Micro-grid system and operation method thereof

Abstract

The application discloses a micro-grid system and an operation method thereof, and relates to the technical field of micro-grid, wherein the method comprises the following steps: combining new energy output fluctuation, power grid power and frequency modulation requirements, iteratively simulating to calculate the minimum rated capacity of energy storage satisfying the frequency modulation requirements, and then combining hydrogen production equipment operation fault conditions to correct the capacity. Taking the maximum full life cycle economic benefit as the target, a target function is established by comprehensively considering various benefits and costs. Under various operation constraints, the operation parameters of power generation, hydrogen production, energy storage and power grid interaction power in the next stage are determined through rolling optimization. Through the optimized system configuration and collaborative operation strategy, the application can ensure the system frequency stability under the premise of meeting the strict on-grid power and electricity quantity limit of the power grid, can suppress the start-stop impact of the hydrogen production system, and can realize the optimization of the full life cycle economy.

Description

Technical Field

[0001] This invention relates to the field of microgrid technology, and in particular to a microgrid system and its operation method. Background Technology

[0002] Hydrogen production through water electrolysis has become an important component of microgrid systems as an effective way to utilize surplus renewable energy. However, for the sake of overall safe and stable operation, the power grid imposes strict onboard and offboard power limits and electricity consumption limits on connected microgrids.

[0003] The volatility and intermittency of renewable energy output conflict with the rigid constraints of the power grid, posing a significant challenge to the real-time power balance and grid stability of microgrids. Current conventional methods to mitigate power fluctuations involve configuring power-type flexible regulation devices such as energy storage batteries and supercapacitors. However, this undoubtedly increases the initial investment cost and subsequent maintenance costs of the system.

[0004] Currently, no mature microgrid system can systematically solve this problem. Most systems either over-configure energy storage to meet stability requirements, resulting in poor economic efficiency, or prioritize economic efficiency at the expense of strict adherence to grid dispatch requirements and long-term system stability. Therefore, there is an urgent need for a microgrid system solution and its operation method that can simultaneously address dispatch requirements, operational stability, and life-cycle economics. Summary of the Invention

[0005] The main objective of this invention is to provide a microgrid system and its operation method.

[0006] Another object of the present invention is to provide a microgrid system and its operating device.

[0007] The third objective of this invention is to provide an electronic device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention provides a microgrid system and its operation method, comprising:

[0010] S1. Based on the fluctuation characteristics of renewable energy power generation, grid interaction power limitations and system frequency stability requirements, the minimum rated capacity of the energy storage unit to meet the frequency stability constraints is determined through iterative simulation. S2, based on the failure rate of the hydrogen production unit and the number of unplanned shutdowns, the minimum rated capacity is corrected to obtain the final configuration capacity of the energy storage unit considering the impact of failures; S3, based on the final configured capacity, aims to optimize the economic efficiency throughout the entire life cycle by constructing an objective function that includes power generation revenue, hydrogen production revenue, equipment depreciation cost, and equipment loss cost. S4, under the conditions of satisfying the grid interaction constraints, hydrogen production unit operation constraints and energy storage unit state of charge constraints, uses rolling optimization to solve for the set values ​​of the power generation unit, hydrogen production unit, energy storage unit and power interaction with the grid in the next time period.

[0011] Optionally, based on the fluctuation characteristics of renewable energy power generation, grid interaction power limitations, and system frequency stability requirements, the minimum rated capacity of the energy storage unit to meet frequency stability constraints is determined through iterative simulation, including: A frequency dynamic response model is constructed based on the maximum allowable frequency deviation and the equivalent inertial time constant of the system. The frequency dynamic response model is then used in conjunction with the second-level fluctuation data of renewable energy power generation. The system is simulated in the time domain starting from the initial energy storage capacity. During the simulation, the system frequency deviation is monitored. When the system frequency deviation exceeds the maximum frequency deviation, the energy storage capacity is increased and the simulation is repeated until the system frequency deviation remains within the maximum frequency deviation.

[0012] Optionally, the minimum rated capacity is adjusted based on the failure rate of the hydrogen production unit and the number of unplanned outages to obtain the final configured capacity of the energy storage unit considering the impact of failures, including: The power surge of a single fault is determined based on the rated power and failure rate of the hydrogen production unit. The minimum power correction value required by the energy storage unit is calculated based on the power surge value and the safety factor. The additional capacity correction value required by the energy storage unit is calculated based on the power surge value and the system's allowable fault response adjustment time. The sum of the minimum rated capacity and the additional capacity correction value is taken as the final configured capacity.

[0013] Optionally, based on the final configured capacity, and with the goal of optimizing the economics over the entire lifecycle, an objective function is constructed that includes power generation revenue, hydrogen production revenue, equipment depreciation costs, and equipment depreciation costs, including: The formula for calculating the objective function is:

[0014]

[0015] in, Let be the market price of hydrogen at time t. Let be the amount of hydrogen produced at time t. Let t be the grid electricity price. Let be the power interacting with the power grid at time t. For energy storage depreciation costs, For hydrogen production depreciation costs, For operation and maintenance costs, The cost of equipment wear and tear under conditions of no hydrogen production system failure. The cost of equipment loss due to failure events throughout the year; This represents the total energy storage charge and discharge volume caused by malfunctions throughout the year. For the rated capacity of energy storage, To determine the maximum depth of discharge permissible for energy storage, To replace the entire energy storage system at a unit capacity cost, To reduce the failure rate of hydrogen production systems, Cost of loss to the hydrogen production system due to a single failure.

[0016] Optionally, a model predictive control framework is used in the rolling optimization process to obtain the optimal setpoints for the power generation unit, hydrogen production unit, energy storage unit, and power interaction with the grid in the next time period based on the predicted renewable energy output, electricity price, and hydrogen price data.

[0017] To achieve the above objectives, a second aspect of the present invention provides a microgrid system and its operating apparatus, comprising: The simulation module is used to determine the minimum rated capacity of the energy storage unit to meet the frequency stability constraints based on the fluctuation characteristics of renewable energy power generation, grid interaction power limits and system frequency stability requirements through iterative simulation. The correction module is used to correct the minimum rated capacity based on the failure rate and number of unplanned shutdowns of the hydrogen production unit, so as to obtain the final configuration capacity of the energy storage unit considering the impact of failures. The module is used to construct an objective function based on the final configured capacity, with the goal of optimizing the economics over the entire life cycle, which includes power generation revenue, hydrogen production revenue, equipment depreciation costs, and equipment loss costs. The solution module is used to perform rolling optimization to solve for the set values ​​of the power generation unit, hydrogen production unit, energy storage unit, and power interaction with the grid in the next time period, under the conditions of satisfying grid interaction constraints, hydrogen production unit operation constraints, and energy storage unit state of charge constraints.

[0018] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0019] To achieve the above objectives, a third aspect of this application provides an electronic device, including a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing the microgrid system and its operation method as described in the first aspect embodiment.

[0020] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the microgrid system and its operation method as described in the first aspect embodiment.

[0021] The embodiments of the present invention have the following beneficial effects: This invention, through systematic modeling and optimization, can flexibly adapt to different grid on / off power and electricity constraints, as well as fluctuations in downstream hydrogen market demand and prices. For the first time, it integrates grid dispatch requirements, system frequency stability (through energy storage lower limit configuration), equipment reliability (impact of hydrogen production failure rate), and full lifecycle economics into a unified framework for comprehensive optimization, ensuring that the system achieves the best balance between stability and economy even under harsh conditions. It proposes operational strategies for different scenarios (such as full or partial hydrogen consumption), considering not only real-time optimization but also predictive information through the MPC framework. This provides a clear and executable reference solution for the long-term, efficient, and stable operation of microgrids, significantly improving the system's practicality and investment value. Attached Figure Description

[0022] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a microgrid system and its operation method provided in an embodiment of the present invention; Figure 2 This is a structural diagram of a microgrid system and its operating device provided in an embodiment of the present invention. Detailed Implementation

[0023] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0025] The following description, with reference to the accompanying drawings, describes a microgrid system and its operation method and apparatus according to embodiments of the present invention.

[0026] Example 1 This invention provides a microgrid system and its operation method, such as... Figure 1 As shown, the method includes the following steps: S1, based on the fluctuation characteristics of renewable energy power generation, grid interaction power limitations and system frequency stability requirements, determines the minimum rated capacity of the energy storage unit to meet the frequency stability constraints through iterative simulation.

[0027] In order to determine the lower limit of energy storage configuration that meets the requirements of frequency, power grid and equipment operation, this application builds a simulation model by combining multiple types of constraints and uses iterative simulation to solve for the minimum rated capacity of the energy storage unit.

[0028]

[0029] in, for System frequency deviation at time t, for The net power deficit of the time system, and , for Total power generation of renewable energy sources (wind and solar) at any given time for The actual operating power of the hydrogen production system at any given time. for The charging and discharging power of energy stored at all times.

[0030] In this application embodiment, to construct a complete and rigorous simulation solution boundary, this application standardizes the entire key parameter system of the microgrid system, wherein the system component parameters include the rated capacity of the generator set. Rated capacity of hydrogen production system Rated power of energy storage device With rated capacity Based on the above basic parameters, the rated energy storage duration can be further derived. and the maximum charge / discharge rate of energy storage The system operating parameters include the power grid connection limit. Power limits Internet power limit Offline power limit Equivalent charge-discharge cycle count External operating parameters include real-time hydrogen market prices. Time-of-use pricing with the power grid This provides all the input data for modeling and simulation.

[0031] In this embodiment of the application, the energy storage configuration lower bound solution model constructed in this step aims to minimize the energy storage configuration cost. At the same time, it is configured with multi-level, full-coverage rigid constraints to ensure that the solution results are engineering feasible, grid compliant, and stable in operation.

[0032] In this embodiment, a real-time power balance constraint is first configured to ensure dynamic balance of power generation, power consumption, energy storage, and grid-connected power throughout the entire time period. Its expression is: At the same time, the interaction power between the microgrid and the grid must always meet the requirements. The rate exceeds the limit.

[0033] In this embodiment of the application, to meet the grid connection and dispatch specifications and prevent the periodic grid connection power from exceeding the limit, this application configures a complete power accumulation constraint, which is divided into grid-connected power constraint and grid-off power constraint, wherein the periodic grid-connected power constraint is as follows: The system limits the cumulative maximum amount of electricity it can deliver to the grid, with a periodic grid disconnection constraint of [missing information]. It is used to limit the maximum cumulative amount of electricity absorbed by the system from the grid, thereby achieving dual compliance constraints on power and electricity.

[0034] In this embodiment, to accurately characterize the dynamic frequency shift of the system caused by the second-level fluctuations of new energy sources, this application establishes a frequency dynamic response model based on the power system swing equation. This model is used to quantify the coupling relationship between the system's net power imbalance and the rate of frequency change. The calculation formula is as follows: The system net power deficit , The system represents the real-time frequency deviation, where H is the system's equivalent inertial time constant and D is the system's equivalent damping coefficient. The system must satisfy frequency stability constraints throughout the entire process. , The maximum permissible frequency deviation is preset, typically ±0.2Hz.

[0035] In this embodiment, to closely match the actual operating characteristics of the hydrogen production equipment, a time-series operation model of the hydrogen production system is established, and the energy management system (EMS) operates according to a fixed instruction cycle. Update the hydrogen production power setpoint. The formula for calculating the setpoint is as follows: The average power generation algorithm smooths out high-frequency fluctuations in renewable energy, ensuring that hydrogen production power closely matches the average power generation. Simultaneously, the rate of change in hydrogen production power is strictly limited within a single instruction cycle, satisfying… This is to avoid secondary power disturbances caused by sudden changes in hydrogen production capacity.

[0036] In this embodiment, the energy storage system, as the main smoothing and regulation unit, needs to simultaneously satisfy power constraints and state of charge constraints. The real-time charging and discharging power constraints of the energy storage system must satisfy... To prevent operation at excessive rates; the energy storage state of charge constraints must be satisfied. By controlling the charging and discharging efficiency and the real-time accumulated power, the energy storage operation range is constrained, thus preventing damage to the equipment from overcharging and over-discharging.

[0037] In this embodiment, an iterative time-domain simulation method is used to solve for the minimum energy storage capacity. Starting with the initial energy storage capacity, a full-system time-domain simulation is continuously run, monitoring the system frequency deviation throughout the process. When a problem occurs during the simulation... This indicates that the current energy storage capacity is insufficient to smooth power fluctuations and maintain frequency stability. The energy storage capacity is gradually increased, and the simulation iterations are restarted until the frequency deviation at all times throughout the entire simulation cycle meets the limit requirements. Finally, the minimum rated power of the energy storage unit that satisfies all power, energy, frequency, and equipment constraints is obtained. Rated capacity The system provides a precise lower limit for hardware configuration to ensure stable and safe operation.

[0038] In this embodiment, by constructing a frequency response model, setting multiple operating constraints, and conducting iterative time-domain simulations, the minimum rated capacity of the energy storage unit is calculated, laying the foundation for subsequent capacity correction and operation optimization.

[0039] S2, based on the failure rate of the hydrogen production unit and the number of unplanned shutdowns, corrects the minimum rated capacity to obtain the final configuration capacity of the energy storage unit considering the impact of failures.

[0040] In order to adapt the energy storage configuration to the failure impact conditions of the hydrogen production unit and improve the system's anti-disturbance capability, this application calculates the compensation capacity by combining parameters such as the failure rate, and corrects it to obtain the final configuration capacity of the energy storage unit.

[0041] In this embodiment, the calculated minimum capacity only applies to a fault-free steady-state operation scenario where all equipment in the system is functioning correctly. It can only mitigate typical random power fluctuations in new energy sources and cannot cope with significant power surges caused by sudden shutdowns of the hydrogen production system. Therefore, a fault compensation capacity must be superimposed using a reliability model. This application establishes a fault frequency model for the hydrogen production system, constructing the number of unplanned shutdowns throughout the year as a function relating the fault rate to the operating conditions. ,in To reduce the failure rate of hydrogen production systems, The number of system start-ups and shutdowns throughout the year can accurately characterize the failure patterns of hydrogen production equipment under different operating strategies.

[0042] In this embodiment, when an unplanned shutdown occurs in the hydrogen production system, the hydrogen production power is instantly reduced to zero, resulting in a significant power surplus. The power surge corresponding to a single fault is equal to the rated power of the hydrogen production system, thus satisfying the requirement... This instantaneous high-power disturbance can easily cause the system power to exceed the limit, the power consumption to exceed the standard, and the frequency to fluctuate drastically.

[0043] In this application embodiment, in order to reasonably allocate the fault regulation responsibilities of energy storage and the power grid and avoid the power interaction between the power grid exceeding the limit at the moment of fault, this application introduces a safety factor with a value range of 0 to 1. Used to distribute fault impact loads, the rated power of the energy storage must meet the following requirements. Energy storage has sufficient instantaneous fault response power.

[0044] In this embodiment of the application, to ensure continuous stability throughout the fault adjustment process, a system-preset fault allowable response adjustment time is used. The calculation requires additional fault compensation capacity for energy storage, and the additional capacity correction value meets the following requirements. It is used to support energy storage to continuously absorb excess power during the entire fault regulation period and stabilize the system power and power balance.

[0045] In this embodiment, the final energy storage configuration capacity is obtained by superimposing the steady-state base capacity and the fault compensation capacity, which satisfies... It can be configured to cover both daily new energy fluctuation conditions and sudden hydrogen production failure impact conditions, comprehensively improving the system's operational reliability and grid constraint adaptability.

[0046] In this embodiment, by establishing a fault frequency model, calculating the power surge and capacity correction value, the minimum steady-state capacity is compensated to obtain the final energy storage configuration capacity that takes into account both steady-state and fault conditions, laying the foundation for the subsequent construction of the objective function.

[0047] S3, based on the final configured capacity, aims to optimize the economics throughout the entire life cycle by constructing an objective function that includes power generation revenue, hydrogen production revenue, equipment depreciation costs, and equipment loss costs.

[0048] In order to achieve the economically optimal operation of the wind-solar-storage-hydrogen production microgrid throughout its entire life cycle while taking into account equipment reliability, this application constructs a quantitative objective function and a condition-coupled failure rate model that covers multi-dimensional benefits and loss costs, with the final energy storage configuration capacity as the boundary.

[0049] In this embodiment, this step abandons the traditional single-period instantaneous optimization mode, and instead focuses on the full life cycle operation characteristics of the equipment. It comprehensively considers multiple factors such as equipment aging, failure losses, and fluctuations in market electricity and hydrogen prices to construct an optimization objective function that maximizes the system's net revenue. The overall expression of the objective function is as follows: In this embodiment, the objective function's revenue side includes revenue from hydrogen production and revenue from grid interaction. The hydrogen market price at time t, Let t represent the real-time hydrogen production rate. The hydrogen production rate is directly related to the real-time operating power of the hydrogen production unit. The grid electricity price at time t, The power exchange between the microgrid and the grid determines the revenue from grid-connected electricity.

[0050] In this embodiment of the application, the cost includes fixed equipment depreciation and dynamic operating losses. For the depreciation cost of energy storage system capacity, For the depreciation cost of the hydrogen production system, For the annual operation and maintenance costs of the system, The cost of natural aging under fault-free steady-state operation is mainly related to the energy storage cycle life and hydrogen production start-up and shutdown losses. In this application embodiment, to accurately quantify the additional equipment losses caused by hydrogen production failures, this application separately constructs a formula for calculating failure loss costs, specifically as follows: Total energy storage charging and discharging volume caused by faults throughout the year , Maximum permissible depth of discharge, Replacement cost per unit capacity of energy storage This represents the cost of hydrogen production equipment loss corresponding to a single failure.

[0051] In this embodiment, to achieve deep coupling between operating strategy and equipment reliability, this application further constructs an effective failure rate correction model for the hydrogen production system, the formula of which is: Where A is the normalized power fluctuation intensity, B is the normalized start-stop frequency, and C is the proportion of low-load operation. , , The acceleration factor coefficients obtained by fitting measured reliability data can dynamically correct the equipment failure rate according to actual operating conditions, so that the economic optimization results can take into account both short-term benefits and long-term equipment life, and achieve comprehensive optimization throughout the entire life cycle.

[0052] In this embodiment, by establishing an objective function to maximize net revenue over the entire lifecycle, subdividing steady-state and fault loss costs, and introducing a fault rate model modified by operating conditions, a foundation is laid for subsequent power rolling optimization.

[0053] S4, under the conditions of satisfying the grid interaction constraints, hydrogen production unit operation constraints and energy storage unit state of charge constraints, uses rolling optimization to solve for the set values ​​of the power generation unit, hydrogen production unit, energy storage unit and power interaction with the grid in the next time period.

[0054] In order to achieve optimal scheduling of multiple microgrid units under the premise of fully meeting various system operation, power grid and reliability constraints, this application relies on the model predictive control framework to carry out rolling optimization solution to obtain the optimal setpoint values ​​of the interactive power of each unit and the power grid.

[0055] In this embodiment of the application, the optimization problem in this step is a multi-constraint mixed integer programming problem. The solution process needs to strictly inherit all the power constraints, energy constraints, and frequency constraints mentioned above, while adding equipment operation constraints, market constraints, and reliability constraints.

[0056] In this application embodiment, the newly added constraints include minimum start-up and shutdown time constraints for the hydrogen production system, power ramp-up rate constraints, upper and lower limits of energy storage SOC constraints and cycle life constraints, and hydrogen market absorption constraints. This ensures that hydrogen production meets market demand; system reliability constraints are satisfied. The frequency of backup failures must be strictly controlled within the allowable range.

[0057] In this embodiment, the application adopts the Model Predictive Control (MPC) rolling optimization framework. Based on the time-series data of renewable energy output, the time-of-use electricity price data of the power grid, the hydrogen market price data, and the hydrogen energy demand data, the rolling optimization time domain is constructed. By solving iteratively time by time, the operating power of each unit and the power grid interaction setpoint are dynamically updated in the next time step.

[0058] In this embodiment, the rolling optimization strategy can track new energy fluctuations, market price changes, and equipment operating status in real time. Under the premise of strictly meeting all formula constraints, grid power limits, frequency stability, equipment safety, and fault redundancy, it dynamically balances system benefits and equipment losses, maximizes the comprehensive economic benefits of the microgrid throughout its entire life cycle, suppresses the frequency of hydrogen production equipment failures, delays equipment aging, and achieves the optimal synergistic operation of the wind-solar-storage-hydrogen production coupled microgrid system in terms of stability, reliability, and economy.

[0059] In this embodiment, by superimposing multiple system constraints, constructing a rolling optimization time domain using predicted data, and iteratively solving the power setpoints of each unit, the optimal operation of microgrid stability, reliability, and full life-cycle economy is achieved.

[0060] Example 2 This invention relates to a microgrid system, comprising: Power generation units: These include renewable energy generator sets with fluctuating output, such as wind turbines and photovoltaic generator sets.

[0061] Hydrogen production unit: used to consume the electrical energy generated by the power generation unit to produce hydrogen, including: The generator set is equipped with a rectifier transformer and rectifier power supply to convert AC power into DC power required for hydrogen production; an electrolyzer is used to electrolyze water; and a gas-liquid separation device, purification device, and compression system are used for subsequent processing and storage of the generated hydrogen.

[0062] Energy storage unit: includes at least one power-type energy storage device, such as lithium battery, supercapacitor, etc., used to provide rapid power support.

[0063] Energy Management System: Connected to the power generation unit, hydrogen production unit, energy storage unit, and power grid, it is used to collect system operation data and execute the following operation methods to achieve coordinated control of the entire system.

[0064] Example 3 This invention provides a microgrid system and its operating device 10, such as... Figure 2 As shown, the device includes: Simulation module 100 is used to determine the minimum rated capacity of energy storage units to meet frequency stability constraints through iterative simulation based on the fluctuation characteristics of renewable energy power generation, grid interaction power limits and system frequency stability requirements. The correction module 200 is used to correct the minimum rated capacity based on the failure rate and unplanned downtime of the hydrogen production unit, so as to obtain the final configuration capacity of the energy storage unit considering the impact of failure. Module 300 is used to construct an objective function based on the final configured capacity, with the goal of optimizing the economics throughout the entire life cycle, which includes power generation revenue, hydrogen production revenue, equipment depreciation costs, and equipment loss costs. The solver module 400 is used to perform rolling optimization to solve for the set values ​​of the power generation unit, hydrogen production unit, energy storage unit, and power interaction with the grid in the next time period, under the conditions of satisfying the grid interaction constraints, hydrogen production unit operation constraints, and energy storage unit state of charge constraints.

[0065] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0066] Example 4 To implement the methods of the above embodiments, the present invention also provides an electronic device, which includes a memory and a processor; wherein the processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the various steps of the methods described above.

[0067] Example 5 To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0068] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0069] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0070] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A microgrid system and its operation method, characterized in that, include: S1. Based on the fluctuation characteristics of renewable energy power generation, grid interaction power limitations and system frequency stability requirements, the minimum rated capacity of the energy storage unit to meet the frequency stability constraints is determined through iterative simulation. S2, based on the failure rate of the hydrogen production unit and the number of unplanned shutdowns, the minimum rated capacity is corrected to obtain the final configuration capacity of the energy storage unit considering the impact of failures. S3, based on the final configured capacity, aims to optimize the economic efficiency throughout the entire life cycle by constructing an objective function that includes power generation revenue, hydrogen production revenue, equipment depreciation cost, and equipment loss cost. S4, under the conditions of satisfying the grid interaction constraints, hydrogen production unit operation constraints and energy storage unit state of charge constraints, uses rolling optimization to solve for the set values ​​of the power generation unit, hydrogen production unit, energy storage unit and power interaction with the grid in the next time period.

2. The method according to claim 1, characterized in that, Based on the fluctuation characteristics of renewable energy power generation, grid interaction power limitations, and system frequency stability requirements, the minimum rated capacity of the energy storage unit to meet frequency stability constraints is determined through iterative simulation, including: A frequency dynamic response model is constructed based on the maximum allowable frequency deviation and the equivalent inertial time constant of the system. The frequency dynamic response model is then used in conjunction with the second-level fluctuation data of renewable energy power generation. The system is simulated in the time domain starting from the initial energy storage capacity. During the simulation, the system frequency deviation is monitored. When the system frequency deviation exceeds the maximum frequency deviation, the energy storage capacity is increased and the simulation is repeated until the system frequency deviation remains within the maximum frequency deviation.

3. The method according to claim 1, characterized in that, The minimum rated capacity is adjusted based on the failure rate of the hydrogen production unit and the number of unplanned outages to obtain the final configured capacity of the energy storage unit considering the impact of failures, including: The power surge of a single fault is determined based on the rated power and failure rate of the hydrogen production unit. The minimum power correction value required by the energy storage unit is calculated based on the power surge value and the safety factor. The additional capacity correction value required by the energy storage unit is calculated based on the power surge value and the system's allowable fault response adjustment time. The sum of the minimum rated capacity and the additional capacity correction value is taken as the final configured capacity.

4. The method according to claim 1, characterized in that, Based on the final configured capacity, and with the goal of optimizing economic efficiency throughout the entire lifecycle, an objective function is constructed that includes power generation revenue, hydrogen production revenue, equipment depreciation costs, and equipment depreciation costs. The formula for calculating the objective function is: in, Let be the market price of hydrogen at time t. Let be the amount of hydrogen produced at time t. Let t be the grid electricity price. Let be the power interacting with the power grid at time t. For energy storage depreciation costs, For hydrogen production depreciation costs, For operation and maintenance costs, The cost of equipment wear and tear under conditions of no hydrogen production system failure. The cost of equipment loss due to failure events throughout the year; This represents the total energy storage charge and discharge volume caused by fault events throughout the year. For the rated capacity of energy storage, To determine the maximum depth of discharge permissible for energy storage, To replace the entire energy storage system at a unit capacity cost, To reduce the failure rate of hydrogen production systems, Cost of loss in hydrogen production system due to a single failure.

5. The method according to claim 1, characterized in that, The rolling optimization solution process employs a model predictive control framework, which, based on predicted renewable energy output, electricity price, and hydrogen price data, obtains the optimal setpoints for the power generation unit, hydrogen production unit, energy storage unit, and power interaction with the grid for the next time period.

6. A microgrid system and its operating device, characterized in that, include: The simulation module is used to determine the minimum rated capacity of the energy storage unit to meet the frequency stability constraints based on the fluctuation characteristics of renewable energy power generation, grid interaction power limits and system frequency stability requirements through iterative simulation. The correction module is used to correct the minimum rated capacity based on the failure rate and number of unplanned shutdowns of the hydrogen production unit, so as to obtain the final configuration capacity of the energy storage unit considering the impact of failures. The module is used to construct an objective function based on the final configured capacity, with the goal of optimizing the economics over the entire life cycle, which includes power generation revenue, hydrogen production revenue, equipment depreciation costs, and equipment loss costs. The solution module is used to perform rolling optimization to solve for the set values ​​of the power generation unit, hydrogen production unit, energy storage unit, and power interaction with the grid in the next time period, under the conditions of satisfying grid interaction constraints, hydrogen production unit operation constraints, and energy storage unit state of charge constraints.

7. An electronic device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the method as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method as described in any one of claims 1-5.

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