Optimization scheduling method, system, equipment, and media for an electro-hydrogen-ethanol coupling system based on dynamic feed ratio in methanol synthesis.

Abstract

This invention belongs to the field of optimization scheduling technology for electro-hydrogen-ethanol coupled systems. It discloses a method, system, equipment, and medium for optimization scheduling of electro-hydrogen-ethanol coupled systems based on the dynamic feed ratio of methanol synthesis, overcoming the shortcomings of existing simplified models and inefficient solutions. The method includes: determining the composition framework of the electro-hydrogen-ethanol coupled system and establishing operational constraints for each device within the system; establishing a refined nonlinear operating model for the methanol synthesis equipment in the system; establishing an optimization objective function with the goal of minimizing system operating costs, and constructing a system optimization scheduling model by integrating operational constraints and the refined nonlinear operating model; and solving the system optimization scheduling model using a preference-guided multi-strategy improved genetic algorithm to output the optimal scheduling scheme. By using the dynamic feed ratio of methanol synthesis as the key optimization variable, this invention achieves a leap from simple "power scheduling" to "power-material coordinated scheduling."

Description

Technical Field

[0001] This invention belongs to the field of optimization scheduling technology for electro-hydrogen-ethanol coupling systems, specifically relating to optimization scheduling methods, systems, equipment, and media for electro-hydrogen-ethanol coupling systems based on the dynamic feed ratio of methanol synthesis. Background Technology

[0002] The electro-hydrogen-ethanol coupling system, a key energy system connecting renewable energy, hydrogen energy, and methanol chemical processes, integrates multiple stages such as power generation, energy storage, hydrogen conversion, and chemical synthesis. It enables the synergistic conversion and complementary utilization of various energy forms, including electricity, hydrogen, and alcohols. This system is of great significance for the large-scale absorption of intermittent renewable energy sources such as wind and solar power, reducing carbon emissions in the chemical industry, and improving overall energy efficiency.

[0003] However, the stable and economical operation of the electro-hydrogen-methanol coupling system highly depends on the precise characterization of the operating characteristics of its internal equipment and the optimized scheduling of the system. In this system, the methanol synthesis process is the core process that converts "green hydrogen" and captured carbon dioxide into the high-value chemical "green methanol," and it is a key node where energy and material flows converge. The actual methanol synthesis reaction process is complex, and its output efficiency is not constant but exhibits a strong nonlinear coupling relationship with the feed ratio of the input reactor (usually referring to the mass ratio or molar ratio of carbon dioxide to hydrogen). To simplify the model and facilitate the solution, most existing technical solutions assume that the efficiency of the methanol synthesis equipment is a constant value or a piecewise linear value. This simplification cannot accurately reflect the actual operating characteristics of the methanol synthesis process under different operating conditions, resulting in a significant deviation between the established optimized scheduling model and physical reality. Consequently, the scheduling scheme generated based on this model cannot achieve the expected economic and energy efficiency levels in practical applications, limiting the full realization of the system's potential.

[0004] On the other hand, the strong nonlinear relationship between methanol synthesis efficiency and feed ratio makes the optimal scheduling model of the entire electro-hydrogen-methanol coupling system a complex nonlinear optimization problem, difficult to solve effectively using traditional linear programming or mixed-integer linear programming solvers. To address this, existing solutions employ artificial intelligence algorithms, such as traditional genetic algorithms and particle swarm optimization. However, these traditional intelligent algorithms generally suffer from inherent defects when dealing with such high-dimensional, nonlinear, and multi-constraint complex engineering optimization problems. These defects include premature convergence, susceptibility to local optima, and difficulty in balancing global exploration and local exploitation capabilities, resulting in low-quality scheduling schemes and inaccurate solutions that fail to meet practical engineering requirements.

[0005] In summary, the main problems in the existing technology can be summarized into two points: first, the model accuracy is insufficient, and the nonlinear dynamic characteristics of the core conversion process (methanol synthesis) are not accurately characterized; second, the solution capability is lacking, and there is a lack of optimization algorithms that can efficiently and accurately handle such complex nonlinear scheduling models. Summary of the Invention

[0006] Based on the aforementioned shortcomings and deficiencies in the existing technology, one of the objectives of this invention is to at least solve one or more of the aforementioned problems in the existing technology. In other words, one of the objectives of this invention is to provide an optimized scheduling method, system, device, and medium for an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio of methanol synthesis that meets one or more of the aforementioned requirements, so as to overcome the defects of existing model simplification and inefficient solution, and to take into account the accuracy and practicality of the scheduling scheme.

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

[0008] In a first aspect, the present invention provides an optimized scheduling method for an electro-hydrogen-ethanol coupling system based on a dynamic feed ratio for methanol synthesis, comprising the following steps:

[0009] S1. Determine the composition framework of the electro-hydrogen alcohol coupling system and establish the operational constraints of each device in the system;

[0010] S2. A refined nonlinear operation model is established for the methanol synthesis equipment in the system. The output efficiency of the methanol synthesis equipment is modeled as a nonlinear function of its feed ratio.

[0011] S3. Establish an optimization objective function with the goal of minimizing system operating costs, and construct a system optimization scheduling model by combining the aforementioned operating constraints with the refined nonlinear operating model.

[0012] S4. The system optimization scheduling model is solved by using a preference-guided multi-strategy improved genetic algorithm, and the optimal scheduling scheme is output.

[0013] As a preferred embodiment, the electro-hydrogen-ethanol coupling system includes a fan, photovoltaic system, load, battery, electrolyzer, hydrogen storage tank, hydrogen fuel cell, carbon capture equipment, and methanol synthesis equipment;

[0014] The operational constraints of each device within the system include battery power constraints, battery energy constraints, electrolyzer start / stop constraints, electrolyzer power constraints, electrolyzer output constraints, hydrogen fuel cell start / stop constraints, hydrogen fuel cell power constraints, hydrogen fuel cell output constraints, hydrogen storage tank power constraints, hydrogen storage tank energy constraints, carbon capture equipment constraints, and methanol synthesis equipment constraints.

[0015] As a preferred embodiment, the refined nonlinear operation model in step S2 is established using an interpolation fitting method, and the nonlinear function is a polynomial function.

[0016] The feed ratio is the mass ratio of carbon dioxide to hydrogen fed into the methanol synthesis equipment.

[0017] As a preferred option, the objective function in step S3 is to minimize the total system operating cost, which is the sum of the cost of purchasing electricity and the cost of purchasing hydrogen, minus the revenue from selling electricity, selling hydrogen and selling methanol.

[0018] The system optimization scheduling model must meet the following constraints: system equipment model constraints, electricity purchase and sale constraints, hydrogen purchase and sale power constraints, methanol sale power constraints, wind turbine output constraints, photovoltaic output constraints, power balance constraints, hydrogen power balance constraints, feed ratio constraints, and methanol synthesis equipment output efficiency constraints.

[0019] As a preferred embodiment, step S4 employs a preference-guided multi-strategy improved genetic algorithm to solve the system optimization scheduling model, including the following steps:

[0020] S41. Initialize the population. Individuals in the population include decision variables for equipment start-up and shutdown, power allocation, and feed ratio. A multivariate mutation strategy is used to enhance diversity.

[0021] S42. Calculate the objective function value of each individual in the population as the fitness;

[0022] S43. Perform the selection operation, embedding a preference guidance mechanism during the selection process to prioritize individuals that perform better in terms of methanol synthesis efficiency and operating cost;

[0023] S44. Perform a greedy crossover operation based on extreme value tracking on the selected individuals;

[0024] S45. Perform multivariate mutation operations on some new individuals;

[0025] S46. Perform multi-level search operations, including local search based on individual extreme values ​​for elite individuals, and global refined search triggered for populations with similarity exceeding the threshold.

[0026] As a preferred approach, the multivariate mutation strategy includes exchange mutation, reverse mutation, and insertion mutation.

[0027] As a preferred embodiment, step S44 specifically involves:

[0028] For each decision variable, a set of candidate values ​​is constructed from the parent individual and the global historical best solution, and the crossover action that minimizes the objective function value is selected from them.

[0029] In a second aspect, the present invention provides an optimized scheduling system for an electro-hydrogen-alcohol coupling system based on the dynamic feed ratio of methanol synthesis, for implementing the optimized scheduling method for the electro-hydrogen-alcohol coupling system as described in the first aspect.

[0030] Thirdly, the present invention provides an electronic device, the computer device including a memory, a processor and a computer program, wherein when the computer program is executed by the processor, it implements the electrohydrogen alcohol coupling system optimization scheduling method as described in the first aspect.

[0031] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the optimized scheduling method for the electrohydrogen-alcohol coupling system as described in the first aspect.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] 1. This invention overcomes the limitations of existing technologies that simplify methanol synthesis efficiency to a constant or piecewise linear value. By establishing a refined nonlinear operating model based on dynamic feed ratio, it can accurately characterize the actual working characteristics of methanol synthesis equipment. This fundamentally solves the problem of large deviations between existing scheduling models and physical reality, making the generated optimized scheduling scheme not only mathematically optimal but also directly applicable to real systems efficiently and stably.

[0034] 2. By using the dynamic feed ratio for methanol synthesis as a key optimization variable, this invention achieves a leap from simple "power scheduling" to "power-material coordinated scheduling." This method can dynamically find the optimal feed ratio that maximizes methanol production or system benefits while meeting system constraints. This reduces external energy purchase costs while increasing the output of high-value-added products (methanol), achieving a synergistic increase in system operating economy and energy conversion efficiency.

[0035] 3. To address the challenges posed by nonlinear models, this invention designs a preference-guided, multi-strategy improved genetic algorithm. This algorithm ensures the search direction closely aligns with the core optimization objective through a preference-guided mechanism, maintains population diversity and accelerates convergence through multivariate mutation and greedy crossover strategies, and dynamically balances global exploration and local exploitation through a multi-level search mechanism. These targeted improvements effectively overcome the shortcomings of traditional intelligent algorithms, such as premature convergence and susceptibility to local optima, significantly improving the accuracy and reliability of finding the optimal scheduling scheme.

[0036] Further or more detailed beneficial effects will be described in conjunction with specific embodiments in the detailed implementation. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0038] Figure 1 This is a flowchart illustrating the optimized scheduling method for the electro-hydrogen alcohol coupling system described in Embodiment 1 of the present invention.

[0039] Figure 2 This is a schematic diagram of the electrohydrogen alcohol coupling system described in Embodiment 1 of the present invention.

[0040] Figure 3 This is a curve showing the production efficiency of the methanol synthesis equipment described in Embodiment 1 of the present invention.

[0041] Figure 4 This is a flowchart illustrating the preference-guided multi-strategy improved genetic algorithm described in Embodiment 1 of the present invention.

[0042] Figure 5 This is a structural diagram of the electronic device described in Embodiment 3 of the present invention.

[0043] Icon labels:

[0044] 500. Electronic equipment;

[0045] 501. Processor; 502. Communication bus; 503. User interface; 504. Network interface; 505. Memory. Detailed Implementation

[0046] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0047] In the following description, several embodiments of the present invention are provided. Different embodiments can be substituted or combined. Therefore, the present invention can also be considered to include all possible combinations of the same and / or different embodiments described. Thus, if one embodiment includes features A, B, and C, and another embodiment includes features B and D, then the present invention should also be considered to include embodiments containing one or more other possible combinations of A, B, C, and D, even if such embodiments are not explicitly described in the following text.

[0048] The following description provides examples and does not limit the scope, applicability, or examples set forth in the claims. Changes may be made to the function and arrangement of the described elements without departing from the scope of the invention. Various processes or components may be appropriately omitted, substituted, or added to the various examples. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Furthermore, features described with respect to some examples may be combined into other examples.

[0049] To facilitate a better understanding of the embodiments of the present invention, its application scenarios will be explained before providing a detailed explanation of the specific implementation methods.

[0050] The electro-hydrogen-ethanol coupling system optimization scheduling method described in the embodiments of this specification is applied to the planning, design, and real-time operation scheduling of integrated energy systems that include renewable energy power generation, hydrogen conversion, and methanol synthesis. In these scenarios, the application of the electro-hydrogen-ethanol coupling system optimization scheduling method aims to improve system economy, increase energy utilization efficiency, ensure stable system operation, and achieve refined scheduling.

[0051] The following is a brief explanation of the electro-hydrogen alcohol coupling system, the refined nonlinear operating model, and the preference-guided multi-strategy improved genetic algorithm involved in several embodiments of this specification:

[0052] An electro-hydrogen-methanol coupling system is a comprehensive energy system that tightly couples multiple processes, including renewable energy power generation, water electrolysis for hydrogen production, carbon dioxide capture, and methanol synthesis. Typical components include, but are not limited to: renewable energy power generation equipment such as wind turbines and photovoltaic systems; energy storage equipment such as batteries; hydrogen energy conversion and storage equipment such as electrolyzers, hydrogen storage tanks, and hydrogen fuel cells; carbon capture equipment; and methanol synthesis equipment. This system achieves the synergistic conversion and complementary utilization of the three energy carriers—electricity, hydrogen, and methanol—and is a key infrastructure for enhancing renewable energy absorption capacity and producing green fuels and chemical feedstocks ("green methanol").

[0053] A refined nonlinear operating model refers to a mathematical model established for methanol synthesis equipment that accurately reflects its actual operating characteristics. Unlike the constant efficiency or piecewise linear models commonly used in existing technologies, the core of this model lies in modeling the output efficiency of the methanol synthesis equipment as a nonlinear function of its feed ratio (mass ratio of carbon dioxide to hydrogen). This model preferably uses interpolation fitting methods to fit experimental or theoretical data into a polynomial function, thereby continuously and accurately describing the complex impact of dynamic changes in the feed ratio on output efficiency. This model is a key technological foundation for improving the accuracy of the entire optimization scheduling method.

[0054] The preference-guided multi-strategy improved genetic algorithm is an intelligent optimization algorithm specifically designed for efficiently and accurately solving the complex nonlinear optimization scheduling model constructed in this invention. It is a multi-faceted improvement on the traditional genetic algorithm framework, with core features including: 1. Preference-guided mechanism: In the selection operation, individuals are prioritized not only based on their overall objective function value but also on their superior performance in specific key performance indicators (such as methanol synthesis efficiency and operating cost), thus guiding the population towards a more practically beneficial direction. 2. Multi-strategy collaboration: The algorithm integrates multiple mutation strategies (such as exchange, reversal, and insertion), greedy crossover based on extreme value tracking, and multi-level search (combining local refined search and global perturbation search), effectively balancing the algorithm's global exploration capability and local development capability, significantly overcoming the shortcomings of traditional algorithms such as premature convergence and getting trapped in local optima. 3. Targeted design: The improved strategy of this algorithm is designed specifically for the characteristics of decision variables (such as start / stop state, power, and feed ratio) in the optimal scheduling model of the electro-hydrogen-ethanol coupling system, thus outperforming general algorithms in both solution accuracy and efficiency.

[0055] Example 1:

[0056] like Figure 1 As shown, this embodiment provides an optimized scheduling method for an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio of methanol synthesis, including the following steps:

[0057] Step S1: Determine the composition framework of the electrohydrogen alcohol coupling system and establish the operational constraints of each device within the system.

[0058] For details, please refer to Figure 2 The electrohydrogen-ethanol coupling system includes a fan, photovoltaic system, load, battery, electrolyzer, hydrogen storage tank, hydrogen fuel cell, carbon capture equipment, and methanol synthesis equipment.

[0059] Specifically, the operational constraints of each device within the system include battery power constraints, battery energy constraints, electrolyzer start / stop constraints, electrolyzer power constraints, electrolyzer output constraints, hydrogen fuel cell start / stop constraints, hydrogen fuel cell power constraints, hydrogen fuel cell output constraints, hydrogen storage tank power constraints, hydrogen storage tank energy constraints, carbon capture equipment constraints, and methanol synthesis equipment constraints.

[0060] More specifically, the expression for the battery power constraint is:

[0061] (1),

[0062] In equation (1), For time period index, and These represent the battery's charging and discharging states, and These represent the charging and discharging power of the battery, and These are the upper limits of battery charging and discharging power.

[0063] More specifically, the expression for the battery energy constraint is:

[0064] (2),

[0065] In equation (2), , and These represent the battery's stored capacity at the beginning, end, and time period t, respectively. and These are the upper and lower limits of the battery's storage capacity, respectively. and These refer to the charging and discharging efficiency of the battery, respectively. This refers to the stored energy during the t+1 time period.

[0066] More specifically, the expression for the start-up and shutdown constraints of the electrolytic cell includes:

[0067] (3),

[0068] (4),

[0069] (5),

[0070] (6),

[0071] (7),

[0072] In equations (3)-(7), This represents the total number of sampling periods. and These represent the start-up and shutdown states of the electrolytic cell, respectively. For 0-1 variables, This indicates that the electrolytic cell is in operation. This indicates that the electrolytic cell is in a shut-down state. For unit time intervals, This indicates the operating or shut-down status of the electrolytic cell in the previous period. and These are the daily maximum number of times an electrolytic cell can be started and stopped.

[0073] More specifically, the expression for the electrolytic cell power constraint includes:

[0074] The upper and lower power limits for electrolytic cell operation are constrained, and their expressions are as follows:

[0075] (8),

[0076] In equation (8), This refers to the operating power of the electrolytic cell. and These are the upper and lower limits of the operating power of the electrolytic cell under working conditions.

[0077] The power constraint for ramping up the electrolytic cell is given by the following expression:

[0078] (9),

[0079] In equation (9), This represents the operating power of the electrolytic cell in the previous time period. This represents the maximum ramping power of the electrolytic cell per unit time period during operation.

[0080] More specifically, the expression for the output constraint of the electrolytic cell is:

[0081] (10),

[0082] In equation (10), The equivalent power of hydrogen production in the electrolyzer. This represents the hydrogen production efficiency of the electrolyzer.

[0083] More specifically, the expression for the start-stop constraint of the hydrogen fuel cell includes:

[0084] (11),

[0085] (12),

[0086] (13),

[0087] (14),

[0088] (15),

[0089] In equations (11)-(15), and These represent the start-up and shutdown states of the hydrogen fuel cell, respectively. For 0-1 variables, This indicates that the hydrogen fuel cell is in operation. This indicates that the hydrogen fuel cell is in a shut-down state. For unit time intervals, This indicates the previous operating or shut-down state of the hydrogen fuel cell. and These are the daily maximum number of times a hydrogen fuel cell can be started and stopped, respectively.

[0090] More specifically, the expression for the power constraint of the hydrogen fuel cell includes:

[0091] The upper and lower power limits for hydrogen fuel cells are defined, and their expressions are as follows:

[0092] (16),

[0093] In equation (16), This represents the equivalent input power of a hydrogen fuel cell. and These are the upper and lower limits of the operating power of a hydrogen fuel cell when it is powered on.

[0094] The ramp-up power constraint for hydrogen fuel cells is given by the following expression:

[0095] (17),

[0096] In equation (17), This represents the operating power of the hydrogen fuel cell in the previous time period. This represents the maximum ramp power of the hydrogen fuel cell per unit time period when it is powered on.

[0097] More specifically, the expression for the hydrogen fuel cell output constraint is:

[0098] (18),

[0099] In equation (18), For the power output of hydrogen fuel cells, This refers to the power generation efficiency of hydrogen fuel cells.

[0100] More specifically, the expression for the power constraint of the hydrogen storage tank is:

[0101] (19),

[0102] In equation (19), and These represent the charging and discharging states of the hydrogen storage tank, respectively. and These refer to the hydrogen filling and discharging power of the hydrogen storage tank, respectively. and These are the upper limits for hydrogen filling and hydrogen discharging power of the hydrogen storage tank, respectively.

[0103] More specifically, the expression for the energy constraint of the hydrogen storage tank is:

[0104] (20),

[0105] In equation (20), , and These represent the hydrogen storage capacity of the hydrogen storage tank at the beginning, end, and time period t, respectively. and These represent the upper and lower limits of hydrogen storage capacity in the hydrogen storage tank. and These refer to the hydrogen filling and discharging efficiency of the hydrogen storage tank. This represents the amount of hydrogen stored during the t+1 time period.

[0106] More specifically, the expression for the constraint of the carbon capture device includes:

[0107] (twenty one),

[0108] (twenty two),

[0109] In equations (21)-(22), The upper limit of carbon dioxide mass during the carbon dioxide capture process. The mass of carbon dioxide gas captured during the carbon capture process. The electrical power consumed in the carbon capture process. For unit time intervals, The amount of electricity consumed to capture 1 kg of carbon dioxide gas.

[0110] More specifically, the expressions constraining the methanol synthesis equipment include:

[0111] (twenty three),

[0112] (twenty four),

[0113] In equations (23)-(24), This represents the upper limit of methanol mass during methanol synthesis. The mass of the synthesized methanol. This represents the electrical power consumed during the methanol synthesis process. The output efficiency of the methanol synthesis equipment. For unit time intervals, The amount of electricity consumed to synthesize 1 kg of methanol.

[0114] Step S2: Establish a refined nonlinear operating model for the methanol synthesis equipment in the system. The output efficiency of the methanol synthesis equipment is modeled as a nonlinear function of its feed ratio.

[0115] Specifically, the refined nonlinear operation model is established using an interpolation fitting method, and the nonlinear function is a polynomial function. The feed ratio is the mass ratio of carbon dioxide to hydrogen input into the methanol synthesis equipment.

[0116] More specifically, the expression for the raw material feed ratio constraint is as follows:

[0117] (25),

[0118] (26),

[0119] (27),

[0120] (28),

[0121] In equations (25)-(28), This refers to the feed ratio in the methanol synthesis process, specifically the mass ratio of carbon dioxide to hydrogen. This represents the minimum feed ratio in the methanol synthesis process. This represents the maximum feed ratio in the methanol synthesis process. and These are the equivalent power of hydrogen and methanol used in the synthesis of methanol, respectively. and These are the calorific value equivalence coefficients for hydrogen and methanol, respectively, representing the equivalent amount of electricity produced by 1 kg of hydrogen and 1 kg of methanol. This represents the mass of hydrogen gas used in the synthesis of methanol.

[0122] After interpolation fitting, the fitting curve of the output efficiency of the methanol synthesis equipment versus the feed ratio is as follows: Figure 3 As shown, the nonlinear output efficiency constraint of the methanol synthesis equipment is:

[0123] (29).

[0124] Step S3: Establish an optimization objective function with the goal of minimizing system operating costs, and construct a system optimization scheduling model by combining the operating constraints and the refined nonlinear operating model.

[0125] Specifically, the optimization objective function is to minimize the total system operating cost, which is the sum of the electricity purchase cost and the hydrogen purchase cost, minus the revenue from selling electricity, hydrogen, and methanol.

[0126] More specifically, the expression for the optimization objective function is:

[0127] (30),

[0128] (31),

[0129] (32),

[0130] (33),

[0131] (34),

[0132] (35),

[0133] In equations (30)-(35), For time intervals, and The costs are for purchasing electricity and hydrogen, respectively. , and Revenue from electricity sales, hydrogen sales, and methanol sales, respectively. and These are the purchase price and the sale price of electricity, respectively. and These are the prices for purchasing and selling hydrogen, respectively. The selling price of methanol, and They are respectively the power of electricity purchased and sold. and These represent the hydrogen purchase and sales capacities, respectively. Power for selling methanol.

[0134] Specifically, the system optimization scheduling model must meet the following constraints, including system equipment model constraints (as in equations (1) to (24) above), electricity purchase and sale constraints, hydrogen purchase and sale power constraints, methanol sale power constraints, wind turbine output constraints, photovoltaic output constraints, electricity power balance constraints, hydrogen power balance constraints, feed ratio constraints (as in equations (25) to (28) above), and methanol synthesis equipment output efficiency constraints (as in equation (29) above).

[0135] More specifically, the expression for the power purchase and sale constraint is:

[0136] (36),

[0137] In equation (36), and These represent the power purchased and the power sold, respectively. and These are the state variables for electricity purchase and electricity sale, respectively. and These are the upper limits for electricity purchase and electricity sales, respectively.

[0138] More specifically, the expression for the hydrogen purchase and sale power constraint is:

[0139] (37),

[0140] In equation (37), and These represent the hydrogen purchase and sales capacities, respectively. and These are the state variables for purchasing and selling hydrogen, respectively. and These are the upper limits for hydrogen purchase and hydrogen sales capacity, respectively.

[0141] More specifically, the expression for the methanol sales power constraint is:

[0142] (38),

[0143] In equation (38), For methanol sales power, This is the upper limit for methanol sales capacity.

[0144] More specifically, the expression for the wind turbine output constraint is:

[0145] (39),

[0146] In equation (39), This refers to the output power of the fan. This is the upper limit of the fan's output power.

[0147] More specifically, the expression for the photovoltaic output constraint is:

[0148] (40),

[0149] In equation (40), For photovoltaic output power, This represents the upper limit of photovoltaic output power.

[0150] More specifically, the expression for the power balance constraint is:

[0151] (41),

[0152] In equation (41), This refers to the output power of the fan. For photovoltaic output power, For electrical load, and These are the charging and discharging power, respectively. and These represent the power purchased and the power sold, respectively. To input the power of the electrolytic cell, This refers to the electrical power output of the hydrogen fuel cell. The electrical power consumed in the carbon capture process. This represents the electrical power consumed during the methanol synthesis process.

[0153] More specifically, the expression for the hydrogen power balance constraint is:

[0154] (42),

[0155] In equation (42), For hydrogen load data, and These represent the hydrogen charging and discharging power, respectively. and These represent the hydrogen purchase and sales capacities, respectively. and These represent the power output from the electrolyzer and the power input to the fuel cell, respectively. This represents the equivalent power of hydrogen used in the synthesis of methanol.

[0156] Step S4: Use a preference-guided multi-strategy improved genetic algorithm to solve the system optimization scheduling model and output the optimal scheduling scheme.

[0157] For details, please refer to Figure 4 The preference-guided multi-strategy improved genetic algorithm described in the above steps includes the following steps:

[0158] Step 1: Initialization. Set the maximum number of generations and randomly generate N individuals. Individual variables include equipment start / stop status, power allocation, methanol feed ratio, and energy storage charging / discharging decisions. To increase population diversity, a multivariate mutation strategy is introduced during initialization. Three mutation methods are selected: exchange mutation, reverse mutation, and insertion mutation. Different selection probabilities are assigned to these three mutations to avoid getting trapped in local optima. The new individuals after multivariate mutation are used as the initial population.

[0159] Step 2: Individual Evaluation. Calculate the objective function value for each individual in the population;

[0160] Step 3: Selection Operation. The selection operator is applied to the population. Based on the individual objective function values, a roulette wheel selection method is used to select the best individuals from the current population as parents, which are then passed on to the next generation. During this process, a preference-guided mechanism is embedded: individuals with better performance in methanol synthesis efficiency and operating costs are prioritized, improving the targeting of the scheduling scheme.

[0161] Step 4: Crossover Operation. The crossover operator is applied to the population. For the selected parent individuals, a greedy crossover strategy based on extreme value tracking is used to generate new individuals. For each time period, a local greedy selection mechanism is first used to extract a candidate set for a specific individual variable from the parent and global optimal solutions, and then the crossover action that minimizes the objective function is selected from the candidate set. If there are insufficient candidates, the corresponding solution of the historical best individual is referenced according to the extreme value tracking mechanism.

[0162] Step 5: Mutation Operations. Perform multivariate mutation operations, such as exchange, reversal, and insertion, on some offspring individuals to maintain population diversity and escape local optima.

[0163] Step 6: Multi-level search: After selection, crossover, and mutation operations, the next generation of the population is obtained. The objective function value is calculated and the population is sorted according to the objective function value. For elite individuals, a local search strategy based on individual extrema is further applied: greedy improvements are used for high-impact individual variables such as feed ratio and power allocation to bring them closer to the optimal solution; reversals or interval perturbations are applied to low-impact variables such as partially redundant start-stop states to increase diversity. If the population similarity exceeds a threshold, a global refined search strategy based on assimilation degree is triggered: first, continuous neighborhood perturbations are added to the continuous variables of the population, and local state reversals are performed on discrete start-stop variables to form new population individuals.

[0164] Step 7: Terminate Output. When the number of times the objective function is called reaches the maximum number of iterations, output the individual with the smallest objective function value obtained during the evolution process as the optimal solution, i.e., the optimal scheduling scheme, and terminate the calculation.

[0165] Example 2:

[0166] This embodiment provides an optimized scheduling system for an electro-hydrogen-alcohol coupling system based on the dynamic feed ratio of methanol synthesis, used to implement the optimized scheduling method for the electro-hydrogen-alcohol coupling system as described in Embodiment 1.

[0167] Example 3:

[0168] like Figure 5 As shown, this embodiment provides an electronic device, which may include: at least one processor, at least one network interface, a user interface, a memory, and at least one communication bus.

[0169] The communication bus can be used to enable communication between the various components mentioned above.

[0170] The user interface may include buttons, and optional user interfaces may also include standard wired interfaces and wireless interfaces.

[0171] The network interface may include, but is not limited to, Bluetooth modules, NFC modules, Wi-Fi modules, etc.

[0172] The processor may include one or more processing cores. It connects various parts of the electronic device via various interfaces and lines, executing instructions, programs, code sets, or instruction sets stored in memory, and accessing data stored in memory to perform various functions and process data. Optionally, the processor can be implemented using at least one hardware form of DSP, FPGA, or PLA. The processor may integrate one or more of the following: CPU, GPU, and modem. The CPU primarily handles the operating system, user interface, and applications; the GPU is responsible for rendering and drawing the content required for display; and the modem handles wireless communication. It is understood that the modem may also be implemented as a separate chip without being integrated into the processor.

[0173] The memory may include RAM or ROM. Optionally, the memory may include a non-transitory computer-readable medium. The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-described method embodiments, etc.; the data storage area may store data involved in the above-described method embodiments, etc. Optionally, the memory may also be at least one storage device located remotely from the aforementioned processor. The memory, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a scheduling application. The processor can be used to call the scheduling application stored in the memory and execute the steps of the electro-hydrogen alcohol coupling system optimization scheduling method mentioned in the foregoing embodiments.

[0174] Example 4:

[0175] This embodiment provides a computer-readable storage medium storing instructions that, when executed on a computer or processor, cause the computer or processor to perform the above-described instructions. Figure 1 One or more steps in the illustrated embodiment. If the constituent modules of the above-described electronic device are implemented as software functional units and sold or used as independent products, they can be stored in the computer-readable storage medium.

[0176] In the above embodiments, implementation can be achieved, in whole or in part, through software, hardware, firmware, or any combination thereof. When implemented in software, it can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this specification are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in or transmitted through a computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available media may be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., Digital Versatile Discs (DVDs)), or semiconductor media (e.g., Solid State Disks (SSDs)).

[0177] Those skilled in the art will understand that all or part of the processes in the method of Embodiment 1 described above can be implemented by a computer program instructing related hardware. This program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks. Unless otherwise specified, the technical features of this embodiment and the implementation scheme can be combined arbitrarily.

[0178] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0179] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0180] The above description is merely an exemplary embodiment of the present invention and should not be construed as limiting the scope of the invention. Any equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of embodiments of the invention upon considering the specification and practicing the disclosure herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not described herein. The specification and embodiments are to be considered exemplary only, and the scope and spirit of the invention are defined by the claims.

Claims

1. An optimized scheduling method for an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio for methanol synthesis, characterized in that, Including the following steps: S1. Determine the composition framework of the electro-hydrogen alcohol coupling system and establish the operational constraints of each device in the system; The electro-hydrogen-ethanol coupling system includes a fan, photovoltaic system, load, battery, electrolyzer, hydrogen storage tank, hydrogen fuel cell, carbon capture equipment, and methanol synthesis equipment; The operational constraints of each device in the system include battery power constraints, battery energy constraints, electrolyzer start-up and shutdown constraints, electrolyzer power constraints, electrolyzer output constraints, hydrogen fuel cell start-up and shutdown constraints, hydrogen fuel cell power constraints, hydrogen fuel cell output constraints, hydrogen storage tank power constraints, hydrogen storage tank energy constraints, carbon capture equipment constraints, and methanol synthesis equipment constraints. S2. A refined nonlinear operation model is established for the methanol synthesis equipment in the system. The output efficiency of the methanol synthesis equipment is modeled as a nonlinear function of its feed ratio. The refined nonlinear operation model described in step S2 is established using an interpolation fitting method, and the nonlinear function is a polynomial function. The feed ratio is the mass ratio of carbon dioxide to hydrogen fed into the methanol synthesis equipment. S3. Establish an optimization objective function with the goal of minimizing system operating costs, and construct a system optimization scheduling model by combining the aforementioned operating constraints with the refined nonlinear operating model. S4. The system optimization scheduling model is solved by using a preference-guided multi-strategy improved genetic algorithm, and the optimal scheduling scheme is output.

2. The method for optimizing the scheduling of an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio for methanol synthesis according to claim 1, characterized in that: The optimization objective function in step S3 is to minimize the total system operating cost, which is the sum of the cost of purchasing electricity and the cost of purchasing hydrogen, minus the revenue from selling electricity, selling hydrogen and selling methanol. The system optimization scheduling model must meet the following constraints: system equipment model constraints, electricity purchase and sale constraints, hydrogen purchase and sale power constraints, methanol sale power constraints, wind turbine output constraints, photovoltaic output constraints, power balance constraints, hydrogen power balance constraints, feed ratio constraints, and methanol synthesis equipment output efficiency constraints.

3. The method for optimizing the scheduling of an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio for methanol synthesis according to claim 1, characterized in that, Step S4 employs a preference-guided multi-strategy improved genetic algorithm to solve the system optimization scheduling model, including the following steps: S41. Initialize the population. Individuals in the population include decision variables for equipment start-up and shutdown, power allocation, and feed ratio. A multivariate mutation strategy is used to enhance diversity. S42. Calculate the objective function value of each individual in the population as the fitness; S43. Perform the selection operation, embedding a preference guidance mechanism during the selection process to prioritize individuals that perform better in terms of methanol synthesis efficiency and operating cost; S44. Perform a greedy crossover operation based on extreme value tracking on the selected individuals; S45. Perform multivariate mutation operations on some new individuals; S46. Perform multi-level search operations, including local search based on individual extreme values ​​for elite individuals, and global refined search triggered for populations with similarity exceeding the threshold.

4. The method for optimizing the scheduling of an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio for methanol synthesis according to claim 3, characterized in that: The multivariate mutation strategies include exchange mutation, reverse mutation, and insertion mutation.

5. The method for optimizing the scheduling of an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio for methanol synthesis according to claim 3, characterized in that, Step S44 is as follows: For each decision variable, a set of candidate values ​​is constructed from the parent individual and the global historical best solution, and the crossover action that minimizes the objective function value is selected from them.

6. An optimized scheduling system for an electro-hydrogen-ethanol coupling system based on the dynamic feed ratio of methanol synthesis, characterized in that, This method is used to implement the optimized scheduling method for the electrohydrogen alcohol coupling system as described in any one of claims 1 to 5.

7. A computer device, the computer device comprising a memory, a processor, and a computer program, characterized in that, When the computer program is executed by the processor, it implements the optimized scheduling method for the electrohydrogen alcohol coupling system as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the optimized scheduling method for the electrohydrogen alcohol coupling system as described in any one of claims 1 to 5.

Images