Wind-solar-storage integrated hydrogen production capacity coordination optimization method

Abstract

The invention relates to a wind-solar-storage integrated hydrogen production capacity coordinated optimization method, which comprises the steps of scene analysis, optimization mechanism analysis, index evaluation, layout and optimization, and is used for communicating the coordinated operation of a plurality of power generation projects and hydrogen production and energy storage projects, reasonably allocating resources, reducing energy waste and ensuring green energy supply. The economic, environmental and social benefits of the project group are considered, the static collaborative configuration model for the capacity of the project group is constructed, the sustainable development capability of the project can be improved, and development and implementation of the project are facilitated. Aiming at the capacity collaborative configuration difficulties of huge project group scale, complex structural relationship, diversified optimization targets and various decision variables and constraint conditions, the method comprises the following steps: carding collaborative operation relationships of various types of equipment in a project group, introducing a double-layer optimization strategy, and carrying out outer-layer collaborative configuration optimization on the basis of optimal inner-layer collaborative operation; and the capacity of each type of equipment is reasonably configured, and the resource utilization value in the project group is maximized.

Description

Technical Field

[0001] The present invention belongs to the technical field of clean energy power generation dispatching, and relates to a method for coordinated optimization of hydrogen production capacity with wind, solar and storage integration. Background Art

[0002] At present, wind-solar-hydrogen coupled energy storage and fuel cell power generation systems have not yet reached the level of commercial operation. There is insufficient experience in the design of large-scale wind-hydrogen coupled energy storage demonstration projects. The main problems faced include: The low capacity of wind-solar-hydrogen coupling projects leads to poor project economics. The scale of existing wind power hydrogen production systems is generally within a few megawatts, which is far smaller than the hundreds of megawatts or even larger power generation capacity of centralized wind power. The insufficient capacity of electrolytic hydrogen production systems seriously restricts the practical application of wind-chlorine coupling systems. It is of great significance to explore larger-scale and larger-capacity wind power hydrogen production systems. The quality of wind-photovoltaic power and the power system's ability to absorb wind power urgently need to be improved, which puts forward high requirements on the capacity configuration and optimization control of wind turbines, electrolyzers, hydrogen storage, and fuel cells in the wind-hydrogen coupling system under off-grid / grid connection, as well as the intelligent coordination with grid demand; There is a spatial mismatch between the wind-photovoltaic energy resource endowment and the demand for electric hydrogen, and the large-scale use of hydrogen is relatively single, and the high-purity hydrogen market is not yet mature, which restricts the efficiency and economy of wind-hydrogen coupling projects. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a method for coordinated optimization of hydrogen production capacity with wind, solar and storage integration, which connects the coordinated operation of multiple power generation projects and hydrogen production and energy storage projects, reasonably allocates resources, reduces energy waste, and ensures green energy supply.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity, comprising the following steps: S1, scenario analysis, by generating typical wind turbine output scenarios, determining the preconditions for the coordinated operation optimization of the project group, and paving the way for the coordinated configuration optimization of the project group capacity; S2, optimization mechanism analysis, optimization path after determining optimization principles and key influencing factors; S3, indicator evaluation, including scientificity, pertinence, systematicness, comprehensiveness and dominance; S4, layout, based on geographic information big data, preliminary screening of the locations for the layout of wind, solar, storage and hydrogen production project groups in the region, excluding restrictive areas that do not meet the requirements of the veto index system, grading the suitability under each indicator, assigning corresponding scores and superimposing analysis, and preliminarily selecting the layout points of wind, solar and shared hydrogen energy centers with higher suitability based on the comprehensive suitability; S5, optimization, uses the feedback form from the inner layer to the outer layer of the double-layer optimization model to make decisions and continue to optimize; including data input, optimization model and output results.

[0005] In S1, the output power of the wind turbine is calculated according to the wind speed and normalized, and the basic output scenes are divided into spring, summer, autumn and winter. The output power of the wind turbine is calculated according to the wind speed and normalized, and the Gaussian kernel density estimation method is used to obtain the probability density and cumulative probability distribution of the unit output, and Latin hypercube sampling is performed on it to extract a large number of output scenes.

[0006] In S2, the optimization principle is a process of guiding all projects in the project group with a common optimization goal and obtaining greater overall economic, social and environmental benefits through unified coordination than the optimization of single project capacity configuration.

[0007] In S2, key influencing factors include layout planning, operation strategy, electric hydrogen demand and market price.

[0008] In S2, the optimization path is capacity coordination configuration from “project group layout optimization” to “static coordination configuration of project group capacity” and then to “dynamic coordination configuration of project group capacity”.

[0009] In S3, we consult data on optimal decision-making, wind power site selection, and hydrogen energy layout, and collect industry reports, policy requirements, and planning documents to analyze the actual decision-making needs of wind power, solar power, and hydrogen energy projects; classify and organize the data to provide reference and reference for the subsequent identification of factors affecting the optimization of project group layout and the determination of the evaluation index system.

[0010] In S4, the weight of the optimization index system is calculated, and the relative importance of the first-level indicators in the wind power layout point optimization and hydrogen energy layout point optimization index systems and the second-level indicators under the same first-level indicators are evaluated in a hierarchical manner based on the expert scoring method. The language value evaluation method that conforms to human thinking habits is adopted, and the indicators are divided into five levels: equally important, slightly important, relatively important, very important, and extremely important.

[0011] In S5, data input includes determining the maximum number of wind and solar power generators, energy storage battery electrolyzers, fuel cells and hydrogen storage tanks to be configured at each project layout point, and inputting environmental benefit parameters, equipment investment costs, and fixed construction cost project group planning data.

[0012] In S5, the optimization model is a multi-objective outer-layer collaborative configuration optimization model, which takes into account the economic, environmental and social benefits of the project group, takes the minimum total life cycle cost, the maximum carbon dioxide emission reduction and the minimum wind curtailment rate as the objective function, and is constrained by the site restrictions of each layout point of the project group.

[0013] In S5, the output structure is optimized by the outer layer to output the capacity configuration of the equipment at each layout point of the project group, and is input as a parameter into the inner layer model to optimize the coordinated operation of the project group, and output the equipment output, power transmission and grid connection, hydrogen sales and backup power generation at each layout point when the typical daily net profit is maximum; the output results of the inner layer model are fed back to the outer layer to obtain the values ​​of the total investment cost, carbon dioxide emission reduction and wind curtailment rate of the objective function.

[0014] The main beneficial effects of the present invention are: Analyzing the collaborative configuration mechanism of project group capacity provides decision makers with a relatively complete research framework and optimization path.

[0015] In response to the development bottleneck of weak scale effect and poor economic efficiency of wind power, solar power and energy storage hydrogen production projects, the concept of project group management was introduced, a collaborative development model was proposed, and the collaborative mechanism of the project group and the synergy effect among multiple projects were analyzed, which is conducive to the promotion of the project.

[0016] The key influencing factors of the optimization of project group capacity collaborative configuration were further refined and sorted out, and an optimization idea from "project group layout optimization" to "static capacity collaborative configuration" and then to "dynamic capacity collaborative configuration" was proposed to form a systematic research framework and implementation path.

[0017] Taking into account the economic, environmental and social benefits of the project group, a static collaborative configuration model of the project group capacity is constructed, which can improve the sustainable development capability of the project and facilitate the development and implementation of the project.

[0018] In view of the difficulty of capacity collaborative configuration in project groups with large scale, complex structural relationships, diverse optimization objectives, and numerous decision variables and constraints, this paper introduces a two-layer optimization strategy by sorting out the collaborative operation relationship of various types of equipment within the project group. On the basis of the optimal inner-layer collaborative operation, the outer-layer collaborative configuration is optimized, and the capacity of various equipment is reasonably allocated, thus maximizing the resource utilization value within the project group. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0020] Figure 1 This is a typical output scenario framework diagram of the project group of the present invention.

[0021] Figure 2 The capacity collaborative configuration optimization roadmap of the present invention is provided.

[0022] Figure 3 A flow chart is constructed for the evaluation index system of the present invention.

[0023] Figure 4 It is the optimization decision framework diagram of the present invention.

[0024] Figure 5It is a double-layer optimization framework diagram of the present invention. DETAILED DESCRIPTION

[0025] like Figure 1~Figure 5 A method for coordinating and optimizing the capacity of hydrogen production by integrating wind, solar and storage includes the following steps: S1, scenario analysis, by generating typical wind turbine output scenarios, determining the preconditions for the coordinated operation optimization of the project group, and paving the way for the coordinated configuration optimization of the project group capacity; S2, optimization mechanism analysis, optimization path after determining optimization principles and key influencing factors; S3, indicator evaluation, including scientificity, pertinence, systematicness, comprehensiveness and dominance; S4, layout, based on geographic information big data, preliminary screening of the locations for the layout of wind, solar, storage and hydrogen production project groups in the region, excluding restrictive areas that do not meet the requirements of the veto index system, grading the suitability under each indicator, assigning corresponding scores and superimposing analysis, and preliminarily selecting the layout points of wind, solar and shared hydrogen energy centers with higher suitability based on the comprehensive suitability; S5, optimization, uses the feedback form from the inner layer to the outer layer of the double-layer optimization model to make decisions and continue to optimize; including data input, optimization model and output results.

[0026] In the preferred scheme, in S1, the output power of the wind turbine group is calculated according to the wind speed, and it is standardized, and the basic output scenes are divided into spring, summer, autumn and winter; the output power of the wind turbine group is calculated according to the wind speed, and it is standardized, and the Gaussian kernel density estimation method is used to obtain the probability density and cumulative probability distribution of the unit output, and Latin hypercube sampling is performed on it to extract a large number of output scenes.

[0027] In the preferred solution, in S2, the optimization principle is to guide all projects in the project group with a common optimization goal, and obtain greater overall economic benefits, social benefits and environmental benefits through unified coordination than the optimization of single project capacity configuration.

[0028] Embodiment 1, like Figure 2 In the project group capacity collaborative configuration, there are static collaborative configuration and dynamic collaborative configuration: static collaborative configuration is based on the current economic and social development and regional energy demand, clarifies the collaborative operation strategy of the project group, and finds the Pareto frontier of capacity configuration solutions under multiple objectives on the basis of achieving optimal operation. Dynamic collaborative configuration is based on static collaborative configuration, taking into account multiple uncertain factors such as demand growth, price fluctuations and cost changes faced by the medium and long-term development of the project group, dividing the project group planning cycle into multiple stages, and determining the optimal solution of when, where and what capacity to configure through dynamic optimization.

[0029] The following basic principles are mainly followed in the optimization of project group capacity collaborative configuration: Principle of maximizing benefits. The project group obtains economic benefits by selling clean wind power and green hydrogen energy. The wind power and hydrogen produced by the project group can meet the growing energy demand, make beneficial contributions to economic and social development, and also have certain social benefits. In addition, the wind power and hydrogen produced by the project group are clean energy, which can replace the electricity and hydrogen produced by traditional high-carbon emission methods, reduce carbon emissions in the power sector and industrial sector, and have obvious environmental benefits. The coordinated configuration optimization of project group capacity is based on the principle of maximizing benefits. It takes the maximization of economic benefits, social benefits and environmental benefits throughout the life cycle as the optimization goal, and seeks the optimal solution for the entire process of coordinated configuration of project group capacity.

[0030] The principle of overall optimization. There are two optimization ideas for phased planning. The first is to find the optimal capacity allocation plan for each stage. In this case, the capacity coordination configuration sequence obtained is not necessarily the optimal solution for the entire planning cycle; the second is to find the optimal capacity coordination configuration strategy for the entire planning cycle. In this case, the capacity configuration plan for each stage is not necessarily the optimal solution for that stage. The project group capacity coordination configuration follows the second optimization idea, dynamically considering multiple uncertain factors such as changes in electric hydrogen energy demand and price fluctuations, and taking the overall optimization of the project group throughout the life cycle as the goal to find the optimal strategy for capacity coordination configuration throughout the entire process.

[0031] Demand-driven principle. In order to meet the growing energy demand in the future, the project group needs to be equipped with larger capacity energy production, transmission, storage and conversion equipment. If too much is configured in the early planning stage, it will cause a waste of resources due to the inability to absorb, greatly reducing the economic efficiency of the project group. At the same time, it does not meet the requirements of national policies and brings hidden dangers to the long-term development of the project group. The capacity coordination configuration of the project group is dominated by energy demand. The planning stage is reasonably divided according to the demand growth and other conditions. On the premise of meeting the energy demand of this stage, the optimal capacity coordination configuration plan is sought to maximize resource utilization.

[0032] In the preferred solution, in S2, the key influencing factors include layout planning, operation strategy, electric hydrogen demand and market price.

[0033] Embodiment 2, Layout planning: The project cluster consists of multiple wind power projects and hydrogen energy projects, which work together to meet the regional electricity and hydrogen needs. The wind resource conditions of the wind and photovoltaic layout points, the site constraints of the wind power and hydrogen energy layout points, and the power transmission distance between each wind power layout point and each hydrogen energy layout point are all important prerequisites for the coordinated configuration of the project cluster capacity.

[0034] Wind power generation is the energy source for the entire project group. The better the wind resource conditions at the wind power layout point, the higher the average output level of a single wind turbine unit, and the smaller the unit capacity required to meet the regional electricity and hydrogen demand, which is conducive to improving the overall economic benefits of the project group; the site constraints at the project group layout point determine the maximum number of equipment that can be configured for wind power projects and hydrogen energy projects; the distance between the wind power layout point and the hydrogen energy layout point affects the efficiency and cost of power transmission. The shorter the power transmission distance, the smaller the power loss within the project group and the lower the cost.

[0035] Operation strategy: The coordinated operation strategy of the project group determines the output of each device, which in turn affects the output of wind power and hydrogen. The coordinated configuration of project group capacity is to pursue greater economic, environmental and social benefits while meeting multiple constraints. For the same capacity configuration plan, different operation strategies will result in different benefit levels achieved by the project group. Inappropriate operation strategies cannot fully coordinate various resources within the project group, which may lead to waste of resources. In other words, if the operation strategy is not formulated properly, the comprehensive benefits obtained by the better project group capacity configuration plan may be smaller, causing the capacity coordinated configuration optimization to deviate from the optimal path.

[0036] Therefore, in order to seek the optimal capacity configuration scheme for the project group to obtain greater benefits, the optimal coordinated operation of the project group should be ensured first. Due to the large scale, complex structure, and numerous optimization variables and constraints of the project group, it is more difficult to formulate an operation strategy. Moreover, due to the limitations of the decision-makers' ability, artificially given operation strategies usually cannot guarantee optimal operation. Therefore, in the coordinated configuration of project group capacity, an inner-layer coordinated operation optimization model should be constructed, and through the iteration of the inner and outer layers, the optimal configuration scheme of the project group can be obtained on the basis of the optimal operation of the project group.

[0037] Electric hydrogen demand: The market demand for electricity and hydrogen in the project cluster layout area determines whether the produced wind power and hydrogen can be smoothly consumed and thus obtain benefits. For the short-term development plan of the wind-hydrogen coupling project cluster, the static coordinated configuration optimization of the project cluster capacity should be carried out based on the current acceptance level of wind power and hydrogen energy.

[0038] With the advent of the intelligent information age, the market demand for green electricity and hydrogen energy is bound to continue to rise. In order to meet the needs of future economic and social development, it is necessary to invest in more wind power and hydrogen energy projects and configure various types of equipment with larger capacity. Market demand changes in real time, and the dynamic and coordinated configuration of the capacity of the project group is a discrete decision. If the capacity configuration in the early stage of planning is too large, the wind power and hydrogen energy produced by the project group cannot be fully consumed, which not only causes a waste of resources but also does not meet the requirements of national policies, reduces the economic efficiency of the project group, and is not conducive to its sustainable development. Therefore, for the medium- and long-term development plan of the project group, it is necessary to combine the changes in the level of wind power and hydrogen energy consumption during the entire life cycle of the project group, reasonably divide the investment stages, and dynamically coordinate the capacity in stages to maximize resource utilization.

[0039] Market price: Electricity prices, hydrogen prices and major equipment costs directly affect the income and expenditure of the construction and operation of the wind-hydrogen coupling project group, and thus affect the economic benefits of the project group.

[0040] When electricity prices are more competitive, the project group will give priority to outputting electricity under the condition of meeting constraints in order to achieve higher economic benefits. When hydrogen prices are more competitive, the opposite is true. For the purchase of major equipment, if the price tends to decrease in the future, the later the installed capacity is increased, the lower the total cost of the project group; if the price tends to increase in the future, it is necessary to compare with the time value of money. If the price increase rate is greater than the fund discount rate, the later the installed capacity is increased, the higher the total cost of the project group. If the price increase rate is less than the fund discount rate, the opposite is true. With the development of the economy and society, the technological level continues to improve, the installed capacity and R&D investment of major equipment are gradually increasing, and the unit cost is reduced accordingly. In addition, the electricity price and hydrogen price will also change due to market fluctuations. The price and cost uncertainties faced by the future development of the project group should be comprehensively considered to carry out dynamic collaborative configuration of the project group capacity.

[0041] In the preferred solution, in S2, the optimization path is capacity coordination configuration from "project group layout optimization" to "static coordination configuration of project group capacity" and then to "dynamic coordination configuration of project group capacity".

[0042] Embodiment 3, The primary link in the optimization of the coordinated configuration of project cluster capacity is to identify the key factors affecting the layout of the project cluster from multiple dimensions such as nature, environment, economy and society, to refine the evaluation index system for layout optimization, and to construct a two-stage layout optimization model for the wind-hydrogen coupling project cluster based on GIS and PROMETHEE method.

[0043] Firstly, based on geographic information big data, restricted development areas are excluded and the layout suitability of the area is analyzed, and alternative layout points with high suitability for project groups are identified. Then, an optimization plan for the project group layout is given through preferential sorting decisions, and the layout locations of wind power projects and hydrogen energy projects in the project group are clarified, providing input parameters such as power transmission distance, wind resource conditions and site constraints for subsequent static and dynamic capacity collaborative configuration models.

[0044] Under the optimized project group layout, the static collaborative configuration of project group capacity is based on the current economic and social development and regional energy demand, taking into account the economic, environmental and social benefits of the project group, and adopts a two-layer optimization strategy to optimize the project group capacity configuration plan.

[0045] The inner layer collaborative operation optimization takes the maximum typical daily net income as the optimization goal, finds the optimal output of each device under the conditions of satisfying the equipment power constraints and the electric hydrogen capacity constraints, and clarifies the collaborative operation strategy of the project group; the outer layer collaborative configuration optimization takes the minimum total cost of the project group over the entire life cycle, the maximum carbon dioxide emission reduction and the minimum wind abandonment rate as the optimization goals, and finds the Pareto frontier of capacity configuration solutions under multiple objectives based on the improved non-dominated sorting genetic algorithm under the premise of optimal operation of the project group. Dynamic collaborative configuration of project group capacity is based on project group layout optimization and static collaborative configuration. It considers multiple uncertain factors such as demand growth, price fluctuations and cost changes faced by the medium- and long-term development of the project group, and analyzes and processes them through robust linear optimization, geometric Brownian motion and two-factor learning curve.

[0046] The project group planning cycle is divided into multiple stages. The inner model uses the maximum typical daily net profit of each stage as the optimization goal to find the optimal collaborative operation plan for the project group. The outer model uses the maximum total net profit of all stages as the optimization goal. The investment nodes corresponding to the stage division are also used as decision variables. The optimal plan of when, where and how to configure the capacity is determined through dynamic optimization.

[0047] In the preferred solution, in S3, data on optimal decision-making, wind power site selection, and hydrogen energy layout are consulted. At the same time, industry reports, policy requirements, and planning documents are collected to analyze the actual decision-making needs of wind power, solar power, and hydrogen energy projects. The data are classified and organized to provide reference for the subsequent identification of factors affecting the optimization of project group layout and the determination of the evaluation index system.

[0048] Embodiment 4, like Figure 3 In the design process of the evaluation index system, the principles of scientificity, pertinence, systematicness, comprehensiveness and dominance should be implemented.

[0049] Scientific principle: The evaluation index system for the optimization decision of the project group layout should be established on a scientific and reasonable basis, based on the essential characteristics and basic goals of the evaluation object and the evaluation problem, to achieve an effective evaluation of the suitability of the alternative regional layout projects. The design of this index system needs to be reasonable and traceable. The connotation and calculation method of the relevant indicators cannot be created arbitrarily, and should be identified, mined and referenced from academic papers, industry specifications, expert advice and other materials. Qualitative indicators should ensure that the connotation and evaluation criteria of the indicators are scientific and clear to avoid evaluation bias caused by insufficient understanding of the interviewees; quantitative indicators should ensure the rationality of the calculation method and the accessibility of the data to ensure the operability of the evaluation process. For indicators with ambiguous meanings and difficult data acquisition, scientific treatment should be carried out by comprehensively considering their necessity and substitutability.

[0050] Principle of targeting: The evaluation index system of layout optimization decision serves the specific decision-making problems of specific evaluation objects, and the characteristics of different service objects determine the different influencing factors of concern, and the focus of different research problems is different. The two decision-making elements should be clarified first, and the indicators should be sorted, analyzed and screened in a targeted manner. Focusing on the layout optimization problem of the project group, grasping the research focus of the problem, exploring and analyzing the characteristics of project and project group decisions, and constructing a targeted evaluation index system to ensure the high-quality completion of layout optimization.

[0051] Systematic principles: The optimization of project group layout is a complex decision-making problem with a multi-level structure, which requires systematic consideration of various factors. The construction of its evaluation index system needs to start from a systematic perspective and comprehensively consider the decision-making characteristics at all levels. The various influencing factors are not simply combined, but form an organic whole. The evaluation indicators do not overlap, complement each other, and are coordinated and unified, so as to obtain a logically clear and hierarchical evaluation index system for the optimization decision of project group layout.

[0052] Comprehensiveness principle: When planning the layout of a project group, it is necessary not only to evaluate whether the objective conditions of the layout area meet the needs of the construction and operation of the project group, but also to pay full attention to the economic benefits, social benefits and environmental friendliness of the project group. In the process of constructing the evaluation index system, the factors affecting the optimization of the layout of the project group should be deeply analyzed and explored from all aspects, and try to avoid omissions and full coverage. At the same time, it is necessary to avoid the situation where the functions of the indicators are similar and the evaluation content overlaps, so as to fully evaluate the suitability of all aspects of the candidate area without increasing the complexity of the evaluation process.

[0053] Dominant principles: There are many factors in the decision-making of project group layout optimization. If the constructed evaluation index system takes all the details into account, it will greatly increase the complexity of comprehensive evaluation, consume too much cost and energy, and reduce decision-making efficiency. For the problem of project group layout optimization, some evaluation indicators play a leading role, which can reflect the differences between different candidate regions and have a significant impact on the decision-making results. Therefore, when designing the evaluation index system, we should fully understand the connotation of the indicators, grasp the leading indicators, and eliminate the secondary factors that are insensitive to regional differences, so as to achieve scientific and efficient layout optimization decisions.

[0054] In the preferred scheme, in S4, the weight of the optimization index system is calculated, and the relative importance of the first-level indicators in the wind power layout point optimization and hydrogen energy layout point optimization index systems and the second-level indicators under the same first-level indicators are evaluated in a hierarchical manner based on the expert scoring method. The language value evaluation method that conforms to human thinking habits is adopted, and the indicators are divided into five levels: equally important, slightly important, relatively important, very important, and extremely important.

[0055] In the preferred solution, in S5, data input includes determining the maximum number of wind and solar power generators, energy storage battery electrolyzers, fuel cells and hydrogen storage tanks to be configured at each project layout point, and inputting environmental benefit parameters, equipment investment costs, and fixed construction cost project group planning data.

[0056] In the preferred scheme, in S5, the optimization model is a multi-objective outer layer collaborative configuration optimization model, which takes into account the economic benefits, environmental benefits and social benefits of the project group, takes the minimum total life cycle cost, the maximum carbon dioxide emission reduction and the minimum wind curtailment rate as the objective function, and is constrained by the site constraints of each layout point of the project group.

[0057] In the preferred solution, in S5, the output structure is optimized by the outer layer to output the capacity configuration of the equipment at each layout point of the project group, and is input as a parameter into the inner layer model to optimize the coordinated operation of the project group, and output the equipment output, power transmission and grid connection, hydrogen sales and backup power generation at each layout point when the typical daily net profit is maximum; the output results of the inner layer model are fed back to the outer layer to obtain the values ​​of the total investment cost, carbon dioxide emission reduction and wind curtailment rate of the objective function.

[0058] Embodiment 5, Firstly, based on geographic information big data, the locations suitable for the layout of wind-hydrogen coupling project groups in the study area are preliminarily screened, and the restrictive areas that do not meet the requirements of the veto index system are excluded. The suitability of the study area under each indicator is graded, and corresponding scores are assigned and superimposed for analysis. Based on the comprehensive suitability, the layout points of wind power and shared hydrogen energy centers with higher suitability are preliminarily selected.

[0059] Next, based on the optimization index system, a comprehensive evaluation is conducted on the advantages and disadvantages of each alternative layout point from multiple aspects such as nature, environment, economy and society. By determining the weight of each evaluation index and substituting it into the comprehensive ranking of the alternative layout points, the layout points of the wind power and shared hydrogen energy center of the project group are finally optimized.

[0060] like Figure 5 As shown in the figure, there are two types of feedback from the inner layer to the outer layer in the two-layer optimization model: One is to feed back the objective function value corresponding to the optimal decision of the inner optimization to the outer model. This form does not require the uniqueness of the decision, allowing the inner optimization to obtain multiple optimal solutions, and continuing to optimize the outer objective only based on the optimal value fed back. The second is to feed back the optimal decision solution of the inner optimization, and then calculate the objective function value of the outer model according to the decision variable value and continue to optimize.

[0061] Input data: The outer optimization model needs to determine the maximum number of wind turbines, energy storage batteries, electrolyzers, fuel cells and hydrogen storage tanks for each wind power project and hydrogen energy project layout point according to the site conditions of the wind-hydrogen coupling project group layout points obtained in the previous chapter, and also needs to input environmental benefit parameters, equipment investment costs, fixed construction costs and other project group planning related data. The input data of the inner optimization model includes regional electricity and hydrogen demand loads, technical and economic parameters of major equipment and other project group collaborative operation related data.

[0062] Optimizing the model: The outer collaborative configuration optimization model is a multi-objective optimization model that takes into account the economic, environmental and social benefits of the wind-hydrogen coupling project group, with the minimum total cost of the entire life cycle, the maximum carbon dioxide emission reduction and the minimum wind abandonment rate as the objective function, and is constrained by the site constraints of each layout point of the project group. The inner collaborative operation optimization model is a single-objective optimization model that takes the maximum typical daily operation net profit as the objective function to solve the optimal operation plan of the project group. The constraints include the output power constraints of the main equipment, the balance constraints of electricity / hydrogen, the demand constraints of electricity / hydrogen, the state and capacity constraints of storage and discharge / hydrogen, etc.

[0063] Output: The outer optimization outputs the capacity configuration of the main equipment at each layout point of the project group, and inputs it as a parameter into the inner model to optimize the coordinated operation of the project group, and outputs the equipment output, power transmission and grid connection, hydrogen sales and backup power generation at each layout point when the typical daily net income is maximum. The output results of the inner model are fed back to the outer layer to obtain the total investment cost, carbon dioxide emission reduction and wind abandonment rate of the objective function. Through repeated iterative optimization of the inner and outer layers, the optimization results of the coordinated configuration of the project group capacity are finally output.

[0064] The above-mentioned embodiments are only preferred technical solutions of the present invention and should not be regarded as limitations of the present invention. The embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. The protection scope of the present invention shall be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity, characterized in that: The steps include: S1, scenario analysis, by generating typical wind turbine output scenarios, determining the preconditions for the coordinated operation optimization of the project group, and paving the way for the coordinated configuration optimization of the project group capacity; S2, optimization mechanism analysis, optimization path after determining optimization principles and key influencing factors; S3, indicator evaluation, including scientificity, pertinence, systematicness, comprehensiveness and dominance; S4, layout, based on geographic information big data, preliminary screening of the locations for the layout of wind, solar, storage and hydrogen production project groups in the region, excluding restrictive areas that do not meet the requirements of the veto index system, grading the suitability under each indicator, assigning corresponding scores and superimposing analysis, and preliminarily selecting the layout points of wind, solar and shared hydrogen energy centers with higher suitability based on the comprehensive suitability; S5, optimization, uses the feedback form from the inner layer to the outer layer of the double-layer optimization model to make decisions and continue to optimize; including data input, optimization model and output results.

2. The method for coordinated optimization of wind-solar-storage integrated hydrogen production capacity according to claim 1 is characterized in that: In S1, the output power of the wind turbine is calculated according to the wind speed, and is normalized to divide the basic output scenarios into spring, summer, autumn and winter. The output power of wind turbines is calculated based on the wind speed and normalized. The Gaussian kernel density estimation method is used to obtain the probability density and cumulative probability distribution of the unit output. Latin hypercube sampling is performed to extract a large number of output scenarios.

3. The method for coordinated optimization of wind-solar-storage integrated hydrogen production capacity according to claim 1 is characterized in that: In S2, the optimization principle is a process in which all projects in the project group are guided by a common optimization goal and, through unified coordination, greater overall economic, social and environmental benefits are obtained than the optimization of the capacity configuration of a single project.

4. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized by: In S2, key influencing factors include layout planning, operation strategy, electric hydrogen demand and market price.

5. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized in that: In S2, the optimization path is capacity coordination configuration from "project group layout optimization" to "static coordination configuration of project group capacity" and then to "dynamic coordination configuration of project group capacity".

6. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized by: In S3, we consult data on optimal decision-making, wind power site selection, and hydrogen energy layout, and collect industry reports, policy requirements, and planning documents to analyze the actual decision-making needs of wind power, solar power, and hydrogen energy projects; classify and organize the data to provide reference and reference for the subsequent identification of factors affecting the optimization of project group layout and the determination of the evaluation index system.

7. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized in that: In S4, the weight of the optimization index system is calculated, and the relative importance of the first-level indicators in the wind power layout point optimization and hydrogen energy layout point optimization index systems and the second-level indicators under the same first-level indicators are evaluated in a hierarchical manner based on the expert scoring method. The language value evaluation method that conforms to human thinking habits is adopted, and the indicators are divided into five levels: equally important, slightly important, relatively important, very important, and extremely important.

8. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized by: In S5, data input includes determining the maximum number of wind and solar power generators, energy storage battery electrolyzers, fuel cells and hydrogen storage tanks to be configured at each project layout point, and inputting environmental benefit parameters, equipment investment costs, and fixed construction cost project group planning data.

9. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized by: In S5, the optimization model is a multi-objective outer-layer collaborative configuration optimization model, which takes into account the economic, environmental and social benefits of the project group, takes the minimum total life cycle cost, the maximum carbon dioxide emission reduction and the minimum wind curtailment rate as the objective function, and is constrained by the site restrictions of each layout point of the project group.

10. The method for coordinated optimization of wind, solar and storage integrated hydrogen production capacity according to claim 1 is characterized in that: In S5, the output structure is optimized by the outer layer to output the capacity configuration of the equipment at each layout point of the project group, and is input as a parameter into the inner layer model to optimize the coordinated operation of the project group, and output the equipment output, power transmission and grid connection, hydrogen sales and backup power generation at each layout point when the typical daily net profit is maximum; the output results of the inner layer model are fed back to the outer layer to obtain the values ​​of the total investment cost, carbon dioxide emission reduction and wind curtailment rate of the objective function.

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