Optimal configuration method and device of integrated energy system, electronic equipment and storage medium

Abstract

The invention discloses an optimal configuration method and device for an integrated energy system, electronic equipment and a storage medium, and belongs to the technical field of integrated energy system planing.The method comprises the steps that according to basic data of a to-be-configured park, the maximum net present value of the whole life cycle of the integrated energy system serves as the target, and the to-be-configured park is established; constructing a target function and a constraint condition corresponding to the configuration of the integrated energy system; under the constraint of the constraint condition, solving the target function to obtain the number of photovoltaic modules, the number of fans, the equipment configuration capacity, the total input power of an electrolytic bath and the power for purchasing electricity from a power grid of the integrated energy system when the net present value of the whole life cycle of the integrated energy system is maximum; and a configuration scheme corresponding to the integrated energy system is generated to perform optimal configuration on the integrated energy system of the to-be-configured park, so that the problem of lack of comprehensive evaluation of project full-life-cycle economic feasibility during configuration in the prior art can be solved.

Description

Technical Field

[0001] This invention relates to the field of integrated energy system planning technology, and in particular to an optimized configuration method, apparatus, electronic device and storage medium for integrated energy systems. Background Technology

[0002] Against the backdrop of global efforts to address climate change and achieve carbon neutrality, replacing fossil fuels with renewable energy (RES) has become central to energy system transformation. However, major renewable energy sources such as wind and solar power are significantly intermittent and unpredictable, leading to a temporal and spatial mismatch between their power generation and actual energy demand. This supply-demand imbalance not only challenges the stability of the power grid but also often results in significant curtailment of wind and solar power during peak power generation periods, causing energy waste.

[0003] Power-to-X (PtX) technology, particularly the technology for producing green hydrogen through water electrolysis, is considered one of the key solutions to the aforementioned problems. It can convert excess renewable electricity into chemical energy (hydrogen) for storage, providing not only flexibility to the power grid but also a pathway for deep decarbonization in industries that are difficult to directly electrify, such as transportation and chemical industries, which are notoriously difficult to reduce emissions. Therefore, building zero-carbon industrial parks around PtX technology to achieve on-site energy production, conversion, and consumption has become a research hotspot in the energy sector.

[0004] However, integrating PtX technology with a high proportion of renewable energy presents new challenges. On the one hand, core PtX equipment such as electrolyzers incurs high capital expenditures, and their economic viability is highly dependent on high utilization rates. On the other hand, the intermittent nature of renewable energy naturally leads to drastic fluctuations in the operating load of electrolyzers and low utilization rates, which directly drives up the levelized cost of green hydrogen production, making it difficult to compete with traditional fossil fuel-based hydrogen production. This contradiction constitutes the core difficulty in project planning: project designers need to strike a balance between increasing renewable energy capacity (to reduce operating costs and carbon emissions) and controlling total investment (to ensure the utilization rate and economic viability of capital-intensive equipment). However, existing project planning methods often have limitations, focusing on component selection and capacity configuration at the technical level, but lacking a comprehensive assessment of the economic feasibility of the entire project lifecycle. For example, they fail to fully consider key financial indicators such as net present value (NPV), which leads to significant uncertainty and investment risks in the project planning stage, making it impossible to meet the expected carbon reduction targets. Summary of the Invention

[0005] This invention provides an optimized configuration method, apparatus, electronic device, and storage medium for an integrated energy system, which can solve the problem of lack of comprehensive assessment of the economic feasibility of the entire project life cycle when configuring in the prior art.

[0006] To address the aforementioned technical problems, embodiments of the present invention provide an optimized configuration method for an integrated energy system, comprising: Acquire basic data for the industrial park to be configured; wherein, the basic data includes: meteorological data, load data, and power grid data; the meteorological data includes: hourly total solar irradiance, ambient temperature, and hub-height wind speed; the load data includes: the rigid hydrogen demand and heat load demand of the chemical production line; the power grid data includes: time-of-use electricity price curve and real-time carbon emission factor of the power grid; Based on the aforementioned basic data, a renewable energy power generation model, an electrolyzer piecewise linearization model, and an energy storage model are constructed for the integrated energy system of the park to be configured. Based on the renewable energy power generation model, the piecewise linearization model of the electrolyzer, and the energy storage model, with the goal of maximizing the net present value of the integrated energy system over its entire life cycle, an objective function and constraints corresponding to the configuration of the integrated energy system are constructed. Under the constraints described, the objective function is solved to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the integrated energy system is maximized. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, a configuration scheme corresponding to the integrated energy system is generated, and the integrated energy system of the park to be configured is optimized according to the configuration scheme.

[0007] As a preferred embodiment, under the constraints of the given conditions, solving the objective function to obtain the integrated energy system at its maximum net present value over its entire life cycle includes the following parameters: number of photovoltaic modules, number of wind turbines, equipment configuration capacity, total input power of the electrolyzer, and power purchased from the grid. The objective function and constraints are input into a preset optimization solver and the parameters of the optimization solver are set so that the optimization solver performs relaxation operations on the integer variables in the objective function and solves the linear programming problem in the objective function to obtain the lower bound of the optimal solution of the objective function. Based on the lower bound of the optimal solution, the integer variables in the objective function are branched in the preset search tree to generate several child nodes. A cutting plane constraint is added to each child node. Then, under the constraint of the cutting plane constraint, each child node is solved to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the entire life cycle of the integrated energy system is maximized.

[0008] As a preferred embodiment, the step of generating a configuration scheme for the integrated energy system based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid includes: Based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, calculate the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity for the integrated energy system. Based on the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity, the over-allocation ratio of the power generation unit capacity to the rated capacity of the hydrogen production equipment in the integrated energy system is obtained. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, the power purchased from the grid, and the over-allocation ratio, the configuration scheme corresponding to the integrated energy system is generated.

[0009] As a preferred embodiment, the objective function is: ; in, This refers to the net present value over the entire lifecycle of the integrated energy system. This represents the total initial investment cost; The project lifecycle; For the first Total annual income; For the first Total operating expenses for the year; The discount rate; A collection of devices; For equipment The cost per unit capacity; For equipment Configuration capacity.

[0010] As a preferred embodiment, the constraints include: energy and material balance constraints, equipment operation constraints, power grid interaction constraints, and environmental carbon intensity constraints. The energy-matter balance constraint is: ; in, for The output power of the photovoltaic system at any given time; for The output power of the wind farm at any given time; and These represent the power purchased from the grid and the power sold to the grid, respectively. and These are the battery's discharge power and charging power, respectively. This refers to the total input power of the electrolytic cell; This refers to the standard electrical load of the park to be configured; The operating constraints of the equipment are: ; in, and These are the maximum allowable downward ramp rate and the maximum allowable upward ramp rate for the electrolytic cell, respectively. This refers to the rated capacity of the electrolytic cell; The power grid interaction constraints are as follows: ; in, and Binary variables of 0 and 1 respectively represent Whether it is in a state of purchasing or selling electricity at any given time; The environmental carbon intensity constraint is: ; in, For statistical periods; for Real-time carbon emission factor of the power grid; for Hydrogen production at any given time; The maximum permissible carbon emission intensity threshold per unit of hydrogen.

[0011] Based on the above embodiments, another embodiment of the present invention provides an optimization configuration device for an integrated energy system, comprising: a basic data acquisition module, a model building module, an objective function construction module, and an optimization configuration module; The basic data acquisition module is used to acquire basic data of the park to be configured; wherein, the basic data includes: meteorological data, load data and power grid data; the meteorological data includes: hourly total solar irradiance, ambient temperature and hub height wind speed; the load data includes: rigid hydrogen demand and heat load demand of chemical production lines; the power grid data includes: time-of-use electricity price curve and real-time carbon emission factor of the power grid; The model building module is used to build a renewable energy power generation model, an electrolyzer piecewise linearization model, and an energy storage model for the integrated energy system of the park to be configured, based on the basic data. The objective function construction module is used to construct the objective function and constraints corresponding to the configuration of the integrated energy system based on the renewable energy power generation model, the electrolyzer piecewise linearization model and the energy storage model, with the goal of maximizing the net present value of the integrated energy system throughout its entire life cycle. The optimization configuration module is used to solve the objective function under the constraints of the constraints, to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the integrated energy system is maximized. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, a configuration scheme corresponding to the integrated energy system is generated, and the integrated energy system of the park to be configured is optimized according to the configuration scheme.

[0012] As a preferred embodiment, under the constraints of the given conditions, solving the objective function to obtain the integrated energy system at its maximum net present value over its entire life cycle includes the following parameters: number of photovoltaic modules, number of wind turbines, equipment configuration capacity, total input power of the electrolyzer, and power purchased from the grid. The objective function and constraints are input into a preset optimization solver and the parameters of the optimization solver are set so that the optimization solver performs relaxation operations on the integer variables in the objective function and solves the linear programming problem in the objective function to obtain the lower bound of the optimal solution of the objective function. Based on the lower bound of the optimal solution, the integer variables in the objective function are branched in the preset search tree to generate several child nodes. A cutting plane constraint is added to each child node. Then, under the constraint of the cutting plane constraint, each child node is solved to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the entire life cycle of the integrated energy system is maximized.

[0013] As a preferred embodiment, the step of generating a configuration scheme for the integrated energy system based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid includes: Based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, calculate the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity for the integrated energy system. Based on the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity, the over-allocation ratio of the power generation unit capacity to the rated capacity of the hydrogen production equipment in the integrated energy system is obtained. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, the power purchased from the grid, and the over-allocation ratio, the configuration scheme corresponding to the integrated energy system is generated.

[0014] Based on the above embodiments, another embodiment of the present invention provides an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the optimized configuration method of the integrated energy system described in the above embodiments of the invention.

[0015] Based on the above embodiments, another embodiment of the present invention provides a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the optimized configuration method of the integrated energy system described in the above embodiments of the invention.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention provides an optimized configuration method, apparatus, electronic device, and storage medium for an integrated energy system. The method includes: acquiring basic data of the industrial park to be configured; wherein the basic data includes: meteorological data, load data, and power grid data; the meteorological data includes: hourly total solar irradiance, ambient temperature, and hub-height wind speed; the load data includes: the rigid hydrogen demand and heat load demand of the chemical production line; the power grid data includes: time-of-use electricity price curves and real-time carbon emission factors of the power grid; based on the basic data, constructing a renewable energy generation model, an electrolyzer piecewise linearization model, and an energy storage model for the integrated energy system of the industrial park to be configured; and based on the renewable energy generation model and the electrolyzer piecewise linearization model... The model and energy storage model aim to maximize the net present value (NPV) of the integrated energy system throughout its entire life cycle. An objective function and constraints are constructed for the configuration of the integrated energy system. Under these constraints, the objective function is solved to obtain the number of photovoltaic modules, wind turbines, equipment capacity, total input power of the electrolyzer, and the power purchased from the grid when the NPV of the integrated energy system is maximized. Based on these parameters, a configuration scheme for the integrated energy system is generated, and the integrated energy system of the park to be configured is optimized according to this scheme.

[0017] This invention, through the construction of a renewable energy power generation model, a piecewise linearized electrolyzer model, and an energy storage model, aims to maximize the net present value (NPV) of the integrated energy system throughout its entire lifecycle. It constructs an objective function and constraints for the configuration of the integrated energy system, solves the objective function, and optimizes the configuration of the integrated energy system in the industrial park based on the solution results. The objective function of this invention, constructed with the goal of maximizing the NPV of the integrated energy system throughout its entire lifecycle, fully considers the economic feasibility of the project throughout its entire lifecycle during the configuration process, thereby improving the feasibility and scientific rigor of the optimized configuration of the integrated energy system. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating an optimized configuration method for an integrated energy system according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the integrated energy system of a zero-carbon industrial park; Figure 3 This is a schematic diagram of the structure of an optimized configuration device for an integrated energy system provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0020] Unless otherwise defined, 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 application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0021] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0024] In the description of the embodiments of this application, the terms "multiple" and "several" refer to two or more (including two), similarly, "multiple groups" refer to two or more (including two groups), and "multiple pieces" refer to two or more (including two pieces).

[0025] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0026] Example 1 Please refer to Figure 1 To address the lack of comprehensive assessment of the economic feasibility of a project throughout its entire lifecycle when configuring systems in existing technologies, an embodiment of the present invention provides a flowchart of an optimized configuration method for an integrated energy system, comprising the following specific steps: S1. Obtain the basic data of the park to be configured; wherein, the basic data includes: meteorological data, load data and power grid data; the meteorological data includes: hourly total solar irradiance, ambient temperature and hub height wind speed; the load data includes: rigid hydrogen demand and heat load demand of chemical production lines; the power grid data includes: time-of-use electricity price curve and real-time carbon emission factor of the power grid; In a specific embodiment, for step S1 above, please refer to... Figure 2 This is a schematic diagram of a comprehensive energy system for a zero-carbon industrial park. This embodiment takes a typical chemical industrial park as an example. First, the following basic data is obtained: Meteorological data: hourly total solar irradiance of the location to be evaluated. Ambient temperature Hub height and wind speed Load data: Rigid hydrogen demand of the industrial park's chemical production lines Heat load demand Grid data: Time-of-use electricity price curve and real-time carbon emission factors of power grid .

[0027] S2. Based on the aforementioned basic data, construct a renewable energy power generation model, an electrolyzer piecewise linearization model, and an energy storage model for the integrated energy system of the park to be configured. In a specific embodiment, for step S2 above, based on the basic data, a renewable energy power generation model, an electrolyzer piecewise linearization model, and an energy storage model are constructed for the integrated energy system of the park to be configured. The specific model construction process is as follows: 1. Renewable energy power generation models: including photovoltaic power generation models and wind power generation models, specifically: (1) Photovoltaic power generation model (PV): Considering the effect of temperature on photovoltaic conversion efficiency: ; ; in, yes The output power of the photovoltaic system at any given time; It refers to the number of photovoltaic modules; It is the rated power of a single component under standard test conditions; It refers to inverter efficiency; yes Solar irradiance at any given time; It is the standard irradiance (usually 1000W / m²). It is the power temperature coefficient; It refers to the operating temperature of the solar cells; This is the standard test temperature (usually 25℃). It is the ambient temperature; This is the battery's rated operating temperature.

[0028] Specifically, this formula originates from a simplified form of the commonly used single-diode model in photovoltaic engineering. The rationale for this design is that the output power of a photovoltaic module is primarily affected by irradiance and temperature. This formula has the following characteristics: Irradiance correction: Based on the physical property that photocurrent is proportional to incident light intensity, power and irradiance have an approximately linear relationship. Therefore, an irradiance correction is introduced. The item is linearly scaled to the rated power; Temperature correction: Since the open-circuit voltage of crystalline silicon cells decreases with increasing temperature, resulting in a negative temperature coefficient of power, a temperature correction factor is introduced to accurately reflect the impact of the environment on power generation efficiency. System losses: introduced This includes inverter conversion efficiency and cable loss; In summary, this formula maps the rated parameters under standard test conditions (STC) to the actual variable operating environment, ensuring the physical accuracy of the power generation prediction.

[0029] (2) Wind power generation model (WT): The wind speed-power relationship is described by a piecewise function, specifically: ; in, yes The output power of the wind farm at any given time; It refers to the number of wind turbines; It is the real-time wind speed at the wheel hub height; It is the cut-in wind speed; This is the rated wind speed; It is the cut-off wind speed; This is the rated power of a single fan.

[0030] Specifically, the formula is based on Betz's Law and the aerodynamic characteristic curve of the wind turbine, and has the following characteristics: Cube Relationship Area ( The formula for the kinetic energy of airflow is: The air mass swept by the wind turbine per unit time area Therefore, wind power This model uses a cubic function for fitting within this interval to reflect the significant impact of small changes in wind speed on power. Constant power region ( When the wind speed exceeds the rated value, the captured power is limited by the variable pitch control to protect the unit; therefore, it is set to a constant value. ; Cut-in / cut-out zone: Based on physical start-up inertia and safety shutdown protection mechanism, the power is set to 0.

[0031] 2. Piecewise Linearization Model for Electrolyzer: The hydrogen production efficiency of the electrolyzer varies nonlinearly with the load. To solve this in MILP, a piecewise linearization model is used to approximate its characteristic curve. ; in, yes Hydrogen yield at any given time; It is the number of segments; and They are the first The slope and intercept of the linearized interval; The electrolytic cell is in the first Operating power components within a segment interval; This indicates whether the electrolytic cell is working at the [number]th [stage]. The binary variable of the segment (0 or 1); It is the recovered heat power; It is the total input power of the electrolytic cell; It is the electrical efficiency of the electrolytic cell; It refers to the heat exchanger's recovery efficiency.

[0032] This formula is based on piecewise linearization of the electrolytic cell polarization curve (UI curve). Its underlying principle is as follows: Nonlinear Problem Transformation: The hydrogen production efficiency of actual electrolyzers varies nonlinearly with the loading rate (efficiency is typically low under low load). Directly using nonlinear functions would transform the model into a mixed-integer nonlinear programming (MINLP) problem, making it difficult to find the global optimum. This invention introduces binary variables... The original nonlinear efficiency curve is cut into K linear segments. This represents the slope (marginal hydrogen production rate) of the k-th segment. This represents the intercept. This setting transforms complex physicochemical processes into a mixed-integer linear programming (MILP) compatible format, ensuring both computational accuracy and solution speed and convergence.

[0033] 3. Energy storage model (hydrogen storage tank): Based on the difference equation of mass conservation: ; in, yes The amount of hydrogen stored in the hydrogen storage tank at any given time; yes The amount of hydrogen produced and injected into the storage tank at any given time; yes The amount of hydrogen consumed by industrial load at any given time.

[0034] This formula is based on the law of conservation of mass in physics and is in the form of a continuous-time integral. The discretized difference form.

[0035] Rationale: At a discrete time step (e.g., 1 hour), the current storage capacity of the tank must equal the storage capacity at the previous moment plus the net flow rate (production minus consumption) for the current period. This is a key constraint for decoupling energy production and demand in time.

[0036] S3. Based on the renewable energy power generation model, the electrolyzer piecewise linearization model, and the energy storage model, construct the objective function and constraints corresponding to the configuration of the integrated energy system with the goal of maximizing the net present value of the entire life cycle of the integrated energy system. In a specific embodiment, for step S3 above, based on the constructed renewable energy power generation model, electrolyzer piecewise linearization model, and energy storage model, a corresponding objective function is constructed with the goal of maximizing the net present value (NPV) over the entire life cycle. and its constraints.

[0037] Preferably, the objective function is: ; in, This refers to the net present value over the entire lifecycle of the integrated energy system. This represents the total initial investment cost; The project lifecycle; For the first Total annual revenue (including hydrogen sales, surplus electricity sold to the grid and by-product revenue). For the first Total annual operating expenses (including electricity purchase costs and operation and maintenance costs); The discount rate; A collection of devices; For equipment The cost per unit capacity; For equipment Configuration capacity.

[0038] Specifically, the objective function is derived from the Net Present Value (NPV) method and Discounted Cash Flow (DCF) model in engineering economics. The rationale for this design is as follows: The time value of money: Considering a project cycle of up to 25 years, future cash flows must be discounted to their present value using a discount rate *r*. The formula... This is the discount factor. Compared to a simple investment return period, NPV more comprehensively reflects the profitability of a project throughout its entire lifecycle. This invention constructs this formula as the objective function to directly select the allocation scheme with the highest economic value while simultaneously seeking technological optimization.

[0039] Specifically, there is a tight constraint-objective coupling relationship between each equipment model (electrolyzer, renewable energy, energy storage) and the objective function: The transitivity of decision variables: in the objective function Directly from the capacity decision variables in the equipment model (such as , The values ​​of power flow and product flow are determined by the power flow variables and product flow rates calculated by the device model during the operation phase.

[0040] Definition of solution space: The electrolyzer, power generation, and energy storage models are not merely physical descriptions; mathematically, they constitute the constraints of the optimization problem. These models define the physical feasible region of the system (e.g., hydrogen production cannot exceed the upper limit corresponding to the rated power of the electrolyzer, and hydrogen storage must comply with mass conservation).

[0041] Driving Mechanism: The solver aims to find a solution that maximizes the objective function (NPV), while the individual device models restrict the solver from taking arbitrary values, forcing the system to find the optimal balance between "increasing device capacity to obtain more benefits" and "reducing device capacity to reduce investment costs" in accordance with the laws of physics.

[0042] Preferably, the constraints include: energy and material balance constraints, equipment operation constraints, power grid interaction constraints, and environmental carbon intensity constraints; The energy-matter balance constraint is: ; in, for The output power of the photovoltaic system at any given time; for The output power of the wind farm at any given time; and These represent the power purchased from the grid and the power sold to the grid, respectively. and These are the battery's discharge power and charging power, respectively. This refers to the total input power of the electrolytic cell; This refers to the standard electrical load of the park to be configured; The operating constraints of the equipment are: ; in, and These are the maximum allowable downward ramp rate and maximum upward ramp rate (percentage of rated capacity) for the electrolytic cell, respectively. This refers to the rated capacity of the electrolytic cell; The power grid interaction constraints are as follows: ; in, and Binary variables of 0 and 1 respectively represent Whether the system is in a power purchase or power sale state at any given time; this constraint prevents the system from engaging in power purchase and sale arbitrage simultaneously. The environmental carbon intensity constraint is: ; in, The statistical period is (e.g., one year or the entire life cycle). for Real-time carbon emission factor of the power grid (unit: kgCO2 / kWh). for Hydrogen production at any given time; The maximum permissible carbon emission intensity threshold per unit of hydrogen (unit: kgCO2 / kgH2).

[0043] S4. Under the constraints of the above conditions, the objective function is solved to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the entire life cycle of the integrated energy system is maximized. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, a configuration scheme corresponding to the integrated energy system is generated, and the integrated energy system of the park to be configured is optimized according to the configuration scheme.

[0044] Preferably, the step of solving the objective function under the constraints of the constraints to obtain the number of photovoltaic modules, number of wind turbines, equipment configuration capacity, total input power of electrolyzers, and power purchased from the grid for the integrated energy system when the net present value of the integrated energy system over its entire life cycle is maximized includes: inputting the objective function and constraints into a preset optimization solver and setting the parameters of the optimization solver so that the optimization solver performs relaxation operations on the integer variables in the objective function and solves the linear programming problem in the objective function to obtain the lower bound of the optimal solution of the objective function; based on the lower bound of the optimal solution, branching the integer variables in the objective function in a preset search tree to generate several child nodes, adding a cutting plane constraint to each child node, and then solving each child node under the constraints of the cutting plane constraint to obtain the number of photovoltaic modules, number of wind turbines, equipment configuration capacity, total input power of electrolyzers, and power purchased from the grid for the integrated energy system when the net present value of the integrated energy system over its entire life cycle is maximized.

[0045] Preferably, generating the configuration scheme corresponding to the integrated energy system based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid includes: calculating the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity corresponding to the integrated energy system based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid; obtaining the over-allocation ratio of the installed capacity of the power generation unit to the rated capacity of the hydrogen production equipment of the integrated energy system based on the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity; and then generating the configuration scheme corresponding to the integrated energy system based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, the power purchased from the grid, and the over-allocation ratio.

[0046] In a specific embodiment, for step S4 above, this step uses a preset optimization solver (such as Gurobi, CPLEX, etc.) to solve the established MILP model.

[0047] Solution method: A combination of branch and bound and cutting plane method is used. The solver first relaxes integer variables to solve the linear programming subproblem and determines the lower bound; then it searches the search tree for integer variables (such as the number of devices). Segmented state The solution space is branched and tightened by adding cutting plane constraints, which quickly converges to the global optimum.

[0048] Output result: (The output will be...) The decision variables to be maximized include configuration variables (number of photovoltaic modules, number of wind turbines, and equipment configuration capacity) and operating variables (total input power of the electrolyzer and power purchased from the grid).

[0049] After determining the maximum net present value (NPV) over the entire lifecycle of the integrated energy system, considering the number of photovoltaic modules, wind turbines, equipment capacity, total input power of the electrolyzer, and the power purchased from the grid, the following analysis is performed on the solution results of the objective function: The following two key indicators are extracted and calculated from the solution results, and based on these two key indicators, the over-allocation ratio of the power generation unit installed capacity to the rated capacity of the hydrogen production equipment in the integrated energy system is obtained: Levelized Cost of Hydrogen (LCOH): ; in, It is the capital recovery coefficient; yes Operating costs at any given time; yes Revenue from the sale of by-products (oxygen, heat, and residual electricity).

[0050] The ratio of renewable energy to electrolyzer capacity ( ): ; in, Total installed capacity of renewable energy; This refers to the installed capacity of the electrolytic cell.

[0051] This indicator is used to guide the power supply over-sizing design of the system in order to balance equipment utilization and power curtailment rate.

[0052] The mechanism by which the indicator γ (the ratio of renewable energy to electrolyzer capacity) guides oversizing design is as follows: Addressing the pain point of "low utilization": Due to the fluctuations in wind and solar power, if the power supply and electrolytic cell are configured at a 1:1 ratio (i.e., γ=1), the expensive electrolytic cell will be under low load or even shut down during periods when wind and solar power output is insufficient (most of the time), resulting in idle equipment and increased LCOH.

[0053] The economic trade-off of over-sizing: When γ increases (over-sizing): the installed capacity at the source increases, and the electrolyzer can obtain sufficient power during periods of low irradiance / low wind speed, thereby increasing the annual utilization hours (full load rate) of the electrolyzer and reducing the CAPEX of the hydrogen production equipment.

[0054] Negative effects: As γ continues to increase, the electricity generated during peak wind and solar periods far exceeds the capacity of the electrolyzers to absorb it, leading to an increase in curtailment and pushing up energy costs at the power source.

[0055] Guiding significance: This invention presents a U-shaped curve by calculating LCOH under different γ values; on the left side of the curve (smaller γ): low utilization rate of the electrolyzer leads to high LCOH; on the right side of the curve (excessively large γ): excessively high cost of abandoned electricity leads to high LCOH.

[0056] Conclusion: The index γ can quantify this trade-off relationship, guide designers to find the optimal over-allocation ratio corresponding to the lowest point of LCOH, and thus find the economically optimal solution between "wasting some electricity" and "making the electrolyzer fully utilized", realizing a complete closed loop from data input to optimal configuration output.

[0057] It should be noted that the calculation of the above key indicators is entirely based on the solution results of the objective function. For the LCOH formula, CAPEXtotal is not a fixed value, but is calculated by substituting the optimal installed capacity output from the optimization into the formula. LCOH is a post-processing indicator, calculated by dividing the "numerator" (optimal total lifecycle cost) obtained from the optimization by the "denominator" (optimal total hydrogen production over the entire lifecycle). Regarding the capacity ratio γ formula, NPV and NWT in the formula actually refer to the optimal number of photovoltaic modules (NPV) and wind turbines (NWT*) output by the optimization solver, while CapEL in the formula is the optimal electrolyzer capacity output by the optimization. These two indicators are a secondary refinement of the optimization results, used to evaluate the rationality of the system configuration from a macro-technological and economic perspective.

[0058] In another preferred embodiment, to more clearly demonstrate the value of this method, it can be applied to a specific sensitivity analysis scenario. Taking the Ordos Zero-Carbon Industrial Park project as an example, this park aims to achieve 100% green zero-carbon energy supply, with 80% coming from direct supply of wind power, photovoltaics, and energy storage, and 20% traded with the grid. We can fix the electrolyzer capacity and then use the ratio r of renewable energy to electrolyzer capacity as a variable, performing the optimization method of this invention on a series of discrete values ​​(e.g., r = 0.5, 1, 2, ..., 10).

[0059] This analysis reveals a complex nonlinear relationship between system design (i.e., the r-value) and key performance indicators (NPV, LCOH, carbon intensity). For example, the study finds that LCOH does not decrease monotonically as the r-value increases. Initially, increasing the proportion of renewable energy (increasing r) replaces expensive grid power, thus reducing LCOH. However, when the r-value is too high, the large-scale installation of renewable energy leads to a decrease in its utilization hours, and the high CAPEX amortization cost begins to dominate the calculation of LCOH, causing it to rise instead. This convex relationship of first decreasing and then increasing indicates the existence of an optimal r-value that minimizes LCOH.

[0060] The method of this invention aims to systematically find this non-obvious optimal point. Without such integrated optimization, project developers may intuitively overinvest in renewable energy, leading to LCOH levels exceeding necessary levels; or underinvest, resulting in failure to meet carbon intensity targets and achieve the strategic goal of 100% green electricity supply for the industrial park. This invention provides a navigation tool to help decision-makers find the optimal balance in this complex, multi-dimensional trade-off space.

[0061] To enable those skilled in the art to reproduce the present invention, an exemplary input parameter table and corresponding optimization result table are provided below, as shown in Tables 1 and 2. Table 1 Examples of input parameters for the optimization model Table 2 Examples of optimization results under different renewable energy / electrolyzer capacity ratios (r) As shown in Table 2, by implementing the method of the present invention, it can be clearly seen that when the capacity ratio r is 2.0, the LCOH reaches its lowest point, while when r is 3.0, the NPV reaches its highest point. Decision-makers can select the final system configuration scheme based on the project's primary objective (whether to pursue the lowest production cost or the highest return on investment), while all schemes meet the preset carbon intensity constraints.

[0062] Therefore, this invention provides an optimized configuration method for integrated energy systems. By constructing a renewable energy generation model, a piecewise linearized electrolyzer model, and an energy storage model, and with the goal of maximizing the net present value (NPV) of the integrated energy system throughout its entire lifecycle, an objective function and constraints for the integrated energy system configuration are established. The objective function is then solved, and the integrated energy system configuration of the industrial park to be configured is optimized based on the solution results. The objective function of this invention, constructed with the goal of maximizing the NPV of the integrated energy system throughout its entire lifecycle, fully considers the economic feasibility of the project throughout its entire lifecycle during the configuration process, improving the feasibility and scientific rigor of the optimized configuration. This invention, through an integrated optimization framework, combines long-term investment decisions with short-term operational strategies, providing a scientific, rigorous, and commercially viable solution for the planning and design of zero-carbon industrial parks.

[0063] Example 2 Please refer to Figure 3 This is a schematic diagram of the structure of an optimization configuration device for an integrated energy system provided in an embodiment of the present invention. The device includes: a basic data acquisition module, a model building module, an objective function construction module, and an optimization configuration module. The basic data acquisition module is used to acquire basic data of the park to be configured; wherein, the basic data includes: meteorological data, load data and power grid data; the meteorological data includes: hourly total solar irradiance, ambient temperature and hub height wind speed; the load data includes: rigid hydrogen demand and heat load demand of chemical production lines; the power grid data includes: time-of-use electricity price curve and real-time carbon emission factor of the power grid; The model building module is used to build a renewable energy power generation model, an electrolyzer piecewise linearization model, and an energy storage model for the integrated energy system of the park to be configured, based on the basic data. The objective function construction module is used to construct the objective function and constraints corresponding to the configuration of the integrated energy system based on the renewable energy power generation model, the electrolyzer piecewise linearization model and the energy storage model, with the goal of maximizing the net present value of the integrated energy system throughout its entire life cycle. The optimization configuration module is used to solve the objective function under the constraints of the constraints, to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the integrated energy system is maximized. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, a configuration scheme corresponding to the integrated energy system is generated, and the integrated energy system of the park to be configured is optimized according to the configuration scheme.

[0064] Preferably, the step of solving the objective function under the constraints of the given conditions to obtain the maximum net present value of the integrated energy system over its entire life cycle, includes the following: the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid. The objective function and constraints are input into a preset optimization solver and the parameters of the optimization solver are set so that the optimization solver performs relaxation operations on the integer variables in the objective function and solves the linear programming problem in the objective function to obtain the lower bound of the optimal solution of the objective function. Based on the lower bound of the optimal solution, the integer variables in the objective function are branched in the preset search tree to generate several child nodes. A cutting plane constraint is added to each child node. Then, under the constraint of the cutting plane constraint, each child node is solved to obtain the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid when the net present value of the entire life cycle of the integrated energy system is maximized.

[0065] Preferably, the step of generating a configuration scheme for the integrated energy system based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid includes: Based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, and the power purchased from the grid, calculate the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity for the integrated energy system. Based on the levelized cost of hydrogen and the ratio of renewable energy to electrolyzer capacity, the over-allocation ratio of the power generation unit capacity to the rated capacity of the hydrogen production equipment in the integrated energy system is obtained. Then, based on the number of photovoltaic modules, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, the power purchased from the grid, and the over-allocation ratio, the configuration scheme corresponding to the integrated energy system is generated.

[0066] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0067] Those skilled in the art will clearly understand that, for convenience and brevity, the specific working process of the device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0068] Example 3 Accordingly, embodiments of the present invention provide an electronic device, the device including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the optimized configuration method of the integrated energy system described in the above embodiments of the invention.

[0069] The electronic device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The device may include, but is not limited to, a processor and a memory.

[0070] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the device, connecting various parts of the device via various interfaces and lines.

[0071] Example 4 Accordingly, embodiments of the present invention provide a storage medium, the storage medium including a stored computer program, wherein, when the computer program is running, it controls the device where the storage medium is located to execute the optimized configuration method of the integrated energy system described in the above embodiments of the invention.

[0072] The memory can be used to store the computer program. The processor implements various functions of the device by running or executing the computer program stored in the memory and calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, applications required for at least one function, etc.; the data storage area may store data created based on the use of the mobile phone, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as hard disk, RAM, plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, at least one disk storage device, flash memory device, or other volatile solid-state storage device.

[0073] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0074] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for optimal configuration of an integrated energy system, characterized in that, The method comprises the following steps: acquiring basic data of a to-be-configured park; wherein, the basic data comprises meteorological data, load data and power grid data; the meteorological data comprises hourly total solar irradiance, ambient temperature and hub height wind speed; the load data comprises rigid hydrogen demand and heat load demand of a chemical production line; the power grid data comprises time-of-use price curve and real-time carbon emission factor of a power grid; constructing a renewable energy power generation model, an electrolyzer segmented linearization model and an energy storage model of a comprehensive energy system of the to-be-configured park according to the basic data; constructing a corresponding objective function and constraint condition of the comprehensive energy system configuration with the maximum life cycle net present value of the comprehensive energy system as the target according to the renewable energy power generation model, the electrolyzer segmented linearization model and the energy storage model; solving the objective function under the constraint of the constraint condition to obtain the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power of buying electricity from the power grid of the comprehensive energy system when the life cycle net present value of the comprehensive energy system is maximum, and then generating a corresponding configuration scheme of the comprehensive energy system according to the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power of buying electricity from the power grid, and optimizing the configuration of the comprehensive energy system of the to-be-configured park according to the configuration scheme.

2. The method for optimal configuration of an integrated energy system according to claim 1, wherein, The solving of the objective function under the constraint of the constraint condition to obtain the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power of buying electricity from the power grid of the comprehensive energy system when the life cycle net present value of the comprehensive energy system is maximum comprises: inputting the objective function and constraint condition into a preset optimization solver and setting parameters of the optimization solver to enable the optimization solver to perform relaxation operation on integer variables in the objective function and solve linear programming problems in the objective function to obtain a lower bound of an optimal solution of the objective function; branching integer variables in the objective function in a preset search tree according to the lower bound of the optimal solution to generate a plurality of child nodes, adding a cutting plane constraint to each child node, and then solving each child node under the constraint of the cutting plane constraint to obtain the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power of buying electricity from the power grid of the comprehensive energy system when the life cycle net present value of the comprehensive energy system is maximum.

3. The method for optimal configuration of integrated energy system according to claim 1, wherein, The generation of the corresponding configuration scheme of the comprehensive energy system according to the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power of buying electricity from the power grid comprises: calculating a corresponding levelized hydrogen cost index and a renewable energy to electrolyzer capacity ratio index of the comprehensive energy system according to the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power of buying electricity from the power grid. According to the cost index of the hydrogen equalization and the index of the renewable energy and the electrolytic tank capacity ratio, a super-proportion of a total installed capacity of a power generation unit and a rated capacity of a hydrogen production device of the integrated energy system is obtained, and then a configuration scheme corresponding to the integrated energy system is generated according to the number of photovoltaic modules, the number of wind turbines, the device configuration capacity, the total input power of the electrolytic tank, the power purchased from the power grid, and the super-proportion.

4. The method for optimal configuration of integrated energy system according to claim 1, wherein, The objective function is: ； wherein, is the net present value of the full life cycle of the integrated energy system; is the total initial investment cost; is the project life cycle years; is the total revenue in year ; is the total operating expenditure in year ; is the discount rate; is the set of equipment; is the unit capacity cost of equipment ; is the configuration capacity of equipment .

5. The method for optimal configuration of an integrated energy system according to claim 4, wherein, The constraint conditions include: energy and material balance constraints, device operation constraints, power grid interaction constraints, and environmental carbon intensity constraints; The energy and material balance constraints are: ； wherein, is the output power of the photovoltaic system at time t; is the output power of the wind farm at time t; and are the power bought from and sold to the grid, respectively; and are the discharging and charging power of the battery, respectively; is the total input power of the electrolyzer; is the conventional electrical load of the park to be configured; The device operation constraints are: ； wherein, and are the maximum allowed downward and upward ramp rates for the electrolytic cell, respectively; is the rated capacity of the electrolytic cell; The power grid interaction constraints are: ； wherein, and are binary variables of 0-1, respectively indicating whether the time is in the state of buying electricity or selling electricity; The environmental carbon intensity constraints are: ； wherein, is a statistical period; is a real-time carbon emission factor of the power grid at the moment; is hydrogen production at the moment; is a maximum carbon emission intensity threshold allowed per unit of hydrogen.

6. An optimal configuration device of an integrated energy system, characterized in that, It comprises: a basic data acquisition module, a model construction module, an objective function construction module, and an optimization configuration module; The basic data acquisition module is configured to acquire basic data of a to-be-configured park, wherein the basic data includes meteorological data, load data, and power grid data; the meteorological data includes hourly total solar irradiance, ambient temperature, and hub height wind speed; the load data includes rigid hydrogen demand and heat load demand of a chemical production line; and the power grid data includes a time-of-use price curve and a real-time carbon emission factor of the power grid. The model construction module is configured to construct a renewable energy power generation model, an electrolytic tank segmented linearization model, and an energy storage model of the integrated energy system of the to-be-configured park according to the basic data. The objective function construction module is configured to construct an objective function and constraint conditions corresponding to the integrated energy system configuration, with the maximum net present value of the whole life cycle of the integrated energy system as the target, according to the renewable energy power generation model, the electrolytic tank segmented linearization model, and the energy storage model. The optimization configuration module is configured to solve the objective function under the constraint of the constraint conditions, to obtain the number of photovoltaic modules, the number of wind turbines, the device configuration capacity, the total input power of the electrolytic tank, and the power purchased from the power grid of the integrated energy system when the net present value of the whole life cycle of the integrated energy system is maximum, and then generate a configuration scheme corresponding to the integrated energy system according to the number of photovoltaic modules, the number of wind turbines, the device configuration capacity, the total input power of the electrolytic tank, and the power purchased from the power grid, and optimize the configuration of the integrated energy system of the to-be-configured park according to the configuration scheme.

7. The apparatus for optimized configuration of integrated energy system as claimed in claim 1 wherein, The solving of the objective function under the constraint of the constraint conditions to obtain the number of photovoltaic modules, the number of wind turbines, the device configuration capacity, the total input power of the electrolytic tank, and the power purchased from the power grid of the integrated energy system when the net present value of the whole life cycle of the integrated energy system is maximum comprises: The objective function and the constraint conditions are input into a preset optimization solver, and parameters of the optimization solver are set, so that the optimization solver performs a relaxation operation on integer variables in the objective function and solves a linear programming problem in the objective function to obtain a lower bound of an optimal solution of the objective function. According to the optimal solution lower bound, branching is performed on integer variables in the objective function in a preset search tree, a plurality of child nodes are generated, a cut plane constraint is added to each child node, and each child node is solved under the constraint of the cut plane constraint, so that when the net present value of the whole life cycle of the integrated energy system is maximum, the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power purchased from the power grid of the integrated energy system pair are obtained.

8. The apparatus for optimized configuration of integrated energy system as claimed in claim 1 wherein, The configuration scheme corresponding to the integrated energy system is generated according to the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power purchased from the power grid, comprising: The configuration scheme corresponding to the integrated energy system is generated according to the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer and the power purchased from the power grid, comprising: According to the flat hydrogen cost index and the renewable energy to electrolyzer capacity ratio index, the over-provisioning ratio of the installed capacity of the power generation unit and the rated capacity of the hydrogen production equipment of the integrated energy system is obtained, and then the configuration scheme corresponding to the integrated energy system is generated according to the number of photovoltaic components, the number of wind turbines, the equipment configuration capacity, the total input power of the electrolyzer, the power purchased from the power grid and the over-provisioning ratio.

9. An electronic device, comprising: The storage medium comprises a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to perform the optimal configuration method of the integrated energy system according to any one of claims 1 to 5.

10. A storage medium, characterized by The storage medium comprises a stored computer program, wherein when the computer program is running, the device where the storage medium is located is controlled to perform the optimal configuration method of the integrated energy system according to any one of claims 1 to 5.

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