coefficient, respectively. Indicates the rated power of the carbon capture equipment;

[0036] 3.2) Methanol reactor

[0037]

[0038] In the formula, Indicates the power of the methanol production equipment; Indicates the amount of hydrogen gas in the reactant; μ M λ represents the conversion factor; M express and Feed ratio; Indicates the rated power of the methanol production equipment;

[0039] 3.3) Methanol storage tank

[0040]

[0041] Φ0=Φ T (28)

[0042]

[0043] In the formula, Φ t Indicates the amount of methanol stored; Indicates the amount of methanol entering the system; Indicate the amount of methanol; Indicates the rated power for methanol inlet and outlet; Φ T Φ represents the methanol storage at the end of time period T; Φ0 represents the initial methanol storage; Φ max Indicates the planned capacity of the methanol storage tank;

[0044] 4) Market constraints

[0045]

[0046] In the formula, Indicates the volume of electricity sold; Indicates the amount of electricity purchased; Indicates the maximum transmitted electrical power; This indicates the volume of hydrogen sold in the hydrogen market; This indicates the volume of methanol sold in the methanol market. and These represent the maximum hydrogen / methanol sales volume, respectively. This indicates the sale of carbon allowances in the carbon trading market; R C ,k, λ t , λ s and These represent carbon market revenue, the conversion factor between carbon credits and carbon dioxide emission reductions, the amount of methanol produced by the system, the emission factor of the traditional process, the emission factor of this system, and the maximum carbon allowance traded in the carbon market, respectively.

[0047] 5) Load shedding constraint

[0048]

[0049] In the formula, This indicates the range constraints of the electrical cutting load; Predicted electrical load;

[0050] 6) System node balance constraints

[0051]

[0052] Equation (40) represents the electrical node balance constraint; Equation (41) represents the methanol node balance constraint; and Equation (42) represents the hydrogen node balance constraint.

[0053] The optimized scheduling method for an integrated green electricity-hydrogen-methanol energy system includes the following:

[0054] S1. Multi-source data acquisition and preprocessing:

[0055] Historical time-series data on wind power, solar power output, system load, and electricity market prices were collected. A standardized training dataset was constructed through data cleaning (outlier removal) and normalization (eliminating the influence of units). An LSTM neural network time series forecast algorithm was used to generate 24-hour forecasts, including predicted wind power / solar power output. Predicted internal electrical load values ​​of the system Time-of-use electricity pricing

[0056] S2. Loading system constant parameters:

[0057] Initialize the physical limit parameters of the equipment, including: rated power, tank capacity, energy efficiency coefficient, and market constraints. The rated power includes: P EL,max P HC,max , The storage tank capacity includes: Ψ max Φ max The energy efficiency coefficient includes: μ EL , λ M The market constraints include:

[0058] S3. Construction of Mixed Integer Linear Programming Model:

[0059] Construct a nonlinear model, transform it into MILP form, and define the integer variables:

[0060] Hydrogen storage tank operation mode (Hydrogen charge = 1, hydrogen release = 0);

[0061] Electricity trading status vt ∈{0,1}(Purchased electricity = 1, Sold electricity = 0);

[0062] Linear reconstruction mutual exclusion constraint:

[0063]

[0064] The mathematical forms of the objective functions (1)-(3) and the linear constraints (4)-(15) and (17)-(42) are retained;

[0065] S4. Optimization Model Solving and Convergence Control:

[0066] The CPLEX mathematical programming solver is configured with parameters, the branch and bound method is selected, the objective function is Equation (1), the convergence condition is relative gap <1% or computation time <300 seconds, and the optimal solution vector X for 24 time periods is output. * ;

[0067] S5. Feasibility verification of the scheduling scheme:

[0068] Perform a posteriori analysis on the solution vector obtained in S4 to verify the multi-energy flow balance (Equations (40)-(42) are strictly valid), check that the equipment power does not exceed the limit, and confirm that the storage tank state cycle is closed; if the verification fails, start the feasibility repair algorithm to adjust the boundary constraints;

[0069] S6. Real-time control command issuance and execution:

[0070] The optimization scheme (the optimal solution vector X after feasibility verification) is implemented through the Energy Management System (EMS). * This is converted into equipment control signals, such as those used in electrolytic cells / methanol reactors. Run, press Participating in electricity / hydrogen energy trading, the system's operational data is transmitted back in real time, forming a closed-loop optimization.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] (1) This invention proposes an integrated green electricity-hydrogen-methanol energy system and its optimization scheduling method. Using wind power / photovoltaic as primary energy, it realizes the bidirectional conversion of green electricity to green hydrogen through a hydrogen energy storage unit (including an electrolyzer, compressor, hydrogen storage tank and hydrogen fuel cell), and forms a carbon closed-loop path in coordination with an electro-methanol production unit (coupled with a carbon capture device and a methanol synthesis reactor). It also designs a day-ahead scheduling optimization model based on multi-market interaction, with the goal of minimizing total operating cost. It integrates equipment operation constraints (Equation 4-30), multi-energy flow balance (Equation 40-42) and market trading mechanism (Equation 31-38), and realizes day-ahead scheduling decision that minimizes operating cost through S1-S6, thereby achieving dynamic synergistic optimization of electricity-hydrogen-methanol multi-energy flow and efficient recycling of carbon resources.

[0073] (2) This invention significantly improves the wind and solar energy absorption rate and reduces operating costs through the dynamic synergy mechanism of hydrogen energy storage charging and discharging capacity and electric methanol production. The market-coupled scheduling strategy constructed simultaneously optimizes the revenue from electricity, hydrogen, methanol and carbon trading, resulting in a significant improvement in overall benefits. Attached Figure Description

[0074] Figure 1 This is a system structure diagram of the integrated green electricity-hydrogen-methanol energy system proposed in this invention;

[0075] Figure 2 This is a flowchart of the optimized scheduling method for the integrated green electricity-hydrogen-methanol energy system proposed in this invention. Detailed Implementation

[0076] The following will provide a detailed description of an integrated green electricity-hydrogen-methanol energy system and its optimized scheduling method, which are based on the present invention. It should be emphasized that the following description is merely exemplary and not intended to limit the scope or application of the invention.

[0077] The following description, in conjunction with relevant accompanying drawings and specific examples, illustrates the integrated green electricity-hydrogen-methanol energy system and its optimized scheduling method proposed in this invention, specifically including the following contents.

[0078] Example 1:

[0079] This invention proposes an integrated green electricity-hydrogen-methanol energy system and its optimized scheduling method, specifically including the following:

[0080] 1. Model Introduction

[0081] The integrated green electricity-hydrogen-methanol energy system architecture constructed in this invention (e.g.) Figure 1As shown, the system uses wind and solar power as the core renewable energy generation units, along with hydrogen storage and methanol electrolysis units to form the main framework. It achieves multi-dimensional energy interaction through the external power grid, electricity load, hydrogen market, and methanol market. Specifically, the green electricity generated by renewable energy flows into the power hub and is distributed through multiple paths—part of it drives the electrolyzer in the hydrogen storage unit to electrolyze water to produce hydrogen. The produced hydrogen is compressed and stored in a hydrogen storage tank with charging and discharging capabilities, which can provide fuel for hydrogen fuel cells to generate electricity to feed back into the grid or be directly supplied to the hydrogen market; another part of the green electricity supplies the methanol electrolysis unit, where it is catalytically converted into methanol in a methanol synthesis reactor using carbon sources obtained from a carbon dioxide capture device. The product is stored in a methanol storage tank that also supports bidirectional flow before being exported to the methanol market, while simultaneously ensuring the system's internal electricity load needs. This system achieves a deep coupling and coordinated operation of power flow (driving electricity-to-hydrogen, hydrogen-to-electricity, and electricity-to-chemical energy conversion), hydrogen flow (realizing storage, conversion, and commercialization), and methanol flow (connecting carbon capture to the market, constructing a closed-loop carbon cycle), forming an integrated architecture with multi-energy complementarity and close connection of multiple links.

[0082] The core value of this system lies in its high integration of large-scale hydrogen energy storage, diversified energy carrier conversion (electricity-hydrogen-methanol), and the output of multiple types of green energy products (green electricity, green hydrogen, and green methanol). It also innovatively couples carbon capture with resource utilization pathways, directly converting carbon dioxide as a synthetic feedstock into methanol, significantly improving carbon utilization efficiency. This architecture not only effectively promotes the efficient conversion and value diversification of clean energy but also simultaneously achieves significant improvements in deep decarbonization and operational economics.

[0083] The day-ahead scheduling optimization model for the green electricity-to-methanol system of this invention is as follows:

[0084]

[0085] Formula (1) is the formula for minimizing the total daily operating cost C of the green electricity-to-methanol system. total This includes the sum of all system operating costs, C0, and the sum of all revenues, R0.

[0086] Formula (2) represents the various operating costs of a green electricity-to-methanol system, including the operating and maintenance costs of each piece of equipment and the load shedding penalty cost. Specifically, in formula (2), c EL (constant) and (Variables) represent the unit output operation and maintenance cost of the electrolytic cell and the electrical power of the electrolytic cell at time t, respectively; c HC (constant) and (Variables) represent the unit output operation and maintenance cost of the hydrogen compressor and the electrical power of the hydrogen compressor at time t, respectively; c HFC (constant) and (Variables) represent the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at time t, respectively; c HS c MS (constant), as well as (Variables) represent the unit hydrogen (methanol) filling and discharging operation and maintenance cost of the hydrogen storage tank (methanol storage tank) and the hydrogen (methanol) discharging and filling power of the hydrogen storage tank (methanol storage tank) at time t; c cut (constant), (Variables) represent the unit penalty cost for load shedding and the load shedding at time t, respectively; c M c c c W c P (constant) and (Variables) represent the unit output operation and maintenance costs of the methanol reactor, carbon capture, and wind and solar power generation, as well as the carbon capture, methanol production, and wind and solar power generation power at time t.

[0087] Formula (3) represents the revenue from each component of a green electricity-to-methanol system, including revenue from electricity sales, hydrogen sales, methanol sales, and carbon market revenue. Specifically, in formula (3), (constant), and (Variables) represent the electricity sales price, electricity sales volume, and electricity purchase volume of the system at time t, respectively; ρ H ρ M (constant), and (Variables) represent the system's hydrogen and methanol prices, and the hydrogen / methanol quantity sold at time t, respectively; R C This indicates the returns from the carbon market.

[0088] 2. Constraints

[0089] (1) Constraints of hydrogen energy storage

[0090] 1) Electrolytic cell

[0091]

[0092] Formula (4) represents hydrogen production. (Variable) is determined by electrical power input. (Variable) determines; Formula (5) represents the... The scheduling must not exceed the rated power P of the electrolytic cell. EL,max (Constant), η EL (Constant) represents the hydrogen production coefficient of the electrolyzer.

[0093] 2) Hydrogen compressor

[0094]

[0095]

[0096] Formulas (6) and (7) represent the electrical power input required for hydrogen compression. (variable); Formula (8) represents the expression for... The scheduling must not exceed the planned capacity of HC. (Constant), k HC (Constant) represents the hydrogen compression coefficient of the hydrogen compressor.

[0097] 3) Hydrogen fuel cells

[0098]

[0099] Formula (9) represents the consumption of a certain amount of hydrogen. (Variable) can generate electricity (variable), η represents the amount of hydrogen consumed at time t. HFC The formula (10) indicates the power generation efficiency of the hydrogen-oxygen fuel cell; formula (10) indicates that the difference in power production between two adjacent time periods must not exceed the ramp rate limit, which is related to the rated power of the hydrogen fuel cell. (Constant) related, (constant) and (Constants) represent the downward and upward ramp coefficients, respectively; Formula (11) represents the coefficients for... The dispatching capacity must not exceed the rated power of the hydrogen fuel cell. (constant).

[0100] 4) Hydrogen storage tank

[0101]

[0102] Ψ0=Ψ T (15)

[0103]

[0104] Formula (12) represents hydrogen charging. (Variable) or hydrogen release Hydrogen storage Ψ caused by (variable) t (Variable) dynamic change; formulas (13) and (14) respectively represent the changes in rated hydrogen charging and discharging power. Determined by (constant) and The feasible range; Formula (15) specifies the hydrogen storage Ψ at the end of time period T. T (Variable) equals the initial hydrogen storage capacity Ψ0 (constant) to ensure the recycling of the hydrogen storage tank; Formula (16) indicates that hydrogen filling and hydrogen release cannot occur simultaneously; Formula (17) indicates the hydrogen storage capacity Ψt (Variable) must not exceed its planned capacity Ψ max (constant).

[0105] (2) Constraints on the main body of renewable energy power generation

[0106]

[0107] Formulas (18) and (19) respectively represent the waste light (variables) and wind curtailment (Variables) must not exceed the predicted output of photovoltaic and wind turbine units. (constant), (constant).

[0108] (3) Main constraints of electro-methanol production

[0109] 1) Carbon capture

[0110]

[0111] Formula (20) represents the carbon capture power. (Variable), Amount of CO2 captured (Variable) transformation relationship, λ C μ C (Constants) represent the capture efficiency and conversion coefficient, respectively; Formula (21) represents the... The dispatching power must not exceed the rated power of the carbon capture equipment. (constant).

[0112] 2) Methanol reactor

[0113]

[0114] Formula (22) represents the power of the methanol production equipment. (Variable) and the amount of hydrogen gas reactant (Variable) transformation relationship, μ M (Constant) represents the conversion coefficient; formula (23) represents the quantitative relationship of substances in the methanol reactor, λ M (Constant) represents the reactant hydrogen gas. and carbon dioxide Feed ratio; Formula (24) represents the ratio of feed to feed. The dispatching power must not exceed the rated power of the methanol production equipment. (constant).

[0115] 3) Methanol storage tank

[0116]

[0117] Φ0=Φ T (28)

[0118]

[0119] Formula (25) represents the methanol input (Variable) or methanol The amount of methanol stored caused by (variable) Φ t (Variable) Dynamic change; Formulas (26) and (27) respectively represent the changes in the rated methanol inlet and outlet power. Determined by (constant) and The feasible range; Formula (28) specifies the methanol storage quantity Φ at the end of time period T. t (Variable) equals the initial methanol storage amount Φ0 (constant) to ensure the recycling of the methanol storage tank; formula (29) indicates that methanol inflow and outflow cannot occur simultaneously; formula (30) indicates the methanol storage amount Φ t (Variable) must not exceed its planned capacity Φ max (constant).

[0120] (4) Market constraints

[0121]

[0122] Formulas (31) and (32) represent the electricity sales of the green electricity-to-methanol system participating in the electricity market. (variables) and electricity purchase (Variable) restrictions, (Constant) represents the maximum transmitted electrical power; Equation (33) indicates that purchasing and selling electricity cannot occur simultaneously. Equations (34) and (35) indicate that the green electricity-to-methanol system participates in the hydrogen market to sell hydrogen. (Variables) and methanol market sales (Variable) restrictions, (constant) and These represent the maximum hydrogen / methanol sales volume, respectively. Formulas (36)-(38) represent the carbon quotas sold by the green electricity-to-methanol system participating in the carbon trading market. (Variable) constraints, R C (variable), k (constant) (variable), λ t (constant), λ s (constant) and (Constants) represent the carbon market revenue, the conversion factor between carbon credits (such as CCER) and carbon dioxide emission reductions, the amount of methanol produced by the system, the emission factor of the traditional process, the emission factor of this system, and the maximum carbon allowance traded in the carbon market, respectively.

[0123] (5) Load shedding constraint

[0124]

[0125] Formula (39) represents the electrical load shearing. (Variable) range constraints, The predicted electrical load must not be exceeded. (constant).

[0126] (6) System node balance constraints

[0127]

[0128] Formula (40) is the electrical node balance constraint, formula (41) is the methanol node balance constraint, and formula (42) is the hydrogen node balance constraint, ensuring the conservation of hydrogen production, consumption, storage, and trading volume within the system.

[0129] In summary, the model {Formula (1)-Formula (42)} (with Formula (1)-(3) as the objective function and Formula (4)-(42) as the constraint) is the day-ahead scheduling optimization model for minimizing the cost of the green electricity-to-methanol integrated energy system of the present invention. Based on the model construction, and combined with historical data analysis to predict the day-ahead wind and solar power output and the internal electrical load of the system, the day-ahead scheduling of minimizing the operating cost of the green electricity-to-methanol integrated energy system can be achieved through this model.

[0130] Based on the constructed day-ahead scheduling optimization model of the green electricity-to-methanol integrated energy system (objective functions (1)-(3), constraints (4)-(42)), as follows Figure 2 As shown in the model solution flowchart, the day-ahead scheduling decision with minimal operating costs is achieved through the following steps:

[0131] Step 1: Multi-source data acquisition and preprocessing

[0132] Historical time-series data on wind and solar power output, system load, and electricity market prices were collected. A standardized training dataset was constructed through data cleaning (outlier removal) and normalization (eliminating the influence of units). An LSTM neural network time series forecast algorithm was used to generate 24-hour forecasts for wind and solar power output. Predicted internal electrical load values ​​of the system Time-of-use electricity pricing

[0133] Step 2: Load system constant parameters

[0134] Initialize the physical limit parameters of the equipment: rated power (P) EL,max P HC,max , etc.), storage tank capacity (Ψ) max Φ max Energy efficiency coefficient (μ) EL , λ M), market constraints )

[0135] Step 3: Constructing a Mixed Integer Linear Programming Model

[0136] The original nonlinear model was transformed into a MILP form, defining integer variables and specifying the hydrogen storage tank operation mode. (Hydrogen charging = 1, hydrogen release = 0), electricity trading status v t ∈{0,1} (electricity purchase = 1, electricity sale = 0). Further, linear reconstruction of mutual exclusion constraints:

[0137]

[0138] And retain the mathematical forms of the objective function (1)-(3) and the linear constraints (4)-(15)(17)-(42).

[0139] Step 4: Optimize model solution and convergence control

[0140] Call the CPLEX mathematical programming solver configuration parameters, select the branch and bound method, objective function (1), convergence condition is relative gap <1% or computation time <300 seconds, output the optimal solution vector X for 24 time periods. * .

[0141] Step 5: Feasibility verification of the scheduling scheme

[0142] Perform a post-hoc analysis on the solution vector obtained in the previous step to verify the multi-energy flow balance (Equations (40)-(42) are strictly valid), check that the equipment power does not exceed the limit, and confirm that the storage tank state is closed in a cycle. If the verification fails, start the feasibility repair algorithm to adjust the boundary constraints.

[0143] Step 6: Issuance and execution of real-time control commands

[0144] The optimization scheme (the optimal solution vector X after feasibility verification) is implemented through the Energy Management System (EMS). * This is converted into equipment control signals, such as those used in electrolytic cells / methanol reactors. Run, press Participating in electricity / hydrogen energy trading, the system's operational data is transmitted back in real time, forming a closed-loop optimization.

[0145] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A green electricity-hydrogen-methanol integrated energy system, characterized in that, It includes renewable energy power generation units, hydrogen energy storage units, and electric methanol production units, and achieves multi-dimensional energy interaction through external power grid, power load, hydrogen energy market, and methanol market; The green electricity generated by the renewable energy power generation unit is fed into the power hub and then distributed through multiple paths, specifically including: Some green electricity drives the electrolyzer in the hydrogen energy storage unit to electrolyze water to produce hydrogen. The produced hydrogen is compressed and stored in a hydrogen storage tank with charging and discharging capabilities. The stored hydrogen serves two purposes: first, it provides raw materials for hydrogen fuel cells to generate electricity and feed it back to the grid; second, it is directly transported to the hydrogen energy market. Another portion of the green electricity supplies the methanol electrochemical unit, which, combined with the carbon source obtained from the carbon dioxide capture device, catalytically converts the carbon into methanol in the methanol synthesis reactor. The product is then stored in a methanol storage tank that also supports bidirectional flow before being output to the methanol market to ensure the system's internal power load demand.

2. The integrated green electricity-hydrogen-methanol energy system according to claim 1, characterized in that, Based on the aforementioned system, an integrated optimal scheduling model for green electricity, hydrogen, and methanol is constructed. The functional representation of the model is as follows: In the formula, C total C0 represents the total daily operating cost of the integrated green electricity-hydrogen-methanol energy system; C0 represents the sum of all operating costs of the system. R0 represents the sum of all types of revenue generated by the system; c EL and These represent the unit output operation and maintenance cost of the electrolytic cell and the electrolytic cell power at time t; c HC and Let $\mathbf{t}$ represent the unit output operation and maintenance cost of the hydrogen compressor and the electrical power of the hydrogen compressor at time $t$, respectively. c HFC and c represents the unit output operation and maintenance cost of the hydrogen fuel cell and the electric power of the hydrogen fuel cell at time t, respectively; HS c MS These represent the unit cost of filling / discharging hydrogen / methanol into the hydrogen storage tank / methanol storage tank, respectively. These represent the hydrogen / methanol charging power of the hydrogen storage tank / methanol storage tank at time t, respectively. c represents the hydrogen / methanol release power of the hydrogen storage tank / methanol storage tank at time t; cut , c represents the unit penalty cost for load shedding and the load shedding at time t, respectively; M c c c W c P These represent the unit output operation and maintenance costs of methanol reactors, carbon capture, wind power generation, and photovoltaic power generation, respectively. Let represent the carbon capture, methanol production, wind power generation, and photovoltaic power generation at time t, respectively. and ρ represents the system's electricity sales / purchase price, electricity sales volume, and electricity purchase volume at time t; H ρ M These represent the prices for hydrogen and methanol, respectively. and R represents the amount of hydrogen and methanol sold at time t, respectively; C This indicates the returns from the carbon market.

3. The integrated green electricity-hydrogen-methanol energy system according to claim 2, characterized in that, The constraints of the integrated green electricity-hydrogen-methanol scheduling model include the following: 1) Constraints of Hydrogen Energy Storage 1.1) Electrolytic cell In the formula, Indicates hydrogen production; Indicates the electrical power input of the electrolytic cell; P EL,max Indicates the rated power of the electrolytic cell; η EL Indicates the hydrogen production coefficient of the electrolyzer; 1.2) Hydrogen compressor In the formula, P represents the electrical power input for hydrogen compression; HC,max Indicates the planned capacity of the hydrogen compressor; η HC This indicates the hydrogen compression coefficient of the hydrogen compressor; 1.3) Hydrogen fuel cells In the formula, Indicates hydrogen consumption; P t HFC Indicates the amount of electricity generated; This represents the amount of hydrogen consumed at time t; η HFC This indicates the power generation efficiency of a hydrogen-oxygen fuel cell; This indicates the rated electrical power of the hydrogen fuel cell; and These represent the downward and upward ramp coefficients, respectively. 1.4) Hydrogen storage tank Ψ0=Ψ T (15) In the formula, Ψ t This indicates the amount of hydrogen stored; Ψ0 indicates the initial amount of hydrogen stored. Indicates the amount of hydrogen charged; Indicates the amount of hydrogen released; Indicates the rated hydrogen charge / discharge power; Ψ T Indicates the amount of hydrogen stored at the end of time period T; Ψ max Indicates the planned capacity of the hydrogen storage tank; 2) Constraints on the main body of renewable energy power generation In the formula, Indicates the amount of light discarded; Indicates the amount of wind curtailed; Indicates photovoltaic power output; Indicates the output of the wind turbine unit; 3) Main constraints of electro-methanol production 3.1) Carbon capture In the formula, Indicates carbon capture efficiency; Indicates CO2 capture rate; λ C μ C These represent the capture efficiency and the conversion coefficient, respectively. Indicates the rated power of the carbon capture equipment; 3.2) Methanol reactor In the formula, Indicates the power of the methanol production equipment; Indicates the amount of hydrogen gas in the reactant; μ M λ represents the conversion factor; M express and Feed ratio; Indicates the rated power of the methanol production equipment; 3.3) Methanol storage tank Φ0=Φ T (28) In the formula, Φ t Indicates the amount of methanol stored; Indicates the amount of methanol entering the system; Indicate the amount of methanol; Indicates the rated power for methanol inlet and outlet; Φ T Φ represents the methanol storage at the end of time period T; Φ0 represents the initial methanol storage; Φ max Indicates the planned capacity of the methanol storage tank; 4) Market constraints In the formula, Indicates the volume of electricity sold; Indicates the amount of electricity purchased; Indicates the maximum transmitted electrical power; This indicates the volume of hydrogen sold in the hydrogen market; This indicates the volume of methanol sold in the methanol market. and These represent the maximum hydrogen / methanol sales volume, respectively. This indicates the sale of carbon allowances in the carbon trading market; R C ,k, λ t , λ s and These represent carbon market revenue, the conversion factor between carbon credits and carbon dioxide emission reductions, the amount of methanol produced by the system, the emission factor of the traditional process, the emission factor of this system, and the maximum carbon allowance traded in the carbon market, respectively. 5) Load shedding constraint In the formula, This indicates the range constraints of the electrical cutting load; Predicted electrical load; 6) System node balance constraints Equation (40) represents the electrical node balance constraint; Equation (41) represents the methanol node balance constraint; and Equation (42) represents the hydrogen node balance constraint.

4. The optimized scheduling method for the integrated green electricity-hydrogen-methanol energy system as described in any one of claims 1-3, characterized in that, Includes the following: S1. Multi-source data acquisition and preprocessing: Collect time-series data on historical wind power, photovoltaic power output, system load, and electricity market prices; construct a standardized training dataset through data cleaning and normalization. The LSTM neural network time series forecast algorithm is used to generate the predicted values ​​for the previous 24 hours, including the predicted output of wind power / solar power. Predicted internal electrical load values ​​of the system Time-of-use electricity pricing S2. Loading system constant parameters: Initialize the physical limit parameters of the equipment, including: rated power, tank capacity, energy efficiency coefficient, and market constraints. The rated power includes: P EL,max P HC,max , The storage tank capacity includes: Ψ max Φ max The energy efficiency coefficient includes: μ EL , λ M The market constraints include: S3. Construction of Mixed Integer Linear Programming Model: Construct a nonlinear model, transform it into MILP form, and define the integer variables: Hydrogen storage tank operation mode (Hydrogen charge = 1, hydrogen release = 0); Electricity trading status v t ∈{0,1}(Purchased electricity = 1, Sold electricity = 0); Linear reconstruction mutual exclusion constraint: The mathematical forms of the objective functions (1)-(3) and the linear constraints (4)-(15) and (17)-(42) are retained; S4. Optimization Model Solving and Convergence Control: The CPLEX mathematical programming solver is configured with parameters, the branch and bound method is selected, the objective function is Equation (1), the convergence condition is relative gap <1% or computation time <300 seconds, and the optimal solution vector X for 24 time periods is output. * ; S5. Feasibility verification of the scheduling scheme: Perform posterior analysis on the solution vector obtained in S4 to verify the multi-energy flow balance, check that the equipment power does not exceed the limit, and confirm that the tank state cycle is closed; if the verification fails, start the feasibility repair algorithm to adjust the boundary constraints. S6. Real-time control command issuance and execution: The energy management system transforms optimization schemes into equipment control signals, and system operation data is transmitted back in real time, forming a closed-loop optimization.

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