A system for generating hydrogen and synthesizing methanol from wind, solar, and energy storage

Abstract

This invention discloses a system for generating hydrogen and synthesizing methanol from wind, solar, and energy storage, belonging to the field of wind, solar, and energy storage hydrogen production and methanol synthesis technology. It includes a photovoltaic power generation module; a wind power generation module; an alkaline electrolyzer module electrically connected to the photovoltaic, wind, and substation modules; a hydrogen storage tank module gas-connected to the alkaline electrolyzer module; a methanol generation equipment module connected to the alkaline electrolyzer module, the hydrogen storage tank module, and a carbon dioxide supply source, receiving hydrogen and carbon dioxide and synthesizing methanol; a substation for power interaction and dispatch; and a system optimization module employing a two-layer optimization framework. The inner layer uses a Pyomo algorithm to solve a short-term dispatch model, optimizing the interaction strategies of the alkaline electrolyzer module, the hydrogen storage tank module, the methanol generation equipment module, and the power grid, generating annual data multiple times in series. The outer layer uses a particle swarm optimization algorithm to optimize the capacity of core equipment, aiming to maximize annual net profit. This invention can solve the problems of poor economic efficiency and insufficient stability in existing systems.

Description

Technical Field

[0001] This invention relates to the field of wind, solar, and energy storage hydrogen production and methanol synthesis technology, and in particular to a system for wind, solar, and energy storage hydrogen production and methanol synthesis. Background Technology

[0002] The installed capacity of renewable energy sources such as wind power and photovoltaics continues to expand, but the volatility of wind and solar power generation leads to significant curtailment, hindering the efficient utilization of new energy sources. Energy storage technology is key to solving this problem. Among them, hydrogen energy storage has the advantages of being unrestricted by geography, having large capacity, and long cycle time, but hydrogen storage and transportation costs are high, and safety requirements are stringent. Methanol, as a derivative of hydrogen, is easily liquefied, has low storage and transportation costs, and is widely used in the chemical and transportation sectors, making it an ideal hydrogen energy carrier.

[0003] Existing wind-solar power generation and methanol synthesis systems are divided into two categories: off-grid and grid-connected. Off-grid systems can achieve energy self-sufficiency but cannot interact with the power grid, limiting their economic benefits. Grid-connected systems, while possessing power interaction capabilities, lack a global optimization scheme for coordinating equipment capacity configuration and real-time scheduling, and their stability under extreme operating conditions is insufficient. Therefore, there is an urgent need to construct a wind-solar power generation and storage system for hydrogen production and methanol synthesis that balances economic efficiency and stability. Summary of the Invention

[0004] The purpose of this invention is to provide a system for generating hydrogen from wind, solar, and energy storage to synthesize methanol, thereby solving the problems of poor economic efficiency and insufficient stability of existing systems.

[0005] To achieve the above objectives, the present invention provides a system for generating hydrogen from wind, solar, and energy storage to synthesize methanol, comprising:

[0006] Photovoltaic power generation modules are used to convert solar energy into electrical energy;

[0007] Wind power generation module, used to convert wind energy into electrical energy;

[0008] The alkaline electrolyzer module is electrically connected to the photovoltaic power generation module, wind power generation module, and substation to receive electrical energy and electrolyze water to generate hydrogen and oxygen.

[0009] The hydrogen storage tank module is gas-connected to the alkaline electrolyzer module and is used to store the hydrogen produced by the alkaline electrolyzer and supply hydrogen to the methanol production equipment module as needed.

[0010] The methanol generation equipment module is connected to the alkaline electrolyzer module, the hydrogen storage tank module, and the carbon dioxide supply source, respectively, to receive hydrogen and carbon dioxide and synthesize methanol;

[0011] The substation is connected to the photovoltaic power generation module, wind power generation module, alkaline electrolysis cell module, methanol generation equipment module and external power grid respectively to realize power interaction and dispatch;

[0012] The system optimization module adopts a two-layer optimization framework. The inner layer is based on the Pyomo solution of the short-term scheduling model to optimize the alkaline electrolyzer module, hydrogen storage tank module, methanol generation equipment module and power grid interaction strategy, and generates annual data multiple times in series. The outer layer optimizes the capacity of core equipment through particle swarm optimization algorithm with the goal of maximizing annual net profit.

[0013] Therefore, the present invention employs the above-described system for generating hydrogen from wind, solar, and energy storage to synthesize methanol, which has the following beneficial effects:

[0014] 1. Through high-value conversion of methanol and grid interaction optimization, the annual net profit is higher than that of traditional wind and solar power sales.

[0015] 2. Enhance the capacity for wind and solar energy absorption and alleviate the problem of wind and solar curtailment.

[0016] 3. The system relies on flexible equipment adjustment, coordinated energy replenishment between hydrogen storage and the power grid, and dynamic economic dispatch mode to ensure stable system operation.

[0017] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of an embodiment of the present invention;

[0019] Figure 2 This is a system optimization flowchart (based on particle swarm optimization algorithm) of an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram illustrating the relevant parameters for calculating wind and solar power generation in an embodiment of the present invention. Figure 3 In the table, (a) is the meridional wind speed of a certain economic development zone, (b) is the zonal wind speed of a certain economic development zone, (c) is the net solar radiation intensity of a certain economic development zone, and (d) is the temperature of a certain economic development zone.

[0021] Figure 4 This is a schematic diagram illustrating electricity and oxygen prices according to an embodiment of the present invention. Figure 4 In the table, (a) is the hourly electricity price of a certain economic development zone, and (b) is the daily market price (yuan / ton) of oxygen (99.5% liquid oxygen) in a certain economic development zone.

[0022] Figure 5 This is a schematic diagram illustrating the wind power and photovoltaic output on a typical day throughout the four seasons, according to an embodiment of the present invention. Figure 5 In the diagram, (a) shows the wind power output on typical days of the four seasons, and (b) shows the photovoltaic power output on typical days of the four seasons. The red lines represent typical winter days (1.3), the yellow lines represent typical spring days (3.28), the green lines represent typical summer days (8.15), and the blue lines represent typical autumn days (10.25).

[0023] Figure 6 This is a schematic diagram of a typical daily optimized scheduling according to an embodiment of the present invention. Figure 6 In the table, (a) shows the system scheduling on a typical winter day (1.3), (b) shows the system scheduling on a typical spring day (3.28), (c) shows the system scheduling on a typical summer day (8.15), and (d) shows the system scheduling on a typical autumn day (10.25).

[0024] Figure 7 This is a schematic diagram illustrating the scenario where the total wind and solar power generation is at its minimum for three consecutive days, according to an embodiment of the present invention. Figure 7 In the table, (a) shows the scheduling of each unit under the condition of the minimum total wind and solar power generation for three consecutive days, (b) shows the load rate changes of photovoltaic units, wind turbine units, electrolyzers, and methanol production units, (c) shows the change in the proportion of hydrogen in the hydrogen storage tank during this period, and (d) shows the fluctuation of electricity price during this period.

[0025] Figure 8 This is a schematic diagram illustrating the cost and revenue of the system under different methanol prices according to an embodiment of the present invention. Figure 8 In the table, (a) is a bar chart of the system's revenue and cost under different methanol prices, (b) is a stacked bar chart of the system's cost percentage under different methanol prices, and (c) is a stacked bar chart of the system's revenue percentage under different methanol prices.

[0026] Figure 9 This is a schematic diagram illustrating the sensitivity analysis of methanol and carbon dioxide prices in an embodiment of the present invention. Figure 9 In the table, (a) represents the impact of methanol price changes on the system's annual net profit, (b) represents the impact of methanol price changes on the system's curtailment rate, and (c) represents the impact of carbon dioxide price changes on the system's annual net profit. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages disclosed in the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the embodiments of the present invention and are not intended to limit the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments in this application without creative effort are within the scope of protection of this application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout.

[0028] Example

[0029] like Figure 1 As shown, this embodiment provides a system for generating hydrogen from wind, solar, and energy storage to synthesize methanol, comprising:

[0030] Photovoltaic power generation modules are used to convert solar energy into electrical energy.

[0031] Model for constructing a photovoltaic power generation module:

[0032] The power output of a photovoltaic (PV) power generation module is affected by solar radiation and ambient temperature. The model of a PV power generation module is shown below:

[0033] ;

[0034] In the formula, This refers to the output power of the photovoltaic cells. This represents the actual solar irradiance. The solar irradiance under standard operating conditions is 1 kW / m². 2 ; The standard solar cell temperature is 25°C. This refers to the surface temperature of the solar photovoltaic panel, a value that is approximately the same as the ambient temperature. This represents the maximum power output of the photovoltaic module under standard test conditions. This is the temperature coefficient, with a value of -3.5 × 10⁻⁶. -3 / ℃.

[0035] ;

[0036] In the formula, Net solar radiation intensity, expressed in kJ / m². 2 .

[0037] Wind power generation modules are used to convert wind energy into electrical energy.

[0038] Model for constructing a wind power generation module:

[0039] The power output of a wind turbine module is related to the wind speed near the hub, the rated power of the turbine, and the turbine's cut-in wind speed, rated wind speed, and cut-out wind speed. The model of the wind turbine module is shown below:

[0040] ;

[0041] In the formula, This refers to the actual output power of the wind turbine. This refers to the rated output power of the wind turbine generator; The actual wind speed is the hub height of the wind turbine. The rated wind speed of the wind turbine; Cut-in wind speed of wind turbine; This refers to the cut-out wind speed of the wind turbine.

[0042] The wind speed at the height of the wind turbine hub is shown below:

[0043] ;

[0044] In the formula, The wind speed near the ground near the wind turbine; The height of the ground near the wind turbine; The wind speed at the wind turbine hub; The height of the wind turbine hub; This is the surface roughness coefficient, with a value of 1 / 7.

[0045] The alkaline electrolyzer module is electrically connected to the photovoltaic power generation module, wind power generation module, and substation to receive electrical energy and electrolyze water to generate hydrogen and oxygen.

[0046] Model for constructing an alkaline electrolyzer module:

[0047] In an alkaline electrolyzer under direct current, a reduction reaction occurs at the cathode, generating hydrogen gas and hydroxide ions. Under the influence of the electric field and the concentration difference between the hydrogen and oxygen sides, the hydroxide ions pass through the diaphragm to the anode, where an oxidation reaction occurs, producing oxygen and water. The relationship between hydrogen production and electrical energy consumption in the electrolyzer is shown below:

[0048] ;

[0049] In the formula, This represents the amount of hydrogen produced by the electrolyzer per hour, expressed in kg / h. Electrolyzer efficiency, expressed as % The power consumption of the electrolytic cell is expressed in kW. The lower calorific value of hydrogen is expressed in kJ / kg.

[0050] The hydrogen storage tank module is gas-connected to the alkaline electrolyzer module and is used to store the hydrogen produced by the alkaline electrolyzer and supply hydrogen to the methanol production equipment module as needed.

[0051] Modeling a hydrogen storage tank module:

[0052] The hydrogen storage tank converts instantaneous excess electrical energy into hydrogen energy for storage and releases it on demand, solving the "time mismatch" problem in the methanol production process from wind and solar power. The rate at which the alkaline electrolyzer module produces hydrogen and the rate at which the methanol generation equipment module consumes hydrogen affect the amount of hydrogen stored in the storage tank at any given time. The model of the hydrogen storage tank module is shown below:

[0053] ;

[0054] In the formula, for The amount of hydrogen stored inside the hydrogen storage tank at all times; This refers to a specific moment in the entire system. for The amount of hydrogen stored inside the hydrogen storage tank at all times; for Time's up The hydrogen level in the hydrogen storage tank at all times; for Time's up The amount of hydrogen released from the hydrogen storage tank at any given time; The efficiency of filling and discharging hydrogen into the hydrogen storage tank.

[0055] The methanol generation equipment module is connected to the alkaline electrolyzer module, the hydrogen storage tank module, and the carbon dioxide supply source, respectively, to receive hydrogen and carbon dioxide and synthesize methanol.

[0056] Modeling a methanol generation equipment module:

[0057] The methanol generation equipment uses carbon dioxide and hydrogen to react and produce methanol and water. The chemical reaction mechanism is as follows:

[0058] .

[0059] The relationship between the amount of methanol produced and the amount of hydrogen consumed by the methanol generation equipment is shown in the following model:

[0060] ;

[0061] In the formula, The amount of methanol produced by the methanol generation equipment, in kg; The efficiency of a methanol generation device is the amount of methanol produced per unit mass of hydrogen consumed. The amount of hydrogen consumed to produce methanol is expressed in kg.

[0062] The substation is connected to the photovoltaic power generation module, wind power generation module, alkaline electrolysis cell module, methanol generation equipment module, and external power grid to realize power interaction and dispatch.

[0063] System optimization modules, such as Figure 2 As shown, a two-layer optimization framework is adopted. The inner layer is based on Pyomo to solve the short-term scheduling model and optimizes the alkaline electrolyzer module, hydrogen storage tank module, methanol generation equipment module and power grid interaction strategy, and generates annual data multiple times in series. The outer layer optimizes the capacity of core equipment through particle swarm optimization algorithm with the goal of maximizing annual net profit.

[0064] The outer-layer optimization of the system optimization module uses the capacity parameters of key equipment as decision variables, including the rated power of the electrolyzer, the capacity of the hydrogen storage tank, the rated power of the methanol production unit, and the substation capacity. Particle Swarm Optimization (PSO) is used to optimize these parameters, determining the optimal capacity combination through iterative search. The number of particles is set to 50, corresponding to 50 combinations of variables for the rated power of the electrolyzer, the capacity of the hydrogen storage tank, the rated power of the methanol production unit, and the substation capacity. This number covers the multidimensional solution space of the four decision variables, balancing comprehensive optimization with computational efficiency. The maximum number of iterations is set to 100, providing sufficient iterative optimization time to ensure that particles can complete global exploration and local fine-grained search within the solution space. Variable boundaries are sequentially matched to the value ranges of the four decision variables to ensure the optimality of the objective function. The inertia weight is set to 0.8, balancing global exploration and local optimization. Learning factors c1=c2=1.5 balance individual and group experience guidance, avoiding constraint variables from getting trapped in local optima and ensuring that the variable combinations are suitable for system grid connection and energy consumption requirements. The velocity initialization range [-1,1], combined with boundary constraints, prevents the initial position of particles from exceeding the rated parameter range of the device, ensuring that the iteration starts from a feasible combination of variables.

[0065] The inner-layer optimization of the system optimization module is based on the equipment capacity parameters given by the outer layer. With a continuous 5-day scheduling cycle, a Pyomo-based mixed-integer linear programming model is constructed to optimize the hourly operation strategy, including the output of the electrolyzer, the amount of hydrogen charged and discharged from the hydrogen storage tank, the load of the methanol production unit, the power interaction with the power grid, and the amount of abandoned electricity. By cycling the 5-day scheduling cycle 73 times, the annual system operation data and net profit are accumulated and used as the fitness evaluation index of the outer-layer particle swarm optimization algorithm.

[0066] The objective function is the system's maximum annual net profit, and its expression is as follows:

[0067] ;

[0068] In the formula, This represents the highest annual net profit. For the system's electricity sales revenue; For the revenue from selling methanol by the system; Revenue from the sale of oxygen produced by the electrolyzer; The cost of CO2 consumed by the system in producing methanol; The operating and maintenance costs of each module of the system; This refers to the depreciation cost of each module in the system.

[0069] The expression for the system's electricity sales revenue is as follows:

[0070] ;

[0071] In the formula, for Power grid interaction power at any time; for Electricity price at any time; For time intervals; The system's computation cycle is 8760 hours;

[0072] The expression for the system's revenue from selling methanol is as follows:

[0073] ;

[0074] In the formula, for The mass of methanol produced by the time-controlled methanol production unit; The price per unit mass of methanol;

[0075] The expression for the revenue from selling oxygen produced by the electrolyzer is as follows:

[0076] ;

[0077] In the formula, for The mass of oxygen produced by the electrolysis of water in the electrolytic cell at any given time; for Price of oxygen per unit mass at any given time;

[0078] The expression for the CO2 cost consumed in the system's methanol production is shown below:

[0079] ;

[0080] In the formula, for The mass of carbon dioxide consumed by the methanol production unit at any given time; The purchase price per unit mass of carbon dioxide;

[0081] The expressions for the operation and maintenance costs of each module of the system are as follows:

[0082] ;

[0083] In the formula, The unit operation and maintenance cost of each module of the system; This refers to the rated power of each module in the system. It is a module of the system;

[0084] The expressions for the depreciation costs of each module in the system are as follows:

[0085] ;

[0086] In the formula, This refers to the unit depreciation cost of each module in the system, including the depreciation cost of the substation.

[0087] The system's constraints include:

[0088] Power balance constraints:

[0089] ;

[0090] In the formula, for The electricity generated by photovoltaic power generation at all times; The electrical energy generated by wind power per unit time; Electrical energy exchanged between the power grid and the system per unit time; The electrical energy consumed by the alkaline electrolyzer per unit time; The electrical energy consumed per unit time in a methanol production unit; The system wastes power per unit time;

[0091] Hydrogen storage tank constraints:

[0092] The hydrogen storage tank operates in two states: hydrogen filling and hydrogen discharging. These two states satisfy a mutual exclusion constraint.

[0093] ;

[0094] In the formula, for The state variables of hydrogen storage tank filling at any given time; for The hydrogen storage tank's hydrogen release status variable is displayed at all times; 0 indicates stop, and 1 indicates operation.

[0095] Hydrogen balance constraints in hydrogen storage tanks:

[0096] ;

[0097] In the formula, Hydrogen production from the electrolyzer; Hydrogen consumption for methanol production unit; Hydrogen storage tank The amount of hydrogen released at any given moment; Hydrogen storage tank The amount of hydrogen charged at any given time;

[0098] Constraints on the percentage of hydrogen in the hydrogen storage tank:

[0099] ;

[0100] In the formula, for The amount of hydrogen in the hydrogen storage tank at any given time; This refers to the capacity of the hydrogen storage tank;

[0101] Hydrogen percentage constraints before hydrogen storage tank scheduling:

[0102] ;

[0103] In the formula, This represents the initial hydrogen quantity in the hydrogen storage tank.

[0104] Hydrogen storage tank filling and discharging hydrogen quality constraints:

[0105] ;

[0106] ;

[0107] In the formula, for The hydrogen level in the hydrogen storage tank at all times; for The amount of hydrogen released from the hydrogen storage tank at any given time; This represents the maximum hydrogen capacity of the hydrogen storage tank. This represents the maximum hydrogen release capacity of the hydrogen storage tank.

[0108] Power grid interaction constraints:

[0109] The system interacts with the power grid to reduce the impact of the volatility of wind and solar power generation on the methanol production process. This interaction involves two states: purchasing electricity from the grid and selling electricity to the grid. These two states satisfy mutual exclusion constraints.

[0110] ;

[0111] In the formula, The state variable is the purchase of electricity from the power grid; This is a state variable for selling electricity to the grid; 0 indicates stop, and 1 indicates operation.

[0112] During grid interaction, the system passes through substations. The substation capacity constrains the electrical energy purchased by the system from the grid. Simultaneously, due to the volatility and uncertainty of wind and solar power generation, power curtailment occurs. Constraints are imposed on grid interaction and system power curtailment.

[0113] ;

[0114] ;

[0115] In the formula, for Purchase electricity from the power grid at all times; The capacity of the substation connecting the system to the power grid;

[0116] Electrolytic cell constraints:

[0117] The electrolyzer operates in two states: standby and operating. In standby mode, the electrolyzer does not produce hydrogen. The two states satisfy a mutual exclusion constraint.

[0118] ;

[0119] In the formula, These are the operating state variables of the electrolytic cell; This refers to the standby state variables of the electrolytic cell;

[0120] Constraining the power of the electrolytic cell during operation:

[0121] ;

[0122] ;

[0123] In the formula, for The operating power of the electrolytic cell at all times; for Standby power of the electrolytic cell at all times; This is the rated power of the electrolytic cell.

[0124] Methanol generation equipment constraints:

[0125] The methanol generation equipment must remain running continuously and cannot be shut down. The operating power constraints of the methanol generation equipment are as follows:

[0126] ;

[0127] In the formula, for Operating power of the methanol production equipment at specific times; The rated power of the methanol production equipment.

[0128] The scenic resources of a certain economic development zone are selected as a case study for analysis. The specific details are as follows:

[0129] 1. Basic Settings:

[0130] The total installed capacity for wind power is set at 90MW, and the installed capacity for photovoltaic power is set at 10MW. For example... Figure 3 As shown, Figure 3 In the table, (a) represents the meridional wind speed of a certain economic development zone, (b) represents the zonal wind speed of a certain economic development zone, (c) represents the net solar radiation intensity of a certain economic development zone, and (d) represents the air temperature of a certain economic development zone.

[0131] The economic indicators and technical parameters of the equipment in the system are shown in Tables 1-6:

[0132] Table 1. Economic Indicators of Equipment

[0133]

[0134] Table 2 Parameters of Methanol Production Unit

[0135]

[0136] Table 3 Parameters of Photovoltaic Power Generation System

[0137]

[0138] Table 4 Hydrogen Storage Tank Parameters

[0139]

[0140] Table 5 Parameters of Wind Power Generation System

[0141]

[0142] Table 6 Electrolyzer Parameters

[0143]

[0144] The price of raw material carbon dioxide is 300 yuan / ton, and the price of the product green methanol is 2880 yuan / ton, which is the third and fourth quartile of the methanol price in the Yangtze River Delta region from January 1, 2007 to April 20, 2025. The service life of wind turbines, photovoltaic units, hydrogen storage tanks, and alkaline electrolyzers is 20 years, and the service life of the methanol production unit is 15 years. The net residual value rate of each unit is 5%. Oxygen price data and electricity price data are selected from a certain economic development zone. Figure 4 As shown, Figure 4 In the table, (a) represents the hourly electricity price in a certain economic development zone, and (b) represents the daily market price (yuan / ton) of oxygen (99.5% liquid oxygen) in the same economic development zone. The relevant parameters for the particle swarm optimization algorithm are as follows:

[0145] Particle count: Set to 50, representing the total number of particles in the population, which determines the diversity of the search process.

[0146] Maximum number of iterations: Set to 100, this is one of the termination conditions of the algorithm. The search stops after the number of iterations reaches this number.

[0147] Variable boundaries: set as [(1500,2500),(65000,75000),(10000,25000),(50000,60000)], corresponding to the legal value range of the four optimization variables (each element is a "lower bound - upper bound" pair), determined by the physical or business constraints of the actual problem, and used to limit the particle position from exceeding the legal range.

[0148] Cognitive learning factor (c1): set to 1.5, it is the weight of the particle learning from its own historical best position, controlling the degree of dependence of the particle on its own experience, and the classic value range is 1.5~2.0.

[0149] Social learning factor (c2): set to 1.5, it is the weight for particles to learn to the global optimal position of the group, controlling the degree to which particles depend on the group's experience.

[0150] Inertia weight (w): set to 0.8, representing the degree of influence of the particle's previous velocity on the current velocity. Its core function is to balance the "global exploration" and "local utilization" capabilities of the algorithm.

[0151] Velocity initialization range: set to [-1, 1]. The particle velocity is initialized by randomly generating values ​​within this range to avoid the initial velocity being too large and causing the particles to rush out of the boundary.

[0152] 2. Capacity configuration results:

[0153] As shown in Table 7, a comparison of key indicators between the wind-solar power generation grid-connected electricity sales system and the wind-solar-storage power generation hydrogen-to-methanol synthesis system reveals that the latter exhibits significant advantages in both economic benefits and energy utilization efficiency. Regarding the core economic indicator of annual net profit, the wind-solar-storage power generation hydrogen-to-methanol synthesis system achieved 17.9 million yuan, an increase of 18.23% compared to the 15.14 million yuan of the wind-solar power generation grid-connected electricity sales system. This directly demonstrates the economic superiority of the wind-solar power generation hydrogen-to-methanol synthesis technology path compared to simply selling electricity. This revenue growth stems from the market value transformation of methanol as a high-value-added energy product, breaking through the limitations of the traditional electricity sales model.

[0154] In terms of energy efficiency, the curtailment rate of the wind-solar-storage hydrogen-to-methanol synthesis system is only 9.51%, which is 58.62% lower than the 22.98% curtailment rate of the grid-connected power sales system. This is due to the energy buffering and conversion system composed of the electrolyzer, hydrogen storage tank, and methanol production unit within the system: on the one hand, the electrolyzer can efficiently absorb unstable wind and solar energy and convert excess electricity into hydrogen energy for storage; on the other hand, the hydrogen storage tank can balance the intermittency of wind and solar power generation with the continuity of the methanol production process, avoiding curtailment caused by the mismatch between power supply and demand, while the substation provides support for the interaction between the system and the grid and for stable operation.

[0155] Table 7 Comparison of electricity sales and methanol sales from wind and solar power systems

[0156]

[0157] 3. Execution and scheduling results:

[0158] like Figure 5 As shown, Figure 5In the diagram, (a) shows the wind power output on typical days of the four seasons, and (b) shows the photovoltaic power output on typical days of the four seasons. The red lines represent typical winter days (1.3), the yellow lines represent typical spring days (3.28), the green lines represent typical summer days (8.15), and the blue lines represent typical autumn days (10.25). Figure 5 The study presents the typical daily processing patterns of wind power and photovoltaic power in the four seasons, showing significant seasonal complementarity and differentiation between the two in terms of time and power.

[0159] Wind power output peaks in spring and troughs in summer, with irregular intraday fluctuations. Autumn and winter wind power output fall between spring and summer, with autumn output slightly higher than winter output. Solar power output peaks in spring and troughs in winter, with output concentrated between 6:00 AM and 6:00 PM during the day, and no output at night. Summer solar power output peaks slightly lower than spring, but high output periods are longer in summer. Autumn solar power output falls between winter and spring / summer output.

[0160] like Figure 6 As shown, Figure 6 In the table, (a) shows the system scheduling on a typical winter day (1.3), (b) shows the system scheduling on a typical spring day (3.28), (c) shows the system scheduling on a typical summer day (8.15), and (d) shows the system scheduling on a typical autumn day (10.25).

[0161] Depend on Figure 6 As can be seen from the system scheduling curve, the system scheduling strategy is dynamically adjusted according to the seasonal characteristics of wind and solar power generation. The coordinated response of each device and the wind and solar power output shows significant seasonal differences, with the specific characteristics as follows:

[0162] On a typical winter day (a), the total output of wind and solar power is generally low and fluctuates gently. In order to maintain the stable operation of the methanol production unit and the electrolyzer, the proportion of electricity purchased from the grid is significantly higher than in other seasons. There is no power curtailment throughout the day, and there is no situation of selling electricity to the grid. The dispatching core focuses on purchasing electricity from the grid to supplement energy and ensuring the stability of core equipment.

[0163] On a typical spring day (b), the total output of wind and solar power increased significantly compared to winter, and the intraday fluctuation range was larger. The load on the grid purchased electricity decreased compared to winter. During the peak hours of wind and solar power output, the system generated revenue by selling excess electricity to the grid, while no power was wasted. This reflects the scheduling logic of prioritizing wind and solar power consumption and moderate grid interaction. The loads of electrolyzers and methanol production units were adjusted slightly with the changes in wind and solar power output to maintain production continuity.

[0164] On a typical summer day (c), due to the concentrated and high intensity of photovoltaic power output, the total wind and solar power output reaches its peak for the four seasons, and the daily output curve exhibits a single-peak distribution. During the corresponding period, the peak load for electricity sold to the grid is significantly higher than in other seasons, while the load for electricity purchased from the grid drops to its lowest level. Because of the ample wind and solar power output and its high degree of matching with equipment load, the curtailment load is almost zero. The methanol production unit and electrolyzer operate stably at high loads, achieving synergistic optimization of high wind and solar power consumption, high equipment efficiency, and high electricity sales revenue.

[0165] The total wind and solar power output on a typical autumn day (d) exhibits a multi-peak fluctuation characteristic, with intraday output stability lower than in spring and summer. The system adapts to changes in wind and solar power output by dynamically adjusting the load of the electrolyzer and methanol production unit to match the load changes. The system alternates between purchasing electricity from the grid and selling electricity to the grid. During periods of low wind and solar power output, a small amount of electricity is purchased to supplement energy, while during periods of peak output, a small amount of electricity is sold. There is no curtailment of electricity load, forming a flexible load adjustment and on-demand grid interaction scheduling mode, which ensures a dynamic balance between wind and solar power consumption and equipment operation.

[0166] like Figure 7 As shown, Figure 7 In the table, (a) shows the scheduling of each unit under the condition of the minimum total wind and solar power generation for three consecutive days, (b) shows the load rate changes of photovoltaic units, wind turbine units, electrolyzers, and methanol production units, (c) shows the change in the proportion of hydrogen in the hydrogen storage tank during this period, and (d) shows the fluctuation of electricity price during this period.

[0167] Under extreme conditions where wind and solar power generation were at their lowest levels for three consecutive days, a comprehensive analysis of multi-dimensional operational data verified that the system possesses stable operational capabilities. From the perspective of equipment operation, Figure 7 (b) The load factor curves show that the load factors of the electrolyzer and methanol production unit fluctuate dynamically within a reasonable range, without exceeding the upper and lower limits of the rated load. The utilization rates of photovoltaic and wind power generation have also not remained at zero for extended periods. Core equipment can adapt to changes in wind and solar input by flexibly adjusting its output. In terms of energy flow, Figure 7 (a) Optimized scheduling exhibits dynamic adjustments in electrolyzer power and methanol production power over time. During periods of lowest wind and solar power generation, hydrogen is released using hydrogen storage tanks. Figure 7 (c) The hydrogen content in the hydrogen storage tank decreased. In coordination with grid power purchases, energy supply and demand were balanced, and no sudden power outages or forced equipment shutdowns occurred. During the operation of the hydrogen storage system, Figure 7 (c) The hydrogen content in the hydrogen storage tank remains within a controllable range of 0.1-0.9%, avoiding the extreme risks of "empty tank" or "full tank" and ensuring dynamic adjustment capability of hydrogen supply and demand. In terms of economic strategy implementation, [the following is combined with...] Figure 7 (d) Electricity price curve and Figure 7(a) Scheduling logic: During periods of low wind and solar power output and high electricity prices, the electrolyzer power is proactively reduced, while during periods of low electricity prices, the production load is increased and grid interaction is optimized. The rationality of operation is ensured through a mechanism that seeks to maximize benefits and minimize risks. The system is verified to achieve stable operation under conditions of minimal wind and solar power generation for three consecutive days, relying on the flexible adjustment of equipment, the coordinated energy replenishment of hydrogen storage and the grid, and the dynamic economic dispatch mode.

[0168] like Figure 8 As shown, Figure 8 In the figure, (a) is a bar chart of the system's revenue and cost under different methanol prices, (b) is a stacked bar chart of the system's cost percentage under different methanol prices, and (c) is a stacked bar chart of the system's revenue percentage under different methanol prices.

[0169] From the correlation between annual returns and methanol prices ( Figure 8 (a) As the price of methanol gradually increased from 3,000 yuan / ton to 4,000 yuan / ton, the system's annual revenue showed a significant upward trend. When the price of methanol was 3,000 yuan / ton, the annual revenue was relatively low; while when the price rose to 4,000 yuan / ton, the annual revenue reached a higher level. This directly reflects the positive driving effect of methanol price on the overall revenue of the system, that is, the higher the price of methanol, the more considerable the revenue obtained by the system through selling methanol and other means.

[0170] Further analysis of the cost-benefit ratio characteristics ( Figure 8 (b) and Figure 8 (c) In the cost structure, electricity purchase cost dominates, consistently accounting for 56% or more. Behind this, besides electricity price factors, the uncertainty and volatility of wind and solar power generation are the core contributing factors. Wind and solar power generation depends on natural conditions, and its output is intermittent and random. When wind and solar power generation cannot meet the system's electricity demand, large amounts of electricity need to be purchased from the external grid to maintain the continuous and stable operation of processes such as hydrogen production and methanol synthesis, thus driving up electricity purchase costs. In terms of revenue composition, revenue from methanol sales consistently accounts for around 80%, making it the main source of income.

[0171] From the perspective of equipment constraints, apart from factors related to electricity purchase, substations and electrolytic cells are key equipment that restrict the annual revenue of the system, in addition to the cost of electricity purchase. Their high depreciation costs compress profit margins from the perspective of fixed costs.

[0172] Substations, as core equipment for system-grid interaction, have a significant proportion of depreciation costs. As a hub for wind and solar power consumption and external power purchases, reducing substation depreciation costs will improve annual net revenue. Electrolyzers, as core equipment in hydrogen production, are more significantly affected by depreciation costs and efficiency issues, which directly impact profits. Low electrolyzer efficiency, coupled with the instability of wind and solar power generation and reliance on purchased electricity, will further increase energy consumption, raise hydrogen production costs, and consequently compress profit margins for subsequent methanol synthesis and sales. In terms of cost dominance, electricity costs have a more pronounced impact on system annual revenue.

[0173] 4. Comparative analysis of grid-connected and semi-off-grid results:

[0174] Significant differences in technical feasibility and economic viability exist between semi-off-grid and grid-connected modes in wind, solar, and energy storage power generation hydrogen production and methanol synthesis systems. The core difference lies in the depth of interaction with the power grid and the degree of dependence on energy storage equipment.

[0175] The semi-off-grid mode uses local wind and solar power as the main energy source, and only purchases electricity from the grid to maintain the operation of the electrolyzer and methanol production unit (non-full-load production) when the wind and solar output is insufficient. This limited interaction characteristic broadens its feasibility boundary compared with the pure off-grid mode.

[0176] Studies show that semi-off-grid systems require large-capacity hydrogen storage tanks to achieve a feasible solution: the tanks need to store sufficient hydrogen during peak wind and solar power generation periods to support methanol synthesis feedstock demand during off-peak periods; simultaneously, electricity from the grid should be used to compensate for the power output gap from wind and solar power, preventing equipment from shutting down due to prolonged power shortages. However, due to limitations such as electricity purchases only being used for equipment insulation and not for increasing production capacity, and the high investment and maintenance costs of large-capacity hydrogen storage tanks, the system's annual net profit remains negative, resulting in limited economic improvement.

[0177] As the capacity of hydrogen storage tanks is gradually reduced, the feasibility of the semi-off-grid system deteriorates rapidly: insufficient hydrogen storage buffer capacity leads to a decrease in the stability of hydrogen supply, and methanol production units frequently face raw material shortages; the demand for electricity increases during off-peak wind and solar power periods, but the contradiction of "high electricity purchase cost - low methanol output" becomes prominent, ultimately resulting in an infeasible solution because the system cannot meet equipment load factor constraints and energy balance conditions. This result reveals the core limitation of the semi-off-grid model: grid-purchased electricity can only guarantee basic equipment operation and cannot replace energy storage to achieve efficient energy conversion, and it still has a strong dependence on hydrogen storage capacity.

[0178] In contrast, the grid-connected model overcomes these constraints through full-condition power interaction. It can not only purchase electricity to maintain equipment operation when wind and solar power are insufficient, but also dynamically adjust the scale of electricity purchase based on real-time electricity prices: prioritizing hydrogen storage and wind / solar power generation during periods of high electricity prices, and increasing electricity purchases to boost capacity during periods of low electricity prices; simultaneously, generating revenue through electricity sales when wind and solar power generation is in surplus, forming a synergistic optimization mechanism of "electricity purchase-electricity sales-energy storage." This characteristic allows the grid-connected system to operate stably without relying on ultra-large capacity hydrogen storage tanks, ensuring stable equipment operation through grid peak shaving; and it can optimize its revenue structure through electricity spot trading and carbon emission tax subsidies, resulting in significantly better annual net profit than the semi-off-grid model.

[0179] The comparison results of the two modes show that the grid-connected mode achieves dual optimization of energy flow and economic flow through deep power interaction, while the semi-off-grid mode, although expanding the feasibility through limited power purchase, still struggles to overcome the bottlenecks of hydrogen storage dependence and economic efficiency, providing a clear quantitative basis for the selection of system modes.

[0180] 5. Sensitivity analysis:

[0181] like Figure 9 As shown, Figure 9 In the table, (a) represents the impact of methanol price changes on the system's annual net profit, (b) represents the impact of methanol price changes on the system's curtailment rate, and (c) represents the impact of carbon dioxide price changes on the system's annual net profit.

[0182] Depend on Figure 9 Sensitivity analysis of annual net profit and curtailment rate under different methanol prices in a wind-solar-storage power generation and hydrogen production to methanol synthesis system reveals a unique correlation between the two. When the methanol price is below 2800 yuan / ton, the system will not produce hydrogen to synthesize methanol, but will simply sell electricity from wind and solar power. As the methanol price increases, the annual net profit continues to grow due to the increased product value, but the curtailment rate does not decrease as expected, but instead gradually increases. This is because curtailment mostly occurs during periods of negative electricity prices, when selling electricity yields no profit or even incurs losses. Increased methanol prices will drive the system to expand substation capacity. When substation capacity increases, and there is a surplus of wind and solar power with negative electricity prices, curtailment becomes a better option due to the influence of power purchase quotas and revenue, leading to an increase in the curtailment rate as the methanol price (within a certain range) rises. This reflects the complex response of the system's operating strategy under the coupling of multiple factors. Within the range of 280-320 yuan / ton for carbon dioxide prices, the annual net profit gradually decreases as the carbon dioxide price increases. The goodness of fit (R0) of the fitting curve between carbon dioxide price and system annual profit is... 2 The value is 0.9981, and the relationship between the two is approximately linear.

[0183] In summary, this invention addresses the economic optimization problem of a wind-solar-storage power generation system for hydrogen production and methanol synthesis. It constructs a two-layer optimization framework of "inner-layer scheduling optimization - outer-layer capacity optimization," and achieves global optimization of system operation strategies and equipment capacity configuration through the collaborative solution of Pyomo and particle swarm optimization algorithms. Furthermore, by comparing grid-connected and off-grid modes, the following conclusions are drawn:

[0184] The photovoltaic power generation hydrogen synthesis methanol system has significant advantages: annual net profit reaches 17.9 million yuan, an increase of 18.23% compared with the grid-connected power sales system; the curtailment rate is only 9.51%, a decrease of more than 58%, thanks to the energy conversion system consisting of electrolyzers, hydrogen storage tanks, etc., and the support of substations.

[0185] Under extreme conditions where wind and solar power generation was at its lowest for three consecutive days, the wind-solar-storage hydrogen-to-methanol synthesis system achieved stable operation by relying on flexible equipment adjustment, coordinated energy replenishment from hydrogen storage and the grid, and dynamic economic dispatch mode. When the price of methanol was 2,880 yuan / ton, the hydrogen storage tank had a capacity of 20.37 tons. Without a source of hydrogen production from the electrolyzer, the hydrogen storage tank could support the methanol production unit to operate at full load for up to 46.57 hours, verifying the system's ability to cope with the uncertainties and fluctuations of wind and solar power generation.

[0186] In a wind-solar-storage power generation system for hydrogen production and methanol synthesis, rising methanol prices increase annual net profit but conversely increase the curtailment rate. Sensitivity analysis results show that the annual net profit of the system gradually decreases with increasing CO2 prices, and the goodness of fit (R²) of the fitted curves between the two is... 2 The coefficient of variation reached 0.9981, indicating that the negative correlation between CO2 price and the system's annual net profit has a very strong linear correlation, and the linear model can accurately describe the quantitative relationship between the two.

[0187] Therefore, the present invention employs the above-mentioned system for generating hydrogen from wind, solar, and energy storage to synthesize methanol, which can solve the problems of poor economic efficiency and insufficient stability of existing systems.

[0188] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A system for producing hydrogen and methanol from wind-solar power storage and generation, characterized in that: Comprise: Photovoltaic power generation module for converting solar energy into electrical energy; Wind power generation module for converting wind energy into electrical energy; Alkaline electrolyzer module, electrically connected with photovoltaic power generation module, wind power generation module, substation, receives electrical energy and electrolyzes water to generate hydrogen and oxygen; Hydrogen storage tank module, gas connected with alkaline electrolyzer module, for storing hydrogen produced by alkaline electrolyzer and supplying hydrogen to methanol generation device module as needed; Methanol generation device module, connected with alkaline electrolyzer module, hydrogen storage tank module, carbon dioxide supply source, receives hydrogen and carbon dioxide and synthesizes methanol; Substation, connected with photovoltaic power generation module, wind power generation module, alkaline electrolyzer module, methanol generation device module and external power grid, realizes power interaction and scheduling; System optimization module adopts double-layer optimization framework, outer layer optimization of system optimization module takes capacity parameters of key equipment as decision variables, adopts particle swarm optimization algorithm to optimize above parameters, determines optimal capacity combination through iterative search; Inner layer optimization of system optimization module is based on given equipment capacity parameters of outer layer, determines scheduling period, constructs mixed integer linear programming model based on Pyomo, optimizes operation strategy of each hour, through cycling scheduling period multiple times, accumulates to obtain annual system operation data and net profit, as fitness evaluation index of outer layer particle swarm optimization algorithm; Constraint conditions of system include: Electricity balance constraint: ； In the formula, is the electric energy generated by photovoltaic power generation at the moment; is the electric energy generated by wind power generation per unit time; is the electric energy exchanged by the system power grid per unit time; is the electric energy consumed by the alkaline electrolytic cell per unit time; is the electric energy consumed by the methanol production device per unit time; is the system abandoned electricity per unit time; Hydrogen storage tank constraint: Working state of hydrogen storage tank is divided into two kinds: hydrogen charging state and hydrogen discharging state, two working states meet state mutual exclusion constraint: ； In the formula, is hydrogen storage tank hydrogen charging state variable at time t; is hydrogen storage tank hydrogen discharging state variable at time t, 0 indicates stop, and 1 indicates work; Hydrogen balance constraint of hydrogen storage tank: ； In the formula, is the hydrogen production amount of the electrolytic tank; is the hydrogen amount for methanol production device; is the hydrogen storage tank is the hydrogen discharge amount at the time; is the hydrogen storage tank is the hydrogen charge amount at the time; Hydrogen storage tank hydrogen ratio constraint: ； wherein is the amount of hydrogen in the hydrogen tank at the moment; is the capacity of the hydrogen tank; Hydrogen storage tank hydrogen ratio constraint before scheduling: ； In the formula, is the initial hydrogen amount of the hydrogen storage tank; Hydrogen storage tank charging and discharging mass constraint: ； ； In the formula, is hydrogen charging amount of the hydrogen storage tank at the time t; is hydrogen discharging amount of the hydrogen storage tank at the time t; is the maximum hydrogen charging amount of the hydrogen storage tank; is the maximum hydrogen discharging amount of the hydrogen storage tank; Power grid interaction constraint: Interaction of system and power grid is divided into two states: purchasing power from power grid and selling power to power grid, two states meet state mutual exclusion constraint: ； In the formula, is a state variable for purchasing power from the power grid; is a state variable for selling power to the power grid, 0 means stop, 1 means work; System in the process of power grid interaction passes through substation, capacity of substation constraints electricity purchased from power grid, at the same time, due to volatility and uncertainty of wind and light generation, there will be abandoned electricity, which is constrained: ； ； wherein is the amount of electricity purchased from the grid at the moment; is the substation capacity of the system connected to the grid; Electrolyzer constraint: Running state of electrolyzer is divided into standby state and working state, electrolyzer does not produce hydrogen in standby state, two states meet state mutual exclusion constraint: ； In the formula, is an operating state variable of the electrolytic cell; is a standby state variable of the electrolytic cell; Power of running state of electrolyzer is constrained: ； ； wherein is the electrolyzer working power at the moment; is the electrolyzer standby power at the moment; is the electrolyzer rated power; Methanol generation device constraint: Methanol generation device always maintains running state and cannot stop, methanol generation device running power constraint: ； In the formula, is the working power of the methanol plant at the moment; is the rated power of the methanol plant. 2.The system for hydrogen production and methanol synthesis from wind-solar-storage power generation according to claim 1, characterized in that: Model of photovoltaic power generation module is constructed: Power of photovoltaic power generation module is influenced by solar radiation and environmental temperature, model of photovoltaic power generation module is as follows: ； wherein, Pout is the output power of the photovoltaic cell; E is the actual solar irradiance; Estd is the standard solar irradiance, value 1 kW / m 2 ; Tstd is the standard solar cell temperature, value 25°C; Tpv is the solar photovoltaic panel surface temperature, value approximately the ambient temperature; Pmax is the maximum power value of the photovoltaic power module under standard test conditions; K is the temperature coefficient, value -3.5 x 10 -3 / °C; ； wherein is the net intensity of solar radiation in kJ / m 2 . 3.The system of claim 1, wherein: Model of wind power generation module is constructed: Power of wind power generation module is related to wind speed near hub, rated power of fan and cut-in wind speed, rated wind speed and cut-out wind speed of fan, model of wind power generation module is as follows: ； wherein is the actual output power of the wind turbine; is the rated output power of the wind turbine; is the hub height actual wind speed of the wind turbine; is the rated wind speed of the wind turbine; is the cut-in wind speed of the wind turbine; is the cut-out wind speed of the wind turbine; Wind speed at hub height of fan is as follows: ； wherein is the wind speed at the ground near the fan; is the height of the ground near the fan; is the wind speed at the hub of the fan; is the height of the hub of the fan; is the roughness coefficient of the ground, having a value of 1 / 7. 4.The system of claim 1, wherein: Model of alkaline electrolyzer module is constructed: The alkaline electrolyzer generates hydrogen and hydroxide ions at the cathode under the action of direct current. The hydroxide ions pass through the diaphragm to the anode under the action of electric field and concentration difference, and generate oxygen and water at the anode. The relationship between the hydrogen generated by the electrolyzer and the power consumed is shown as follows: ； wherein is the amount of hydrogen produced per hour by the electrolyzer, in kg / h; is the efficiency of the electrolyzer, in %; is the amount of consumed electricity by the electrolyzer, in kW; is the low calorific value of hydrogen, in kJ / kg. 5.The system for hydrogen production and methanol synthesis from wind-solar- storage power generation according to claim 1, characterized in that: The model of the hydrogen storage tank module is constructed as follows: The hydrogen storage tank converts the instantaneous excess power into hydrogen energy and stores it for release on demand. The rate at which the alkaline electrolyzer module generates hydrogen and the rate at which the methanol generation device module consumes hydrogen affect the amount of hydrogen stored in the hydrogen storage tank at each moment. The model of the hydrogen storage tank module is constructed as follows: ； In the formula, is the hydrogen storage amount in the hydrogen storage tank at time t; is the time of the entire system; is the hydrogen storage amount in the hydrogen storage tank at time t; is from time t to time t+1 the hydrogen charging amount of the hydrogen storage tank at time t; is from time t to time t+1 the hydrogen discharging amount of the hydrogen storage tank at time t; is the hydrogen charging and discharging efficiency of the hydrogen storage tank. 6.The system for hydrogen production and methanol synthesis from wind-solar- storage power generation according to claim 1, characterized in that: The model of the methanol generation device module is constructed as follows: The methanol generation device reacts carbon dioxide and hydrogen to generate methanol and water, and the chemical reaction mechanism is as follows: ； The relationship model between the amount of methanol generated by the methanol generation device and the amount of hydrogen consumed is as follows: ； wherein is the amount of methanol produced by the methanol production plant, in kg; is the efficiency of the methanol production plant, i.e. the amount of methanol produced per unit mass of hydrogen consumed; is the amount of hydrogen consumed for the production of methanol, in kg. 7.The system of claim 1, wherein: The annual maximum net profit of the system is taken as the objective function, and the expression of the objective function is as follows: ； In the formula, is the annual maximum net profit; is the system electricity sales revenue; is the system methanol sales revenue; is the oxygen sales revenue generated by the electrolytic cell; is the CO2 cost consumed by the system for methanol production; is the operation and maintenance cost of each module of the system; is the depreciation cost of each module of the system; The expression of the system's electricity sales revenue is as follows: ； wherein is the grid interaction power at time instant t; is the electricity price at time instant t; is the calculation period of the system of 8760 hours; The expression of the system's methanol sales revenue is as follows: ； wherein is the mass of methanol produced by the methanol plant at the time instant t; is the price of methanol per unit mass; is the price of methanol per unit mass; The expression of the oxygen sales revenue generated by the electrolyzer is as follows: ； In the formula, is the mass of oxygen generated by the electrolytic cell at the moment; is the unit mass price of oxygen at the moment; The expression of the CO2 cost consumed by the system's methanol production is as follows: ； wherein is the mass of carbon dioxide consumed by the methanol plant at the time instant; is the purchase price per unit mass of carbon dioxide. The expression of the operation and maintenance cost of each module of the system is as follows: ； wherein is the unit operating and maintenance cost of each module of the system; is the rated power of each module of the system; is a module of the system; The expression of the depreciation cost of each module of the system is as follows: ； In the formula, is the unit depreciation cost of each module of the system, including the depreciation cost of the substation. 8.The system of claim 1, wherein: In the outer optimization of the system optimization module, the decision variables include the rated power of the electrolyzer, the capacity of the hydrogen storage tank, the rated power of the methanol production device, and the capacity of the substation. 9.The system of claim 8, wherein: In the inner optimization of the system optimization module, the operation strategy includes the output of the electrolyzer, the hydrogen charging and discharging amount of the hydrogen storage tank, the load of the methanol production device, the interactive power with the power grid, and the amount of abandoned electricity.

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