A method and device for coordinated planning and economic evaluation of a wind-solar-hydrogen storage system

Abstract

The application provides a wind-solar-hydrogen storage system collaborative planning and economic evaluation method and device, belonging to the technical field of new energy and hydrogen energy system planning. The method: obtains input data of wind power and photovoltaic, processes known output or resource data mode according to the data type of the application scene, generates annual time sequence output sequence, and configures the operation time interval of the chemical device; determines the optimization variable, the objective function and the constraint condition based on the annual output sequence and the operation time interval, constructs a full-industry-chain collaborative optimization mathematical model; calls a solver to solve the model, obtains a capacity configuration scheme, links the dynamic financial parameters in the built-in database, uses a preset financial evaluation rule to calculate the economic evaluation index of the whole life cycle; and visually displays the capacity configuration scheme and the economic evaluation index of the whole life cycle. The application realizes full-industry-chain collaborative optimization and intelligent diagnosis through double-mode data and dynamic attenuation simulation.

Description

Technical Field

[0001] This application belongs to the field of new energy and hydrogen energy system planning technology, specifically involving a method and device for collaborative planning and economic evaluation of wind-solar-hydrogen storage systems. Background Technology

[0002] With the development of new power systems based on new energy sources, integrated wind-solar-hydrogen energy storage systems, by combining fluctuating wind and solar power generation with hydrogen production and energy storage, can effectively improve the capacity for new energy consumption and produce green hydrogen energy, with broad application prospects.

[0003] However, the planning methods for integrated wind, solar, and hydrogen energy storage systems still have the following problems: First, there is the problem of independent planning across multiple stages. Traditional methods design wind and solar power generation, hydrogen production, and energy storage independently, lacking overall collaborative optimization, making it difficult to achieve global optimization in system energy efficiency and investment costs. Second, data processing flexibility is insufficient. The acquisition conditions for wind and solar power output data vary, and existing methods have a single data input mode, which cannot adapt to different scenarios with existing historical operating data of known output modes or raw resource data only. Furthermore, it fails to dynamically match the operating constraints of chemical plants (such as hydrogen production plants). Third, there is a disconnect between technical and economic indicators. The calculation of equipment capacity planning and technical and financial indicators usually requires manual coupling, which is inefficient and error-prone, and cannot achieve real-time, coordinated evaluation of the economics throughout the entire life cycle. Finally, parameter debugging efficiency is low. In complex multi-constraint optimization calculations, there is a lack of automatic diagnosis mechanisms for parameter conflicts, which relies heavily on manual experience for debugging, resulting in long planning cycles and high costs.

[0004] Therefore, there is an urgent need for a planning method for wind-solar-hydrogen storage systems that can achieve collaborative modeling across the entire industry chain, flexibly adapt to data scenarios, conduct technical and economic assessments, and possess intelligent diagnostic capabilities. Summary of the Invention

[0005] In a first aspect, embodiments of this application provide a method for the coordinated planning and economic evaluation of a wind-solar-hydrogen storage system, comprising the following steps: S1. Acquire input data from wind power and photovoltaics, select known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. S2. Based on the annual power output sequence and operating time interval, the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply are used as optimization variables, the preset economic indicators are used as objective functions, and the system energy balance and equipment operation characteristics are used as constraints to construct a mathematical model for collaborative optimization of the entire industry chain. S3. Call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; if the solution fails and a preset error code is returned, the parameter conflict location will be automatically located and prompted according to the preset intelligent diagnostic rules. S4. Take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. S5. Visualize the capacity configuration scheme and the economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files.

[0006] Furthermore, the specific steps of step S1 are as follows: S11. Determine the application scenario; If it is an existing power plant, proceed to step S12; If it is a planned power plant, proceed to step S13; S12. If a known output mode is selected, an xlsx format template containing normalized data between [0,1] is directly imported to represent the proportion of the total output to the maximum output capacity within the corresponding time period. For wind power or photovoltaic, the actual output power is equal to the product of the installed capacity and the corresponding normalization coefficient. Proceed to step S2. S13. Select the resource data mode and determine the power output mode; For wind power generation, proceed to step S14; For photovoltaic power generation, proceed to step S15; S14. Obtain wind speed data measured by a wind measurement tower at a preset height. Wind shear parameters Wheel hub height Cut-in wind speed Cut-off wind speed and rated wind speed Calculate the normalization coefficients according to the following procedure. : Calculate the wind speed at the hub height ,in, This is the preset reference height for the wind measurement tower; according to Determine the normalization coefficient for wind power generation : like or ,but ; like ,but ; like ,but ; Wind power generation capacity ; S15. Obtain ambient temperature and total solar irradiance and reference irradiance Reference temperature and short-circuit current temperature coefficient The normalized coefficient of photovoltaic power generation is calculated using the following formula. :

[0007] Photovoltaic power generation ; S16. Configure the operating time range of the chemical plant and determine the operating mode; If the operation is to be continuous, the default start and end points are used, which correspond to the start and end points of the preset time period. If it is a non-continuous operation, the end point of the first segment is set earlier than the end point of the preset time period, and the start and end points of the next segment are configured. The start point of the next segment is later than the end point of its adjacent previous segment, until all segments are configured, resulting in several non-continuous operation segments.

[0008] Furthermore, in step S1, after the normalization coefficient calculation in S14 or S15 is completed, and before step S16, the resource data model also includes simulating the degradation of wind and solar resources or equipment performance over time. The specific steps are as follows: S101. Set an attenuation coefficient that decreases linearly according to a preset time period, wherein the attenuation coefficient includes the wind power generation attenuation coefficient. and photovoltaic power generation attenuation coefficient These are used to simulate the performance degradation of wind turbine generators and photovoltaic modules as they age; S102. Wind power generation normalization coefficient calculated based on step S14 Combined with the wind power attenuation coefficient and operating years Calculate the actual normalized coefficient of wind power generation after considering attenuation. :

[0009] in, This represents the year the system was launched, and its value is... , This indicates the first year of operation, during which there is no degradation. S103. Photovoltaic power generation normalization coefficient calculated based on step S15 Combined with photovoltaic power generation attenuation coefficient and operating years Calculate the actual normalized coefficient of photovoltaic power generation after considering degradation. :

[0010] S104. Substitute the normalized coefficient, which takes into account attenuation, into the power calculation formulas in S14 and S15 to obtain the wind power generation considering long-term attenuation. and photovoltaic power generation :

[0011] ; S105. The power sequence after attenuation is taken as the input to the mathematical model of the whole industry chain collaborative optimization in step S2, so as to carry out multi-year collaborative optimization and economic evaluation.

[0012] Furthermore, the specific steps of step S2 are as follows: S21. Set optional optimization variables, including wind turbine scale, wind turbine output sequence, photovoltaic scale, photovoltaic output sequence, maximum grid-connected power, grid-connected power sequence, green electricity grid-connected power sequence, electrolysis unit scale, electrolysis unit electricity consumption sequence, energy storage scale, energy storage power sequence, and hydrogen production volume sequence; S22. Determine whether each optional optimization variable has been given a definite value in the interactive interface configuration; If so, then the optional optimization variables that give definite values ​​will be removed; If not, then each optional optimization variable will be used as the final optimization variable; S23. Set the objective function to maximize the annual revenue. The components of the annual revenue include hydrogen sales revenue, green electricity grid connection revenue, maintenance and management insurance costs for each module, depreciation of each module, hydrogen production circulating water cost, chemical plant personnel cost, and the discounted total construction investment. S24. Set constraints, the constraints including: Energy balance constraints of integrated wind-solar-hydrogen-storage energy systems:

[0013] in, , , , , They represent Real-time wind power generation, photovoltaic power generation, lithium battery energy storage output, hydrogen energy storage output, and electrical load power input from the grid; Power upper and lower limit constraints:

[0014] in, , These represent the minimum and maximum permissible operating power of the wind power generation system, respectively. , These represent the minimum and maximum permissible operating power of the photovoltaic power generation system, respectively. , These represent the minimum and maximum charge / discharge power allowed by the lithium battery energy storage system, respectively. Negative values ​​indicate the maximum charging power, and positive values ​​indicate the maximum discharging power. , These represent the minimum and maximum permissible power output of the hydrogen energy storage system, respectively. Energy storage system operating state constraints:

[0015] in, , These represent the state of charge of a lithium battery energy storage system and the maximum permissible state of hydrogen pressure of a hydrogen energy storage system, respectively. Hydrogen production constraints:

[0016] in, This represents the volume of hydrogen produced by the electrolytic hydrogen production system during time period t. This indicates the hydrogen supply demand required by downstream chemical plants during the corresponding time period. Hydrogen production constraints ensure that the total hydrogen production in each period is not less than the hydrogen supply demand in the corresponding period.

[0017] Furthermore, after setting the constraints in step S24, step S2 also uses a module dynamic selection method to configure the whole industry chain collaborative optimization model. The specific steps are as follows: S25. A set of preset module types, including a set of mandatory modules, a set of power supply modules, a set of hydrogen consumption modules, and a set of auxiliary modules; Required module set The required modules are forcibly associated in the whole industry chain collaborative optimization model; Power-side module collection At least one module must be selected from the power supply module set; Hydrogen consumption side module set The modules in the hydrogen consumption module set are optional. Auxiliary module collection The modules in the auxiliary module set are optional modules; S26. Generate a set of modules that actually participate in the optimization based on the user's configuration selection in the interactive interface. The set of modules actually involved in the optimization satisfies:

[0018]

[0019] in, It is a set of required modules. It is a collection of power supply side modules; S27. Based on the set of modules actually involved in the optimization generated in step S26. The optional optimization variables set in step S21 are filtered, and only the set of modules that actually participate in the optimization are retained. The optimization variables corresponding to the modules are used to generate a dynamic set of optimization variables. :

[0020] in, This represents the set of all optional optimization variables in step S21. Represents optimization variables The module to which it belongs; S28. Based on the set of modules actually involved in the optimization generated in step S26. The constraints set in step S24 are dynamically adjusted: S281. The energy balance constraint is adjusted to include only the power term of the selected module:

[0021] in, Representation module exist The power contribution at any given moment is positive for power generation and negative for power consumption; It is the electrical load power input from the power grid; S282. Power upper and lower limit constraints are adjusted to include only the constraint equations of the selected module:

[0022] in, This represents the set of all modules with power constraints. , Representing modules respectively Minimum and maximum permissible operating power; It is the set of modules that actually participate in the optimization; S283. The energy storage system operating status constraint is adjusted to be enabled only when the electrochemical energy storage or hydrogen storage module is selected; S284. Hydrogen production capacity constraint is adjusted to be enabled only when the hydrogen production plant module is selected; S29. The set of dynamic optimization variables generated in step S27. The dynamic constraints generated in step S28 are used as the final mathematical model for collaborative optimization of the entire industry chain, and are input into step S3 for solving.

[0023] Furthermore, the specific steps of step S3 are as follows: S31. Call the solver to solve the whole industry chain collaborative optimization model and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; S32. If the solver returns a preset error code, conflict diagnosis is performed according to preset intelligent diagnostic rules; the intelligent diagnostic rules include power generation-power consumption mismatch diagnosis and hydrogen production-hydrogen supply mismatch diagnosis. The mismatch between power generation and consumption is diagnosed as follows: the problem of power curtailment caused by new energy power generation being far greater than the demand for electricity and limited grid capacity, or insufficient number of electrolyzers or hydrogen consumption; and the problem of power outages caused by new energy power generation being far less than the demand for electricity and excessive number of electrolyzers or hydrogen consumption, or the existence of periods with high minimum power load. The mismatch between hydrogen production and supply is diagnosed as the following issues: the downstream hydrogen demand is too stringent, the electrolyzer is too small to produce the required hydrogen, and the hydrogen storage tank capacity is too small to meet peak shaving requirements.

[0024] Furthermore, the specific steps of step S4 are as follows: S41. Obtain a capacity configuration plan, which includes total investment, wind turbine scale, photovoltaic scale, total power generation, and hydrogen production volume; S42. Obtain dynamic financial parameters from the built-in database, including benchmark rate of return, various tax rates, various fee rates, depreciation and amortization period, residual value rate, various cost parameters, and various unit price parameters; S43. Based on the preset financial evaluation criteria, the linkage capacity configuration scheme and dynamic financial parameters, calculate the economic evaluation indicators for the entire life cycle; the economic evaluation indicators include the total investment return rate, the average annual return rate, the internal rate of return after income tax and the levelized cost of hydrogen production.

[0025] Furthermore, the specific steps of step S5 are as follows: S51. The capacity configuration scheme and the economic evaluation indicators of the whole life cycle are visualized in multiple dimensions, including the power dispatch curve and hydrogen production curve in the time dimension, the energy flow topology map in the spatial dimension, and the economic and technical parameter comparison charts in the indicator dimension. S52. In response to the modular export request, export the capacity optimization results and financial data into a general data file format according to the preset module division. The modules include wind power, photovoltaic, energy storage, hydrogen production and grid interaction. The data includes power generation, abandoned power, equipment scale, cost allocation and economic indicators.

[0026] Furthermore, the method also includes a forward optimization mode and a reverse optimization mode: In the forward optimization mode, step S2 takes the wind turbine scale and photovoltaic scale as known inputs, and the electrolysis unit scale and energy storage scale as variables to be optimized. In the reverse optimization mode, step S2 takes the scale of the electrolysis unit and the energy storage scale as known inputs, and takes meeting the hydrogen supply demand of downstream chemical plants as the constraint, and takes the scale of wind turbines and photovoltaics as variables to be optimized. The capacity configuration result obtained from step S3 of the forward optimization mode can be exported and used as input data for the reverse optimization mode to perform collaborative planning and economic evaluation under the reverse optimization mode.

[0027] Secondly, embodiments of this application also provide a device for collaborative planning and economic evaluation of wind-solar-hydrogen storage systems, comprising: The data acquisition and dual-mode preprocessing module is used to acquire input data from wind power and photovoltaics, select the known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. The whole industry chain collaborative optimization model construction module is used to construct a mathematical model of whole industry chain collaborative optimization based on the annual time-series output sequence and the operation time interval, with the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply as optimization variables, the preset economic indicators as objective functions, and the system energy balance and equipment operation characteristics as constraints. The optimization and intelligent diagnosis module is used to call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link. If the solution fails and a preset error code is returned, the module will automatically locate and prompt the parameter conflict location according to the preset intelligent diagnosis rules. The technology-economic linkage assessment module is used to take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. The results visualization and export module is used to visualize capacity configuration schemes and economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files.

[0028] As can be seen from the above technical solutions, this application has the following advantages: The wind-solar-hydrogen-storage system collaborative planning and economic evaluation method and device provided in this application achieve precise adaptation of historical data of existing power plants and meteorological resources of planned power plants through a dual-modal data processing approach using known output modes and resource data modes, ensuring the standardization and physical authenticity of input data. By introducing the linear attenuation coefficients of wind and solar power generation and multi-year operation simulation algorithms, a quantitative evaluation of the performance degradation throughout the entire life cycle of the equipment is achieved, avoiding long-term overestimation of power generation and deviation in revenue prediction due to neglecting attenuation. By constructing a mathematical model with the goal of maximizing annual revenue, covering energy balance, power constraints, energy storage status, and supply and demand matching, and adopting a module dynamic selection mechanism, the system automatically selects and optimizes variables and constraints according to user configuration, achieving full coverage under multiple scenarios. The system achieves optimal collaborative configuration of equipment across the industrial chain; it enables automatic identification of parameter conflict root causes when solutions fail by pre-setting intelligent diagnostic rules for mismatches between power generation and consumption, and between hydrogen production and supply, reducing the difficulty of manual troubleshooting and trial-and-error costs; it achieves automated and accurate calculation of full life-cycle economic indicators such as total investment return rate, internal rate of return, and levelized cost of hydrogen production by linking dynamic financial parameters and capacity configuration schemes in the built-in database; it realizes flexible planning strategies and closed-loop verification of schemes under different boundary conditions by setting up bidirectional modes of forward optimization based on source and reverse optimization based on load and their data iteration interfaces; and it realizes intuitive presentation and standardized output of complex planning results through multi-dimensional visualization and modular data export functions, improving decision-making efficiency and data interaction capabilities. Attached Figure Description

[0029] To more clearly illustrate the technical solution of this application, the accompanying drawings used in the description will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a flowchart illustrating the collaborative planning and economic evaluation method for wind-solar-hydrogen storage systems of the present invention.

[0031] Figure 2 This is a schematic diagram of the wind-solar-hydrogen storage system collaborative planning and economic evaluation device of the present invention. Detailed Implementation

[0032] The various embodiments of this disclosure will be described more fully in the detailed steps of the collaborative planning and economic evaluation method for wind-solar-hydrogen storage systems described below. This disclosure may have various embodiments, and adjustments and changes may be made therein. However, it should be understood that there is no intention to limit the various embodiments of this disclosure to the specific embodiments disclosed herein, but rather this disclosure should be understood to cover all adjustments, equivalents, and / or alternatives falling within the spirit and scope of the various embodiments of this disclosure.

[0033] This embodiment provides a collaborative planning and economic evaluation method for wind-solar-hydrogen storage systems. Through dual-mode data adaptation, collaborative modeling of the entire industry chain considering equipment degradation, dynamic module selection, and intelligent diagnostic mechanisms, it achieves optimal capacity configuration, automatic parameter conflict location, and accurate quantitative evaluation of economic indicators throughout the entire life cycle of wind-solar-hydrogen storage systems in multiple scenarios.

[0034] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0035] Please see Figure 1 The diagram shows a flowchart of a method for coordinated planning and economic evaluation of a wind-solar-hydrogen storage system in a specific embodiment. The method includes the following steps: S1. Acquire input data from wind power and photovoltaics, select known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. It should be noted that by distinguishing between known output and resource data, the problem of inconsistent data sources at different project stages was solved; a standardized annual time series was generated and chemical operation intervals were configured, providing a unified and high-quality time resolution input basis for optimization and ensuring the time series accuracy of the simulation. S2. Based on the annual power output sequence and operating time interval, the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply are used as optimization variables, the preset economic indicators are used as objective functions, and the system energy balance and equipment operation characteristics are used as constraints to construct a mathematical model for collaborative optimization of the entire industry chain. It should be noted that by coupling multiple links such as wind and solar power, energy storage, hydrogen production, and grid interaction, and taking economic efficiency as the goal and physical laws as the constraint, a globally optimal mathematical model was constructed to achieve collaborative planning. This avoids capacity redundancy or gaps caused by independent planning of each link and ensures the optimal configuration of the system. S3. Call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; if the solution fails and a preset error code is returned, the parameter conflict location will be automatically located and prompted according to the preset intelligent diagnostic rules. It should be noted that in this step, when unreasonable parameter settings lead to solution failure, the system can automatically diagnose and prompt specific conflict points, improving user experience and planning efficiency, and preventing users from obtaining incorrect conclusions or being unable to obtain conclusions due to incorrect parameters. S4. Take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. It should be noted that this step achieves a deep integration of technology and economy. By using a dynamic financial parameter library, the physical capacity configuration is transformed into economic indicators throughout the entire life cycle, making the planning results not only technically feasible but also economically assessable, directly serving investment decisions. S5. Visualize the capacity configuration scheme and the economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files; It should be noted that this step transforms complex optimization data and financial indicators into intuitive charts and topology diagrams, reducing the difficulty of understanding professional data; at the same time, it supports modular data export, meeting the needs of report preparation, data archiving, and integration with other systems, thus completing the planning work.

[0036] This embodiment achieves accurate planning and economic evaluation of the entire life cycle of wind-solar-hydrogen storage systems through dual-mode data processing, collaborative modeling considering attenuation, and intelligent diagnosis.

[0037] Furthermore, as a refinement and extension of the specific implementation of the above embodiments, in order to fully illustrate the specific implementation process in this embodiment, another method for collaborative planning and economic evaluation of wind-solar-hydrogen storage system is provided. Taking a newly built chemical industrial park as an example, it is planned to build a wind-solar-hydrogen storage integrated system to provide green hydrogen energy for the chemical plants in the park; the planned construction period of the park is 1 year, and the operation period is 20 years. The method includes the following steps: S1. Acquire input data from wind power and photovoltaics, select known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. For example, since the project is in the planning stage and there is no historical operational data, the resource data model is selected for processing. The specific steps of step S1 are as follows: S11. Determine the application scenario; If it is an existing power plant, proceed to step S12; If it is a planned power plant, proceed to step S13; S12. If a known output mode is selected, an xlsx format template containing normalized data between [0,1] is directly imported to represent the proportion of the total output to the maximum output capacity within the corresponding time period. For wind power or photovoltaic, the actual output power is equal to the product of the installed capacity and the corresponding normalization coefficient. Proceed to step S2. S13. Select the resource data mode and determine the power output mode; For wind power generation, proceed to step S14; For photovoltaic power generation, proceed to step S15; For example, the application scenario is a planned power plant, and the resource data mode is selected; The power output includes both wind power generation and photovoltaic power generation, therefore S14 and S15 are executed respectively; S14. Obtain wind speed data measured by a wind measurement tower at a preset height. Wind shear parameters Wheel hub height Cut-in wind speed Cut-off wind speed and rated wind speed Calculate the normalization coefficients according to the following procedure. : Calculate the wind speed at the hub height ,in, This is the preset reference height for the wind measurement tower; according to Determine the normalization coefficient for wind power generation : like or ,but ; like ,but ; like ,but ; Wind power generation capacity ; For example, wind power generation data processing is performed: Input resource data: Obtain wind speed data for 8760 hours throughout the year from a local 10m high anemometer tower. For example, the wind speed from 0:00 to 1:00 on January 1st was ; The following device parameters are entered through user configuration: Wind shear parameters Wheel hub height Cut into wind speed Cut off the wind speed Rated wind speed Reference height of the wind measurement tower ; The calculation process using 1 hour as an example: Calculate the wind speed at the wheel hub height:

[0038] Determine the interval and calculate the normalization coefficient: because It belongs to the second interval:

[0039] Assuming the installed capacity of the wind turbines needs to be optimized, the normalization coefficient for this 1-hour period is recorded as 0.1008; By repeating the above calculations for all 8760 hours of the year, a normalized coefficient sequence for wind power generation is obtained. ; S15. Obtain ambient temperature and total solar irradiance and reference irradiance Reference temperature and short-circuit current temperature coefficient The normalized coefficient of photovoltaic power generation is calculated using the following formula. :

[0040] Photovoltaic power generation ; For example, photovoltaic power generation data processing is performed: Input resource data: Obtain the local temperature over 8760 hours throughout the year. and total solar irradiance For example, the ambient temperature at the same hour on the same day. irradiation intensity (at night); The user-configured input device parameters are as follows: Reference irradiance Reference temperature Temperature coefficient of short-circuit current ; The calculation process using 1 hour as an example: Due to irradiance The calculation yields:

[0041] By repeating the above calculations for all 8760 hours of the year, a normalized coefficient sequence for photovoltaic power generation is obtained. ; S16. Configure the operating time range of the chemical plant and determine the operating mode; If the operation is to be continuous, the default start and end points are used, which correspond to the start and end points of the preset time period. If it is a non-continuous operation, set the end point of the first segment earlier than the end point of the preset time period, and continue to configure the start and end points of the next segment. The start point of the next segment is later than the end point of its adjacent previous segment, until all segments are configured, resulting in several non-continuous operation segments. For example, configure the operating time range of the chemical plant: Downstream chemical plants do not operate continuously throughout the year; user configurations are as follows: First segment: Start and end points = 1st hour to 2160th hour, corresponding to continuous operation from January to March; Second section: Start and end points = 3625 hours to 8016 hours, corresponding to continuous operation from June to November; The system generates the corresponding hydrogen demand sequence. During the aforementioned period, the hourly hydrogen requirement is fixed at [value missing]. The hydrogen requirement is 0 during other periods. In step S1, after the normalization coefficient calculation in S14 or S15 is completed, and before step S16, the resource data model also includes simulating the degradation of wind and solar resources or equipment performance over time. The specific steps are as follows: S101. Set an attenuation coefficient that decreases linearly according to a preset time period, wherein the attenuation coefficient includes the wind power generation attenuation coefficient. and photovoltaic power generation attenuation coefficient These are used to simulate the performance degradation of wind turbine generators and photovoltaic modules as they age; S102. Wind power generation normalization coefficient calculated based on step S14 Combined with the wind power attenuation coefficient and operating years Calculate the actual normalized coefficient of wind power generation after considering attenuation. :

[0042] in, This represents the year the system was launched, and its value is... , This indicates the first year of operation, during which there is no degradation. S103. Photovoltaic power generation normalization coefficient calculated based on step S15 Combined with photovoltaic power generation attenuation coefficient and operating years Calculate the actual normalized coefficient of photovoltaic power generation after considering degradation. :

[0043] S104. Substitute the normalized coefficient, which takes into account attenuation, into the power calculation formulas in S14 and S15 to obtain the wind power generation considering long-term attenuation. and photovoltaic power generation :

[0044] ; S105. The power sequence after attenuation is taken as the input to the mathematical model of the whole industry chain collaborative optimization in step S2, so as to carry out multi-year collaborative optimization and economic evaluation. For example, prepare to perform equipment degradation simulation over a multi-year period: Set wind power attenuation coefficient Photovoltaic power generation attenuation coefficient ; For the 5th year of operation The normalized coefficient for wind power generation in the above hours is corrected as follows:

[0045] The first The corrected sequence of the year and As input for step S2; S2. Based on the annual power output sequence and operating time interval, the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply are used as optimization variables, the preset economic indicators are used as objective functions, and the system energy balance and equipment operation characteristics are used as constraints to construct a mathematical model for collaborative optimization of the entire industry chain. The specific steps of step S2 are as follows: S21. Set optional optimization variables, including wind turbine scale, wind turbine output sequence, photovoltaic scale, photovoltaic output sequence, maximum grid-connected power, grid-connected power sequence, green electricity grid-connected power sequence, electrolysis unit scale, electrolysis unit electricity consumption sequence, energy storage scale, energy storage power sequence, and hydrogen production volume sequence; S22. Determine whether each optional optimization variable has been given a definite value in the interactive interface configuration; If so, then the optional optimization variables that give definite values ​​will be removed; If not, then each optional optimization variable will be used as the final optimization variable; For example, the user sets the maximum grid-connected power to a fixed value of 50MW in the interactive interface, and it is not included in the optimization. Other variables, such as wind turbine scale, photovoltaic scale, electrolysis unit scale, and energy storage scale, are all considered as variables to be optimized. S23. Set the objective function to maximize the annual revenue. The components of the annual revenue include hydrogen sales revenue, green electricity grid connection revenue, maintenance and management insurance costs for each module, depreciation of each module, hydrogen production circulating water cost, chemical plant personnel cost, and the discounted total construction investment. For example, the objective function is defined as follows: The goal is to maximize annual returns; in the revenue composition, it is assumed that the hydrogen selling price is... The feed-in tariff for green electricity is The electricity price purchased online is Equipment maintenance rates, depreciation periods, etc., are all read from the database; S24. Set constraints, the constraints including: Energy balance constraints of integrated wind-solar-hydrogen-storage energy systems:

[0046] in, , , , , They represent Real-time wind power generation, photovoltaic power generation, lithium battery energy storage output, hydrogen energy storage output, and electrical load power input from the grid; Power upper and lower limit constraints:

[0047] in, , These represent the minimum and maximum permissible operating power of the wind power generation system, respectively. , These represent the minimum and maximum permissible operating power of the photovoltaic power generation system, respectively. , These represent the minimum and maximum charge / discharge power allowed by the lithium battery energy storage system, respectively. Negative values ​​indicate the maximum charging power, and positive values ​​indicate the maximum discharging power. , These represent the minimum and maximum permissible power output of the hydrogen energy storage system, respectively. Energy storage system operating state constraints:

[0048] in, , These represent the state of charge of a lithium battery energy storage system and the maximum permissible state of hydrogen pressure of a hydrogen energy storage system, respectively. Hydrogen production constraints:

[0049] in, This represents the volume of hydrogen produced by the electrolytic hydrogen production system during time period t. This indicates the hydrogen supply demand required by downstream chemical plants during the corresponding time period. Hydrogen production constraints ensure that the total hydrogen production in each period is not less than the hydrogen supply demand in the corresponding period. For example, set the constraints: Energy balance constraints, taking a certain hour as an example: The system must satisfy:

[0050] in, This represents the power purchased from the grid at time t. This represents the power consumed by the electrolytic cell at time t. This represents the power sold to the grid at time t. (Maximum power consumption limit); It should be noted that the electrical load power input from the power grid... This refers to the net power actually absorbed by the wind-solar-hydrogen-storage integrated energy system from the external power grid. It also implicitly includes the power consumption of the system's electrical equipment (such as electrolyzers) and the offsetting of power fed back to the grid. Specifically:

[0051] That is, the net electrical load input from the grid is equal to the purchased power minus the sold power, plus the power of the electrolyzer inside the wind-solar-hydrogen-storage integrated energy system, which serves as the electrical equipment; ≥0, ≥0, and neither of them are positive at the same time, that is × =0; Hydrogen production constraints: set up This refers to the set of operating segments for the chemical plant configured in step S16.

[0052] That is, within the two operating sections, the hourly hydrogen production must meet the requirement of at least 1000 Nm³; After setting the constraints in step S24, step S2 further configures the whole industry chain collaborative optimization model using a module dynamic selection method. The specific steps are as follows: S25. A set of preset module types, including a set of mandatory modules, a set of power supply modules, a set of hydrogen consumption modules, and a set of auxiliary modules; Required module set The required modules are forcibly associated in the whole industry chain collaborative optimization model; Power-side module collection At least one module must be selected from the power supply module set; Hydrogen consumption side module set The modules in the hydrogen consumption module set are optional. Auxiliary module collection The modules in the auxiliary module set are optional modules; S26. Generate a set of modules that actually participate in the optimization based on the user's configuration selection in the interactive interface. The set of modules actually involved in the optimization satisfies:

[0053]

[0054] in, It is a set of required modules. It is a collection of power supply side modules; S27. Based on the set of modules actually involved in the optimization generated in step S26. The optional optimization variables set in step S21 are filtered, and only the set of modules that actually participate in the optimization are retained. The optimization variables corresponding to the modules are used to generate a dynamic set of optimization variables. :

[0055] in, This represents the set of all optional optimization variables in step S21. Represents optimization variables The module to which it belongs; S28. Based on the set of modules actually involved in the optimization generated in step S26. The constraints set in step S24 are dynamically adjusted: S281. The energy balance constraint is adjusted to include only the power term of the selected module:

[0056] in, Representation module exist The power contribution at any given moment is positive for power generation and negative for power consumption; This represents the net power exchange between the system and the external power grid; purchasing power from the grid is positive, and selling power to the grid is negative. If the user has not selected a grid power supply module, then it is forced... This indicates that the system is operating in isolated grid mode and has internal power self-balancing. S282. Power upper and lower limit constraints are adjusted to include only the constraint equations of the selected module:

[0057] in, This represents the set of all modules with power constraints. , Representing modules respectively Minimum and maximum permissible operating power; This represents the set of all modules with power constraints. S283. The energy storage system operating status constraint is adjusted to be enabled only when the electrochemical energy storage or hydrogen storage module is selected; S284. Hydrogen production capacity constraint is adjusted to be enabled only when the hydrogen production plant module is selected; S29. The set of dynamic optimization variables generated in step S27. The dynamic constraints generated in step S28 are used as the final mathematical model for collaborative optimization of the entire industry chain, and are input into step S3 for solving. For example, the execution module is dynamically selected: User-selected modules: Select hydrogen production plant and waste power treatment from the mandatory module set; select wind power generation and photovoltaic power generation from the power supply module set; select electrochemical energy storage from the auxiliary module set; the grid power supply module is not selected, i.e., it operates in islanded mode; The system generates a set of dynamic optimization variables accordingly. Remove variables related to grid power supply; The constraints were adjusted accordingly: the energy balance equation was removed. and Remove grid-related terms from power constraints; S3. Call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; if the solution fails and a preset error code is returned, the parameter conflict location will be automatically located and prompted according to the preset intelligent diagnostic rules. The specific steps of step S3 are as follows: S31. Call the solver to solve the whole industry chain collaborative optimization model and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; For example, the Gurobi solver is invoked to solve the above model; Scenario 1: Solution successful. The resulting capacity configuration scheme is as follows: The wind turbine capacity is 150MW, the photovoltaic capacity is 80MW, the electrolysis unit capacity is 25MW, and the electrochemical energy storage capacity is 40MWh / 20MW; The timing operation strategy is based on hourly power allocation data for 8760 hours, which includes the allocation of wind power output, photovoltaic power output, energy storage charging and discharging, and electrolytic cell power. Scenario 2 involves a solution failure, followed by a simulation diagnosis: If the user mistakenly manually configures the electrolysis unit to 5MW, i.e., the configuration is too small, the solver returns error code 10005; Intelligent diagnostic trigger: The diagnosis of the mismatch between hydrogen production and supply is as follows: The system detected that during the operation period of the chemical plant, even with full wind and solar power generation and full energy storage support, the maximum hydrogen production of the 5MW electrolyzer (i.e., 5MW × conversion efficiency) is still far below the demand of 1000Nm³ / h, resulting in the inability to meet the hydrogen production constraint. Prompt message: The interface displays a message: "Optimization failed. Reason: Hydrogen production capacity constraint cannot be met. Recommendation: The electrolysis unit is too small (currently 5MW) and cannot meet the hourly hydrogen demand of 1000Nm³. It is recommended to increase the electrolyzer capacity to at least 20MW or reduce the hydrogen demand." After the user adjusted the electrolysis unit scale to 25MW according to the prompts, the solution was successfully obtained again. S32. If the solver returns a preset error code, conflict diagnosis is performed according to preset intelligent diagnostic rules; the intelligent diagnostic rules include power generation-power consumption mismatch diagnosis and hydrogen production-hydrogen supply mismatch diagnosis. The mismatch between power generation and consumption is diagnosed as follows: the problem of power curtailment caused by new energy power generation being far greater than the demand for electricity and limited grid capacity, or insufficient number of electrolyzers or hydrogen consumption; and the problem of power outages caused by new energy power generation being far less than the demand for electricity and excessive number of electrolyzers or hydrogen consumption, or the existence of periods with high minimum power load. The mismatch between hydrogen production and supply is diagnosed as the problem that the downstream hydrogen demand is too demanding, the electrolyzer is too small to produce the required hydrogen, and the hydrogen storage tank is too small to meet the peak shaving demand. S4. Take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. The specific steps of step S4 are as follows: S41. Obtain a capacity configuration plan, which includes total investment, wind turbine scale, photovoltaic scale, total power generation, and hydrogen production volume; S42. Obtain dynamic financial parameters from the built-in database, including benchmark rate of return, various tax rates, various fee rates, depreciation and amortization period, residual value rate, various cost parameters, and various unit price parameters; S43. Based on the preset financial evaluation criteria, the linkage capacity configuration scheme and dynamic financial parameters, calculate the economic evaluation indicators for the entire life cycle; the economic evaluation indicators include the total investment return rate, the average annual return rate, the after-tax internal rate of return and the levelized cost of hydrogen production. For example, the capacity configuration plan obtained in step S3 (i.e., 150MW wind turbines, 80MW photovoltaic, 25MW electrolyzer, and 40MWh energy storage) is automatically imported into the financial module, and the dynamic financial parameters in the database (assuming an income tax rate of 15% for the first 5 years and 25% thereafter) are linked to calculate the following: The total return on investment is 8.5%, the after-tax internal rate of return is 9.8%, the levelized cost of hydrogen production is 1.95 yuan / Nm³, and the average annual return is 12.3%. S5. Visualize the capacity configuration scheme and the economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files; The specific steps of step S5 are as follows: S51. The capacity configuration scheme and the economic evaluation indicators of the whole life cycle are visualized in multiple dimensions, including the power dispatch curve and hydrogen production curve in the time dimension, the energy flow topology map in the spatial dimension, and the economic and technical parameter comparison charts in the indicator dimension. For example, a visualization is performed: The system generates displays in the following dimensions: Time dimension: Displays power dispatch curves for typical weeks (such as the first week of January), clearly showing the hourly matching relationship between wind power, photovoltaic output, energy storage charging and discharging, and electrolyzer load; Spatial dimension: Energy flow topology map, showing the distribution of energy throughout the year from wind and solar power generation to hydrogen production, energy storage, and curtailment; Metrics: Compare the levelized cost of hydrogen production and internal rate of return curves under different configuration schemes (such as energy storage capacity from 20MWh to 60MWh); S52. In response to the modular export request, export the capacity optimization results and financial data into a general data file format according to the preset module division. The modules include wind power, photovoltaic, energy storage, hydrogen production and grid interaction. The data includes power generation, abandoned power, equipment scale, cost allocation and economic indicators. For example, performing data export: The user selects the wind power, photovoltaic, and hydrogen production modules, clicks export, and the system generates an Excel file containing: Wind power module: 8760 hours of output data throughout the year, annual power generation, equipment scale, and detailed cost allocation; Photovoltaic modules: 8760 hours of output data throughout the year, annual power generation, equipment scale, and detailed cost allocation; Hydrogen production module: details of annual power consumption (8760 hours), hydrogen production, equipment scale, and levelized cost of hydrogen production.

[0058] In another embodiment of this application, unlike the embodiments described above, the method further includes a forward optimization mode and a reverse optimization mode: In the forward optimization mode, step S2 takes the wind turbine scale and photovoltaic scale as known inputs, and the electrolysis unit scale and energy storage scale as variables to be optimized. In the reverse optimization mode, step S2 takes the scale of the electrolysis unit and the energy storage scale as known inputs, and takes meeting the hydrogen supply demand of downstream chemical plants as the constraint, and takes the scale of wind turbines and photovoltaics as variables to be optimized. The capacity configuration result obtained from step S3 of the forward optimization mode can be exported and used as input data for the reverse optimization mode to perform collaborative planning and economic evaluation under the reverse optimization mode.

[0059] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0060] like Figure 2 As shown, the following are embodiments of the wind-solar-hydrogen-storage system collaborative planning and economic evaluation device provided in this disclosure. This system and the wind-solar-hydrogen-storage system collaborative planning and economic evaluation method of the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the wind-solar-hydrogen-storage system collaborative planning and economic evaluation device, please refer to the embodiments of the wind-solar-hydrogen-storage system collaborative planning and economic evaluation method of the above embodiments.

[0061] The device includes: The data acquisition and dual-mode preprocessing module is used to acquire input data from wind power and photovoltaics, select the known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. The whole industry chain collaborative optimization model construction module is used to construct a mathematical model of whole industry chain collaborative optimization based on the annual time-series output sequence and the operation time interval, with the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply as optimization variables, the preset economic indicators as objective functions, and the system energy balance and equipment operation characteristics as constraints. The optimization and intelligent diagnosis module is used to call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link. If the solution fails and a preset error code is returned, the module will automatically locate and prompt the parameter conflict location according to the preset intelligent diagnosis rules. The technology-economic linkage assessment module is used to take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. The results visualization and export module is used to visualize capacity configuration schemes and economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files.

[0062] This embodiment achieves optimal capacity configuration, automatic parameter conflict location, and precise quantitative evaluation of the entire life cycle economic indicators of wind-solar-hydrogen storage systems in multiple scenarios through the interactive collaboration of data acquisition and dual-mode preprocessing modules, whole-industry chain collaborative optimization model construction modules, optimization solution and intelligent diagnosis modules, techno-economic linkage evaluation modules, and result visualization and export modules.

[0063] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for collaborative planning and economic evaluation of wind-solar-hydrogen storage systems, characterized in that, Includes the following steps: S1. Acquire input data from wind power and photovoltaics, select known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. S2. Based on the annual power output sequence and operating time interval, the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply are used as optimization variables, the preset economic indicators are used as objective functions, and the system energy balance and equipment operation characteristics are used as constraints to construct a mathematical model for collaborative optimization of the entire industry chain. After setting the constraints, step S2 further configures the whole industry chain collaborative optimization model using a module dynamic selection method. The specific steps are as follows: S25. A set of preset module types, which includes a set of mandatory modules, a set of power supply modules, a set of hydrogen consumption modules, and a set of auxiliary modules; Required module set The required modules are forcibly associated in the whole industry chain collaborative optimization model; Power-side module collection At least one module must be selected from the power supply module set; Hydrogen consumption side module set The modules in the hydrogen consumption module set are optional. Auxiliary module collection The modules in the auxiliary module set are optional modules; S26. Generate a set of modules that actually participate in the optimization based on the user's configuration selection in the interactive interface. The set of modules actually involved in the optimization satisfies: in, It is a set of required modules. It is a collection of power supply side modules; S27. Based on the set of modules actually involved in the optimization generated in step S26. The set of optional optimization variables are filtered, and only the set of modules that actually participate in the optimization are retained. The optimization variables corresponding to the modules are used to generate a dynamic set of optimization variables. : in, Represents the set of all possible optimization variables. Represents optimization variables The module to which it belongs; S28. Based on the set of modules actually involved in the optimization generated in step S26. The constraints are dynamically adjusted. S281. The energy balance constraint is adjusted to include only the power term of the selected module: in, Representation module exist The power contribution at any given moment is positive for power generation and negative for power consumption; It is the electrical load power input from the power grid; S282. Power upper and lower limit constraints are adjusted to include only the constraint equations of the selected module: in, This represents the set of all modules with power constraints. , Representing modules respectively Minimum and maximum permissible operating power; It is the set of modules that actually participate in the optimization; S283. The energy storage system operating status constraint is adjusted to be enabled only when the electrochemical energy storage or hydrogen storage module is selected; S284. Hydrogen production capacity constraint is adjusted to be enabled only when the hydrogen production plant module is selected; S29. The set of dynamic optimization variables generated in step S27. The dynamic constraints generated in step S28 are used as the final mathematical model for collaborative optimization of the entire industry chain, and are input into step S3 for solving. S3. Call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; if the solution fails and a preset error code is returned, the parameter conflict location will be automatically located and prompted according to the preset intelligent diagnostic rules. The specific steps of step S3 are as follows: S31. Call the solver to solve the whole industry chain collaborative optimization model and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link; S32. If the solver returns a preset error code, then conflict diagnosis is performed according to the preset intelligent diagnostic rules; the intelligent diagnostic rules include power generation and power consumption mismatch diagnosis and hydrogen production and hydrogen supply mismatch diagnosis. S4. Take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. S5. Visualize the capacity configuration scheme and the economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files; The method also includes a forward optimization mode and a reverse optimization mode: In the forward optimization mode, step S2 takes the wind turbine scale and photovoltaic scale as known inputs, and the electrolysis unit scale and energy storage scale as variables to be optimized. In the reverse optimization mode, step S2 takes the scale of the electrolysis unit and the energy storage scale as known inputs, and takes meeting the hydrogen supply demand of downstream chemical plants as the constraint, and takes the scale of wind turbines and photovoltaics as variables to be optimized. The capacity configuration result obtained from step S3 of the forward optimization mode can be exported and used as input data for the reverse optimization mode to perform collaborative planning and economic evaluation under the reverse optimization mode.

2. The method for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems according to claim 1, characterized in that, The specific steps of step S1 are as follows: S11. Determine the application scenario; If it is an existing power plant, proceed to step S12; If it is a planned power plant, proceed to step S13; S12. If a known output mode is selected, an xlsx format template containing normalized data between [0,1] is directly imported to represent the proportion of the total output to the maximum output capacity within the corresponding time period. For wind power or photovoltaic, the actual output power is equal to the product of the installed capacity and the corresponding normalization coefficient. Proceed to step S2. S13. Select the resource data mode and determine the power output mode; For wind power generation, proceed to step S14; For photovoltaic power generation, proceed to step S15; S14. Obtain wind speed data measured by a wind measurement tower at a preset height. Wind shear parameters Wheel hub height Cut-in wind speed Cut-off wind speed and rated wind speed Calculate the normalization coefficients according to the following procedure. : Calculate the wind speed at the hub height ,in, This is the preset reference height for the wind measurement tower; according to Determine the normalization coefficient for wind power generation : like or ,but ; like ,but ; like ,but ; Wind power generation capacity ; S15. Obtain ambient temperature and total solar irradiance and reference irradiance Reference temperature and short-circuit current temperature coefficient The normalized coefficient of photovoltaic power generation is calculated using the following formula. : Photovoltaic power generation ; S16. Configure the operating time range of the chemical plant and determine the operating mode; If the operation is to be continuous, the default start and end points are used, which correspond to the start and end points of the preset time period. If it is a non-continuous operation, the end point of the first segment is set earlier than the end point of the preset time period, and the start and end points of the next segment are configured. The start point of the next segment is later than the end point of its adjacent previous segment, until all segments are configured, resulting in several non-continuous operation segments.

3. The method for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems according to claim 2, characterized in that, In step S1, after the normalization coefficient calculation in S14 or S15 is completed, and before step S16, the resource data model also includes simulating the degradation of wind and solar resources or equipment performance over time. The specific steps are as follows: S101. Set an attenuation coefficient that decreases linearly according to a preset time period, wherein the attenuation coefficient includes the wind power generation attenuation coefficient. and photovoltaic power generation attenuation coefficient These are used to simulate the performance degradation of wind turbine generators and photovoltaic modules as they age; S102. Wind power generation normalization coefficient calculated based on step S14 Combined with the wind power attenuation coefficient and operating years Calculate the actual normalized coefficient of wind power generation after considering attenuation. : in, This represents the year the system was launched, and its value is... , This indicates the first year of operation, during which there is no degradation. S103. Photovoltaic power generation normalization coefficient calculated based on step S15 Combined with photovoltaic power generation attenuation coefficient and operating years Calculate the actual normalized coefficient of photovoltaic power generation after considering degradation. : S104. Substitute the normalized coefficient, which takes into account attenuation, into the power calculation formulas in S14 and S15 to obtain the wind power generation considering long-term attenuation. and photovoltaic power generation : ； S105. The power sequence after attenuation is taken as the input to the mathematical model of the whole industry chain collaborative optimization in step S2, so as to carry out multi-year collaborative optimization and economic evaluation.

4. The method for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems according to claim 2, characterized in that, The specific steps of step S2 are as follows: S21. Set optional optimization variables, including wind turbine scale, wind turbine output sequence, photovoltaic scale, photovoltaic output sequence, maximum grid-connected power, grid-connected power sequence, green electricity grid-connected power sequence, electrolysis unit scale, electrolysis unit electricity consumption sequence, energy storage scale, energy storage power sequence, and hydrogen production volume sequence; S22. Determine whether each optional optimization variable has been given a definite value in the interactive interface configuration; If so, then the optional optimization variables that give definite values ​​will be removed; If not, then each optional optimization variable will be used as the final optimization variable; S23. Set the objective function to maximize the annual revenue. The components of the annual revenue include hydrogen sales revenue, green electricity grid connection revenue, maintenance and management insurance costs for each module, depreciation of each module, hydrogen production circulating water cost, chemical plant personnel cost, and the discounted total construction investment. S24. Set constraints, the constraints including: Energy balance constraints of integrated wind-solar-hydrogen-storage energy systems: in, , , , , They represent Real-time wind power generation, photovoltaic power generation, lithium battery energy storage output, hydrogen energy storage output, and electrical load power input from the grid; Power upper and lower limit constraints: in, , These represent the minimum and maximum permissible operating power of the wind power generation system, respectively. , These represent the minimum and maximum permissible operating power of the photovoltaic power generation system, respectively. , These represent the minimum and maximum charge / discharge power allowed by the lithium battery energy storage system, respectively. Negative values ​​indicate the maximum charging power, and positive values ​​indicate the maximum discharging power. , These represent the minimum and maximum permissible power output of the hydrogen energy storage system, respectively. Energy storage system operating state constraints: in, , These represent the state of charge of a lithium battery energy storage system and the maximum permissible state of hydrogen pressure of a hydrogen energy storage system, respectively. Hydrogen production constraints: in, This represents the volume of hydrogen produced by the electrolytic hydrogen production system during time period t. This indicates the hydrogen supply demand required by downstream chemical plants during the corresponding time period. Hydrogen production constraints ensure that the total hydrogen production in each period is not less than the hydrogen supply demand in the corresponding period.

5. The method for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems according to claim 1, characterized in that, The mismatch between power generation and consumption is diagnosed as follows: the problem of power curtailment caused by new energy power generation being far greater than the demand for electricity and limited grid capacity, or insufficient number of electrolyzers or hydrogen consumption; and the problem of power outages caused by new energy power generation being far less than the demand for electricity and excessive number of electrolyzers or hydrogen consumption, or the existence of periods with high minimum power load. The mismatch between hydrogen production and supply is diagnosed as the following issues: the downstream hydrogen demand is too stringent, the electrolyzer is too small to produce the required hydrogen, and the hydrogen storage tank capacity is too small to meet peak shaving requirements.

6. The method for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems according to claim 1, characterized in that, The specific steps of step S4 are as follows: S41. Obtain a capacity configuration plan, which includes total investment, wind turbine scale, photovoltaic scale, total power generation, and hydrogen production volume; S42. Obtain dynamic financial parameters from the built-in database, including benchmark rate of return, various tax rates, various fee rates, depreciation and amortization period, residual value rate, various cost parameters, and various unit price parameters; S43. Based on the preset financial evaluation criteria, the linkage capacity configuration scheme and dynamic financial parameters, calculate the economic evaluation indicators for the entire life cycle; the economic evaluation indicators include the total investment return rate, the average annual return rate, the internal rate of return after income tax and the levelized cost of hydrogen production.

7. The method for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems according to claim 1, characterized in that, The specific steps of step S5 are as follows: S51. The capacity configuration scheme and the economic evaluation indicators of the whole life cycle are visualized in multiple dimensions, including the power dispatch curve and hydrogen production curve in the time dimension, the energy flow topology map in the spatial dimension, and the economic and technical parameter comparison charts in the indicator dimension. S52. In response to the modular export request, export the capacity optimization results and financial data into a general data file format according to the preset module division. The modules include wind power, photovoltaic, energy storage, hydrogen production and grid interaction. The data includes power generation, abandoned power, equipment scale, cost allocation and economic indicators.

8. A device for coordinated planning and economic evaluation of wind-solar-hydrogen storage systems, using the method described in any one of claims 1-7, characterized in that, include: The data acquisition and dual-mode preprocessing module is used to acquire input data from wind power and photovoltaics, select the known output mode or resource data mode for processing according to the data type of the application scenario, generate a standardized annual time-series output sequence, and configure the operating time interval of the chemical plant. The whole industry chain collaborative optimization model construction module is used to construct a mathematical model of whole industry chain collaborative optimization based on the annual time-series output sequence and the operation time interval, with the equipment scale and operation strategy of each link of wind and solar power generation, grid interaction, energy storage charging and discharging, electrolysis hydrogen production and supply as optimization variables, the preset economic indicators as objective functions, and the system energy balance and equipment operation characteristics as constraints. The optimization and intelligent diagnosis module is used to call the solver to solve the collaborative optimization model of the entire industry chain and obtain a capacity configuration scheme that includes the optimal equipment scale and timing operation strategy of each link. If the solution fails and a preset error code is returned, the module will automatically locate and prompt the parameter conflict location according to the preset intelligent diagnosis rules. The technology-economic linkage assessment module is used to take the capacity configuration plan as input, link the dynamic financial parameters in the built-in database, and use the preset financial evaluation rules to calculate the economic evaluation indicators for the entire life cycle. The results visualization and export module is used to visualize capacity configuration schemes and economic evaluation indicators throughout the entire life cycle, and respond to modular export requests to output data files.

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