Equipment configuration methods, devices, equipment, media and products for green hydrogen microgrids
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- GUANGZHOU POWER SUPPLY BUREAU GUANGDONG POWER GRID CO LTD
- Filing Date
- 2026-01-15
- Publication Date
- 2026-06-02
Smart Images
Figure CN122136965A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of microgrid technology, and more particularly to equipment configuration methods, devices, equipment, media, and products for green hydrogen microgrids. Background Technology
[0002] Green hydrogen microgrids, leveraging their advantages in converting and utilizing clean energy sources such as wind and solar power, have become a crucial platform for achieving low-carbon energy transformation and efficient supply-demand balance. This system converts surplus electricity from new energy sources into hydrogen for storage via an electrolyzer, and then utilizes fuel cells to achieve reversible conversion of hydrogen into electricity and heat during energy shortages. Equipment capacity configuration, as a core element in the planning phase of a green hydrogen microgrid, directly determines whether the system can achieve dynamic matching of energy supply and demand, reasonable control of investment costs, and stable output of service capabilities throughout its entire lifecycle. It is a fundamental prerequisite for ensuring the performance of the green hydrogen microgrid meets standards throughout its entire lifecycle, from planning and design to actual operation.
[0003] Currently, the capacity configuration of green hydrogen microgrid equipment generally adopts a single-layer optimization method based on a steady-state economic model. However, this method has a fundamental flaw when dealing with the electromagnetic transient processes caused by the dense connection of power electronic equipment in actual systems: its optimization model is based entirely on steady-state or quasi-steady-state assumptions, and cannot reflect the dynamic interaction characteristics of equipment such as wind turbine converters, photovoltaic inverters and energy storage converters on the millisecond to second time scale. Furthermore, it does not consider transient problems such as broadband oscillations and voltage instability that may be caused by fault disturbances. As a result, the optimization result is only "optimal" in terms of economics, but may become unstable or even collapse during electromagnetic transient processes, which seriously restricts the reliable implementation and safe operation of equipment capacity configuration schemes in actual engineering. Summary of the Invention
[0004] This invention provides a method, apparatus, equipment, medium, and product for configuring green hydrogen microgrids, which can improve the reliability of equipment capacity configuration in green hydrogen microgrids under electromagnetic transients.
[0005] In a first aspect, an embodiment of the present invention provides a method for configuring equipment in a green hydrogen microgrid, comprising: Obtain the operating data of the green hydrogen microgrid within a preset planning period; The operating data is input into a preset upper-level optimization model. With the goal of maximizing net profit, the upper-level optimization model is optimized and solved under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. A lower-level electromagnetic transient model is constructed based on the initial configuration scheme, and the lower-level electromagnetic transient model is instantiated and simulated under a preset disturbance scenario to obtain first response data. If the first response data meets the preset stability conditions, the target configuration scheme is determined. The capacity of each device in the green hydrogen microgrid is configured based on the target configuration scheme.
[0006] By acquiring operational data of the green hydrogen microgrid within a pre-defined planning period, this approach avoids data gaps or distortions that could lead to a mismatch between the configuration scheme and actual needs. It ensures that the initial economic calculations of the configuration scheme are based on real-world operating scenarios, laying a data foundation for the reliability of subsequent configurations under electromagnetic transients. The operational data is input into the upper-level optimization model, and the initial configuration scheme is obtained by solving under pre-defined constraints with the goal of maximizing net profit. This not only guarantees the core economic requirements of the configuration scheme but also initially limits the reasonable range of equipment capacity through constraints, reducing electromagnetic transient stability risks caused by extreme equipment capacity values and ensuring the reliability of equipment capacity configuration under electromagnetic transients. Based on the initial configuration scheme, a lower-level electromagnetic transient model containing all microgrid equipment is constructed. The model is then instantiated and simulated under pre-defined disturbance scenarios to obtain first-response data, which can replicate the electrical dynamic characteristics and design parameters of each device in the green hydrogen microgrid. This application simulates the interaction behavior between devices and accurately simulates typical operating conditions that may cause electromagnetic transient instability in actual operation. Through instantiated simulation, it can capture key dynamic response data such as voltage, frequency, and oscillation of the initial configuration scheme within a short time scale, breaking the limitation of traditional steady-state optimization that cannot reflect dynamic characteristics and improving the reliability of equipment capacity configuration under electromagnetic transients. By verifying the first response data obtained from the simulation through quantitative stability criteria, reliable configuration schemes that can operate stably under electromagnetic transient scenarios can be directly screened, ensuring that the final target configuration scheme has the stability required for actual engineering operation and ensuring the reliability of equipment capacity configuration under electromagnetic transients. By configuring the capacity of each device in the green hydrogen microgrid using the target configuration scheme, it ensures that the actual configured equipment capacity meets both the optimization requirements and the stable operation requirements under electromagnetic transients, ultimately achieving a significant improvement in the reliability of green hydrogen microgrid equipment capacity configuration. This application can improve the reliability of green hydrogen microgrid equipment capacity configuration under electromagnetic transients.
[0007] Furthermore, determining the target configuration scheme if the first response data meets a preset stability condition includes: Determine whether the first response data satisfies the stability condition; If the first response data does not meet the stability condition, the first response data is analyzed to determine the unstable device and the corresponding capacity data. The capacity constraints of the unstable device are determined using the capacity data, and the upper-level optimization model is updated using the capacity constraints to obtain the target upper-level model. A first configuration scheme is obtained based on the target upper-level model, and a target lower-level model is constructed based on the first configuration scheme until the second response data output by the target lower-level model satisfies the stability condition. Then, the target configuration scheme is determined based on the second response data.
[0008] In this way, when the initial scheme exhibits instability in electromagnetic transient simulation, by analyzing and identifying specific unstable devices, the abstract stability problem can be transformed into a specific device capacity constraint. This constraint is fed back and integrated into the upper-level optimization model, guiding the next round of optimization to search in the direction of avoiding the unstable capacity ratio. Through this closed-loop iterative mechanism, the upper-level optimization not only pursues optimal economy but also continuously eliminates or corrects those capacity schemes that lead to electromagnetic transient instability through repeated dynamic verification. This systematically and automatically converges to a reliable configuration scheme that combines economy and dynamic stability, fundamentally improving the reliability of the configuration scheme in the electromagnetic transient dimension.
[0009] Furthermore, the initial configuration scheme includes configuration values for wind turbine generator capacity, photovoltaic array capacity, electrolyzer capacity, fuel cell capacity, and battery capacity. The construction of the lower-level electromagnetic transient model based on the initial configuration scheme specifically includes: Based on the wind turbine generator capacity configuration value in the initial configuration scheme, a wind turbine converter model is constructed. Based on the photovoltaic array capacity configuration value in the initial configuration scheme, a photovoltaic inverter model is constructed; Based on the electrolytic cell capacity configuration value in the initial configuration scheme, a nonlinear load model of the electrolytic cell is constructed. Based on the fuel cell capacity configuration value in the initial configuration scheme, a controlled voltage source model is constructed. Based on the battery capacity configuration value in the initial configuration scheme, a battery converter model is constructed. Based on the wind turbine converter model, the photovoltaic inverter model, the electrolytic cell nonlinear load model, the controlled voltage source model, and the battery converter model, the lower-level electromagnetic transient model is constructed.
[0010] This approach, based on the initial configuration scheme, constructs a lower-level electromagnetic transient model that includes all devices in the microgrid. This model can replicate the electrical dynamic characteristics of each device in the green hydrogen microgrid and the interaction behavior between devices, providing a realistic simulation basis for subsequent simulations and further improving the reliability of device capacity configuration in the green hydrogen microgrid under electromagnetic transients.
[0011] Furthermore, the step of inputting the operational data into a preset upper-level optimization model, with the goal of maximizing net profit, and optimizing the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid, specifically includes: The operating data, the obtained price data of the green hydrogen microgrid, and its equipment parameters are input into the upper-level optimization model. The upper-level optimization model is solved in combination with a preset optimization algorithm with the goal of maximizing net profit. The solution results that satisfy the constraints are calculated. The operating data includes power generation data and load data. The initial configuration scheme is determined based on the solution results.
[0012] This approach inputs the operational data into the upper-level optimization model and, with the goal of maximizing net profit, solves the initial configuration scheme under preset constraints. This not only ensures the core requirement of economic efficiency in the configuration scheme but also initially limits the reasonable range of equipment capacity through constraints, reducing the potential electromagnetic transient stability risks caused by extreme values of equipment capacity and ensuring the reliability of equipment capacity configuration under electromagnetic transients.
[0013] Furthermore, the step of instantiating and simulating the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data specifically includes: Obtain the scene operation parameters corresponding to the preset disturbance scenario; Using the scenario operating parameters and simulation tools, the lower-level electromagnetic transient model is instantiated and simulated to obtain the first response data, which includes bus voltage, grid current, system frequency, oscillation damping characteristics, and generator power angle.
[0014] By instantiating and simulating the model under a preset disturbance scenario, the first response data can be obtained, which can accurately simulate typical operating conditions that may cause electromagnetic transient instability in actual operation. Through instantiated simulation, key dynamic response data such as voltage, frequency and oscillation of the initial configuration scheme can be captured in a short time scale, breaking the limitation of traditional steady-state optimization that cannot reflect dynamic characteristics and improving the reliability of equipment capacity configuration under electromagnetic transients.
[0015] Furthermore, the method for constructing the upper-level optimization model specifically includes: Obtain the revenue data, cost data, and historical operation data of the green hydrogen microgrid; Using the revenue data and the cost data, a total revenue function and a total cost function are constructed respectively; Based on the total revenue function and the total cost function, a net revenue function is constructed. The objective function is determined based on the net revenue function, and constraints are constructed based on the historical operating data, wherein the constraints include electrothermal coupling constraints, equipment operation constraints, grid interaction power constraints, and capacity boundary constraints. Based on the objective function and the constraints, the upper-level optimization model is constructed.
[0016] By acquiring revenue, cost, and historical operating data and constructing a higher-level optimization model aimed at maximizing net revenue, this approach ensures that the optimization process has a clear economic orientation. It provides a computational foundation for finding the most cost-effective configuration scheme. The constraints constructed based on historical operating data set a preliminary feasible solution space for equipment capacity that conforms to physical laws and actual operating scenarios. This model construction process avoids the generation of physically impossible or seriously deviating capacity schemes from actual operation from the source of optimization. It provides a high-quality candidate starting point with engineering rationality for subsequent electromagnetic transient stability verification, which is an important prerequisite for ensuring the reliability of the final configuration scheme.
[0017] Secondly, an embodiment of the present invention provides an equipment configuration device for a green hydrogen microgrid, including a first module, a second module, a third module and a fourth module; The first module is used to acquire the operating data of the green hydrogen microgrid within a preset planning period; The second module is used to input the operating data into a preset upper-level optimization model, with the goal of maximizing net profit, and to optimize and solve the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. The third module is used to construct a lower-level electromagnetic transient model based on the initial configuration scheme, and to perform instantiation simulation of the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data. If the first response data meets the preset stability conditions, the target configuration scheme is determined. The fourth module is used to configure the capacity of each device in the green hydrogen microgrid based on the target configuration scheme.
[0018] By acquiring operational data of the green hydrogen microgrid within the preset planning period through the first module, it avoids the configuration scheme from deviating from actual needs due to missing or distorted data, ensuring that the economic calculation of the initial configuration scheme is based on the actual operating scenario, and laying a data foundation for the reliability of the configuration under subsequent electromagnetic transients from the source. The second module inputs the operational data into the upper-level optimization model, and solves for the initial configuration scheme under preset constraints with the goal of maximizing net benefits. This not only ensures the core economic requirements of the configuration scheme, but also initially limits the reasonable range of equipment capacity through constraints, reducing the potential electromagnetic transient stability risks caused by extreme values of equipment capacity, and ensuring the reliability of equipment capacity configuration under electromagnetic transients. The third module constructs a lower-level electromagnetic transient model containing all equipment in the microgrid based on the initial configuration scheme, and performs instantiated simulation of the model under preset disturbance scenarios to obtain the first response data, which can replicate the electrodynamics of each device in the green hydrogen microgrid. The system analyzes the dynamic characteristics and interaction behavior between devices, and accurately simulates typical operating conditions that may cause electromagnetic transient instability in actual operation. Through instantiated simulation, it can capture key dynamic response data such as voltage, frequency, and oscillation of the initial configuration scheme within a short time scale, breaking the limitation of traditional steady-state optimization that cannot reflect dynamic characteristics and improving the reliability of device capacity configuration under electromagnetic transients. By verifying the first response data obtained from the simulation through quantitative stability criteria, it can directly screen out reliable configuration schemes that can operate stably under electromagnetic transient scenarios, ensuring that the final target configuration scheme has the stability required for actual engineering operation and ensuring the reliability of device capacity configuration under electromagnetic transients. The fourth module uses the target configuration scheme to configure the capacity of each device in the green hydrogen microgrid, ensuring that the actual configured device capacity meets both the optimization requirements and the stable operation requirements under electromagnetic transients, ultimately achieving a significant improvement in the reliability of green hydrogen microgrid device capacity configuration.
[0019] Thirdly, another embodiment of the present invention provides a terminal device, including: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other through the communication bus; The memory is used to store at least one executable instruction that causes the processor to perform the operation of the device configuration method for the green hydrogen microgrid.
[0020] Fourthly, another embodiment of the present invention provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform a device configuration method for a green hydrogen microgrid.
[0021] Fifthly, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a device configuration method for a green hydrogen microgrid. Attached Figure Description
[0022] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.
[0023] Figure 1 This is a schematic flowchart of an embodiment of a device configuration method for a green hydrogen microgrid provided in this application; Figure 2 This is a schematic diagram of the equipment configuration device for a green hydrogen microgrid provided in this application. Detailed Implementation
[0024] 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.
[0025] 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.
[0026] 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.
[0027] 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.
[0028] 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.
[0029] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).
[0030] 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.
[0031] In the field of microgrid technology, green hydrogen microgrids are an important carrier for low-carbon energy transformation and supply-demand balance. Equipment capacity configuration is a core aspect of its planning, directly determining the system's performance throughout its entire lifecycle. Existing single-layer optimization methods based on steady-state economic models have fundamental flaws. On the one hand, these methods completely ignore electromagnetic transients such as broadband oscillations caused by millisecond-level dynamic interactions of power electronic devices such as wind turbines, photovoltaics, and energy storage converters. On the other hand, their models cannot simulate transient processes under fault disturbances, resulting in optimized schemes that, while economically optimal, carry the risk of operational instability or even collapse. This severely restricts the reliable implementation and safe operation of configuration schemes in practical engineering projects.
[0032] See Figure 1 In order to improve the reliability of equipment capacity configuration of green hydrogen microgrid under electromagnetic transients, an embodiment of the present invention provides a method for configuring equipment in a green hydrogen microgrid, including steps S101 to S104. Step S101: Obtain the operating data of the green hydrogen microgrid within a preset planning period; In some embodiments, acquiring the operational data of the green hydrogen microgrid within a preset planning period specifically includes: acquiring the operational data of the green hydrogen microgrid within a preset planning period from the monitoring system of the green hydrogen microgrid, including collecting data on the actual output power and wind speed changes of the wind turbine generators, the actual output power, light intensity and ambient temperature of the photovoltaic array, the operating power and hydrogen production of the electrolyzer, the power generation and hydrogen consumption of the fuel cell, the charging and discharging power and state of charge of the battery storage, and the electrical load and heat load demand data of the microgrid.
[0033] It should be noted that the green hydrogen microgrid in this application includes equipment such as wind turbine generators (WT), photovoltaic power generation arrays (PV), electrolyzers (EC), fuel cells (FC), and battery energy storage (BESS).
[0034] Step S102: Input the running data into the preset upper-level optimization model, with the goal of maximizing net profit, and optimize the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. In some embodiments, the method for constructing the upper-level optimization model specifically includes: acquiring revenue data, cost data, and historical operating data of the green hydrogen microgrid; constructing a total revenue function and a total cost function using the revenue data and the cost data, respectively; constructing a net revenue function based on the total revenue function and the total cost function; determining an objective function based on the net revenue function, and constructing constraints based on the historical operating data, wherein the constraints include electrothermal coupling constraints, equipment operation constraints, grid interaction power constraints, and capacity boundary constraints; and constructing the upper-level optimization model based on the objective function and the constraints. Specifically, the project acquires revenue data, cost data, and historical operating data for the green hydrogen microgrid. Revenue data includes hydrogen production and sales price data related to hydrogen sales revenue, grid-connected electricity volume and price data related to grid-connected electricity revenue, and ancillary service provision volume and price data related to ancillary service revenue (such as frequency regulation services). Cost data includes unit capacity investment cost, unit power operation and maintenance cost, and electricity purchase cost data related to electricity purchase volume and price. Historical operating data includes power generation data, load data, and equipment operating status data for past pre-planned periods. Using the revenue data, a total revenue function incorporating hydrogen sales revenue, grid-connected electricity revenue, and ancillary service revenue is constructed. Using the cost data, a comprehensive revenue function is constructed. The total cost function includes equipment investment costs, operation and maintenance costs, and electricity purchase costs. A net revenue function is constructed based on the difference between the total revenue function and the total cost function. An objective function is determined based on the net revenue function, with the core objective being maximizing the net present value within the planning period. Constraints are constructed based on historical operating data characteristics such as equipment operation limitations, system power supply and demand, and coordinated demand for electric and thermal loads. These constraints include electric and thermal coupling constraints, equipment operation constraints, grid interaction power constraints, and capacity boundary constraints. Equipment operation constraints include electrolyzer power constraints, fuel cell power constraints, and battery energy storage charging, discharging, and state of charge constraints. Based on the determined objective function and the constructed constraints, an upper-level optimization model is formed.
[0035] It should be noted that the electrothermal coupling constraint ensures that the system's electrical load and thermal load are met in a coordinated manner. Equipment operation constraints include upper and lower power limits for electrolyzers and fuel cells, and state of charge and charge / discharge power constraints for batteries. Capacity boundary constraints define the reasonable range of capacity values for each device. Grid interaction power constraints are the power boundaries that limit the exchange of power between the green hydrogen microgrid and the main grid through the point of common coupling (PCC). Their core function is to ensure the safe and stable operation of the main grid and the economical and efficient scheduling of the microgrid itself.
[0036] In some embodiments, the relevant formulas for constructing the upper-level optimization model specifically include: Objective function: ; In the formula, Total revenue; Total cost; Net income; Indicates maximization; Total revenue function: ; In the formula, Total revenue; For revenue from hydrogen sales; For the revenue from grid-connected electricity; For ancillary service revenue; Formula for calculating revenue from hydrogen sales: ; In the formula, For time; The discount rate; For the first The power corresponding to the hydrogen production at any given time; The selling price of hydrogen; Formula for calculating revenue from grid-connected electricity: ; In the formula, For time; The discount rate; For the first The power of electricity sold online at any given time; The price at which electricity is sold to the grid; Formula for calculating revenue from ancillary services: ; In the formula, For time; The discount rate; For the first The power to provide auxiliary services at all times; Pricing for ancillary services; Total cost function: ; In the formula, Total cost; For equipment investment costs; For operation and maintenance costs; For electricity purchase costs (from the main grid); Formula for calculating equipment investment cost: ; In the formula, For device type (e.g., WT, PV, EC, FC, and BESS); for Equipment capacity of this type of equipment; for The unit capacity investment cost of this type of equipment; Formula for calculating operation and maintenance costs: ; In the formula, For time; The discount rate; For equipment type; for Unit power operation and maintenance cost of this type of equipment; For the first time Operating power of this type of equipment; Formula for calculating electricity purchase cost: ; In the formula, For time; The discount rate; For the first The power consumption of electricity purchased for internet access at any given time; The purchase price of electricity supplied to the grid; Electrothermal coupling constraint conditions: ; ; In the formula, For time; For the first The output power of the wind turbine generator at any given moment; For the first The output power of the photovoltaic array at any given time; For the first The output power of the fuel cell at any given time; For the first The discharge power of the battery at any given time; For the first The power consumption of electricity purchased for internet access at any given time; For the first The electrical load power of the microgrid at any given time; For the first The power consumption of the electrolytic cell at any given time; For the first The charging power of the battery at any given time; For the first The power of electricity sold online at any given time; Electrolytic cell power constraints: ; In the formula, For time; This is the minimum operating power of the electrolytic cell; For the first The operating power of the electrolytic cell at any given time; This refers to the rated capacity of the electrolytic cell; Fuel cell power constraints: ; In the formula, For time; This represents the minimum operating power of the fuel cell; For the first The operating power of the fuel cell at any given time; This refers to the rated capacity of the fuel cell; Battery energy storage charging and discharging and state of charge constraints: ; ; ; ; ; In the formula, For time; This represents the minimum state of charge of the battery. For the first The state of charge of the battery at any given time; This represents the battery's maximum state of charge. For the first The state of charge of the battery at any given time; The charging efficiency of the battery; For the first The charging power of the battery at any given time; For the first The discharge power of the battery at any given time; The discharge efficiency of the battery; For time intervals; This refers to the rated capacity of the battery. For the first The battery charging status indicator at any given time (0 indicates charging is disabled, 1 indicates charging is enabled). For the first The battery discharge status indicator at any given time (0 indicates discharge is prohibited, 1 indicates discharge is allowed). Power grid interaction constraints: ; In the formula, For time; For the first The interaction power between the microgrid and the main grid at any given time (positive value for purchasing electricity, negative value for selling electricity). This is the maximum power limit for interaction between the microgrid and the main grid. Capacity boundary conditions: ; In the formula, For time; for Minimum capacity limit for this type of device; for Maximum capacity limit for this type of device; for Capacity configuration for this type of device.
[0037] It should be noted that, This refers to any time within the preset planning period.
[0038] By acquiring revenue, cost, and historical operating data and constructing a higher-level optimization model aimed at maximizing net revenue, this approach ensures that the optimization process has a clear economic orientation. It provides a computational foundation for finding the most cost-effective configuration scheme. The constraints constructed based on historical operating data set a preliminary feasible solution space for equipment capacity that conforms to physical laws and actual operating scenarios. This model construction process avoids the generation of physically impossible or seriously deviating capacity schemes from actual operation from the source of optimization. It provides a high-quality candidate starting point with engineering rationality for subsequent electromagnetic transient stability verification, which is an important prerequisite for ensuring the reliability of the final configuration scheme.
[0039] In some embodiments, the step of inputting the operating data into a preset upper-level optimization model, with the goal of maximizing net profit, and optimizing the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid, specifically includes: inputting the operating data, the obtained price data of the green hydrogen microgrid, and its equipment parameters into the upper-level optimization model, and combining a preset optimization algorithm to solve the upper-level optimization model with the goal of maximizing net profit, calculating the solution result that satisfies the constraints, wherein the operating data includes power generation data and load data; and determining the initial configuration scheme based on the solution result. Specifically, operational data, acquired price data of the green hydrogen microgrid, and equipment parameters are input into the upper-level optimization model. Operational data includes power generation and load data; price data includes hydrogen sales price, grid connection price, electricity purchase price, and ancillary service prices; and equipment parameters include unit capacity investment cost, unit power operation and maintenance cost, power limits, and charge / discharge efficiency for each device. Using a pre-defined mixed-integer linear programming optimization algorithm, with the goal of maximizing net profit, the upper-level optimization model is solved to obtain a solution that satisfies all constraints. This solution includes capacity configuration data for each device. Based on the capacity data of each device in the solution, an initial configuration scheme is determined.
[0040] This approach inputs the operational data into the upper-level optimization model and, with the goal of maximizing net profit, solves the initial configuration scheme under preset constraints. This not only ensures the core requirement of economic efficiency in the configuration scheme but also initially limits the reasonable range of equipment capacity through constraints, reducing the potential electromagnetic transient stability risks caused by extreme values of equipment capacity and ensuring the reliability of equipment capacity configuration under electromagnetic transients.
[0041] Step S103: Construct a lower-level electromagnetic transient model based on the initial configuration scheme, and perform an instantiation simulation of the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data. If the first response data meets the preset stability conditions, then determine the target configuration scheme. For example, electromagnetic transient simulations typically employ the Dommel (or associated model) method, which equates continuous energy storage elements such as inductors and capacitors to a resistive network connected in parallel or in series with historical current sources at discrete time steps. Therefore, constructing the lower-level electromagnetic transient model first requires modeling the inductors and capacitors. Furthermore, the entire network at each time step can be transformed into a purely resistive node admittance matrix equation, and the instantaneous voltage values of each node can be obtained by solving this equation.
[0042] For example, the relevant formulas for the inductor and capacitor models in the lower-level electromagnetic transient model specifically include: inductance Discretized model: ; ; In the formula, Historical current source; Equivalent resistance; For the first The instantaneous value of the inductor current at any given moment; For the first The instantaneous voltage across the inductor at any given moment; The discrete time step; Inductance value; ; capacitance Discretized model: ; ; In the formula, Historical current source; Equivalent resistance; For the first The instantaneous value of the capacitor current at any given moment; For the first The instantaneous value of the voltage across the capacitor at any given moment; This is the capacitance value; The discrete time step; Purely resistive nodal admittance matrix equation: ; In the formula, The node admittance matrix is determined by the network topology and component parameters. It is a column vector composed of the historical current values of each node, consisting of the historical currents of energy storage components such as inductors and capacitors; For the first A column vector consisting of the instantaneous voltage values of each node at each moment; For the first A column vector consisting of the instantaneous values of the injected current at each node at any given time.
[0043] In some embodiments, the initial configuration scheme includes wind turbine generator capacity configuration values, photovoltaic array capacity configuration values, electrolyzer capacity configuration values, fuel cell capacity configuration values, and battery capacity configuration values. The construction of the lower-level electromagnetic transient model based on the initial configuration scheme specifically includes: constructing a wind turbine converter model based on the wind turbine generator capacity configuration values in the initial configuration scheme; constructing a photovoltaic inverter model based on the photovoltaic array capacity configuration values in the initial configuration scheme; constructing an electrolyzer nonlinear load model based on the electrolyzer capacity configuration values in the initial configuration scheme; constructing a controlled voltage source model based on the fuel cell capacity configuration values in the initial configuration scheme; constructing a battery converter model based on the battery capacity configuration values in the initial configuration scheme; and constructing the lower-level electromagnetic transient model based on the wind turbine converter model, the photovoltaic inverter model, the electrolyzer nonlinear load model, the controlled voltage source model, and the battery converter model.
[0044] Specifically, the initial configuration scheme ( This includes wind turbine generator capacity configuration values. Photovoltaic array capacity configuration value Electrolytic cell capacity configuration value Fuel cell capacity configuration value and battery capacity configuration value ; Based on the wind turbine generator capacity configuration value in the initial configuration scheme, and combined with the working principle of the full-power wind turbine converter, a wind turbine converter model is constructed. This model includes a machine-side converter and a grid-side converter. The machine-side converter is used to control the generator torque or speed to achieve maximum power point tracking. It contains the electromagnetic torque equation of the generator (such as a permanent magnet synchronous generator PMSG). The grid-side converter is used to maintain DC side voltage stability. The model of the grid-side converter is exactly the same as that of the control and photovoltaic inverter. Based on the photovoltaic array capacity configuration in the initial configuration scheme, a photovoltaic inverter model is constructed using a two-level voltage source inverter as the foundation. The model includes the equivalent circuit of the photovoltaic array on the DC side and the DC bus capacitor, the LCL filter (a third-order filter consisting of two inductors and one capacitor) on the AC side, and the control loop consisting of a phase-locked loop (PLL), an inner current loop, and an outer power / voltage loop. The equivalent circuit of the photovoltaic array on the DC side is a current source affected by light and temperature. Similar to a diode model, or simplified to a DC voltage source With output resistance Series connection, including DC bus capacitor The voltage dynamic equation, AC side through switching function (Value is 0 or 1) DC voltage Converted to three-phase pulse voltage The dynamic equations of the LCL filter of the power grid in the abc three-phase coordinate system are included, and the phase-locked loop of the control loop is used to track the phase of the power grid voltage. The commonly used method is SRF-PLL (Synchronous Reference Frame Phase-Locked Loop). The inner current loop control operates in a dq synchronous rotating coordinate system, allowing the command current to... With feedback current In comparison, the command voltage is generated through a PI controller (proportional-integral controller). Its control law is obtained by considering feedforward decoupling. The power / voltage outer loop is used to generate the current reference value of the inner loop according to the operating mode (constant power or constant voltage). For example, under constant voltage control (V / f voltage frequency conversion control), the outer loop adjusts the inverter output voltage amplitude and frequency to the rated value through the PI controller. The electrolytic cell is essentially a nonlinear load. Based on the electrolytic cell capacity configuration value in the initial configuration scheme, and according to the steady-state VI (current-voltage) characteristics of the electrolytic cell, a nonlinear load model of the electrolytic cell is constructed. It is equivalent to a time-varying resistance affected by factors such as temperature, and is powered by a DC / DC (direct current / direct current) or AC / DC (alternating current / direct current) converter. The converter is controlled by adjusting the input power or DC current. Based on the fuel cell capacity configuration value in the initial configuration scheme, the fuel cell is modeled as a controlled voltage source affected by the fuel chemical energy. To simulate the conversion process of fuel chemical energy into electrical energy, a DC / DCBoost converter and a DC / AC inverter model are connected to its output. The inverter adopts a control method similar to that of the energy storage converter. Based on the battery capacity configuration value in the initial configuration scheme, and referring to the power circuit topology of the photovoltaic inverter, a battery converter model is constructed. This model supports flexible switching between constant power control and droop control modes. The constant power control is the same as that of the photovoltaic inverter, and it will receive active / reactive power commands. The droop control (V / f or Q / V, i.e., constant voltage / constant frequency or reactive power / voltage) simulates the frequency and voltage regulation characteristics of a synchronous generator for islanded mode. It includes frequency droop equations and voltage droop equations. The constructed wind turbine converter model, photovoltaic inverter model, electrolytic cell nonlinear load model, controlled voltage source model, and battery converter model are integrated to construct a complete lower-level electromagnetic transient model.
[0045] In some embodiments, the relevant formulas for constructing the lower-level electromagnetic transient model based on the initial configuration scheme specifically include: Wind turbine converter model: Electromagnetic torque equation: ; In the formula, It is the extreme logarithm; For permanent magnet flux linkage; and These are the d-axis and q-axis inductances, respectively. and These are the currents along the d-axis and q-axis, respectively. Electromagnetic torque; Photovoltaic inverter model: DC bus capacitor Voltage dynamic equation: ; In the formula, This is the current drawn by the inverter from the DC side; This is the DC bus capacitance value; This is the DC bus voltage; Output current for the photovoltaic array; For time; Dynamic equations of an LCL filter connected to the power grid: ; ; ; In the formula, For time; This refers to the inverter-side current. This is the voltage across the filter capacitor; This is the grid-connected current; This refers to the grid voltage. This refers to the inductance value on the inverter side. This refers to the inverter output voltage. This refers to the inductor and resistance values on the inverter side. This is the value of the filter capacitor; This is the inductance value on the grid side; This refers to the inductor resistance value on the grid side. Phase-locked loop model: ; In the formula, The component error of the grid voltage along the q-axis in the synchronous rotating coordinate system; This refers to the real-time phase of the grid voltage output by the phase-locked loop; The rated angular velocity of the power grid; The proportional gain of the phase-locked loop PI controller; The integral coefficients of the phase-locked loop PI controller; Control law for current inner loop control: ; ; In the formula, The angular velocity output by the PLL; These are the d-axis and q-axis components of the grid voltage; This is the d-axis command voltage; The proportional coefficient of the inner loop PI controller; The integral coefficient of the inner loop PI controller; It is a complex frequency used to describe the dynamic characteristics of a PI controller; This is the commanded current for the d-axis; This is the d-axis feedback current; This refers to the inductance value on the inverter side. This is the q-axis feedback current; This is the q-axis command voltage; This is the q-axis command current; Constant pressure control (V / f control) is as follows: ; ; In the formula, This is the commanded current for the d-axis; This is the q-axis command current; These are the d-axis and q-axis components of the grid voltage; This is a reference value for active power. This is a reference value for reactive power. Electrolytic cell nonlinear load model: Steady-state VI characteristics: ; In the formula, This represents the number of units connected in series. It is a reversible voltage; This refers to the voltage of the electrolytic cell stack. The first resistivity, which is temperature-dependent, is used to describe the change in the internal resistance of the electrolytic cell with temperature. This is a second resistivity related to temperature, used to further refine the variation of internal resistance with temperature; For temperature; This refers to the current in the electrolytic cell. The coefficient is related to polarization and is used to describe the effect of polarization on voltage in the electrolytic cell. This is the first temperature-dependent polarization coefficient, used to quantify the effect of temperature on polarization characteristics; This is the second temperature-dependent polarization coefficient, used to further refine the influence of temperature on polarization characteristics; This is the third polarization coefficient, which is temperature-dependent and used to more accurately describe the complex relationship between temperature and polarization characteristics. The expression for time-varying resistance: ; In the formula, For the first Equivalent time-varying resistance of the electrolytic cell; For the first Constant-time electrolytic cell stack voltage; For the first The DC current of the electrolytic cell at all times; Controlled voltage source model: ; In the formula, Standard potential; It is the gas constant; It is Faraday's constant; For temperature; The partial pressures of each gas; Battery converter model: Frequency droop equation: ; In the formula, This is the droop coefficient; This refers to the frequency output of the battery energy storage converter in droop control mode. The rated frequency; This refers to the actual active power output of the battery energy storage converter. Rated active power; Voltage droop equation: ; In the formula, This is the droop coefficient; This refers to the voltage output by the battery energy storage converter in droop control mode. Rated voltage; This refers to the actual reactive power output of the battery energy storage converter. This is the rated reactive power.
[0046] This approach, based on the initial configuration scheme, constructs a lower-level electromagnetic transient model that includes all devices in the microgrid. This model can replicate the electrical dynamic characteristics of each device in the green hydrogen microgrid and the interaction behavior between devices, providing a realistic simulation basis for subsequent simulations and further improving the reliability of device capacity configuration in the green hydrogen microgrid under electromagnetic transients.
[0047] In some embodiments, the step of instantiating and simulating the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data specifically includes: acquiring scenario operating parameters corresponding to the preset disturbance scenario; using the scenario operating parameters, combined with simulation tools, instantiating and simulating the lower-level electromagnetic transient model to obtain the first response data, wherein the first response data includes bus voltage, grid current, system frequency, oscillation damping characteristics, and generator power angle. Specifically, acquiring scenario operating parameters corresponding to the preset disturbance scenario, the preset disturbance scenario including a three-phase short-circuit fault occurring at the grid common coupling point (e.g., set in... The simulation process includes scenarios such as sudden drops in wind turbine output power (e.g., a 50% reduction in wind turbine output power due to a sudden drop in wind speed) or the addition of large motor loads. The scenario operation parameters include fault characteristic parameters, power change parameters, and time parameters for each scenario. Using the acquired scenario operation parameters, combined with simulation tools (such as PSCAD / EMTDC or EMTP-RV), the lower-level electromagnetic transient model is instantiated on the simulation platform. The simulation initial conditions, disturbance trigger conditions, and simulation duration are set according to the scenario operation parameters. The simulation process is then started. During the simulation, electrical quantity data of each node in the system are collected in real time, including bus voltage, grid current, system frequency, oscillation damping characteristics, and generator power angle. These collected data are used as the first response data.
[0048] By instantiating and simulating the model under a preset disturbance scenario, the first response data can be obtained, which can accurately simulate typical operating conditions that may cause electromagnetic transient instability in actual operation. Through instantiated simulation, key dynamic response data such as voltage, frequency and oscillation of the initial configuration scheme can be captured in a short time scale, breaking the limitation of traditional steady-state optimization that cannot reflect dynamic characteristics and improving the reliability of equipment capacity configuration under electromagnetic transients.
[0049] In some embodiments, determining the target configuration scheme if the first response data meets a preset stability condition includes: determining whether the first response data meets the stability condition; if the first response data does not meet the stability condition, analyzing the first response data to determine unstable devices and corresponding capacity data; using the capacity data to determine the capacity constraints of the unstable devices, and using the capacity constraints to update the upper-level optimization model to obtain a target upper-level model; obtaining a first configuration scheme based on the target upper-level model, and constructing a target lower-level model based on the first configuration scheme, until the second response data output by the target lower-level model meets the stability condition, then determining the target configuration scheme based on the second response data.
[0050] Specifically, the system determines whether the first response data meets preset stability conditions. These stability conditions include voltage stability (e.g., all bus voltages recover to the range of 0.9–1.1 pu within 0.5 seconds after disturbance clearance), frequency stability (e.g., the absolute value of the system frequency deviation after disturbance does not exceed ±0.5 Hz and can recover to the rated value within ±0.1 Hz within 2 seconds), damping performance (e.g., the damping ratio of any oscillation mode in the microgrid is greater than a predetermined value, such as 3% or 5%), and transient stability (e.g., the power angle difference of all synchronous generators and virtual synchronous generators remains synchronized after disturbance, and the grid-connected current of the inverter does not undergo unstable distortion). If the first response data does not meet the stability conditions, the first response data is analyzed. By comparing the response characteristics of each device before and after the disturbance, the unstable device causing system instability is identified, and the corresponding capacity data of that device is extracted. Using the extracted capacity data, combined with stability simulation... The true result determines the capacity constraints of unstable equipment (such as adjusting the upper and lower limits of equipment capacity, setting the ratio of equipment capacity to other equipment capacity, etc.), and adds this capacity constraint to the upper-level optimization model to update the constraints of the upper-level optimization model, thus obtaining the target upper-level model. Based on the target upper-level model, the acquired operating data, price data, and equipment parameters are input, and the optimization solution is performed again to obtain the first configuration scheme. Based on the first configuration scheme, a target lower-level model is constructed, and the target lower-level model is instantiated and simulated under the same preset disturbance scenario to obtain the second response data. It is determined whether the second response data meets the stability condition. If it still does not meet the condition, the process of analyzing unstable equipment, updating the upper-level model, solving for the new configuration scheme, constructing the new lower-level model, and simulating is repeated until the second response data output by the target lower-level model meets the stability condition. At this point, the configuration scheme corresponding to the response data that meets the condition is determined as the target configuration scheme.
[0051] For example, if the first response data does not meet the stability condition, in addition to adding constraints, a penalty term can be added to the objective function of the upper-level optimization model to impose a penalty cost on the unstable configuration scheme.
[0052] For example, the objective function after adding the penalty term is: ; In the formula, This is an unstable indicator (0 for stable, 1 for unstable). A sufficiently large positive number is used as a penalty; NPV is the net profit. This represents the net benefit after adding the penalty.
[0053] In this way, when the initial scheme exhibits instability in electromagnetic transient simulation, by analyzing and identifying specific unstable devices, the abstract stability problem can be transformed into a specific device capacity constraint. This constraint is fed back and integrated into the upper-level optimization model, guiding the next round of optimization to search in the direction of avoiding the unstable capacity ratio. Through this closed-loop iterative mechanism, the upper-level optimization not only pursues optimal economy but also continuously eliminates or corrects those capacity schemes that lead to electromagnetic transient instability through repeated dynamic verification. This systematically and automatically converges to a reliable configuration scheme that combines economy and dynamic stability, fundamentally improving the reliability of the configuration scheme in the electromagnetic transient dimension.
[0054] Step S104: Configure the capacity of each device in the green hydrogen microgrid based on the target configuration scheme; In some embodiments, the capacity of each device in the green hydrogen microgrid is configured based on the target configuration scheme, specifically including: configuring the capacity of each device in the green hydrogen microgrid based on the wind turbine generator capacity configuration value, photovoltaic array capacity configuration value, electrolyzer capacity configuration value, fuel cell capacity configuration value and battery capacity configuration value in the target configuration scheme.
[0055] See Figure 2 Based on the above method embodiments, corresponding device embodiments are provided; An embodiment of the present invention provides an equipment configuration device for a green hydrogen microgrid, including a first module 100, a second module 200, a third module 300 and a fourth module 400; The first module 100 is used to acquire the operating data of the green hydrogen microgrid within a preset planning period; The second module 200 is used to input the operating data into a preset upper-level optimization model, with the goal of maximizing net profit, and to optimize and solve the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. The third module 300 is used to construct a lower-level electromagnetic transient model based on the initial configuration scheme, and to perform instantiation simulation of the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data. If the first response data meets the preset stability conditions, the target configuration scheme is determined. The fourth module 400 is used to configure the capacity of each device in the green hydrogen microgrid based on the target configuration scheme.
[0056] By acquiring operational data of the green hydrogen microgrid within the preset planning period through the first module, it avoids the configuration scheme from deviating from actual needs due to missing or distorted data, ensuring that the economic calculation of the initial configuration scheme is based on the actual operating scenario, and laying a data foundation for the reliability of the configuration under subsequent electromagnetic transients from the source. The second module inputs the operational data into the upper-level optimization model, and solves for the initial configuration scheme under preset constraints with the goal of maximizing net benefits. This not only ensures the core economic requirements of the configuration scheme, but also initially limits the reasonable range of equipment capacity through constraints, reducing the potential electromagnetic transient stability risks caused by extreme values of equipment capacity, and ensuring the reliability of equipment capacity configuration under electromagnetic transients. The third module constructs a lower-level electromagnetic transient model containing all equipment in the microgrid based on the initial configuration scheme, and performs instantiated simulation of the model under preset disturbance scenarios to obtain the first response data, which can replicate the electrodynamics of each device in the green hydrogen microgrid. The system analyzes the dynamic characteristics and interaction behavior between devices, and accurately simulates typical operating conditions that may cause electromagnetic transient instability in actual operation. Through instantiated simulation, it can capture key dynamic response data such as voltage, frequency, and oscillation of the initial configuration scheme within a short time scale, breaking the limitation of traditional steady-state optimization that cannot reflect dynamic characteristics and improving the reliability of device capacity configuration under electromagnetic transients. By verifying the first response data obtained from the simulation through quantitative stability criteria, it can directly screen out reliable configuration schemes that can operate stably under electromagnetic transient scenarios, ensuring that the final target configuration scheme has the stability required for actual engineering operation and ensuring the reliability of device capacity configuration under electromagnetic transients. The fourth module uses the target configuration scheme to configure the capacity of each device in the green hydrogen microgrid, ensuring that the actual configured device capacity meets both the optimization requirements and the stable operation requirements under electromagnetic transients, ultimately achieving a significant improvement in the reliability of green hydrogen microgrid device capacity configuration.
[0057] It is understood that the above-described device embodiments correspond to the method embodiments of the present invention, and can implement the device configuration method for a green hydrogen microgrid provided by any of the above-described method embodiments of the present invention.
[0058] It should be noted that the device embodiments described above are merely illustrative, and 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 specifically be implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.
[0059] Based on the above-described embodiment of a device configuration method for a green hydrogen microgrid, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a device configuration method for a green hydrogen microgrid according to any embodiment of the present invention.
[0060] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.
[0061] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.
[0062] 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 terminal device, connecting all parts of the terminal device via various interfaces and lines.
[0063] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the device configuration method for a green hydrogen microgrid described in any of the above-described method embodiments of the present invention.
[0064] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it 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 files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.
[0065] Based on the above-described method embodiments, another embodiment of the present invention provides a computer program product, including a computer program or instructions, which, when executed by a communication device, implements a device configuration method for a green hydrogen microgrid.
[0066] 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 configuring equipment in a green hydrogen microgrid, characterized in that, include: Obtain the operating data of the green hydrogen microgrid within a preset planning period; The operating data is input into a preset upper-level optimization model. With the goal of maximizing net profit, the upper-level optimization model is optimized and solved under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. A lower-level electromagnetic transient model is constructed based on the initial configuration scheme, and the lower-level electromagnetic transient model is instantiated and simulated under a preset disturbance scenario to obtain first response data. If the first response data meets the preset stability conditions, the target configuration scheme is determined. The capacity of each device in the green hydrogen microgrid is configured based on the target configuration scheme.
2. The equipment configuration method for the green hydrogen microgrid as described in claim 1, characterized in that, If the first response data meets the preset stability conditions, then the target configuration scheme is determined, specifically including: Determine whether the first response data satisfies the stability condition; If the first response data does not meet the stability condition, the first response data is analyzed to determine the unstable device and the corresponding capacity data. The capacity constraints of the unstable device are determined using the capacity data, and the upper-level optimization model is updated using the capacity constraints to obtain the target upper-level model. A first configuration scheme is obtained based on the target upper-level model, and a target lower-level model is constructed based on the first configuration scheme until the second response data output by the target lower-level model satisfies the stability condition. Then, the target configuration scheme is determined based on the second response data.
3. The equipment configuration method for the green hydrogen microgrid as described in claim 1, characterized in that, The initial configuration scheme includes capacity configuration values for wind turbine generators, photovoltaic arrays, electrolyzers, fuel cells, and batteries. The construction of the lower-level electromagnetic transient model based on the initial configuration scheme specifically includes: Based on the wind turbine generator capacity configuration value in the initial configuration scheme, a wind turbine converter model is constructed. Based on the photovoltaic array capacity configuration value in the initial configuration scheme, a photovoltaic inverter model is constructed; Based on the electrolytic cell capacity configuration value in the initial configuration scheme, a nonlinear load model of the electrolytic cell is constructed. Based on the fuel cell capacity configuration value in the initial configuration scheme, a controlled voltage source model is constructed. Based on the battery capacity configuration value in the initial configuration scheme, a battery converter model is constructed. Based on the wind turbine converter model, the photovoltaic inverter model, the electrolytic cell nonlinear load model, the controlled voltage source model, and the battery converter model, the lower-level electromagnetic transient model is constructed.
4. The equipment configuration method for the green hydrogen microgrid as described in claim 1, characterized in that, The process involves inputting the operational data into a preset upper-level optimization model, aiming to maximize net profit, and optimizing the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. Specifically, this includes: The operating data, the obtained price data of the green hydrogen microgrid, and its equipment parameters are input into the upper-level optimization model. The upper-level optimization model is solved in combination with a preset optimization algorithm with the goal of maximizing net profit. The solution results that satisfy the constraints are calculated. The operating data includes power generation data and load data. The initial configuration scheme is determined based on the solution results.
5. The equipment configuration method for the green hydrogen microgrid as described in claim 1, characterized in that, The step of instantiating and simulating the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data specifically includes: Obtain the scene operation parameters corresponding to the preset disturbance scenario; Using the scenario operating parameters and simulation tools, the lower-level electromagnetic transient model is instantiated and simulated to obtain the first response data, which includes bus voltage, grid current, system frequency, oscillation damping characteristics, and generator power angle.
6. The equipment configuration method for the green hydrogen microgrid as described in claim 1, characterized in that, The method for constructing the upper-level optimization model specifically includes: Obtain the revenue data, cost data, and historical operation data of the green hydrogen microgrid; Using the revenue data and the cost data, a total revenue function and a total cost function are constructed respectively; Based on the total revenue function and the total cost function, a net revenue function is constructed. The objective function is determined based on the net revenue function, and constraints are constructed based on the historical operating data, wherein the constraints include electrothermal coupling constraints, equipment operation constraints, grid interaction power constraints, and capacity boundary constraints. Based on the objective function and the constraints, the upper-level optimization model is constructed.
7. A device configuration apparatus for a green hydrogen microgrid, characterized in that, It includes Module 1, Module 2, Module 3, and Module 4; The first module is used to acquire the operating data of the green hydrogen microgrid within a preset planning period; The second module is used to input the operating data into a preset upper-level optimization model, with the goal of maximizing net profit, and to optimize and solve the upper-level optimization model under preset constraints to obtain the initial configuration scheme of the green hydrogen microgrid. The third module is used to construct a lower-level electromagnetic transient model based on the initial configuration scheme, and to perform instantiation simulation of the lower-level electromagnetic transient model under a preset disturbance scenario to obtain first response data. If the first response data meets the preset stability conditions, the target configuration scheme is determined. The fourth module is used to configure the capacity of each device in the green hydrogen microgrid based on the target configuration scheme.
8. A terminal device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store at least one executable instruction that causes the processor to perform the operation of the device configuration method for the green hydrogen microgrid as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed, it controls the device or apparatus containing the computer-readable storage medium to perform the device configuration method for the green hydrogen microgrid as described in any one of claims 1 to 6.
10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by the communication device, the device configuration method for the green hydrogen microgrid as described in any one of claims 1 to 6 is implemented.