Wind, light, hydrogen, ammonia and alcohol integrated collaborative optimization planning method, system, equipment and medium

Abstract

The invention relates to the technical field of comprehensive energy system planning and operation, and discloses a wind-solar-hydrogen-ammonia-alcohol integrated collaborative optimization planning method, system and equipment and a medium. The method comprises the following steps: constructing an integrated structure comprising a wind-solar power generation unit, an electrolytic hydrogen production unit, an air separation nitrogen production unit, a synthesis ammonia unit, a methanol synthesis unit, a carbon capture unit and a hydrogen storage unit; an electricity-hydrogen-nitrogen-carbon-ammonia-alcohol multi-energy flow network topological structure is established; constructing a capacity planning layer-operation simulation layer double-layer optimization model by taking the lowest total cost of the whole life cycle of the system as a target, and dynamically introducing carbon emission cost in an operation layer; and solving the model by adopting an improved multi-target genetic algorithm, and outputting the optimal planning capacity and the hour-level operation plan of each unit. According to the method, multi-energy flow collaboration, cross-period optimization and economic-low carbon collaboration of the wind-solar-hydrogen-ammonia-alcohol system under multiple uncertainties are realized, and the flexibility, the economical efficiency and the low carbon property of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system planning and operation technology, specifically to a method, system, equipment, and medium for integrated collaborative optimization planning of wind, solar, hydrogen, ammonia, and alcohol. Background Technology

[0002] As the global energy transition and industrial decarbonization accelerate, fluctuating renewable energy sources such as wind and solar power are being integrated into the grid on a large scale. Meanwhile, hydrogen, ammonia, and methanol, as important green energy carriers and chemical raw materials, are crucial for the coordinated production and efficient utilization of these resources, which is essential for building a new energy system. Against this backdrop, multi-energy flow coupling systems integrating "electricity-hydrogen-nitrogen-carbon-ammonia-ethanol" have emerged, but their planning and operation face challenges of high dimensionality, multiple time scales, and strong uncertainty.

[0003] Current planning methods for wind and solar hydrogen and ammonia production systems suffer from several problems: First, existing technologies suffer from insufficient system coupling, mostly limited to single conversion paths such as "electricity-hydrogen" or "electricity-ammonia," failing to deeply integrate multiple energy carriers such as hydrogen, ammonia, and alcohols with carbon capture units. This makes it difficult to construct a complete multi-energy flow collaborative network, limiting system flexibility and resource utilization. Second, planning and operation are severely disconnected. Traditional planning methods are mostly based on static or typical daily scenarios, failing to fully consider the full-time fluctuation characteristics of wind and solar power output, electricity prices, and market demand. This results in the planned capacity failing to achieve the expected economic efficiency and reliability during actual operation. Finally, in terms of optimization objectives, existing methods often unilaterally pursue optimal economic efficiency, lacking a mechanism to dynamically embed carbon emission costs into the operational layer, making it difficult to achieve effective synergy between economic efficiency and low carbon emissions under the "dual carbon" objective.

[0004] Therefore, existing technologies are insufficient to comprehensively consider the system's economy, low carbon emissions, and operational reliability in complex and ever-changing operating environments. A more integrated planning method is needed that can achieve cross-energy carriers, cross-time scales, and multi-objective coordination. Summary of the Invention

[0005] In view of the above-mentioned existing problems, the present invention provides a method, system, equipment and medium for integrated collaborative optimization planning of wind, solar and hydrogen ammonia alcohol.

[0006] Therefore, the technical problem solved by this invention is: how to achieve multi-objective synergistic optimization of system economy, low carbon emissions and operational reliability under multiple uncertainties such as wind and solar power output, electricity price and market demand, by constructing a deep coupling and integrated planning-operation model of multiple energy flows such as electricity-hydrogen-nitrogen-carbon-ammonia-alcohol, and dynamically embedding carbon emission costs.

[0007] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method, comprising, Identify the core system units and construct an integrated wind-solar-hydrogen-ammonia-ethanol system structure; Based on the integrated wind-solar-hydrogen-ammonia-ethanol system structure, the energy and material flow relationships between each unit are analyzed, and a multi-energy flow network topology is constructed. Based on the multi-energy flow network topology, a collaborative optimization planning model is constructed with the goal of minimizing the total cost of the system throughout its entire life cycle. By using a collaborative optimization planning model, the conversion rates of energy flow and material flow and the operational constraints in the model are obtained. An improved multi-objective genetic algorithm is used to solve the collaborative optimization planning model; Based on the solution results, the optimal planned capacity and operation plan are output.

[0008] As a preferred embodiment of the integrated wind-solar-hydrogen-amine-ethanol collaborative optimization planning method of the present invention, the step of determining the core unit of the system and constructing the integrated wind-solar-hydrogen-amine-ethanol system structure includes, The system's various units were analyzed to identify the core units, which were then combined to construct an integrated wind-solar-hydrogen-ammonia-ethanol system structure.

[0009] As a preferred embodiment of the integrated wind-solar-hydrogen-amine-ethanol collaborative optimization planning method described in this invention, the step of analyzing the energy and material flow relationships between each unit based on the integrated wind-solar-hydrogen-amine-ethanol system structure and constructing a multi-energy flow network topology includes: Analyze the energy and material flow relationships between units and construct a multi-energy flow network topology of "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol"; Using hydrogen as the core medium and flexible control hub, coupled with nitrogen and carbon dioxide, a multi-energy flow network is ultimately produced to produce ammonia and methanol.

[0010] As a preferred embodiment of the integrated wind-solar-hydrogen-amine-ethanol collaborative optimization planning method of the present invention, the collaborative optimization planning model is constructed based on a multi-energy flow network topology, with the goal of minimizing the total cost throughout the system's lifecycle. This includes... With the goal of minimizing the total cost throughout the system's lifecycle, a two-layer optimization framework consisting of a capacity planning layer and an operational simulation layer is constructed, and a multi-timescale collaborative optimization planning model is established.

[0011] As a preferred embodiment of the integrated wind-solar-hydrogen-ammonia-ethanol collaborative optimization planning method of the present invention, wherein: the conversion rate of energy flow and material flow and the operating constraints in the model are obtained through the collaborative optimization planning model, including, By establishing mathematical constraints, the units in the model are transformed from independent units into a deeply coupled and collaborative organic whole. The output constraints of wind and solar power generation units coordinate and couple uncontrollable new energy resources with controllable system units, requiring both actual wind and solar power output and the output of key equipment. Not exceeding the maximum theoretical output value: in, and The upper and lower limits of equipment output; Electric power balance constraints are the core of the entire system's energy dispatch, dynamically and collaboratively allocating electricity among generation, conversion, and storage, as shown in the following formula: in, For the output power of the wind farm, For the output power of photovoltaic power plants, In order to purchase electricity from the grid, The power consumption for hydrogen production in the electrolyzer, This refers to the power consumption of the air separation nitrogen production unit. For chemical synthesis of ammonia, The power consumption of methanol. Power consumption of the carbon capture unit; The mass flow balance constraint coordinates the flow of multiple substances, including hydrogen, nitrogen, carbon, ammonia, and alcohol, as shown in the following formula: in, This represents the hydrogen production value. The hydrogen value in the hydrogen storage tank used for chemical synthesis. The hydrogen value used in the synthesis of ammonia. The hydrogen value used in the synthesis of methanol. The value of hydrogen stored in the hydrogen storage tank, The nitrogen value produced by the air separation nitrogen generator. The value of nitrogen gas consumed in the ammonia synthesis unit. The amount of carbon dioxide captured by the carbon capture unit , representing the value of carbon dioxide purchased from external sources. The carbon dioxide value used in the methanol synthesis unit. The value of liquid ammonia produced by the ammonia synthesis unit. The value of liquid ammonia sold externally, This refers to the amount of liquid ammonia stored in the liquid ammonia tank. This refers to the amount of liquid ammonia consumed while stored in the liquid ammonia tank. This represents the amount of methanol produced by the methanol synthesis unit. This refers to the amount of methanol sold externally. The amount of methanol stored in the methanol tank. The amount of methanol in the consumed methanol tank.

[0012] As a preferred embodiment of the integrated wind-solar-hydrogen-amine-ethanol collaborative optimization planning method described in this invention, the method employs an improved multi-objective genetic algorithm to solve the collaborative optimization planning model, including: The collaborative optimization programming model is preprocessed and transformed into a standard optimization problem form. The mathematical model containing nonlinear constraints and mixed variables is transformed into a mixed integer linear programming problem. To address the high-dimensionality, nonlinearity, and mixed-integer characteristics of the integrated wind-solar-hydrogen-ammonia-ethanol collaborative optimization planning model, an improved multi-objective genetic algorithm is adopted as the core solution strategy.

[0013] As a preferred embodiment of the integrated wind-solar-hydrogen-amine-ethanol collaborative optimization planning method of the present invention, wherein: the step of outputting the optimal planning capacity and operation plan based on the solution results includes, After the solution is obtained, the optimal configuration capacity of each unit and the total cost of the system throughout its entire life cycle are taken as the core output results. Through simulation verification, the economy and low carbon emissions of different configuration schemes are compared.

[0014] This invention constructs a multi-energy flow network topology of "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol" with hydrogen as the core medium, and establishes a collaborative optimization planning model with the goal of minimizing the total cost over the entire life cycle. This enables the collaborative optimization and economical and efficient planning of multi-energy flow in wind-solar-hydrogen-ammonia-alcohol systems, thereby outputting the optimal planning capacity and operation plan.

[0015] This invention provides an integrated collaborative optimization planning system for wind, solar, hydrogen, ammonia, and methanol, comprising: The integrated structure construction module identifies the core units of the system, constructs the integrated system structure of wind, solar, hydrogen, ammonia, and methanol, and clarifies the functions and connections between each unit; The multi-energy flow network topology construction module, based on an integrated structure, analyzes the energy and material flow relationships between electricity, hydrogen, nitrogen, carbon, ammonia, and alcohol, and constructs a "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol" multi-energy flow network topology, with hydrogen as the core regulating medium. The collaborative optimization planning model construction module, based on the multi-energy flow network topology, establishes a two-layer optimization model with the goal of minimizing the total cost over the entire life cycle, covering two time scales: capacity planning and operational simulation. The conversion rate and constraint extraction module extracts the conversion rates and operational constraints of energy flow and material flow from the collaborative optimization planning model. The multi-objective optimization solution module uses an improved multi-objective genetic algorithm to solve the collaborative optimization programming model, transforming the model into a mixed-integer linear programming problem and handling high-dimensionality, nonlinearity, and mixed-integer characteristics. The optimal planning output module outputs the optimal configuration capacity and system operation plan for each unit based on the solution results. It then performs simulation verification on the output optimal planning scheme and compares the system performance of different configuration schemes.

[0016] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method.

[0017] 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 steps of a wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method.

[0018] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention constructs an integrated synergistic optimization planning method for wind, solar, hydrogen, ammonia, and alcohol. By establishing a multi-energy flow coupling network of "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol," it deeply integrates multiple energy conversion and chemical production units such as wind and solar power generation, electrolytic hydrogen production, air separation nitrogen production, synthetic ammonia, synthetic methanol, and carbon capture, achieving organic synergy between energy and chemical systems. This method adopts a two-layer optimization framework of "capacity planning layer - operation simulation layer." The upper layer determines the optimal configuration capacity of each unit with the goal of minimizing the total cost over the entire life cycle, while the lower layer formulates operation strategies in hourly increments. Carbon emission costs are dynamically introduced into the operation layer, enabling the system to flexibly adjust power purchase and production plans based on real-time carbon price signals, achieving a balance between long-term planning and short-term operation in terms of economic benefits and low-carbon goals. By establishing a complete mathematical description system covering equipment output constraints, power balance constraints, and multi-material flow balance constraints of hydrogen, nitrogen, carbon, ammonia, and alcohol, the real-time dynamic balance of energy and materials in the system and its safe and efficient operation are ensured. In terms of solution strategy, considering the high-dimensionality, nonlinearity, and mixed-integer characteristics of the model, an improved multi-objective genetic algorithm is adopted for global optimization. Through a population evolution mechanism, the optimal equipment configuration and operation scheme are efficiently searched, outputting the optimal planned capacity and hourly operation plan for each unit. This method significantly improves the system's economy, operational flexibility, and low-carbon performance through multi-energy flow coordination, inter-period optimization, and carbon cost internalization mechanisms. It effectively reduces wind and solar curtailment rates, enhances grid stability, and provides a feasible and reusable planning and operation solution for energy-chemical integrated systems with a high proportion of renewable energy access. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0020] Figure 1 The flowchart illustrates an integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as provided in one embodiment of the present invention.

[0021] Figure 2 This is a system structure diagram of an integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol according to an embodiment of the present invention.

[0022] Figure 3 The flowchart of an integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol is provided in one embodiment of the present invention. Detailed Implementation

[0023] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0024] Example 1, referring to Figure 1 As the first embodiment of the present invention, a wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method is provided, comprising: S1: Determine the core units of the system, construct the integrated wind-solar-hydrogen-amine-ethanol system structure, and based on the integrated wind-solar-hydrogen-amine-ethanol system structure, analyze the energy and material flow relationships between each unit and construct a multi-energy flow network topology.

[0025] S2: Based on the multi-energy flow network topology, a collaborative optimization planning model is constructed with the goal of minimizing the total cost of the system throughout its entire life cycle.

[0026] S3: Through the collaborative optimization planning model, the conversion rate of energy flow and material flow and the operating constraints in the model are obtained.

[0027] S4: An improved multi-objective genetic algorithm is used to solve the collaborative optimization planning model.

[0028] S5: Based on the solution results, output the optimal planned capacity and operation plan.

[0029] It should be noted that traditional wind-solar-hydrogen-ammonia-methanol system planning methods have significant limitations in multi-energy flow coupling. They typically focus only on single conversion paths such as "electricity-hydrogen" or "electricity-ammonia," failing to establish a complete "electricity-hydrogen-nitrogen-carbon-ammonia-methanol" multi-energy flow network, resulting in insufficient system integration and flexibility. Furthermore, existing methods exhibit poor planning and operational coordination, relying heavily on static or typical daily data and failing to fully account for the full-time fluctuations in wind and solar power output, electricity prices, and market demand. This makes it difficult for the planning results to achieve the expected economic efficiency and reliability in actual operation. In addition, traditional optimization models often unilaterally pursue optimal economic performance, failing to incorporate carbon emission costs as a dynamic key factor into operational objectives, and lacking a low-carbon-oriented system coordination mechanism. Therefore, it is necessary to research an integrated wind-solar-hydrogen-ammonia-methanol collaborative optimization planning method. This method should address issues such as low integration, disconnect between planning and operation, and difficulty in coordinating economic and low-carbon goals by constructing a multi-energy flow coupled system structure, establishing a two-layer optimization framework of "capacity planning-operation simulation," introducing a carbon emission cost mechanism, and employing multi-timescale coordination strategies.

[0030] Therefore, addressing the problems of insufficient multi-energy flow coupling, disconnect between planning and operation, and difficulty in balancing economic efficiency and low carbon emissions in traditional wind-solar-hydrogen-ammonia-methanol systems, this paper constructs a "electricity-hydrogen-nitrogen-carbon-ammonia-methanol" multi-energy flow network through steps S1-S5. This deeply couples units such as power generation, electrolytic hydrogen production, air separation nitrogen production, carbon capture, ammonia and methanol synthesis, and energy storage, thereby improving system integration, energy conversion flexibility, and material recycling efficiency. Secondly, a two-layer collaborative optimization framework of "capacity planning-operation simulation" is established. The upper layer plans equipment capacity with the goal of minimizing the total life-cycle cost, while the lower layer introduces carbon emission costs through 8760-hour time-series operation simulation and responds to wind and solar power output and market fluctuations, enhancing the dynamic coordination between planning and operation and balancing economic efficiency and low-carbon reliability. Furthermore, through systematic modeling to describe the operating characteristics and constraints of each unit, a power and multi-material flow balance system is constructed to improve real-time balance and operational safety under multi-energy flow coupling. In terms of solution methodology, an improved multi-objective genetic algorithm is employed to handle the high-dimensional nonlinear mixed-integer programming model. Combined with evolutionary operations and constraint handling mechanisms, the algorithm's global search capability, convergence speed, and solution set quality are enhanced. Ultimately, by outputting the optimal capacity of each unit and hourly operation plans, the economical, low-carbon, and reliable collaborative optimization of the wind-solar-hydrogen-ammonia-methanol integrated system is achieved throughout its entire lifecycle.

[0031] Example 2, refer to Figure 1 - Figure 3 As an embodiment of the present invention, based on the above embodiment, a wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method is provided.

[0032] In this embodiment of the application, step S1 determines the core unit of the system, constructs an integrated wind-solar-hydrogen-amine-ethanol system structure, and based on the integrated wind-solar-hydrogen-amine-ethanol system structure, analyzes the energy and mass flow relationships between each unit, and constructs a multi-energy flow network topology, including the following steps A1-A3: A1: The integrated structure of wind, solar, hydrogen, ammonia, and alcohol includes analyzing each unit of the system, determining the core unit of the system, and combining them to construct an integrated system structure of wind, solar, hydrogen, ammonia, and alcohol.

[0033] Specifically, the core unit of the system is defined as follows: the integrated wind-solar-hydrogen-ammonia-methanol system includes a wind and solar power generation unit, an electrolysis hydrogen production unit, an air separation nitrogen production unit, an ammonia synthesis unit, a methanol synthesis unit, a carbon capture unit, and an energy storage unit. The wind and solar power generation unit, comprising wind turbines and photovoltaic units, serves as the core energy input unit, converting wind and solar energy into electricity to provide power for the operation of subsequent units. This unit needs to rationally select the type of turbine and its installation layout based on the wind and solar resource endowment of the planned area to maximize renewable energy power generation.

[0034] The electrolysis hydrogen production unit, as the core of the system's energy conversion, uses electrolyzers to convert the green electricity generated by the wind and solar power generation units into hydrogen. The electrolyzer cluster can consist of alkaline electrolyzers and proton exchange membrane electrolyzers, and can be flexibly configured according to system response speed requirements and cost budgets. Its operating status directly affects hydrogen production and energy conversion efficiency.

[0035] The air separation nitrogen production unit extracts high-purity nitrogen through air separation technology, providing a key raw material for the ammonia synthesis reaction. This unit consumes electrical energy to operate; by coordinating with the electrolyzer, its operating load can be adjusted according to fluctuations in wind and solar power, thus mitigating power fluctuations and improving system stability.

[0036] The ammonia synthesis unit uses hydrogen produced by the water electrolysis hydrogen production unit and nitrogen provided by the air separation nitrogen production unit as raw materials to synthesize ammonia under catalyst and Haber-Bosch process conditions. This unit serves as a flexible load, allowing for adjustments to production load based on hydrogen supply and market ammonia demand, thus adapting to the fluctuating characteristics of renewable energy.

[0037] The methanol synthesis unit uses hydrogen and carbon dioxide as raw materials to synthesize methanol. The carbon dioxide mainly comes from the carbon capture unit, and when the capture volume is insufficient, it can be supplemented by purchasing carbon from external industrial emission sources. This unit realizes the recycling of carbon resources and is a key link in the system's low-carbon operation.

[0038] The carbon capture unit uses adsorption and separation technologies to capture carbon dioxide from external industrial emissions, which is then purified and transported to the methanol synthesis unit to provide raw materials for methanol synthesis. At the same time, it reduces greenhouse gas emissions and helps achieve carbon reduction targets.

[0039] The energy storage unit includes hydrogen storage tanks and ammonia / methanol product storage tanks, serving as energy and material buffers for the system. The hydrogen storage tanks store excess hydrogen produced by the water electrolysis hydrogen production unit, balancing the time difference between hydrogen supply and demand. The ammonia / methanol product storage tanks store synthesis products, addressing market demand fluctuations and resolving the time mismatch between energy utilization and product production.

[0040] A2: Analyze the energy and material flow relationships between units and construct a multi-energy flow network topology of "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol".

[0041] Specifically, the energy and mass flow relationships between each unit are analyzed to construct a multi-energy flow network topology of "electricity-hydrogen-nitrogen-carbon-ammonia-methanol". Hydrogen serves as the core medium, being the raw material source for the synthesis of ammonia and methanol; carbon, as a key element, is used for internal system circulation through capture technology. The multi-energy flow coupling network of the integrated wind-solar-hydrogen-ammonia-methanol system includes energy flow and mass flow, mainly involving electrical energy balance, hydrogen mass flow balance, nitrogen mass flow balance, carbon dioxide mass flow balance, and ammonia / methanol mass flow balance.

[0042] A3: A multi-energy flow network that uses hydrogen as the core medium and flexible control hub, coupled with nitrogen and carbon dioxide, to ultimately produce ammonia and methanol.

[0043] Specifically, the integrated wind-solar-hydrogen-ammonia-methanol system is a multi-energy flow network driven by electricity, using hydrogen as the core medium and flexible regulation hub, coupled with nitrogen and carbon dioxide, to ultimately produce ammonia and methanol. The electricity generated by the wind and solar power units is prioritized for power-consuming units such as the water electrolysis hydrogen production unit, air separation nitrogen production unit, carbon capture unit, ammonia synthesis unit, and methanol synthesis unit. When wind and solar power generation is insufficient, electricity can be purchased from the grid to supplement it. The energy storage unit participates in power balance regulation through energy storage and release. The hydrogen produced by the water electrolysis hydrogen production unit is allocated to the ammonia synthesis unit and the methanol synthesis unit according to the production plan, with excess hydrogen stored in hydrogen storage tanks. The nitrogen produced by the air separation nitrogen production unit is exclusively supplied to the ammonia synthesis unit. The carbon dioxide captured by the carbon capture unit is transported to the methanol synthesis unit. A portion of the ammonia produced by the ammonia synthesis unit and the methanol produced by the methanol synthesis unit are directly supplied to the chemical market, while the remainder is stored in product storage tanks for future use.

[0044] In an alternative implementation, the core units of the system can also be determined. When constructing an integrated wind-solar-hydrogen-ammonia-methanol system structure, the system units are reduced to a wind-solar power generation unit, a water electrolysis hydrogen production unit, an ammonia synthesis unit, and a methanol synthesis unit, thus constructing a multi-energy flow network topology.

[0045] Specifically, the wind and solar power generation unit includes wind turbines and photovoltaic units; the water electrolysis hydrogen production unit uses an alkaline electrolyzer; the ammonia synthesis unit uses externally purchased nitrogen and hydrogen produced by electrolysis as raw materials; and the methanol synthesis unit uses externally obtained carbon dioxide and hydrogen as raw materials.

[0046] The energy and material flow relationships between the units were analyzed, and a “electricity-hydrogen-ammonia-methanol” network topology was constructed. In this network, electricity is directly supplied from the wind and solar power generation unit to the water electrolysis hydrogen production unit, the ammonia synthesis unit, and the methanol synthesis unit. In the material flow, hydrogen is distributed from the water electrolysis hydrogen production unit to the ammonia synthesis unit and the methanol synthesis unit. Nitrogen and carbon dioxide are directly connected to the synthesis unit as external inputs.

[0047] Electricity is used as the direct driving force, and hydrogen is used as one of the synthesis raw materials. The operation of each unit is based on a fixed production plan.

[0048] In another alternative implementation, the core units of the system can also be determined. When constructing an integrated wind-solar-hydrogen-ammonia-methanol system structure, the system units retain wind and solar power generation units, water electrolysis hydrogen production units, air separation nitrogen production units, ammonia synthesis units, methanol synthesis units, carbon capture units, and energy storage units, and construct a multi-energy flow network topology.

[0049] Specifically, the wind and solar power generation unit includes wind turbines and photovoltaic units; the water electrolysis hydrogen production unit uses a proton exchange membrane electrolyzer; the air separation nitrogen production unit and carbon capture unit are directly connected to the wind and solar power generation unit for power supply; the ammonia synthesis unit and methanol synthesis unit receive the corresponding raw materials; and the energy storage unit includes a hydrogen storage tank and a product storage tank.

[0050] The energy and material flow relationships between the units were analyzed, and a separate network topology was constructed. The electrical energy flow is divided into two branches: one directly drives the air separation nitrogen production unit and the carbon capture unit, and the other supplies the water electrolysis hydrogen production unit and other units. In the material flow, hydrogen is distributed from the water electrolysis hydrogen production unit to the synthesis unit, while nitrogen and carbon dioxide are directly transported from their respective units. There is no cooperative coupling between the material flows.

[0051] Electricity is used as a multi-branch drive, while hydrogen, nitrogen, and carbon dioxide are used as parallel inputs. During system operation, each unit adjusts its load based on independent demand.

[0052] In this embodiment of the invention, step S2, based on the multi-energy flow network topology, constructs a collaborative optimization planning model with the goal of minimizing the total cost throughout the system's lifecycle, including the following steps B1-B4: B1: With the goal of minimizing the total cost of the system's entire lifecycle, a two-layer optimization framework of capacity planning layer and operation simulation layer is constructed, and a multi-timescale collaborative optimization planning model is established.

[0053] Specifically, such as Figure 2As shown, a system structure and unit models are established, and a collaborative optimization planning model is constructed with the goal of minimizing the total cost throughout the system's lifecycle. A two-layer optimization framework of "capacity planning layer - operation simulation layer" is built to achieve cross-period collaboration between long-term planning and short-term operation, ensuring the balance between economic efficiency and low carbon emissions.

[0054] A multi-timescale collaborative optimization architecture is established to achieve cross-period collaboration from long-term annual planning to short-term hourly operation, ensuring that long-term planning and short-term operation are unified in terms of economy and low carbon emissions. The upper layer is the capacity planning layer, with the optimization objective of minimizing the average annual cost over the system's entire lifecycle. Decision variables include the equipment configuration capacity of each unit, such as wind turbine installed capacity, photovoltaic module installed capacity, electrolyzer rated power, air separation unit scale, carbon capture equipment processing capacity, and energy storage unit storage capacity. The lower layer is the operation simulation layer, which, under the equipment configuration determined by the capacity planning layer, uses a typical year of 8760 hours as the time series and hours as the time step to formulate the time-series operation strategy for each unit. The optimization objective is to minimize the system's annual operating cost, with decision variables including the operating power of each power-consuming unit, hydrogen delivery volume, and ammonia and methanol production and storage volume. This layer introduces carbon emission costs to balance the relationship between the economic benefits of purchased resources and carbon costs, ensuring that short-term operation strategies are consistent with long-term low-carbon goals.

[0055] A two-layer optimization framework is constructed, encompassing a capacity planning layer and an operation optimization layer. The capacity planning layer, acting as the operational decision-making body, sets the annual system operation as the planning timeframe and conducts 8760-hour investment planning based on the installed capacity of each unit. The operation optimization layer, acting as the scheduling decision-making body, uses hours as the time step and, given the operating capacity of each unit, formulates the timing operation strategy for each unit.

[0056] B2: Mathematical modeling of the bidirectional collaborative optimization objective. The capacity planning layer mainly considers the total cost of investment and operation, and its optimization objective is to minimize the average annual cost over the entire life cycle.

[0057] Specifically, the cost of the integrated wind-solar-hydrogen-ammonia-ethanol system is divided into initial investment, operation and maintenance costs, personnel costs, consumable costs, and land rent. The objective function is as follows: in, The cost of a wind power / solar power hydrogen production and ammonia / methanol synthesis system, This is the capital recovery factor. For loan interest rates, For the facility's operating cycle, , , , , These include the initial investment, operation and maintenance costs, personnel salaries and benefits, consumable costs, and land rent for a wind, solar, and hydrogen production system for synthesizing ammonia and methanol. These are the initial investments for wind power stations, photovoltaic power stations, power transmission and distribution facilities, water electrolysis hydrogen production stations, raw material gas storage equipment, and air separation and synthetic ammonia stations. These include the operation and maintenance costs of wind power stations, photovoltaic power stations, power transmission and distribution facilities, water electrolysis hydrogen production stations, raw material gas storage equipment, and air separation and synthetic ammonia stations. These are the costs of purchasing electricity from the power grid, the costs of purchasing industrial water, the costs of purchasing chemical catalysts, and the costs of purchasing equipment parts.

[0058] B3: Introducing carbon emission costs into the operation optimization layer can balance the operational relationship between the economic benefits obtained from purchasing external resources and the carbon costs that need to be paid. That is, when the carbon price is high, the amount of electricity purchased is reduced; when the carbon price is low, electricity is purchased during the off-peak period to reduce the total cost, ensuring the consistency between the operation strategy and the planning objectives, and effectively reducing carbon emissions.

[0059] Specifically, the operation optimization layer combines energy costs and carbon emission costs, with the objective function of minimizing the total annual operating cost, taking into account equipment maintenance costs, purchased resource costs, and carbon emission costs. in, To minimize the annual operating cost of a wind power / solar power hydrogen production and ammonia / methanol synthesis system, These include the cost of purchasing electricity from the grid, the cost of purchasing industrial water, the cost of purchasing chemical catalysts, the cost of purchasing carbon dioxide, and the carbon emission cost. The carbon emission cost is related to the carbon emissions from electricity purchases. For carbon price, Purchase electricity for the power grid Power grid emission factors.

[0060] B4: Solve the two-layer collaborative optimization model. Through the above modeling process, the integrated collaborative optimization model constructed by this invention not only ensures the economic efficiency of investment through the upper-layer model, but also ensures deep synergy between short-term operating strategies and long-term low-carbon goals by introducing carbon costs into the lower-layer model. It can automatically find the planning and operation scheme with the lowest total cost throughout the entire life cycle of the system under a specific carbon pricing policy.

[0061] In an alternative implementation, a collaborative optimization planning model can also be constructed based on a capacity configuration scheme for sequential device connections.

[0062] Specifically, the integrated wind-solar-hydrogen-ammonia-ethanol system is viewed as a linear process connected in series, consisting of wind turbines, photovoltaic arrays, electrolyzers, hydrogen storage tanks, and synthesis units. At the capacity planning level, based on historical power generation data and downstream fixed production load demands, the installed capacity is allocated to each equipment unit from top to bottom. At the operation simulation level, power is allocated for each time period according to a fixed energy conversion path and a preset priority order, and an operation strategy is formulated.

[0063] In another alternative implementation, a collaborative optimization planning model can be constructed based on the piecewise planning scheme for independent optimization of isolated subsystems.

[0064] Specifically, the system is divided into two subsystems: a "power generation unit" and a "chemical unit." Energy and material coupling between them is ignored, and separate objectives are set for each subsystem. For the "power generation unit," the objective is to minimize its levelized cost of electricity over its entire lifecycle, independently optimizing the capacity configuration and operation strategies of wind, solar, and energy storage. For the "chemical unit," given the electricity and hydrogen supply curves and prices, the objective is to minimize the production cost of chemical products, independently optimizing the capacity and operation of the electrolyzer, hydrogen storage, and synthesis unit. The planning results of the two subsystems are then simply compared, and the assumed boundary conditions are repeatedly adjusted to achieve a rough match between supply and demand.

[0065] In this embodiment of the invention, step S3 uses a collaborative optimization planning model to obtain the conversion rate of energy flow and material flow and the operating constraints in the model, including the following steps C1-C4: C1: By establishing mathematical constraints, the units in the model are transformed from independent units into a deeply coupled and collaborative organic whole.

[0066] Specifically, such as Figure 3 As shown, the mathematical models of each unit in the system describe the energy conversion efficiency and operational constraints of each unit. By establishing mathematical constraints, the units in the system are transformed from independent units into a deeply coupled, collaborative organic whole. These constraints ensure that the system's planning and operation scheme can achieve the dynamic optimal allocation of matter and energy.

[0067] C2: Output constraints of wind and solar power generation units coordinate and couple uncontrollable new energy resources with controllable system units, requiring actual wind and solar power output and the output of key equipment. Not exceeding the maximum theoretical output value: in, and The upper and lower limits of equipment output.

[0068] C3: The power balance constraint is the core of the entire system's energy dispatch, dynamically and collaboratively allocating electricity among generation, conversion, and storage, as shown in the following formula: in, For the output power of the wind farm, For the output power of photovoltaic power plants, In order to purchase electricity from the grid, The power consumption for hydrogen production in the electrolyzer, This refers to the power consumption of the air separation nitrogen production unit. For chemical synthesis of ammonia, The power consumption of methanol. The power consumption of the carbon capture unit.

[0069] C4: The mass flow balance constraint coordinates the flow of multiple mass substances, including hydrogen, nitrogen, carbon, ammonia, and alcohol, as shown in the following formula: in, This represents the hydrogen production value. The hydrogen value in the hydrogen storage tank used for chemical synthesis. The hydrogen value used in the synthesis of ammonia. The hydrogen value used in the synthesis of methanol. The value of hydrogen stored in the hydrogen storage tank, The nitrogen value produced by the air separation nitrogen generator. The value of nitrogen gas consumed in the ammonia synthesis unit. The value of carbon dioxide captured by the carbon capture unit. The value of carbon dioxide purchased from external sources. The carbon dioxide value used in the methanol synthesis unit. The value of liquid ammonia produced by the ammonia synthesis unit. The value of liquid ammonia sold externally, This refers to the amount of liquid ammonia stored in the liquid ammonia tank. This refers to the amount of liquid ammonia consumed while stored in the liquid ammonia tank. This represents the amount of methanol produced by the methanol synthesis unit. This refers to the amount of methanol sold externally. The amount of methanol stored in the methanol tank. The amount of methanol in the consumed methanol tank.

[0070] Specifically, the optimization model established in this invention is subject to a series of constraints. These constraints not only define the boundaries of independent operation for each unit but are also crucial for achieving collaborative optimization of the integrated wind-solar-hydrogen-amine-ethanol system. Together, they constitute a rule system for the dynamic balance of energy and mass flow within the system, ensuring safe and efficient operation. The integrated wind-solar-hydrogen-amine-ethanol system is described using energy flow and mass flow, ensuring real-time balance and dynamic allocation of energy and mass flow within the system.

[0071] In an alternative implementation, a fixed-ratio allocation model can be used to obtain the conversion rate of energy flow to material flow and the operating constraints in the model.

[0072] Specifically, a fixed output ratio is set between the wind and solar power generation units and each controllable device, and the actual output of wind and solar power is directly allocated to the electrolysis, air separation, and synthesis units according to this ratio. Secondly, the power balance is allocated based on a fixed ratio, with grid power purchase as a fixed supplement. Finally, the material flow balance allocates hydrogen, nitrogen, carbon dioxide, etc. to the synthesis unit according to a fixed production ratio, and storage and transportation operations are performed according to a fixed ratio.

[0073] In another alternative implementation, the conversion rate of energy flow to material flow and the operating constraints in the model can be obtained by sequentially and independently optimizing the model.

[0074] Specifically, firstly, the output of the wind and solar power generation unit is optimized separately; secondly, based on the wind and solar output results, the operation of the electrolysis hydrogen production unit is optimized independently; then, the air separation nitrogen production, ammonia synthesis, methanol synthesis and carbon capture units are optimized independently in sequence, with each unit only accepting the output of the preceding unit as input; finally, the optimization results of each unit are simply superimposed, and the balance between electrical power and mass flow is attempted to be coordinated through post-adjustment.

[0075] In this embodiment of the application, step S4 employs an improved multi-objective genetic algorithm to solve the collaborative optimization planning model, including the following steps D1-D2: D1: Preprocess the collaborative optimization programming model to transform it into a standard optimization problem form. Transform the mathematical model containing nonlinear constraints and mixed variables into a mixed integer linear programming problem.

[0076] Specifically, model preprocessing is performed to reduce computational complexity and ensure solution efficiency. The model is transformed into a standard optimization problem form, using typical annual data (8760 hours of wind and solar power output, electricity prices, market demand), and considering resource characteristics and load features across different seasons and time periods. This simplifies the large-scale time series problem to a computable scale while retaining key time series features. The mathematical model, containing nonlinear constraints and mixed variables, is then transformed into a mixed-integer linear programming problem.

[0077] D2: To address the high-dimensionality, nonlinearity, and mixed-integer characteristics of the integrated wind-solar-hydrogen-ammonia-ethanol collaborative optimization planning model, an improved multi-objective genetic algorithm is adopted as the core solution strategy.

[0078] Specifically, by simulating the natural evolutionary process, the algorithm can efficiently search for the globally optimal or near-optimal solution without strictly linearizing or convexizing the original model. First, algorithm parameters are set, and a set of equipment capacity configuration schemes is randomly generated as the initial population, with each individual corresponding to a complete system capacity configuration. Second, each individual is substituted into the simulation layer model to calculate its total lifecycle cost, carbon emissions, and other objective function values, which are used as the individual's fitness. A new generation of the population is generated through selection, crossover, and mutation operations. The selection operation uses a roulette wheel algorithm to retain individuals with better fitness; the crossover operation combines the equipment capacity parameters of different individuals to generate new configuration schemes; the mutation operation randomly adjusts some parameters of individuals to increase population diversity. Finally, the individuals in the new generation population are checked to ensure they meet all constraints; individuals that do not meet the constraints are removed, and valid individuals are added. These steps are repeated until the maximum number of iterations is reached or the objective function value converges, outputting the equipment capacity configuration scheme corresponding to the optimal individual and the hourly operation plan for each unit.

[0079] In an alternative implementation, a single-objective genetic algorithm can also be used to solve the collaborative optimization planning model.

[0080] Specifically, the collaborative optimization planning model is preprocessed and transformed into a standard optimization problem. Typical year data is selected to simplify the time-series problem, and the model is transformed into a mixed-integer linear programming problem. Multiple objectives, such as total life-cycle cost and carbon emissions, are linearly weighted with fixed weights and merged into a single comprehensive objective function. Algorithm parameters are set, and equipment capacity configuration schemes are randomly generated as the initial population. The fitness of each individual under the single comprehensive objective function is calculated. A new generation of population is generated through roulette wheel selection, two-point crossover, and uniform mutation operations. Constraints are verified, individuals that do not meet the constraints are removed, and legal individuals are added. The iteration is repeated until the maximum number of iterations is reached, and the equipment capacity configuration scheme corresponding to the optimal individual is output.

[0081] In another alternative implementation, the collaborative optimization planning model can be solved based on a gradient-based sequential quadratic programming scheme.

[0082] Specifically, the collaborative optimization planning model is preprocessed and transformed into a standard optimization problem. Typical year data is selected to simplify the time-series problem, but some nonlinear constraints are retained, and it is not completely transformed into a mixed-integer linear programming problem. An equipment capacity configuration scheme is initialized as the starting point, and the gradient information of the objective function and constraints is calculated. A sequential quadratic programming method is adopted, and the solution is updated iteratively by constructing quadratic programming subproblems. The constraints are checked after each iteration. During the iteration process, integer variables are continuously relaxed, and integer rounding is performed near the final solution. The iteration is repeated until the gradient converges or the maximum number of iterations is reached, and the final equipment capacity configuration scheme is output.

[0083] In this embodiment of the application, step S5 outputs the optimal planned capacity and operation plan based on the solution results, including: After the solution is obtained, the optimal configuration capacity of each unit and the total cost of the system throughout its entire life cycle are taken as the core output results. Through simulation verification, the economy and low carbon emissions of different configuration schemes are compared.

[0084] Specifically, after the solution is completed, the core output results include: the optimal configuration capacity of each unit, the total cost composition of the system's entire life cycle, etc. Through simulation verification, with the help of the MATLAB platform and the CPLEX solver, the collaborative optimization model is simulated and analyzed to compare the economic efficiency and low carbon emissions of different configuration schemes. Simulation is carried out based on wind and solar power output data and electricity price data: input data preprocessing, model solving, and result analysis to ensure the feasibility of the optimal scheme.

[0085] In an alternative implementation, the solution process and result output can be simplified to output the optimal planned capacity and operating plan.

[0086] Specifically, after the solution is completed, only the optimal configuration capacity of each unit is output. When evaluating the scheme, the specific composition of the total cost over the entire system life cycle is not analyzed. Instead, the configuration capacity of each unit is directly allocated according to a fixed ratio based on the annual average output ratio of wind and solar resources, and the economic efficiency is estimated only based on typical daily data.

[0087] In another alternative implementation, a single indicator can be used to make decisions, outputting the optimal planned capacity and operating plan.

[0088] Specifically, after the solution is completed, only the optimal configuration capacity of each unit is output. When determining the final solution, the lowest total cost over the entire system lifecycle is used as the sole selection criterion.

[0089] Example 3 is the third embodiment of the present invention, which differs from the previous two embodiments in that: This embodiment also provides an integrated collaborative optimization planning system for wind, solar, hydrogen, ammonia, and methanol, including: The integrated structure construction module identifies the core units of the system, constructs the integrated system structure of wind, solar, hydrogen, ammonia, and methanol, and clarifies the functions and connections between each unit; The multi-energy flow network topology construction module, based on an integrated structure, analyzes the energy and material flow relationships between electricity, hydrogen, nitrogen, carbon, ammonia, and alcohol, and constructs a "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol" multi-energy flow network topology, with hydrogen as the core regulating medium. The collaborative optimization planning model construction module, based on the multi-energy flow network topology, establishes a two-layer optimization model with the goal of minimizing the total cost over the entire life cycle, covering two time scales: capacity planning and operational simulation. The conversion rate and constraint extraction module extracts the conversion rates and operational constraints of energy flow and material flow from the collaborative optimization planning model. The multi-objective optimization solution module uses an improved multi-objective genetic algorithm to solve the collaborative optimization programming model, transforming the model into a mixed-integer linear programming problem and handling high-dimensionality, nonlinearity, and mixed-integer characteristics. The optimal planning output module outputs the optimal configuration capacity and system operation plan for each unit based on the solution results. It then performs simulation verification on the output optimal planning scheme and compares the system performance of different configuration schemes.

[0090] This embodiment also provides an electronic device applicable to a wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning scenario, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement the wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method proposed in the above embodiment.

[0091] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements a wind-solar-hydrogen-ammonia-ethanol integrated collaborative optimization planning method as proposed in the above embodiment.

[0092] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for integrated wind, solar, hydrogen, ammonia, and alcohol optimization planning proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.

[0093] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0094] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. 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 be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A collaborative optimization planning method integrating wind, solar, hydrogen, ammonia, and alcohol, characterized in that: include, Identify the core system units and construct an integrated wind-solar-hydrogen-ammonia-ethanol system structure; Based on the integrated wind-solar-hydrogen-ammonia-ethanol system structure, the energy and material flow relationships between each unit are analyzed, and a multi-energy flow network topology is constructed. Based on the multi-energy flow network topology, a collaborative optimization planning model is constructed with the goal of minimizing the total cost of the system throughout its entire life cycle. By using a collaborative optimization planning model, the conversion rates of energy flow and material flow and the operational constraints in the model are obtained. An improved multi-objective genetic algorithm is used to solve the collaborative optimization planning model; Based on the solution results, the optimal planned capacity and operation plan are output.

2. The integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in claim 1, characterized in that: The core unit of the system is determined, and an integrated wind-solar-hydrogen-amine-ethanol system structure is constructed, including: The system's various units were analyzed to identify the core units, which were then combined to construct an integrated wind-solar-hydrogen-ammonia-ethanol system structure.

3. The integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in claim 2, characterized in that: Based on the integrated wind-solar-hydrogen-amine-ethanol system structure, the energy and material flow relationships between each unit are analyzed, and a multi-energy flow network topology is constructed. include, Analyze the energy and material flow relationships between units and construct a multi-energy flow network topology of "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol"; Using hydrogen as the core medium and flexible control hub, coupled with nitrogen and carbon dioxide, a multi-energy flow network is ultimately produced to produce ammonia and methanol.

4. The integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in claim 3, characterized in that: Based on a multi-energy flow network topology, and with the goal of minimizing the total cost throughout the system's lifecycle, a collaborative optimization planning model is constructed, including: With the goal of minimizing the total cost throughout the system's lifecycle, a two-layer optimization framework consisting of a capacity planning layer and an operational simulation layer is constructed, and a multi-timescale collaborative optimization planning model is established.

5. The integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in claim 4, characterized in that: The aforementioned collaborative optimization planning model yields the conversion rates of energy flow and material flow, as well as the operational constraints. include, By establishing mathematical constraints, the units in the model are transformed from independent units into a deeply coupled and collaborative organic whole. The output constraints of wind and solar power generation units coordinate and couple uncontrollable new energy resources with controllable system units, requiring both actual wind and solar power output and the output of key equipment. Not exceeding the maximum theoretical output value: in, and The upper and lower limits of equipment output; Electric power balance constraints are the core of the entire system's energy dispatch, dynamically and collaboratively allocating electricity among generation, conversion, and storage, as shown in the following formula: in, For the output power of the wind farm, For the output power of photovoltaic power plants, In order to purchase electricity from the grid, The power consumption for hydrogen production in the electrolyzer, This refers to the power consumption of the air separation nitrogen production unit. For chemical synthesis of ammonia, The power consumption of methanol. Power consumption of the carbon capture unit; The mass flow balance constraint coordinates the flow of multiple substances, including hydrogen, nitrogen, carbon, ammonia, and alcohol, as shown in the following formula: in, This represents the hydrogen production value. The hydrogen value in the hydrogen storage tank used for chemical synthesis. The hydrogen value used in the synthesis of ammonia. The hydrogen value used in the synthesis of methanol. The value of hydrogen stored in the hydrogen storage tank, The nitrogen value produced by the air separation nitrogen generator. The value of nitrogen gas consumed in the ammonia synthesis unit. The value of carbon dioxide captured by the carbon capture unit. The value of carbon dioxide purchased from external sources. The carbon dioxide value used in the methanol synthesis unit. The value of liquid ammonia produced by the ammonia synthesis unit. The value of liquid ammonia sold externally, This refers to the amount of liquid ammonia stored in the liquid ammonia tank. This refers to the amount of liquid ammonia consumed while stored in the liquid ammonia tank. This represents the amount of methanol produced by the methanol synthesis unit. This refers to the amount of methanol sold externally. The amount of methanol stored in the methanol tank. The amount of methanol in the consumed methanol tank.

6. The integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in claim 5, characterized in that: The improved multi-objective genetic algorithm is used to solve the collaborative optimization planning model, including: The collaborative optimization programming model is preprocessed and transformed into a standard optimization problem form. The mathematical model containing nonlinear constraints and mixed variables is transformed into a mixed integer linear programming problem. To address the high-dimensionality, nonlinearity, and mixed-integer characteristics of the integrated wind-solar-hydrogen-ammonia-ethanol collaborative optimization planning model, an improved multi-objective genetic algorithm is adopted as the core solution strategy.

7. The integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in claim 6, characterized in that: Based on the solution results, the optimal planned capacity and operation plan are output, including: After the solution is obtained, the optimal configuration capacity of each unit and the total cost of the system throughout its entire life cycle are taken as the core output results. Through simulation verification, the economy and low carbon emissions of different configuration schemes are compared.

8. A wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning system, employing the wind-solar-hydrogen-amine-ethanol integrated collaborative optimization planning method as described in any one of claims 1 to 7, characterized in that, include: The integrated structure construction module identifies the core units of the system, constructs the integrated system structure of wind, solar, hydrogen, ammonia, and methanol, and clarifies the functions and connections between each unit; The multi-energy flow network topology construction module, based on an integrated structure, analyzes the energy and material flow relationships between electricity, hydrogen, nitrogen, carbon, ammonia, and alcohol, and constructs a "electricity-hydrogen-nitrogen-carbon-ammonia-alcohol" multi-energy flow network topology, with hydrogen as the core regulating medium. The collaborative optimization planning model construction module, based on the multi-energy flow network topology, establishes a two-layer optimization model with the goal of minimizing the total cost over the entire life cycle, covering two time scales: capacity planning and operational simulation. The conversion rate and constraint extraction module extracts the conversion rates and operational constraints of energy flow and material flow from the collaborative optimization planning model. The multi-objective optimization solution module uses an improved multi-objective genetic algorithm to solve the collaborative optimization programming model, transforming the model into a mixed-integer linear programming problem and handling high-dimensionality, nonlinearity, and mixed-integer characteristics. The optimal planning output module outputs the optimal configuration capacity and system operation plan for each unit based on the solution results. It then performs simulation verification on the output optimal planning scheme and compares the system performance of different configuration schemes.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the integrated wind-solar-hydrogen-ammonia-ethanol collaborative optimization planning method according to any one of claims 1 to 7.

10. 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 steps of the integrated collaborative optimization planning method for wind, solar, hydrogen, ammonia, and alcohol as described in any one of claims 1 to 7.

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