Wind-light hydrogen storage system capacity optimization configuration method and system

Abstract

The invention belongs to the field of power system planning, and provides a wind-light-hydrogen storage system capacity optimization configuration method and system, and the method comprises the steps: obtaining the setting information of a user for an optimization problem, querying a knowledge base according to the setting information, and constructing a target function of a wind-light-hydrogen storage capacity optimization configuration model and a problem initial boundary condition; querying the knowledge base to construct a constraint condition combination of the wind-light-hydrogen storage system capacity optimization configuration model; according to related historical data in the database, generating related prediction data required for optimizing the configuration model in each time period in the planning time interval; and carrying out primary optimization or secondary optimization by applying a linear programming model according to an optimization method in the set information, solving an optimal solution of a system parameter decision variable and a system state decision variable which maximize the economic benefit of a target function and meet all constraint conditions, and simulating the operation condition in a system planning time interval according to the optimal solution. The method has the advantages of being accurate, efficient, high in adaptability and easy to modify and expand.

Description

Technical Field

[0001] This invention belongs to the field of power system planning, and specifically relates to a method and system for optimizing the capacity configuration of wind-solar-hydrogen-storage systems, which is particularly applicable to the method and system for optimizing the capacity configuration of wind-solar-hydrogen-storage systems in green electricity conversion projects. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Green electricity conversion refers to the on-site conversion and utilization of electricity generated from renewable energy sources such as wind and solar power using innovative technologies. This includes producing hydrogen from green electricity, directly supplying green hydrogen to users based on market demand, or using green hydrogen as the main raw material to convert it into green ammonia, green alcohols, green oil and gas, and other chemical products. Green electricity conversion projects, through the interconnection and mutual support of electricity and hydrogen energy, are of great significance in mitigating the volatility and randomness of wind-solar hybrid power generation, promoting the local consumption of new energy sources, and building a new type of power system.

[0004] A green electricity conversion project is a multi-energy integrated system combining wind, solar, hydrogen, and energy storage. It typically consists of wind turbines, photovoltaic arrays, a water electrolysis hydrogen production unit, a hydrogen storage tank, an electrochemical energy storage device (fuel cell), and other auxiliary equipment. The wind-solar complementary power generation system provides green electricity; the water electrolysis hydrogen production unit (electrolyzer) converts electrical energy into hydrogen energy; the high-pressure hydrogen storage tank centrally stores hydrogen and can supply hydrogen energy to the hydrogen fuel cell; the hydrogen fuel cell, as an electrochemical energy storage device, receives hydrogen from the storage tank and outputs stable electrical energy; and the battery (lithium iron phosphate battery) serves as an electrical energy storage system, storing a small amount of electrical energy for short-term power output supplementation and emergencies, ensuring the normal operation of the system. Depending on whether the project's industrial park power grid is connected to the public power grid, the project system can be divided into grid-connected and off-grid types.

[0005] During the planning phase of green energy conversion projects, the design should be based on the optimal matching of power generation, grid, load, and storage, guided by regional policy requirements and project boundary conditions, and guided by the principles of economic optimization and moderate forward-looking planning. Based on the natural resource conditions of the project location, the optimal system capacity configuration scheme should be calculated. Specifically, using the wind and solar power output characteristics, the regulation capacity of energy storage and hydrogen storage devices, the response characteristics of water electrolysis hydrogen production units, and user-side energy load curves as input data, and the total scale of new energy, investment scale, or hydrogen production scale as boundary conditions, the power load and hydrogen load matching should be fitted. Following the principle of maximizing project economic benefits, the optimal wind power and solar power ratio scheme, hydrogen production scale, energy storage and hydrogen storage scale, and the corresponding power load and hydrogen load matching scheme should be calculated.

[0006] Traditional capacity optimization configuration methods include: (1) manual estimation technology, which provides several alternative schemes based on past experience, manually estimates the system benefits and costs of each alternative scheme, and selects the optimal configuration scheme from them. (2) system simulation technology, which establishes a wind-solar-hydrogen storage system model in foreign simulation software and sets load matching parameters. After the simulation operation of the model, the system provides a system capacity optimization configuration scheme. Manual estimation technology consumes a lot of manpower, material resources and time when comparing the optimal scheme; system simulation technology consumes more time and computer resources, and the application of foreign software cannot guarantee the security of data information. Considering that the relevant policy requirements for green electricity substitution and green electricity conversion projects issued by various provinces are quite different, and the boundary conditions such as electricity price and hydrogen price are greatly affected by regional market transactions and the demands of cooperative users. Once the problem type, system operation constraints and boundary conditions change slightly, it is necessary to start manual estimation or re-complete system simulation, which leads to a sharp increase in the workload and time of scheme comparison and rework. Summary of the Invention

[0007] To address the aforementioned problems, this invention proposes a method and system for optimizing the capacity configuration of a wind-solar-hydrogen-storage system. This invention is applicable to the planning stage of green electricity conversion projects. Given historical data on wind and solar resources and problem boundary conditions, it optimizes decision variables such as the ratio of new energy wind and solar power, the scale of hydrogen production and storage, and energy storage capacity, outputting the optimal wind-solar-hydrogen-storage capacity configuration scheme. By simulating the system's operation, it provides project performance estimates and economic benefit predictions, exhibiting characteristics of accuracy, efficiency, adaptability, and ease of modification and expansion.

[0008] According to some embodiments, the present invention adopts the following technical solution: A method for optimizing the capacity configuration of a wind-solar-hydrogen storage system includes the following steps: Obtain the user's settings for the optimization problem, query the knowledge base based on the settings, and construct the objective function and initial boundary conditions of the wind-solar-hydrogen storage capacity optimization configuration model. Query the knowledge base to construct a combination of constraints for optimizing the capacity configuration of a wind-solar-hydrogen storage system; Based on relevant historical data in the database, generate the relevant forecast data required for the optimization configuration model for each time period within the planning time interval; Based on the optimization method in the settings, a linear programming model is applied to perform a first-order or second-order optimization to find the optimal solution for the system parameter decision variables and system state decision variables that maximizes the economic benefits of the objective function and satisfies all constraints. The system's operation within the planning time interval is then simulated based on the optimal solution.

[0009] As an alternative implementation method, the process of obtaining the user's settings for the optimization problem, querying the knowledge base based on the settings, and constructing the objective function and initial boundary conditions of the wind-solar-hydrogen storage capacity optimization configuration model includes: determining the planning time interval based on the user's settings for time options. Unit time period ; The planning time interval is determined based on the user-defined model that maximizes the objective function. The time required to simulate the system's operation and calculate its benefits; Determine the unit time period for the simulation system to run. That is, the sampling time interval of the simulation system operation; Based on the user-defined planning time interval The objective function of the capacity optimization configuration model is determined to be either maximizing the system's annual net income or maximizing the project's net income over its life cycle. Based on the user-defined optimization problem type options, the initial boundary condition expression of the capacity optimization configuration model is determined by querying the knowledge base; The optimization method for the application is determined based on the optimization method options selected by the user.

[0010] As a further defined implementation method, the process of determining the initial boundary condition expression of the capacity optimization configuration model by querying the knowledge base, based on the user-defined optimization problem type options, includes: New energy scale constraint: Given the total installed capacity of wind power and photovoltaic power, the initial boundary condition expression is: ; Total Investment Constraint: Given the total investment amount, the initial boundary condition expression is as follows: ; Hydrogen production scale constraint: Given a hydrogen production scale, the initial boundary condition expression is: ; Hydrogen loading constraint: Given a hydrogen loading constraint, the initial boundary condition expression is as follows: Hydrogen loading.

[0011] As an alternative implementation method, the process of querying the knowledge base to construct the constraint combination of the capacity optimization configuration model of the wind-solar-hydrogen-storage system includes: the constraint combination includes power balance constraints, hydrogen energy balance constraints, energy storage operation constraints, load power constraints, environmental protection requirements constraints, and policy boundary constraints.

[0012] As an alternative implementation method, the process of generating relevant prediction data for the optimization configuration model for each time period within the planning time interval, based on relevant historical data in the database, includes generating prediction data for each time period by applying wind power and photovoltaic power generation models based on wind speed and solar radiation intensity data in the new energy resource database. Based on the predicted output power data of wind power and photovoltaic power, calculate the output power of wind turbine units. The expression is:

[0013] in, For wind speed, This refers to the cut-in wind speed of the wind turbine. This refers to the cut-off wind speed of the wind turbine. The rated wind speed of the wind turbine unit. This refers to the scale of wind power (number of wind turbine units). This refers to the rated output of the wind turbine generator; Calculate the output power of a photovoltaic power station The expression is:

[0014] in, This refers to the rated installed capacity of the photovoltaic power station. For the efficiency of photovoltaic modules, for Solar irradiance intensity during the period For standard reference irradiation intensity, For temperature coefficient, for Operating temperature during the period For reference temperature; Based on the time-series load database, each time period is calculated and generated. Forecast data on hydrogen load and hydrogen production energy load; For grid-connected projects, the calculations for each time period are based on the electricity spot market database. The predicted values ​​for on-grid and off-grid electricity prices.

[0015] As an alternative implementation method, the process of applying the linear programming model according to the optimization method in the setting information includes: the mathematical expression of the linear programming model is...

[0016]

[0017] in, It is the objective function, and it is the decision variable. The linear function is defined as the project's economic indicator. The objective function is set according to user needs, either as the system's annual net profit or the project's lifecycle net profit. These are decision variables in the model. For linear inequality constraints; Linear equality constraints; for The lower and upper bounds of the value.

[0018] As an alternative implementation, the optimization process includes: achieving optimization by performing a mixed-integer linear programming model, where the objective function of the model is linear, the constraints are linear, and the decision variables include integer variables and continuous variables; Read the unit scale or capacity values ​​of the wind power, photovoltaic, hydrogen production, hydrogen storage and energy storage subsystems from the system operation parameter library, and introduce new system parameter decision variables for each subsystem, including wind power scale, photovoltaic scale, number of hydrogen production units, number of hydrogen storage units and number of energy storage units; Replace the system parameter decision variables in the objective function and boundary constraint expressions of the model with expressions that include the new system parameter decision variables:

[0019]

[0020]

[0021]

[0022]

[0023] The new system parameter decision variables must take integer values, while the system state decision variables do not need to meet this requirement. Therefore, we apply a mixed integer linear programming model to perform an optimization to obtain the optimal solutions for the new system parameter decision variables and the system state decision variables.

[0024] As an alternative implementation method, the process of performing secondary optimization includes: performing optimization by executing the linear programming model twice.

[0025] The first optimization is performed using a linear programming model to obtain the preliminary optimal solutions for the system parameter decision variables, including wind power installed capacity, photovoltaic installed capacity, hydrogen production scale, hydrogen storage scale, and energy storage scale. Based on the unit size of each subsystem, take two integer multiples of the unit size on the left and right sides of the preliminary optimal solution of the system parameter decision variables for each subsystem as candidate values, and form a combination of candidate values ​​for all equipment. Apply a linear programming model to each combination of candidate values ​​for the system parameter decision variables to achieve a second optimization, and obtain the optimal solution of the system state decision variables for each time period. Calculate the predicted economic benefits of the optimal solution for each candidate group, compare the economic benefits among the candidate groups, and select the optimal solution.

[0026] As an alternative implementation method, the process of simulating the operation of the system within the planning time interval based on the optimal solution includes: simulating the operation of the system within the planning time interval based on the optimal solution obtained by solving, including the optimal estimated values ​​of the system parameter decision variables and the optimal predicted values ​​of the system state decision variables, and outputting the variable values ​​of each time period to the dataset, including: wind power and photovoltaic operation data, hydrogen production and storage data, and energy consumption and storage data; Among them, the wind power and photovoltaic operation data include wind power output characteristics, wind curtailment, photovoltaic output characteristics, and photovoltaic curtailment. Hydrogen production and storage data include hydrogen production volume, hydrogen production load, hydrogen charging and discharging volume, and hydrogen storage volume; Energy consumption and storage data include electricity consumption, electricity fed into the grid, electricity discharged from the grid, charging and discharging capacity, and energy storage capacity.

[0027] A capacity optimization configuration system for a wind-solar-hydrogen storage system includes: The database management module is used to obtain the user's settings for the optimization problem and query the knowledge base based on the settings. The model initialization module is used to construct the objective function and initial boundary conditions of the wind-solar-hydrogen-storage capacity optimization configuration model, and to query the knowledge base to construct the constraint combination of the wind-solar-hydrogen-storage system capacity optimization configuration model. The prediction module is used to generate relevant prediction data for the optimization configuration model for each time period within the planning time interval based on relevant historical data in the database. The optimization module is used to apply a linear programming model to perform a first-order or second-order optimization based on the optimization method specified in the settings. It finds the optimal solution for the system parameter decision variables and system state decision variables that maximizes the economic benefits of the objective function and satisfies all constraints. The module then simulates the system's operation within the planning time interval based on the optimal solution.

[0028] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention is applicable to the planning stage of green electricity conversion projects. Given historical data on wind and solar resources and problem boundary conditions, it optimizes decision variables such as the ratio of new energy wind and solar power, the scale of hydrogen production and storage, and the energy storage capacity, and outputs the optimal wind-solar-hydrogen-storage capacity configuration scheme. It provides project performance estimation and economic benefit prediction results through simulation of the system operation process, and has the characteristics of accuracy, efficiency, strong adaptability, and easy modification and expansion.

[0029] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0030] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0031] Figure 1 This is a schematic diagram of the energy structure of a wind-solar-hydrogen-storage system in a green electricity conversion project, as shown in one embodiment. Figure 2 This is a software system architecture diagram of a capacity configuration optimization system for a green electricity conversion project involving wind, solar, hydrogen, and storage; Figure 3 This is an algorithm flowchart of an example of a method for optimizing the configuration of wind, solar, hydrogen, and storage capacity in a green electricity conversion project. Detailed Implementation

[0032] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0034] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0035] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0036] A method for optimizing the capacity configuration of wind-solar-hydrogen storage systems in green electricity conversion projects, such as... Figure 1As shown, it is applied to the wind-solar-hydrogen storage system of green electricity conversion projects, such as... Figure 3 As shown, it includes the following steps: S1 receives the user's settings for the main options of the optimization problem (problem type, time option, optimization objective, optimization method), queries the knowledge base based on the user's settings, and constructs the objective function and initial boundary conditions of the wind-solar-hydrogen storage capacity optimization configuration model.

[0037] The S2 query knowledge base constructs a combination of constraints for the capacity optimization configuration model of wind, solar, hydrogen, and energy storage systems, including: power balance constraints, hydrogen balance constraints, energy storage operation constraints, load power constraints (dynamic operation constraints), environmental protection requirements constraints, and policy boundary constraints.

[0038] S3 generates the planning time interval based on relevant historical data in the database. Each time period The relevant forecast data required for optimizing the configuration model includes: wind power and photovoltaic output power data, hydrogen load and energy load data, as well as grid connection and off-grid electricity price data (only required for grid-connected projects).

[0039] Based on the user-selected optimization method, s4 applies a linear programming model to perform either a first-order or second-order optimization. It finds the optimal solutions for the system parameter decision variables and system state decision variables that maximize the economic benefit of the objective function and satisfy all constraints. This includes the optimal estimates of the system parameter decision variables (including wind power and photovoltaic capacity allocation schemes, hydrogen production scale, hydrogen storage scale, and energy storage scale), and the optimal predicted values ​​of the system state decision variables.

[0040] S5 simulates the system's operation within the planned time interval based on the optimal predicted values ​​of the system state decision variables, and outputs the variable values ​​for each time period to the dataset (each time period corresponds to one observation), including: wind power and photovoltaic operation data (wind power output characteristics, wind power curtailment, photovoltaic power output characteristics, photovoltaic curtailment), hydrogen production and storage data (hydrogen production, hydrogen production load, hydrogen charging and discharging, hydrogen storage), and energy consumption and storage data (energy consumption, grid-connected energy, grid-connected energy, charging and discharging, energy storage).

[0041] The S6 calculates and outputs system performance data. Based on user settings, it calculates annual performance summary data or project lifecycle performance summary data, including: annual wind and solar power generation / total power generation, annual grid-connected power generation / total grid-connected power generation, annual wind and solar curtailment / total wind and solar curtailment, wind and solar curtailment rate, and annual hydrogen production / total hydrogen production.

[0042] S7 calculates and outputs project economic benefit data. Based on user settings, it calculates annual cost-benefit data, including the system's average annual cost (wind power, photovoltaic, hydrogen production, hydrogen storage, and energy storage) and total annual revenue (annual hydrogen sales revenue and annual electricity sales revenue); or project lifecycle cost-benefit data, including total system cost (total wind power cost, total photovoltaic cost, total hydrogen production cost, total hydrogen storage cost, and total energy storage cost) and total revenue (total hydrogen sales revenue and total electricity sales revenue).

[0043] In this embodiment, step s1, which receives the user's settings for the main options of the optimization problem, specifically involves the following operations: s1.1 Determine the planning time interval based on the user's time option settings. Unit time period .

[0044] The planning time interval is determined based on the user-defined model that maximizes the objective function. (Unit: hours), which is the length of time for simulating the system's operation and calculating its benefits.

[0045] If the objective function is the system's annual net profit, then ; If the objective function is the net revenue over the project's lifecycle, then the objective function is then... . This represents the project lifecycle (in years), typically taking the value... .

[0046] Determine the unit time period for the simulation system to run. (Unit: hours), which is the sampling time interval of the simulation system operation, with a default value of . Other possible values ​​include , wait.

[0047] s1.2 Based on the user-defined planning time interval The objective function for maximizing the capacity optimization configuration model is determined to be either "system annual net income" or "project lifecycle net income".

[0048] Optimization Target 1:

[0049] The formula for calculating the system's annual net income is as follows: ; in, ;

[0050] ;

[0051] The unit cost of each device includes construction cost and operation and maintenance cost.

[0052] Optimization Target Two:

[0053] The formula for calculating the net income during the project's operating cycle is as follows:

[0054] in, ; ; ; s1.3 Based on the user-defined optimization problem type options, the initial boundary condition expression for the capacity optimization configuration model is determined by querying the knowledge base. The problem types and their corresponding initial boundary condition expressions are as follows: New energy scale constraints: Given the total installed capacity of wind power and photovoltaic power, the initial boundary condition expression is as follows: ; Total Investment Constraint: Given the total investment amount, the initial boundary condition expression is as follows: ; Hydrogen production scale constraint: Given a hydrogen production scale, the initial boundary condition expression is: ; Hydrogen loading constraint: Given a hydrogen loading constraint, the initial boundary condition expression is as follows: Hydrogen loading; s1.4 Based on the optimization method option selected by the user, determine the optimization method to be applied in step s4. There are two optimization methods to choose from: Method 1 is a single optimization; Method 2 is a double optimization.

[0055] In this embodiment, the specific steps for constructing the constraints of the capacity optimization configuration model in step s2 are as follows: s2.1 The expression for the charge balance constraint (power balance constraint) is constructed as follows: ; in The first step in simulating the operation of the system The constraint means that power generation and power consumption should be balanced in each time period.

[0056] For the system state decision variable "grid power consumption" in the above formula "and "energy storage charge and discharge capacity" The value of is defined as follows: Battery life for both online and offline use This indicates the power transmission relationship between the park's power grid and the public power grid during the current time period. In grid-connected projects:

[0057] In off-grid projects, always maintain " This definition implements the constraint that the system can only be in one of two mutually exclusive states, "power on the network" and "power off the network," at any given time.

[0058] Energy storage charge and discharge capacity :definition:

[0059] This definition achieves the constraint that an energy storage device can only be in one of the two mutually exclusive states of "discharging" and "charging" at any given time.

[0060] s2.2 The expressions for the upper and lower bound constraints of the system parameter decision variables are constructed as follows: Wind power installed capacity constraints: ; Photovoltaic installed capacity constraints: ; Energy storage scale constraints: ; Hydrogen storage capacity constraints: ; Hydrogen production scale constraints: ; s2.3 Construct the system state decision variables in the current time period The expressions for the upper and lower bound constraints for real-time value retrieval are as follows: Wind power generation constraints: ; Constraints of photovoltaic power generation: ; Energy storage capacity constraints: ; Hydrogen storage capacity constraints: ; Hydrogen energy balance constraint: When hydrogen is released, ; During hydrogen charging, ;in, The value of is defined as:

[0061] This definition achieves the constraint that a hydrogen storage device can only be in one of the two mutually exclusive states of "hydrogen release" and "hydrogen filling" at any given time.

[0062] Power balance constraint between grid connection and off-grid: This constraint applies only to grid-connected projects. The power consumption between grid connection and off-grid in each time period must comply with the following constraint expression: Internet power consumption limit: ; Offline power consumption constraints: ; in, .

[0063] Electricity load constraints for hydrogen production: The lower and upper limits are typically set at 30% and 110%, respectively.

[0064] s2.4 The expression for the energy storage balance constraint is constructed as follows: Energy storage available capacity constraints: initial period , ; Subsequent periods , ; This means: Current available energy storage capacity = previous available energy storage capacity - energy storage charging and discharging amount.

[0065] Energy storage charge-discharge balance constraint: During discharge, ; During charging, .

[0066] The hydrogen energy balance constraint expression for s2.5 is as follows: Hydrogen storage capacity constraints: initial period ,

[0067] Subsequent periods , ; This means: Current available hydrogen storage capacity = Previous available hydrogen storage capacity - Hydrogen charging / discharging volume.

[0068] Hydrogen production and consumption balance constraints: .

[0069] The load power constraint (dynamic operation constraint) described in s2.6: ; The load model uses a daily time-series load curve, and the load can be adjusted every few hours. The adjustment time interval can be set to 8 / 12 / 24 (hours) according to the user settings. The load constraint requires that the sum of the electricity load of each hour in each time interval equals the load in that time interval set by the load curve, so as to achieve hourly multi-time scale control.

[0070] S2.7 Environmental Requirements and Constraints Relevant environmental protection requirements and constraints are obtained by searching the knowledge base, such as the requirement that the total amount of wind and solar power curtailed should not exceed 5% of the total power generation, i.e.: .

[0071] s2.8 Policy Boundary Constraints Based on the boundary conditions set by relevant policies in the region where the project is located, such as source-grid-load-storage, wind-solar-hydrogen-storage, and wind-solar-hydrogen production integration, the conditions are obtained by searching the knowledge base and added to the model as additional constraints.

[0072] For example, Inner Mongolia Autonomous Region has the following regulations for integrated wind-solar-hydrogen production projects: For grid-connected projects, the renewable energy scale should be determined according to no more than 1.2 times the electricity required for hydrogen production; for off-grid projects, the renewable energy scale should be determined according to the electricity required for hydrogen production, and the comprehensive utilization rate of renewable energy should not be less than 90%. Energy storage is required, with a peak-shaving capacity of no less than 15% of the renewable energy scale and a duration of no less than 4 hours. For grid-connected projects, the annual on-grid electricity should not exceed 20% of the total annual power generation, and the annual off-grid electricity should not exceed 10% of the project's total annual electricity consumption.

[0073] In this embodiment, step s3 generates the planning time interval. The specific steps for obtaining the relevant prediction data for the internal optimization configuration model are as follows: s3.1 Based on wind speed and solar radiation intensity data from the new energy resource database, wind power and photovoltaic power generation models are applied to generate data for each time period. The predicted output power of wind and solar power. The calculation of the output power of wind turbine generators... The expression is:

[0074] in, For wind speed, This refers to the cut-in wind speed of the wind turbine. This refers to the cut-off wind speed of the wind turbine. The rated wind speed of the wind turbine unit. This refers to the scale of wind power (number of wind turbine units). This refers to the rated output of the wind turbine.

[0075] The calculation of the output power of the photovoltaic power station The expression is:

[0076] in, This refers to the rated installed capacity of the photovoltaic power station. For the efficiency of photovoltaic modules, for Solar irradiance intensity during the period For standard reference irradiation intensity, For temperature coefficient, for Operating temperature during the period This is a reference temperature.

[0077] s3.2 Based on the time-series load database, calculate and generate the time periods for each time period. Forecast data on hydrogen load and energy load for hydrogen production.

[0078] s3.3 For grid-connected projects, calculate the electricity spot market database (real-time electricity price database) for each time period. The predicted on-grid and off-grid electricity prices; skip this step if it is an off-grid project.

[0079] In this embodiment, step s4 applies a linear programming model to optimize system capacity configuration and simulates the production operation of the project within the planned time interval. The mathematical expression of the linear programming model is: ; ; in, It is the objective function, and it is the decision variable. The objective function is a linear function. The objective function is defined as the economic indicator of the project. In one specific embodiment of this invention, the objective function can be set as the system's annual net income or the project's life-cycle net income, depending on user needs. These are decision variables in the model. For linear inequality constraints; Linear equality constraints; for The lower and upper bounds of the value.

[0080] In this embodiment, the decision variables in the model of step s4 are divided into two categories. One category is system parameter decision variables, including wind power installed capacity, photovoltaic installed capacity, hydrogen production scale (number of hydrogen production stations), hydrogen storage scale, and energy storage scale. The other category is system state decision variables (hereinafter referred to as state variables), which are used to record the operating state of the system during the simulated project production process. Each state variable is stored in vector form, totaling [number missing]. There are 10 data points, each recording the value of the state variable for the current time period.

[0081] Table 1 System State Decision Variables

[0082] As a further improvement of the present invention, the optimization process in step s4 can be implemented in two methods: Method 1 is a single-step optimization; Method 2 is a double-step optimization. The selected optimization method is set by the user when executing step s1. The specific implementation steps of the two optimization methods are as follows: s4.1 First-order optimization method. The optimization process is achieved by performing a first-order optimization solution using a mixed-integer linear programming model. The mixed-integer linear programming model combines the characteristics of linear programming and integer programming models. The objective function of the model is linear, the constraints are linear, and the decision variables include integer variables and continuous variables.

[0083] The system reads the default values ​​of unit size or capacity of each subsystem (wind power, photovoltaic, hydrogen production, hydrogen storage, energy storage) from the system operation parameter library, displays them in the interactive interface, and receives user confirmation or modification. New system parameter decision variables are introduced for each subsystem, including wind power scale, photovoltaic scale, number of hydrogen production units, number of hydrogen storage units, and number of energy storage units. The original system parameter decision variables in the objective function and boundary constraint expressions of the original model are replaced by the following expressions that include the new system parameter decision variables:

[0084]

[0085]

[0086]

[0087]

[0088] The new system parameter decision variables must take integer values, while the system state decision variables do not need to meet this requirement. Therefore, by applying the mixed integer linear programming model for optimization, the optimal solutions for both the new system parameter decision variables and the system state decision variables can be obtained simultaneously.

[0089] The optimal solution for the decision variables of the original system parameters can be obtained through the following relational expression:

[0090]

[0091]

[0092]

[0093] .

[0094] s4.2 Quadratic Optimization Method. The optimization process is achieved by executing the linear programming model twice to find the optimal solution.

[0095] The first optimization is performed using a linear programming model to obtain preliminary optimal solutions for the system parameter decision variables, including wind power installed capacity, photovoltaic installed capacity, hydrogen production scale, hydrogen storage scale, and energy storage scale.

[0096] Based on the unit size of each subsystem, for each subsystem, two integer multiples of the unit size on either side of the preliminary optimal solution of the system parameter decision variables are taken as candidate values, forming a combination of candidate values ​​for all equipment. As shown in Table 2, the candidate values ​​for wind power devices are as follows: ; and .

[0097] Table 2

[0098] Note: Operators in the table This indicates rounding down to the nearest integer.

[0099] A second optimization is achieved by applying a linear programming model to each set of alternative combinations of system parameter decision variables, thereby obtaining the optimal solution for the system state decision variables at each time period.

[0100] Calculate the predicted economic benefits of the optimal solution for each candidate group, compare the economic benefits among the candidate groups, and select the optimal solution.

[0101] Example 2 A capacity configuration optimization system for wind-solar-hydrogen storage systems in green electricity conversion projects, such as Figure 2 As shown, it includes the following components: (1) Database Management Module. Used to acquire, store, manage, and retrieve data from the database. The database includes the following: New Energy Resource Database. A long-term, four-dimensional original database of wind and solar energy resources, consisting of multi-dimensional wind speed and solar radiation intensity data across longitude, latitude, altitude, and time scales obtained from historical meteorological databases. The sampling interval is one hour, with a total of 8760 observations (records) throughout the year. Wind power output and photovoltaic power output can be calculated from wind speed and solar radiation intensity using wind power and photovoltaic power output models.

[0102] Time-series load database. Historical data on hydrogen and energy loads, sampled hourly, totaling 8760 observations throughout the year.

[0103] Electricity spot market database (real-time electricity price database). The clearing price (15-minute interval) for each trading session in the electricity spot market of the project area (grid connection price and off-grid price).

[0104] System operation parameter library. Includes cost data (construction cost and operation and maintenance cost of each device), price data (hydrogen sales price, grid-connected electricity price, off-grid electricity price, etc.), performance and energy consumption (upper and lower limits of hydrogen storage equipment capacity, upper and lower limits of energy storage equipment capacity, maximum hydrogen charging and discharging rate, maximum charging and discharging power, etc.), and project operation data (investment recovery factor, discount rate, project operating period, annual loan interest rate, investment loan term, depreciation rate, etc.).

[0105] Knowledge base (knowledge model method library). Includes: types of optimization problems that the methods can solve and their corresponding initial boundary condition expressions; national and regional policies related to the project, constituting additional boundary conditions for the optimization problems.

[0106] (2) User interaction interface module. The module displays the default values ​​of the optimization problem-related options and the simulation system operating parameters through an interactive interface, receives user confirmation and updates the settings, and saves them.

[0107] (3) Model initialization module. Based on system parameters and user settings, the module queries the knowledge base to generate the objective function and initial boundary conditions, and generates predicted values ​​of basic data such as power, load, and price based on the database.

[0108] (4) System capacity configuration algorithm optimization module. A linear programming model is applied to perform a first-order or second-order optimization according to the optimization method selected by the user. The optimal solution obtains the optimal estimated values ​​of the system parameter decision variables and the optimal predicted values ​​of the system state decision variables at each time step.

[0109] (5) Analysis, Calculation and Output Module. Calculate the project operation summary data based on the optimization results, including system performance data and project economic benefit data, and output them in the form of statistical reports and visualizations.

[0110] Example 3 A capacity optimization configuration system for a wind-solar-hydrogen storage system includes: The database management module is used to obtain the user's settings for the optimization problem and query the knowledge base based on the settings. The model initialization module is used to construct the objective function and initial boundary conditions of the wind-solar-hydrogen-storage capacity optimization configuration model, and to query the knowledge base to construct the constraint combination of the wind-solar-hydrogen-storage system capacity optimization configuration model. The prediction module is used to generate relevant prediction data for the optimization configuration model for each time period within the planning time interval based on relevant historical data in the database. The optimization module is used to apply a linear programming model to perform a first-order or second-order optimization based on the optimization method specified in the settings. It finds the optimal solution for the system parameter decision variables and system state decision variables that maximizes the economic benefits of the objective function and satisfies all constraints. The module then simulates the system's operation within the planning time interval based on the optimal solution.

[0111] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0112] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the capacity configuration of a wind-solar-hydrogen storage system, characterized in that, Includes the following steps: Obtain the user's settings for the optimization problem, query the knowledge base based on the settings, and construct the objective function and initial boundary conditions of the wind-solar-hydrogen storage capacity optimization configuration model. Query the knowledge base to construct a combination of constraints for optimizing the capacity configuration of a wind-solar-hydrogen storage system; Based on relevant historical data in the database, generate the relevant forecast data required for the optimization configuration model for each time period within the planning time interval; Based on the optimization method in the settings, a linear programming model is applied to perform a first-order or second-order optimization to find the optimal solution for the system parameter decision variables and system state decision variables that maximizes the economic benefits of the objective function and satisfies all constraints. The system's operation within the planning time interval is then simulated based on the optimal solution.

2. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The process of obtaining user settings for the optimization problem, querying the knowledge base based on these settings, and constructing the objective function and initial boundary conditions for the wind-solar-hydrogen-storage capacity optimization configuration model includes: determining the planning time interval based on the user's time option settings. Unit time period ; The planning time interval is determined based on the user-defined model that maximizes the objective function. The time required to simulate the system's operation and calculate its benefits; Determine the unit time period for the simulation system to run. That is, the sampling time interval of the simulation system operation; Based on the user-defined planning time interval The objective function of the capacity optimization configuration model is determined to be either maximizing the system's annual net income or maximizing the project's net income over its life cycle. Based on the user-defined optimization problem type options, the initial boundary condition expression of the capacity optimization configuration model is determined by querying the knowledge base; The optimization method for the application is determined based on the optimization method options selected by the user.

3. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 2, characterized in that, Based on the user-defined optimization problem type options, the process of determining the initial boundary condition expression for the capacity optimization configuration model by querying the knowledge base includes: New energy scale constraint: Given the total installed capacity of wind power and photovoltaics, the initial boundary condition expression is: ； Total Investment Constraint: Given the total investment amount, the initial boundary condition expression is as follows: ； Hydrogen production scale constraint: Given a hydrogen production scale, the initial boundary condition expression is: ； Hydrogen loading constraint: Given a hydrogen loading constraint, the initial boundary condition expression is as follows: Hydrogen loading.

4. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The process of querying the knowledge base to construct the constraint combination of the capacity optimization configuration model of the wind-solar-hydrogen-storage system includes: the constraint combination includes power balance constraint, hydrogen energy balance constraint, energy storage operation constraint, load power constraint, environmental protection requirement constraint, and policy boundary constraint.

5. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The process of generating relevant forecast data for the optimization configuration model for each time period within the planning time interval, based on historical data from the database, includes applying wind power and photovoltaic power generation models to generate forecast data for each time period based on wind speed and solar radiation intensity data from the new energy resource database. Based on the predicted output power data of wind power and photovoltaic power, calculate the output power of wind turbine units. The expression is: in, For wind speed, This refers to the cut-in wind speed of the wind turbine. This refers to the cut-off wind speed of the wind turbine. The rated wind speed of the wind turbine unit. This refers to the scale of wind power (number of wind turbine units). This refers to the rated output of the wind turbine generator; Calculate the output power of a photovoltaic power station The expression is: in, This refers to the rated installed capacity of the photovoltaic power station. For the efficiency of photovoltaic modules, for Solar irradiance intensity during the period For standard reference irradiation intensity, For temperature coefficient, for Operating temperature during the period For reference temperature; Based on the time-series load database, each time period is calculated and generated. Forecast data on hydrogen load and hydrogen production energy load; For grid-connected projects, the calculations for each time period are based on the electricity spot market database. The predicted values ​​for on-grid and off-grid electricity prices.

6. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, Based on the optimization method specified in the settings, the process of applying the linear programming model includes: the mathematical expression of the linear programming model is as follows: in, It is the objective function, and it is the decision variable. The linear function is defined as the project's economic indicator. The objective function is set according to user needs, either as the system's annual net profit or the project's lifecycle net profit. These are decision variables in the model. For linear inequality constraints; Linear equality constraints; for The lower and upper bounds of the value.

7. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The optimization process includes: performing an optimization solution of a mixed-integer linear programming model. The objective function of the model is linear, the constraints are linear, and the decision variables include integer variables and continuous variables. Read the unit scale or capacity values ​​of the wind power, photovoltaic, hydrogen production, hydrogen storage and energy storage subsystems from the system operation parameter library, and introduce new system parameter decision variables for each subsystem, including wind power scale, photovoltaic scale, number of hydrogen production units, number of hydrogen storage units and number of energy storage units; Replace the system parameter decision variables in the objective function and boundary constraint expressions of the model with expressions that include the new system parameter decision variables: The new system parameter decision variables must take integer values, while the system state decision variables do not need to meet this requirement. Therefore, we apply a mixed integer linear programming model to perform an optimization to obtain the optimal solutions for the new system parameter decision variables and the system state decision variables.

8. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The process of performing secondary optimization involves solving the optimization problem by executing the linear programming model twice. The first optimization is performed using a linear programming model to obtain the preliminary optimal solutions for the system parameter decision variables, including wind power installed capacity, photovoltaic installed capacity, hydrogen production scale, hydrogen storage scale, and energy storage scale. Based on the unit size of each subsystem, take two integer multiples of the unit size on the left and right sides of the preliminary optimal solution of the system parameter decision variables for each subsystem as candidate values, and form a combination of candidate values ​​for all equipment. Apply a linear programming model to each combination of candidate values ​​for the system parameter decision variables to achieve a second optimization, and obtain the optimal solution of the system state decision variables for each time period. Calculate the predicted economic benefits of the optimal solution for each candidate group, compare the economic benefits among the candidate groups, and select the optimal solution.

9. The method for optimizing the capacity configuration of a wind-solar-hydrogen storage system as described in claim 1, characterized in that, The process of simulating the system's operation within the planning time interval based on the optimal solution includes: simulating the system's operation within the planning time interval based on the optimal solution obtained, including the optimal estimated values ​​of system parameter decision variables and the optimal predicted values ​​of system state decision variables, and outputting the variable values ​​for each time period to the dataset, including: wind power and photovoltaic operation data, hydrogen production and storage data, and energy consumption and storage data. Among them, the wind power and photovoltaic operation data include wind power output characteristics, wind curtailment, photovoltaic output characteristics, and photovoltaic curtailment. Hydrogen production and storage data include hydrogen production volume, hydrogen production load, hydrogen charging and discharging volume, and hydrogen storage volume; Energy consumption and storage data include electricity consumption, electricity fed into the grid, electricity discharged from the grid, charging and discharging capacity, and energy storage capacity.

10. A capacity optimization configuration system for a wind-solar-hydrogen storage system, characterized in that, include: The database management module is used to obtain the user's settings for the optimization problem and query the knowledge base based on the settings. The model initialization module is used to construct the objective function and initial boundary conditions of the wind-solar-hydrogen-storage capacity optimization configuration model, and to query the knowledge base to construct the constraint combination of the wind-solar-hydrogen-storage system capacity optimization configuration model. The prediction module is used to generate relevant prediction data for the optimization configuration model for each time period within the planning time interval based on relevant historical data in the database. The optimization module is used to apply a linear programming model to perform a first-order or second-order optimization based on the optimization method specified in the settings. It finds the optimal solution for the system parameter decision variables and system state decision variables that maximizes the economic benefits of the objective function and satisfies all constraints. The module then simulates the system's operation within the planning time interval based on the optimal solution.

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