Off-grid hydrogen production system energy storage capacity optimization method, device, equipment and storage medium

Abstract

This application discloses a method, apparatus, equipment, and storage medium for optimizing the energy storage capacity of an off-grid hydrogen production system, relating to the field of energy management technology. The disclosed method for optimizing the energy storage capacity of an off-grid hydrogen production system includes: inputting target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy, and hydrogen production rate; calculating the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters; determining a dual-objective optimization objective based on energy storage energy, hydrogen production rate, voltage support coefficient, and cost parameters; and determining the Pareto solution set based on the dual-objective optimization objective and an improved non-dominated sorting genetic strategy to obtain the optimal configuration of the target energy storage capacity. Applying this scheme can achieve synergistic optimization of the economic efficiency and voltage support stability of the off-grid hydrogen production system's energy storage capacity configuration.

Description

Technical Field

[0001] This application relates to the field of energy management technology, and in particular to methods, apparatus, equipment and storage media for optimizing the energy storage capacity of off-grid hydrogen production systems. Background Technology

[0002] Off-grid hydrogen production has gained widespread attention due to its advantages, including strong energy self-sufficiency, applicability to remote areas, and ability to utilize wind and solar resources locally. However, wind and solar power generation are characterized by significant randomness and volatility, with output fluctuating frequently with weather conditions. This leads to unstable loads on electrolysis equipment in off-grid hydrogen production systems, affecting hydrogen production efficiency and equipment lifespan. Furthermore, the lack of a large power grid in off-grid operation means the system's voltage stability relies entirely on internal regulation capabilities. Significant power fluctuations can easily trigger voltage instability or even instability.

[0003] Against the backdrop of the aforementioned technologies, existing technologies for configuring energy storage capacity in off-grid hydrogen production systems typically aim to reduce investment costs or improve hydrogen production efficiency. This is achieved by constructing optimization models centered on economic efficiency or hydrogen production capacity, and combining them with conventional optimization algorithms to solve for the energy storage capacity. These methods primarily focus on the system's cost-benefit performance and the continuous operation capability of the equipment, while neglecting to adequately consider the constraints on voltage stability under off-grid operating conditions, and especially lacking an effective quantification and constraint mechanism for voltage support capability. Summary of the Invention

[0004] The main objective of this application is to provide a method, apparatus, equipment, and storage medium for optimizing the energy storage capacity of an off-grid hydrogen production system, aiming to solve the technical problem of balancing economy and voltage stability in the process of configuring energy storage capacity in an off-grid hydrogen production system.

[0005] To achieve the above objectives, this application proposes a method for optimizing the energy storage capacity of an off-grid hydrogen production system, the method comprising: The target weather data and station operation rules are input into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy and hydrogen production rate. The off-grid hydrogen production system output model includes wind farm output model, photovoltaic farm output model, energy storage farm output model and electrolyzer output model. The voltage support coefficient is calculated based on wind power, photovoltaic power, energy storage potential, and AC bus parameters. The dual-objective optimization objective is determined based on energy storage capacity, hydrogen production rate, voltage support coefficient, and cost parameters. The optimal configuration of target energy storage capacity is determined based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy.

[0006] In one embodiment, the step of determining the optimal configuration of the target energy storage capacity based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy includes: Determine decision variables based on dual-objective optimization objectives; An initial population is constructed based on chaotic initialization, and the initial population is mapped to the decision variable space of the decision variables based on chaotic mapping to obtain the initial parent population. The target offspring population is determined from the initial parent population based on the adaptive feasible mutation strategy, and a temporary population is determined based on the initial parent population and the target offspring population. The target energy storage capacity is optimally configured in a temporary population based on an improved non-dominated sorting genetic strategy.

[0007] In one embodiment, the step of determining the offspring population in the parent population according to an adaptive feasible mutation strategy includes: The variation distribution index is calculated based on the iteration parameters and distribution index of the initial parent population; Calculate the variation disturbance term based on the variation distribution index; The initial parent population is mutated according to the adaptive feasible mutation strategy and the mutation perturbation term to generate candidate solutions. By using a truncation boundary strategy and a linear backtracking strategy to handle the decision variable boundary constraints and general inequality constraints of the candidate solutions, respectively, the offspring population is obtained.

[0008] In one embodiment, the step of determining the optimal configuration of the target energy storage capacity in a temporary population according to an improved non-dominated sorting genetic strategy includes: The temporary population is subjected to non-dominated sorting based on the improved non-dominated sorting genetic strategy to obtain the individual crowding degree corresponding to the non-dominated level. Individuals are added to the preset parent population in sequence according to the order of the non-dominant hierarchy to obtain the target population size. When the target population size exceeds a preset size threshold, individuals are selected to be retained based on their crowding levels, and the target population is determined based on the retained individuals. When the target population meets the preset iteration termination condition, the Pareto solution set is determined, and the optimal configuration of the target energy storage capacity is determined based on the Pareto solution set.

[0009] In one embodiment, cost parameters include discount rate, investment period, unit energy investment cost of energy storage, equipment investment capacity, equipment investment cost, and equipment operation and maintenance cost; The steps for determining the dual-objective optimization objective based on energy storage capacity, hydrogen production rate, voltage support coefficient, and cost parameters include: The annualized investment cost is calculated based on the discount rate, investment period, energy storage capacity, energy storage unit energy investment cost, equipment investment capacity, and equipment investment cost. Calculate the annualized operation and maintenance cost based on the equipment investment capacity and equipment operation and maintenance cost; Calculate the annualized total cost based on the annualized investment cost and annualized operation and maintenance cost; Calculate the levelized cost of hydrogen production target based on the annualized total cost and hydrogen production rate; The voltage stability target is determined based on the voltage support coefficient. The dual-objective optimization objective is determined based on the levelized hydrogen production cost objective and the voltage stability objective.

[0010] In one embodiment, the AC bus parameters include AC equivalent impedance, short-circuit capacity, bus nominal voltage, AC equivalent impedance, and bus nominal voltage; The steps for calculating the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters include: Calculate the short-circuit current of the AC bus based on the energy storage potential and AC equivalent impedance; Calculate the short-circuit capacity of grid-type energy storage at the AC bus based on the short-circuit current and the bus nominal voltage. Calculate the total power generated by wind and solar power based on wind power and solar power; The voltage support factor is calculated based on the short-circuit capacity and the total power generated by wind and solar power.

[0011] In one embodiment, before the step of inputting target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy, and hydrogen production rate, the method further includes: Acquire raw meteorological data from the off-grid hydrogen production system, including wind speed data, light intensity data, and ambient temperature data. The raw meteorological data is preprocessed to obtain the target weather data; Based on the dynamic balance strategy of supply and demand, the collaborative operation logic of wind and solar turbines, energy storage systems and electrolyzers in the off-grid hydrogen production system is formulated to obtain the site operation rules.

[0012] Furthermore, to achieve the above objectives, this application also proposes an off-grid hydrogen production system energy storage capacity optimization device. The off-grid hydrogen production system energy storage capacity optimization device includes: an output parameter calculation module, used to input target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy, and hydrogen production rate. The off-grid hydrogen production system output model includes a wind farm output model, a photovoltaic farm output model, an energy storage farm output model, and an electrolyzer output model. The voltage support coefficient calculation module is used to calculate the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters. The optimization objective determination module is used to determine the dual-objective optimization objective based on energy storage energy, hydrogen production rate, voltage support coefficient, and cost parameters. The optimization configuration solution module is used to determine the optimal configuration of the target energy storage capacity based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy.

[0013] In addition, to achieve the above objectives, this application also proposes an off-grid hydrogen production system energy storage capacity optimization device, the device including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the off-grid hydrogen production system energy storage capacity optimization method as described above.

[0014] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the off-grid hydrogen production system energy storage capacity optimization method described above.

[0015] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the off-grid hydrogen production system energy storage capacity optimization method described above.

[0016] One or more technical solutions proposed in this application have at least the following technical effects: By inputting target weather data and station operation rules into the off-grid hydrogen production system's output model, calculating the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters, and further determining dual-objective optimization targets based on energy storage energy, hydrogen production rate, voltage support coefficient, and cost parameters, and by incorporating cost, hydrogen production-related parameters (characterizing system economics), and the voltage support coefficient (characterizing system voltage stability) into the optimization targets, this approach, using dual-objective optimization and an improved non-dominated sorting genetic strategy to determine the optimal configuration of target energy storage capacity, solves the problem of balancing economic efficiency and power efficiency in the configuration of off-grid hydrogen production system energy storage capacity. Regarding the technical issue of voltage stability, compared with existing technologies, this method incorporates cost, hydrogen production-related parameters, and voltage support coefficient (characterizing system economics) into the optimization objectives. This achieves a balance between system operation economics and voltage stability during the configuration of energy storage capacity in off-grid hydrogen production systems. It can ensure the economic rationality of energy storage configuration schemes through the optimization of cost and hydrogen production-related parameters, and ensure the voltage support capability of off-grid systems through the quantification and inclusion of voltage support coefficient in the optimization process. This avoids voltage fluctuations or even instability caused by wind and solar power fluctuations, while also ensuring the load stability, hydrogen production efficiency, and operational lifespan of electrolysis equipment. Attached Figure Description

[0017] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating an embodiment of the method for optimizing the energy storage capacity of an off-grid hydrogen production system in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the method for optimizing the energy storage capacity of an off-grid hydrogen production system in this application. Figure 3 A schematic diagram of the process for determining the target energy storage capacity optimization configuration provided in Embodiment 2 of the method for optimizing the energy storage capacity of off-grid hydrogen production systems in this application; Figure 4 This is a schematic diagram of the module structure of the off-grid hydrogen production system energy storage capacity optimization device according to an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the off-grid hydrogen production system energy storage capacity optimization method in the embodiments of this application.

[0020] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0021] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0022] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0023] The main solution of this application embodiment is as follows: Input the target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy, and hydrogen production rate. The off-grid hydrogen production system output model includes wind farm output model, photovoltaic farm output model, energy storage farm output model, and electrolyzer output model. Calculate the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters. Determine the dual-objective optimization target based on energy storage energy, hydrogen production rate, voltage support coefficient, and cost parameters. Determine the optimal configuration of the target energy storage capacity based on the dual-objective optimization target and the improved non-dominated sorting genetic strategy.

[0024] In this embodiment, for ease of description, the following description will focus on identifying the energy storage capacity optimization equipment of the off-grid hydrogen production system.

[0025] To address the challenge of balancing economic efficiency and voltage stability in off-grid hydrogen production systems during energy storage capacity configuration, this application provides a solution. This solution involves inputting target weather data and site operation rules into the off-grid hydrogen production system's output model. It calculates the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters. Furthermore, it determines a dual-objective optimization goal based on energy storage capacity, hydrogen production rate, voltage support coefficient, and cost parameters. Simultaneously, it incorporates cost (characterizing system economics), hydrogen production-related parameters, and the voltage support coefficient (characterizing system voltage stability) into the optimization goal. This approach, based on the dual-objective optimization goal and an improved non-dominated sorting genetic strategy, addresses the technical challenge of optimizing the target energy storage capacity configuration. This invention addresses the technical challenge of balancing economic efficiency and voltage stability in the configuration of energy storage capacity for off-grid hydrogen production systems. Compared to existing technologies, this invention incorporates cost (characterizing system economics), hydrogen production-related parameters, and voltage support coefficient (characterizing system voltage stability) into the optimization objectives. This allows for the simultaneous consideration of system operation economics and voltage stability during the configuration of energy storage capacity for off-grid hydrogen production systems. It ensures the economic rationality of energy storage configuration schemes through the optimization of cost and hydrogen production-related parameters, while also guaranteeing the voltage support capability of off-grid systems by quantifying and incorporating the voltage support coefficient into the optimization process. This avoids voltage fluctuations and even instability caused by wind and solar power fluctuations, and simultaneously ensures the load stability, hydrogen production efficiency, and operational lifespan of electrolysis equipment.

[0026] The executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as an off-grid hydrogen production system energy storage capacity optimization device. The following description uses an off-grid hydrogen production system energy storage capacity optimization device as an example to illustrate this embodiment and the subsequent embodiments.

[0027] Based on this, embodiments of this application provide a method for optimizing the energy storage capacity of an off-grid hydrogen production system, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the method for optimizing the energy storage capacity of an off-grid hydrogen production system according to this application.

[0028] In this embodiment, the method for optimizing the energy storage capacity of the off-grid hydrogen production system includes steps S10 to S40: Step S10: Input the target weather data and the station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy and hydrogen production rate. The off-grid hydrogen production system output model includes the wind farm output model, the photovoltaic farm output model, the energy storage farm output model and the electrolyzer output model.

[0029] It should be noted that the target weather data is a standardized preprocessed meteorological dataset covering the entire time period of a typical year in the region where the off-grid hydrogen production system is located. It is the core basic input data for calculating the real-time output of wind and solar power equipment. In this embodiment, the data includes wind speed matching the installation height of the wind turbine, solar irradiance and ambient temperature for the corresponding time period, which can completely reconstruct the annual wind and solar resource variation patterns of the site.

[0030] In this embodiment, the data covers the complete time dimension of 8760 hours of weather data in a typical year, which can fully restore the wind and solar resource endowment in different seasons and at different times, providing continuous and accurate meteorological basis for subsequent power output calculation.

[0031] The operating rules of the wind farm are pre-defined logical guidelines used to coordinate the switching of operating states and power distribution among wind farms, photovoltaic farms, energy storage equipment and electrolyzers. They are the core control basis for ensuring the coordinated and safe operation of all equipment.

[0032] The off-grid hydrogen production system output model is a set of mathematical models used to simulate and calculate the real-time operating status and output of each station equipment in the off-grid hydrogen production system. It can accurately reproduce the actual operating physical characteristics and safety constraint boundaries of each equipment.

[0033] Wind power is the active power that a wind farm can stably output under corresponding weather conditions, directly reflecting the real-time power generation capacity of the wind farm.

[0034] Photovoltaic power is the active power that a photovoltaic power station can stably output under corresponding light and temperature conditions. It directly reflects the real-time power generation capacity of the photovoltaic power station and is a core basic parameter for subsequent power balance calculations and voltage support capacity calculations.

[0035] Energy storage potential is the equivalent internal potential value that the grid-type energy storage in an off-grid hydrogen production system can provide. It is a core electrical parameter that characterizes the voltage support capability of energy storage and directly determines the calculation results of short-circuit current and short-circuit capacity at the AC bus.

[0036] Energy storage energy is the electrical energy stored or released by energy storage devices in an off-grid hydrogen production system under corresponding operating conditions. It directly reflects the charging and discharging status of the energy storage devices and the available electrical energy scale, and is a core basic parameter for calculating energy storage investment costs and operating constraints.

[0037] Hydrogen production rate is the volume of hydrogen that an electrolyzer in an off-grid hydrogen production system can produce per unit time under corresponding operating conditions. It directly reflects the hydrogen production efficiency and actual production capacity of the electrolyzer and is a core basic parameter for calculating annualized hydrogen production and levelized cost of hydrogen production.

[0038] The wind farm output model is a core component of the off-grid hydrogen production system output model. It is used to calculate the real-time output power of the wind farm based on wind speed data, accurately reproducing the physical characteristics of wind energy capture and power conversion of wind turbine generators. The wind farm output model is as follows:

[0039] In the formula, This refers to the real-time output active power of the wind turbine generator set, that is, the actual power generation output of the wind farm at the corresponding real-time wind speed. Real-time wind speed; To cut in wind speed; Rated wind speed; To cut off the wind speed; air density; The swept area of ​​the wind turbine; The power coefficient of a wind turbine, also known as wind energy capture efficiency, is related to the tip speed ratio. and propeller pitch angle related; The total efficiency of the fan; This refers to the rated output of the wind turbine generator set.

[0040] The photovoltaic (PV) power plant output model is a core component of the off-grid hydrogen production system output model. It is used to calculate the real-time output power of the PV power plant based on sunlight intensity and ambient temperature data, accurately reproducing the photoelectric conversion characteristics and temperature correction characteristics of the PV modules. The PV power plant output model is as follows:

[0041] In the formula, The real-time output active power of a photovoltaic power station is the actual power generation output of the photovoltaic unit under the corresponding actual solar irradiance and module temperature conditions. Rated output of the photovoltaic unit; This represents the actual solar irradiance. The standard test irradiance is fixed at 1000. ; The power temperature coefficient; This refers to the actual temperature of the photovoltaic module. The standard test temperature is fixed at a specific value. .

[0042] The temperature of the photovoltaic module is different from the ambient temperature, so it needs to be converted to the ambient temperature:

[0043] In the formula, Ambient temperature; The nominal operating temperature of the photovoltaic module; This represents the actual solar irradiance.

[0044] The energy storage station output model is a core component of the off-grid hydrogen production system output model. It is used to calculate the real-time charging and discharging power and remaining energy of the energy storage system based on power allocation commands, accurately reproducing the bidirectional charging and discharging characteristics and state of charge (SOC) changes of the energy storage equipment. The energy storage station output model is as follows:

[0045] In the formula, represent Constant-time energy storage SOC status; This refers to the rated capacity of the energy storage. for Time to The energy stored changes constantly. It can be expressed by the following formula:

[0046] In the formula, for Time-limited energy storage capacity; For energy storage discharge efficiency; To improve energy storage charging efficiency; This refers to the time period for data collection.

[0047] The electrolyzer output model is a core component of the off-grid hydrogen production system output model. It is used to calculate the real-time hydrogen production rate and energy consumption of the electrolyzer based on the input power, accurately reproducing the electrochemical characteristics and safe operation constraints of the alkaline electrolyzer. (Electrolyzer output model terminal voltage) As a core parameter characterizing operating characteristics and energy consumption levels, it can be composed of three parts: ohmic polarization voltage, reversible voltage, and activation polarization voltage. The terminal voltage expression is as follows:

[0048] In the formula, Terminal voltage; This is the reversible voltage, corresponding to the thermodynamic equilibrium voltage of the water electrolysis reaction; This represents the actual operating current of the electrolytic cell; This refers to the operating temperature of the electrolytic cell. This represents the effective electrode area of ​​the electrolytic cell; , , , , as well as The coefficient is an empirical factor determined by the material and structure of the electrolytic cell. The second term in the formula is the ohmic polarization term, which reflects the resistive loss of current flowing through the electrolytic cell; the third term is the activation polarization term, which characterizes the activation energy loss of the electrochemical reaction on the electrode surface based on Tafel's law.

[0049] Furthermore, the electrolyzer output model can also include the hydrogen production rate, the expression for which is as follows:

[0050] In the formula, The hydrogen production rate of the ALK electrolyzer. For Faraday efficiency; This refers to the number of electrolytic cell units; is the actual operating current of the electrolytic cell; F is the Faraday constant.

[0051] Understandably, the target weather data is first input into the wind farm power output model and the photovoltaic power output model respectively to calculate the corresponding wind power and photovoltaic power for the entire period. Then, the energy storage charging and discharging strategy and the electrolyzer operating power are determined in combination with the farm operation rules, and then input into the corresponding models to calculate the energy storage potential, energy storage energy and hydrogen production rate.

[0052] In one feasible implementation, steps S01 to S03 may be included before step S10: Step S01: Obtain raw meteorological data from the off-grid hydrogen production system, including wind speed data, light intensity data, and ambient temperature data.

[0053] The raw meteorological data is collected from authoritative meteorological data sources in the region where the off-grid hydrogen production system is located. It is typical initial meteorological observation data of the year that has not undergone processing and is the most basic data source for subsequent calculation of the real-time output of wind and solar equipment.

[0054] Wind speed data is real-time observational data that characterizes the airflow velocity at a corresponding altitude in the area where the off-grid hydrogen production system is located. It is a core input parameter for calculating the output of wind turbine generators and directly determines the real-time power generation capacity of the wind farm.

[0055] Irradiance data is real-time observational data characterizing the solar radiation power received per unit area in the area where the off-grid hydrogen production system is located. It is the core input parameter for calculating the output of the photovoltaic array and directly determines the real-time power generation capacity of the photovoltaic power station.

[0056] Ambient temperature data is observational data that characterizes the real-time near-surface air temperature in the area where the off-grid hydrogen production system is located. It is a core input parameter for correcting the photoelectric conversion efficiency of photovoltaic modules and directly affects the actual power generation output of the photovoltaic array.

[0057] Understandably, the first step is to determine the specific geographical location of the off-grid hydrogen production system site, match it with authoritative meteorological data sources for typical years corresponding to that location, and collect wind speed data, light intensity data, and ambient temperature data corresponding to the height of the wind turbine blades to complete the acquisition of raw meteorological data.

[0058] Step S02: Preprocess the raw meteorological data to obtain the target weather data.

[0059] Preprocessing is a standardized process for correcting missing or abnormal information in raw meteorological data. Its purpose is to eliminate invalid information in the raw data, improve the continuity and accuracy of the data, and ensure the accuracy of subsequent wind and solar power output calculations.

[0060] The target weather data is a meteorological dataset that has been repaired through preprocessing and possesses continuity, accuracy, and standardization characteristics. It is the core input data for the subsequent off-grid hydrogen production system output model.

[0061] Understandably, the raw meteorological data is first scanned and identified throughout the entire time period to locate missing and outlier values. The missing values ​​are filled using the moving average method, and the outliers are filled using the adjacent data interpolation method, ultimately resulting in standardized target weather data.

[0062] It should be understood that targeted preprocessing effectively eliminated missing and abnormal issues in the original meteorological data, ensuring the continuity and accuracy of the meteorological data, avoiding distortion of wind and solar power output calculation results due to data defects, and providing reliable standardized data support for subsequent optimization of the energy storage capacity of off-grid hydrogen production systems.

[0063] Step S03: Based on the supply and demand dynamic balance strategy, formulate the collaborative operation logic of wind and solar turbines, energy storage system and electrolyzer of off-grid hydrogen production system to obtain the site operation rules.

[0064] The dynamic supply and demand balance strategy is the core control strategy to ensure real-time matching among the power output of wind and solar power generation, the charging and discharging power of energy storage system and the hydrogen production load of electrolyzer in off-grid hydrogen production system. The core objective is to achieve hydrogen production load following wind and solar power, energy storage system ensuring hot standby, and dynamic balance between energy supply and demand.

[0065] Wind and solar power units are a collective term for wind turbine generators and photovoltaic arrays in off-grid hydrogen production systems. They are the core power supply units of off-grid hydrogen production systems, and their output is dynamically adjusted according to changes in meteorological conditions, directly determining the scale of power supply of off-grid hydrogen production systems.

[0066] The energy storage system is a grid-type energy storage unit with bidirectional power regulation capability within the off-grid hydrogen production system. It is the core equipment for smoothing wind and solar power fluctuations and ensuring the stable operation of the electrolyzer, and it is also the only source of voltage support for the off-grid hydrogen production system.

[0067] Electrolyzers are the core hydrogen production equipment in off-grid hydrogen production systems, which convert electrical energy into hydrogen energy through electrochemical reactions. They are the core load unit of off-grid hydrogen production systems, and their operating status directly determines the hydrogen production efficiency and the scale of hydrogen energy output.

[0068] The collaborative operation logic is the logical rule that clarifies the switching of operating states, power distribution, and action priority among the wind and solar generators, energy storage system, and electrolyzer in the off-grid hydrogen production system under different wind and solar power output conditions. It is the core principle to ensure the collaborative and safe operation of all equipment.

[0069] The station operation rules are a standardized rule system based on collaborative operation logic, which can be directly used for the operation control of off-grid hydrogen production system stations. It is the core basis for subsequently establishing equipment output models and calculating equipment operation data.

[0070] In this embodiment, the station operation rules take hydrogen production load following wind and solar power, energy storage system ensuring hot standby, and dynamic balance of energy supply and demand as the core objectives, covering the working condition division and corresponding control strategies for all scenarios of wind and solar power output.

[0071] Off-grid wind, solar, energy storage and hydrogen production stations are hydrogen production stations that do not rely on external power grids. They achieve energy self-sufficiency by generating electricity from wind and solar turbines, regulating the bidirectional power of the energy storage system, and producing hydrogen through electrochemical electrolysis cells. They are the core carriers for the local consumption of wind and solar resources and the production of green hydrogen in remote areas.

[0072] Following the wind and solar power output is one of the core control objectives of the power plant. This means that the hydrogen production input power of the electrolyzer is dynamically adjusted according to the total output of the wind and solar units to maximize the local consumption of wind and solar power resources and reduce the occurrence of wind and solar curtailment.

[0073] Ensuring hot standby is one of the core control objectives of the power station. Specifically, the energy storage system compensates for the power output gap between wind and solar power through bidirectional charging and discharging regulation, keeps the electrolyzer in a hot standby state, avoids frequent cold start-ups and shutdowns of the electrolyzer, and ensures the safety of the hydrogen production process.

[0074] Dynamic balance of energy supply and demand is one of the core control objectives of the power plant. This means coordinating the operating status of various equipment to achieve real-time power matching among the supply of wind and solar power, the charging and discharging regulation of the energy storage system, and the demand for hydrogen production from the electrolyzer, thereby ensuring that the power plant's energy supply and demand are always in a balanced state.

[0075] Wind and solar power units are a collective term for wind turbine generators and photovoltaic arrays in a power station. They are the core power supply units of the power station, and their output power is dynamically adjusted according to changes in weather conditions, which is the core source of power fluctuations in the power station.

[0076] Alkaline electrolyzers (ALK) are the core hydrogen production equipment in a plant that converts electrical energy into hydrogen energy through electrochemical reactions. They have clearly defined operating boundary constraints, such as minimum operating power and maximum operating power, and their operating status directly determines the hydrogen production efficiency and the service life of the equipment.

[0077] Total wind and solar power output is the sum of the active power output of wind turbines and the active power output of photovoltaic arrays at the same time. It is the total power supply of the power station and the core basis for judging the operating conditions of the power station.

[0078] The maximum operating power of an electrolytic cell is the maximum input power that an alkaline electrolytic cell can operate safely and stably for a long period of time. If the input power exceeds this value, it will cause the electrolytic cell equipment to overload and cause safety risks of equipment damage.

[0079] Understandably, the first sub-situation is when the total output of wind and solar power is within the range of the minimum operating power to the maximum operating power of the electrolyzer, the station enters normal operation, and the hydrogen production load is dynamically adjusted according to the total output of wind and solar power, while the energy storage system is on standby.

[0080] Furthermore, in the second sub-situation, when the total output of wind and solar power exceeds the maximum usable power of the electrolyzer, it is first determined whether there is a margin in the energy storage SOC. If there is a margin, the energy storage is charged; if the upper limit has been reached, the excess wind and solar power is discarded.

[0081] State of charge (SOC) is a core parameter that characterizes the ratio of remaining electrical energy to rated capacity in an energy storage system. It is the core basis for judging the charging and discharging capabilities of an energy storage system and formulating charging and discharging strategies.

[0082] Energy storage SOC margin refers to the operating state where the energy storage SOC has not reached the preset charging limit, but still has charging storage space and can receive excess wind and solar power for charging.

[0083] Understandably, the operating logic then divides the second major operating scenario into two categories: the total output of wind and solar power being lower than the minimum operating power of the electrolyzer. In this scenario, control strategies are implemented in stages based on the power gap and the energy storage status.

[0084] Understandably, in this scenario, the first step is to determine whether the energy storage SOC is sufficient. If it is sufficient, the energy storage is discharged to maintain the minimum power of the hydrogen production load. If it is below the discharge limit, the electrolyzer's hot standby mode is activated.

[0085] Hot standby mode is the non-hydrogen production operation mode of the alkaline electrolyzer. In this mode, the electrolyzer does not carry out hydrogen production electrochemical reactions, but maintains the core parameters such as temperature and pressure required for the hydrogen production process, and can quickly switch to normal hydrogen production state.

[0086] Understandably, after the hot standby mode is activated, if the output of new energy is less than the hydrogen production hot standby power, the electrolyzer will enter a cold transition phase. If the long-term power is not met, the electrolyzer will shut down.

[0087] The cold transition stage is the transitional operation stage for alkaline electrolyzers from hot standby mode to shutdown mode. During this stage, the process parameters of the electrolyzer are gradually reduced to smoothly complete the shutdown process and avoid sudden parameter changes from impacting the equipment.

[0088] Understandably, the power station is guided by three core control objectives and uses the matching relationship between the total output of wind and solar power and the operating power boundary of the electrolyzer as the basis for judgment, dividing different operating conditions and coordinating the operating status of each piece of equipment.

[0089] It should be understood that the hierarchical operation logic covering all operating conditions maximizes the utilization of wind and solar resources, effectively reducing the curtailment rate. Simultaneously, the bidirectional regulation of the energy storage system prevents sudden load changes and frequent start-ups and shutdowns of the electrolyzer due to fluctuations in wind and solar power, effectively ensuring the operating efficiency of the hydrogen production equipment and extending its service life. Furthermore, the multi-level operation mode settings fully guarantee the safety and stability of the hydrogen production process, adapting to the operating characteristics of off-grid wind, solar, and energy storage hydrogen production stations without large power grid support, providing a clear control basis for the stable operation of the station.

[0090] Understandably, the core principle is to first divide the total output of wind and solar power into different operating condition ranges, and then formulate the operation status switching rules and power allocation rules for wind and solar units, energy storage systems and electrolyzers for each operating condition range. The rules of all operating conditions are integrated to form a complete collaborative operation logic, and finally the station operation rules are sorted out.

[0091] Step S20: Calculate the voltage support coefficient based on wind power, photovoltaic power, energy storage potential, and AC bus parameters.

[0092] AC bus parameters are core electrical parameters that characterize the electrical characteristics and topological connections of AC buses in off-grid hydrogen production systems. They are the basic input data for calculating the short-circuit capacity and voltage support capability of AC buses.

[0093] The voltage support coefficient is a core evaluation index used to quantitatively characterize the AC bus voltage support strength in off-grid hydrogen production systems. It can intuitively reflect the core ability of grid-type energy storage to resist power fluctuations and maintain stable bus voltage.

[0094] Understandably, the nominal voltage of the AC bus and the AC equivalent impedance of the energy storage system are extracted from the AC bus parameters first. The short-circuit current and short-circuit capacity of the AC bus are then calculated in combination with the energy storage potential. The total power of wind and solar power generation is then calculated based on the wind power and photovoltaic power. Finally, the voltage support coefficient is calculated based on the short-circuit capacity and the total power of wind and solar power generation.

[0095] It should be understood that, considering the operational characteristics of off-grid hydrogen production systems without the support of a large power grid, a voltage support capability quantification method adapted to off-grid scenarios has been constructed. The voltage support coefficient enables the accurate quantification of the system's voltage stability capability, solving the problem that existing technologies cannot effectively quantify the voltage support capability in off-grid scenarios. This provides a feasible quantitative indicator for incorporating voltage stability constraints into energy storage capacity optimization.

[0096] The strong fluctuations in the output of wind and solar power plants can easily cause fluctuations in AC bus voltage, which will not only affect the continuous operation of hydrogen production load, but also threaten the stability of the wind and solar power plants themselves.

[0097] Therefore, establishing a quantitative model of the voltage support strength of off-grid hydrogen production systems is a key prerequisite for setting voltage stability constraints in energy storage capacity configuration, while taking into account both bus voltage and the stable operation of wind and solar power plants.

[0098] The classic quantitative indicator of voltage support strength in grid-connected systems is the short-circuit ratio (SCR), which is quantified by the ratio of the grid's short-circuit capacity to the capacity of the connected power source at the grid connection point.

[0099] In the formula, For the short-circuit capacity at PCC, Rated capacity for new energy power plants.

[0100] Off-grid systems lack external grid support, have no common connection point to grid-connected systems, and lack short-circuit capacity provided by the grid. The voltage support for the system shifts from the external grid to grid-connected energy storage. The physical basis and core nodes for traditional SCR quantification no longer exist. To achieve accurate quantification of the voltage support strength of off-grid hydrogen production systems, this paper draws on the core logic of grid-connected SCR and, combined with the topological characteristics of off-grid systems where "grid-connected energy storage is the sole voltage support source, and wind and solar power plants are the main power disturbance sources," defines its bus voltage support strength as being determined by the voltage support coefficient. express.

[0101] In one feasible implementation, step S20 may include steps S21 to S24: Step S21: Calculate the short-circuit current of the AC bus based on the energy storage potential and AC equivalent impedance.

[0102] AC equivalent impedance is the equivalent impedance value of the AC side of the energy storage system as seen from the AC bus of the off-grid hydrogen production system. It is a core parameter characterizing the electrical connection characteristics between the grid-type energy storage and the AC bus, and directly determines the calculation result of the AC bus short-circuit current.

[0103] Short-circuit current is the current value that grid-type energy storage can provide to the short-circuit point when a short-circuit fault occurs on the AC bus. It is the core intermediate parameter for calculating the short-circuit capacity of the AC bus and directly reflects the fault current support capability of grid-type energy storage.

[0104] Understandably, the corresponding AC equivalent impedance is first extracted from the AC bus parameters, the energy storage potential is retrieved, and the short-circuit current of the AC bus is calculated by the ratio of the energy storage potential to the AC equivalent impedance. The short-circuit current of the AC bus can be determined by the open-circuit voltage before the short circuit and the equivalent impedance of the system. The formula for calculating the short-circuit current of the AC bus is as follows:

[0105] In the formula, This refers to the short-circuit current of the AC busbar. For energy storage internal potential, The AC equivalent impedance of the energy storage system as seen from the AC bus.

[0106] Step S22: Calculate the short-circuit capacity of the grid-type energy storage at the AC bus based on the short-circuit current and the nominal bus voltage.

[0107] The nominal bus voltage is the rated operating voltage of the AC bus in an off-grid hydrogen production system. It is a core electrical parameter for the design and operation of the AC bus and a basic input parameter for calculating short-circuit capacity.

[0108] In this embodiment, the value is determined by the electrical design standards of the off-grid hydrogen production system and is a fixed system rated parameter that will not change with operating conditions.

[0109] Short-circuit capacity is the maximum short-circuit power that grid-type energy storage can provide at the AC bus. It is a core electrical parameter characterizing the ability of grid-type energy storage to support AC bus voltage and directly determines the final voltage support coefficient calculation result.

[0110] Understandably, the nominal bus voltage is first extracted from the AC bus parameters, and the calculated short-circuit current is retrieved. The short-circuit capacity of the grid-type energy storage at the AC bus is then calculated by multiplying the short-circuit current, the nominal bus voltage, and a fixed coefficient. The short-circuit capacity provided by the energy storage to the AC bus is related to the nominal bus voltage. The formula for calculating the short-circuit capacity of the grid-type energy storage at the AC bus is as follows:

[0111] In the formula, It characterizes the short-circuit capacity of grid-connected energy storage at the AC bus; The nominal voltage of the busbar; This refers to the short-circuit current of the AC busbar. For the internal potential of energy storage; The AC equivalent impedance of the energy storage system as seen from the AC bus.

[0112] It should be understood that, based on the short-circuit current calculated in the preceding steps, the equivalent support strength of grid-type energy storage at the AC bus is accurately quantified, the upper limit of the voltage support capability of the off-grid hydrogen production system is clarified, and core support strength quantification parameters are provided for the calculation of the final voltage support coefficient. This is consistent with the core calculation logic of the short-circuit ratio of the large power grid and adapted to the operating characteristics of the off-grid hydrogen production system.

[0113] Step S23: Calculate the total power of wind and solar power generation based on wind power and photovoltaic power.

[0114] The total power of wind and solar power generation is the sum of wind power and photovoltaic power in the off-grid hydrogen production system at the same time. It is the core parameter characterizing the total scale of power disturbance at the power station and also the core input parameter for calculating the voltage support coefficient.

[0115] Understandably, the total wind and solar power output at a given time is calculated by summing the calculated wind and solar power outputs.

[0116] Step S24: Calculate the voltage support coefficient based on the short-circuit capacity and the total power generated by wind and solar power.

[0117] The voltage support coefficient is a core evaluation index used to quantitatively characterize the AC bus voltage support strength in off-grid hydrogen production systems. It can intuitively reflect the core ability of grid-type energy storage to resist wind and solar power fluctuations and maintain stable bus voltage.

[0118] In this embodiment, the voltage support coefficient is positively correlated with the voltage support strength of the off-grid hydrogen production system and the operational stability of wind and solar power stations. The larger the value, the stronger the voltage support capability of the grid-type energy storage to the AC bus and the more stable the bus voltage.

[0119] Understandably, based on the short-circuit capacity and the total wind and solar power generation at the same time, the voltage support coefficient, which characterizes the voltage support strength of the off-grid hydrogen production system at the corresponding time, is calculated by the ratio of the short-circuit capacity to the total wind and solar power generation. The formula for calculating the voltage support coefficient is as follows:

[0120]

[0121] In the formula, This is the voltage support coefficient; It characterizes the short-circuit capacity of grid-connected energy storage at the AC bus; The nominal voltage of the busbar; For the internal potential of energy storage; The AC equivalent impedance of the energy storage system as seen from the AC bus. This represents the total power generated by wind and solar power (assuming an operating power factor of 1). Wind power; Photovoltaic power.

[0122] Step S30: Determine the dual-objective optimization objective based on energy storage capacity, hydrogen production rate, voltage support coefficient, and cost parameters.

[0123] Cost parameters are the basic economic parameters used to calculate the investment and operating costs of off-grid hydrogen production systems throughout their entire life cycle. They are the core input data for calculating the levelized cost of hydrogen production and cover economic indicators related to investment and operation and maintenance throughout the entire project life cycle.

[0124] In this embodiment, the cost parameters include the discount rate, investment period, unit energy investment cost of energy storage, equipment investment capacity, equipment investment cost, and equipment operation and maintenance cost, which can fully restore the economic investment situation of the entire life cycle of the project.

[0125] The dual-objective optimization objective is a synergistic optimization objective that simultaneously considers the operational economy and voltage stability of off-grid hydrogen production systems. It is the core optimization criterion for energy storage capacity optimization and is used to measure the comprehensive performance of different energy storage configuration schemes.

[0126] Understandably, the annualized total cost of the project's entire lifecycle is first calculated based on cost parameters and energy storage capacity. Then, the annualized hydrogen production volume is calculated by combining the hydrogen production rate to obtain the economic objective of minimizing the levelized hydrogen production cost. Finally, the voltage stability objective of maximizing the lowest voltage support coefficient throughout the entire time period is determined based on the voltage support coefficient. The two objectives are then integrated to obtain the dual-objective optimization objective.

[0127] It should be understood that voltage support strength is the core characteristic that characterizes the power system's ability to resist power disturbances and maintain bus voltage stability. For off-grid systems, voltage stability depends entirely on the regulation capability of grid-connected energy storage.

[0128] In one feasible implementation, step S30 may include steps S31 to S36: Step S31: Calculate the annualized investment cost based on the discount rate, investment period, energy storage capacity, energy storage unit energy investment cost, equipment investment capacity, and equipment investment cost.

[0129] The discount rate is the ratio by which a project's future cash flows are discounted to their present value. It is a core parameter in engineering economic accounting for measuring the time value of money. In this embodiment, this parameter is used to discount the initial investment in energy storage and various site equipment into the annualized investment cost over the entire life cycle, ensuring the accuracy of the time dimension in economic accounting.

[0130] The investment period is the design and operation period of the entire life cycle of an off-grid hydrogen production project, and it is also the core time period for investment recovery and cost accounting.

[0131] The unit energy investment cost of energy storage is the initial investment amount corresponding to a unit capacity energy storage system, and it is the core unit price parameter for calculating the total investment cost of an energy storage system.

[0132] Equipment investment capacity refers to the rated installed capacity of core equipment such as wind turbines, photovoltaic arrays, and electrolyzers in an off-grid hydrogen production system. It is the basic parameter for calculating the initial investment of each piece of equipment.

[0133] Equipment investment cost is the initial investment amount of each core piece of equipment within the off-grid hydrogen production system, corresponding to a unit capacity. It is the core unit price parameter for calculating the total investment cost of each piece of equipment.

[0134] Annualized investment cost is the annual equivalent investment cost calculated by discounting the initial total investment over the entire life cycle of a project using a discount rate and the investment period. It is a core component of levelized hydrogen production cost accounting.

[0135] Understandably, the initial total investment for the energy storage system is calculated by first determining the energy storage capacity based on the energy storage capacity; then, the initial total investment is calculated by multiplying the energy storage capacity by the unit energy investment cost; next, the initial total investment for each core device is calculated by multiplying the equipment investment capacity by the corresponding equipment investment cost; finally, all initial investments are summed to obtain the total initial investment for the project, which is then converted into annual equivalent expenditures using a discount rate and the investment period to obtain the annualized investment cost. The formula for calculating the annualized investment cost is as follows:

[0136] In the formula, This represents the annualized investment cost. The discount rate; Investment period; The energy storage capacity configured for the system; Energy investment cost per unit of energy storage; For the first Equipment investment capacity per unit; For the first Investment cost of equipment.

[0137] Step S32: Calculate the annualized operation and maintenance cost based on the equipment investment capacity and equipment operation and maintenance cost.

[0138] Equipment investment capacity refers to the rated installed capacity of core production equipment such as wind turbines, photovoltaic arrays, alkaline electrolyzers, and energy storage systems in an off-grid hydrogen production system. It is the basic scale parameter for calculating the annual operation and maintenance costs of a single piece of equipment.

[0139] Equipment operation and maintenance costs are the annual operation and maintenance expenses for the corresponding type of equipment in the off-grid hydrogen production system, per unit of rated capacity. These costs include daily equipment inspections, regular maintenance, fault repairs, spare parts replacements, consumable replenishment, and other operation and maintenance-related expenses throughout the entire process.

[0140] Annualized operation and maintenance cost is the total cost of operating and maintaining all core equipment in an off-grid hydrogen production project during a full operating year. It is a core component of the project's annual operating cost and a key input parameter for subsequent annualized total cost accounting.

[0141] Understandably, the process involves first retrieving the equipment investment capacity and corresponding unit capacity maintenance cost for each type of equipment. Then, multiplying the equipment investment capacity of a single type of equipment by its corresponding unit maintenance cost yields the annual maintenance cost for that type of equipment. Finally, the annual maintenance costs for all types of equipment are summed to obtain the annualized maintenance cost for all equipment in the project. The formula for calculating the annualized maintenance cost is as follows:

[0142] In the formula, Annualized operation and maintenance costs; For the first Equipment investment capacity per unit; For the first Equipment maintenance costs.

[0143] Step S33: Calculate the annualized total cost based on the annualized investment cost and the annualized operation and maintenance cost.

[0144] The annualized total cost is the total cost of an off-grid hydrogen production project over a full operating year, fully covering the two core cost components: the annual amortization of the initial investment and the annual operation and maintenance expenses.

[0145] Based on the annualized investment cost and annualized operation and maintenance cost, the two cost values ​​are summed to obtain the final annualized total cost of the project. The formula for calculating the annualized total cost is as follows:

[0146] In the formula, For investment costs; For operation and maintenance costs.

[0147] Step S34: Calculate the levelized cost of hydrogen production target based on the annualized total cost and the hydrogen production rate.

[0148] Levelized cost of hydrogen production (LCOE) is the average cost per unit volume of hydrogen produced throughout the entire lifecycle of an off-grid hydrogen production project. It is a core evaluation indicator commonly used in the industry to measure the economic viability of green hydrogen projects.

[0149] Annualized hydrogen production volume is the total volume of hydrogen produced by a project within a full operating year, calculated based on the hydrogen production rate of the alkaline electrolyzer during its continuous operation. It is a core indicator for measuring the project's hydrogen production scale.

[0150] Understandably, based on the hydrogen production rate throughout the entire operating cycle, the annualized hydrogen production volume of the project is obtained by summing the hydrogen production rates over the entire year. Further, the annualized total cost is calculated using the ratio of the annualized total cost to the annualized hydrogen production volume, yielding the levelized cost of hydrogen production (LCOD). Finally, the economic optimization objective is determined with minimizing the LCOD as the core. The formula for calculating the LCOD target is as follows:

[0151] In the formula, To levelize the cost of hydrogen production; This represents the annualized total cost; This represents the annualized hydrogen production volume.

[0152] Step S35: Determine the voltage stability target based on the voltage support coefficient.

[0153] The minimum voltage support factor is the minimum voltage support factor value that can maintain the stable operation of grid-connected wind and solar power equipment in the off-grid hydrogen production system and prevent the equipment from going offline. It is a rigid safety constraint threshold to ensure the stable operation of the system voltage.

[0154] The voltage stability objective is the optimization goal used in this method to ensure the safe and stable operation of the off-grid hydrogen production system throughout the entire time period. The core is to improve the system's ability to withstand wind and solar power fluctuations and maintain the stability of the bus voltage by optimizing the energy storage capacity configuration.

[0155] Based on the voltage support coefficient throughout the entire operating period, the minimum value of the voltage support coefficient at all times is extracted. A voltage stability optimization objective is determined, with maximizing the minimum voltage support coefficient throughout the entire operating period as the core. Simultaneously, a rigid operating constraint is set that the voltage support coefficient throughout the entire operating period must not be lower than the minimum voltage support coefficient. The voltage stability objective expression is as follows:

[0156] In the formula, This is the voltage support coefficient; This indicates the minimum value required to maintain stable operation of network-connected equipment.

[0157] Step S36: Determine the dual-objective optimization objective based on the levelized hydrogen production cost objective and the voltage stability objective.

[0158] Understandably, based on the economic optimization objective of minimizing levelized hydrogen production costs and the voltage stability optimization objective of maximizing the minimum voltage support coefficient throughout the time period, the two optimization sub-objectives are integrated to construct a complete dual-objective collaborative optimization objective system, while clarifying the optimization direction, corresponding constraints, and decision variable range of the two objectives.

[0159] It should be understood that by simultaneously incorporating the levelized cost of hydrogen production, which characterizes the economic efficiency of the system, and the voltage support coefficient, which characterizes voltage stability, into the optimization objectives, a dual-objective synergistic optimization system is constructed. This solves the core defect of existing technologies that only focus on economic efficiency and ignore voltage stability, and realizes the synergistic consideration of economic efficiency and stability in the energy storage capacity configuration of off-grid hydrogen production systems.

[0160] Step S40: Determine the optimal configuration of the target energy storage capacity based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy.

[0161] The improved non-dominated sorting genetic strategy is a multi-objective optimization solution strategy that optimizes the non-dominated sorting genetic algorithm (NSGA-II) with an elitist strategy. It is the core algorithm logic adapted to solve bi-objective optimization models.

[0162] In this embodiment, the strategy solves the problems of traditional algorithms being prone to getting trapped in local optima and uneven distribution of solution sets by optimizing chaotic initialization and nonlinear adaptive mutation, thereby improving the efficiency and accuracy of multi-objective optimization solutions.

[0163] The target energy storage capacity optimization configuration is obtained after solving a dual-objective optimization solution. It is the optimal energy storage capacity configuration scheme that takes into account the economic efficiency and voltage stability of the off-grid hydrogen production system. It includes the core configuration parameters of rated energy storage power and energy storage capacity.

[0164] Understandably, the decision variables and multi-dimensional constraints for energy storage capacity optimization are first determined based on the dual-objective optimization goal. The Pareto optimal solution set is obtained by iteratively solving the problem using an improved non-dominated sorting genetic strategy. Finally, the ideal point method is used to select the equilibrium solution that takes into account both objectives from the solution set, which is the target energy storage capacity optimization configuration.

[0165] This embodiment provides a method for optimizing the energy storage capacity of an off-grid hydrogen production system. By adapting an improved non-dominated sorting genetic strategy to a dual-objective optimization model, it can efficiently solve for a uniformly distributed Pareto optimal solution set, accurately presenting the trade-off between economy and stability, providing diversified decision-making options for engineering design, and avoiding the problem of traditional algorithms easily getting trapped in local optima. It can quickly lock in the optimal energy storage capacity configuration scheme that takes into account both objectives, significantly shortening the design cycle and reducing the cost of trial and error.

[0166] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S40 of the method for optimizing the energy storage capacity of off-grid hydrogen production systems includes steps S41 to S44: Step S41: Determine decision variables based on the dual-objective optimization objective.

[0167] It should be noted that decision variables are the core unknown variables in multi-objective optimization problems that need to be determined through iterative solutions using optimization algorithms. Their values ​​directly determine the calculation results of the optimization objective and the performance of the final solution.

[0168] Understandably, based on the optimization requirements of the dual-objective optimization goal, the rated power and energy capacity of energy storage are determined as the core decision variables for optimization. At the same time, the upper and lower limits of the values ​​of the two decision variables are determined by combining engineering practice and equipment parameters.

[0169] Step S42: Construct an initial population based on chaotic initialization, and map the initial population to the decision variable space of the decision variables according to the chaotic mapping to obtain the initial parent population.

[0170] Chaotic initialization is a population initialization method based on chaos theory. It replaces the traditional random initialization method and can generate an initial set of individuals with ergodicity, randomness and regularity, thus avoiding the problem of uneven distribution of the initial population.

[0171] Chaotic mappings are nonlinear mapping functions capable of generating chaotic sequences. This embodiment employs the logistic mapping, which can generate uniformly distributed, non-repeating chaotic sequences within the interval 0 to 1, providing fundamental data for the initial population construction. The logistic mapping can be represented as a nonlinear difference equation:

[0172] In the formula, For the first The state variables of the next iteration. , For the first The state variables of the next iteration. For control parameters and ,when The system then enters a chaotic state.

[0173] At this point, the sequence of state variables exhibits high randomness and ergodicity. The system then enters a state of complete chaos.

[0174] Mapping the Logistic chaotic sequence to the decision variable space:

[0175] In the formula, For the first One decision variable, , and These are the minimum and maximum values ​​of the decision variables, respectively. It is a Logistic chaotic sequence with dimension . , The Hadamard product is used to map a chaotic Logistic sequence to the actual range of values ​​for the decision variable. The number of decision variables.

[0176] The decision variable space is a multidimensional value space composed of the upper and lower bounds of the values ​​of each decision variable. Each feasible combination of decision variables corresponds to a coordinate point in this space, which is the target interval for the initial population mapping.

[0177] The initial parent population is a set of feasible initial individuals that are completely within the decision variable space after chaotic mapping. Each individual corresponds to a complete energy storage capacity configuration scheme and is the starting population for algorithm iteration and optimization.

[0178] For example, the initial parent population is generated by using the Logistic chaotic mapping initialization method to generate the initial parent population Pt.

[0179] It is understandable that a chaotic sequence of a predetermined size is first generated through chaotic mapping, and then the chaotic sequence is linearly mapped to the decision variable space of the decision variables to obtain initial individuals that meet the value constraints. All initial individuals together form the initial parent population.

[0180] Step S43: Determine the target offspring population in the initial parent population according to the adaptive feasible mutation strategy, and determine the temporary population based on the initial parent population and the target offspring population.

[0181] The adaptive feasible mutation strategy is an optimized and improved strategy of traditional polynomial mutation. It can dynamically adjust the mutation length according to the number of iterations, and at the same time ensure the feasibility of the mutated individuals through constraint processing methods, balancing the global exploration and local development capabilities of the algorithm.

[0182] The target offspring population is a new set of individuals obtained by sequentially performing selection, crossover, and mutation operations on the parent population. Each individual corresponds to a new energy storage capacity configuration scheme, which is a new set of solutions generated by the algorithm's iterative evolution.

[0183] The temporary population is the population obtained by merging the parent population of the current iteration with the target offspring population. Its size is twice that of the initial parent population. It is used for subsequent non-dominated sorting and elite individual selection to implement the elite retention strategy.

[0184] For example, a binary tournament selection strategy is used to select superior individuals from the parent population Pt as evolutionary parents. Then, the selected individuals are subjected to the SBX operation to generate new individual gene combinations; finally, the crossover individuals are subjected to a nonlinear adaptive feasible mutation operation; the parent population Pt is merged with the offspring population Qt to obtain a temporary population Rt, at which point the size of the temporary population is twice that of the original population.

[0185] It is understandable that selection and crossover operations are first performed on the initial parent population, then mutation and constraint processing are completed through an adaptive feasible mutation strategy to obtain the target offspring population, and finally the initial parent population and the target offspring population are merged to obtain a temporary population.

[0186] It should be understood that the adaptive mutation strategy achieves a dynamic balance between the algorithm's global exploration and local development capabilities, while ensuring the feasibility of the mutated individuals, improving the quality of the solution set and the solution efficiency. The population merging method achieves elite preservation and avoids the loss of the best individuals during the iteration process.

[0187] In one feasible implementation, step S43 may include steps S431 to S434: Step S431: Calculate the variation distribution index based on the iteration parameters and distribution index of the initial parent population.

[0188] The iteration parameter is a core parameter characterizing the current iteration progress of a multi-objective optimization algorithm, mainly including two key values: the current iteration count and the preset maximum iteration count. In this embodiment, this parameter is used to dynamically adjust the mutation distribution index, enabling adaptive switching between the algorithm's global exploration and local exploitation capabilities.

[0189] The distribution exponent is the core parameter that controls the variable asynchronous length of the polynomial. When the initial distribution exponent is large, the variable asynchronous length is large, which is suitable for global exploration. When the final distribution exponent is small, the variable asynchronous length is small, which is suitable for local development.

[0190] The mutation distribution index is a dynamically adjusted distribution index actually used for mutation operations. Its value smoothly transitions from the initial distribution index to the final distribution index as the number of iterations increases.

[0191] Understandably, the algorithm first extracts the current iteration count and the preset maximum iteration count from its running state, then retrieves the preset initial and final distribution indices, and calculates the variation distribution index, which dynamically changes with the iteration count, using a power function. The formula for calculating the variation distribution index is as follows:

[0192] In the formula, The variable is the exponent of the variation; t is the current iteration number. , These are the initial and final distribution indices, respectively. denoted as the maximum number of iterations, and k is the nonlinear adjustment coefficient.

[0193] Step S432: Calculate the variation disturbance term based on the variation distribution index.

[0194] The mutation perturbation term is a numerical value used to randomly perturb the decision variables of the parent individual. Its perturbation magnitude is determined by the mutation distribution index and the random number, and it is the core intermediate parameter for generating new candidate solutions.

[0195] In this embodiment, the range of values ​​for the variation disturbance term is constrained by the upper and lower limits of the decision variable and the variation distribution index, ensuring that the decision variable after disturbance does not deviate too far from the feasible range.

[0196] Understandably, we first generate random numbers that conform to a uniform distribution, then combine these with the calculated variance distribution index, and finally calculate the variance perturbation term for each decision variable using the perturbation formula for multinomial variance. The formula for calculating the variance perturbation term is as follows:

[0197] In the formula, The mutation perturbation term corresponding to the i-th decision variable is used to apply random perturbation to the decision variables of the parent individual to generate mutated candidate solutions; A random number that is uniformly distributed between 0 and 1; It is the distribution index of variation; This represents the current iteration number of the algorithm.

[0198] It should be understood that the mutation distribution index, which is based on dynamic adjustment, generates a mutation perturbation term that is adapted to the current iteration stage. The perturbation amplitude is large in the early stage of the iteration, which can quickly jump out of the local optimum. The perturbation amplitude is small in the later stage of the iteration, which can finely adjust the current optimal solution, thus improving the pertinence and effectiveness of the mutation operation.

[0199] Step S433: Perform mutation operation on the initial parent population according to the adaptive feasible mutation strategy and mutation perturbation term to generate candidate solutions.

[0200] The adaptive feasible mutation strategy is a mutation strategy that integrates dynamic variable length and constraint processing logic. Its core is to prioritize the feasibility of individuals during the mutation process and avoid generating a large number of infeasible solutions, thus avoiding wasting computational resources.

[0201] In this embodiment, the adaptive feasible mutation process is as follows: when the population dispersion increases or the optimal fitness decreases, the step size is increased by 4 times (maximum not exceeding 1) to strengthen global exploration; when the population tends to converge or the optimal fitness increases, the step size is reduced by 4 times to focus on local optimization; the mutation probability is set to 0.05 to ensure the randomness and effectiveness of the mutation operation.

[0202] Candidate solutions are new individuals obtained by applying mutation perturbations to the decision variables of the parent individuals. Each candidate solution corresponds to a new energy storage capacity configuration scheme, which may be a feasible solution or an infeasible solution. It needs to be processed by subsequent constraints before it can enter the offspring population.

[0203] Understandably, the decision variable's perturbation term is first applied to each individual in the initial parent population after selection and crossover, generating preliminary mutated individuals. These preliminary mutated individuals are then output as candidate solutions. The candidate solution calculation formula is as follows:

[0204] In the formula, A random number that is uniformly distributed between 0 and 1; For the variation perturbation term; Let be the candidate value of the i-th decision variable after mutation; This is the original value of the i-th decision variable before mutation, that is, the value of the i-th decision variable corresponding to the parent individual; The minimum value boundary is preset for the i-th decision variable; The maximum value boundary is preset for the i-th decision variable.

[0205] Step S434: The decision variable boundary constraints and general inequality constraints of the candidate solutions are processed by the truncation boundary strategy and the linear backtracking strategy, respectively, to obtain the offspring population.

[0206] The boundary truncation strategy is a simple and efficient method for handling boundary constraints of decision variables. When the decision variables of a candidate solution exceed the preset upper and lower limits, they are directly truncated to the corresponding boundary values ​​to ensure that the decision variables meet the value range requirements.

[0207] In this embodiment, the truncation boundary strategy is used to handle the upper and lower limits of the rated power and energy capacity of energy storage.

[0208] Linear backtracking is an effective method for handling general inequality constraints. When a candidate solution does not satisfy the general inequality constraint, linear backtracking is performed in the opposite direction of the mutation until a feasible solution that satisfies the constraint is found or backtracking is performed to the parent individual.

[0209] In this embodiment, the linear backtracking strategy is used to handle general inequality constraints such as the lower limit of the voltage support coefficient.

[0210] Understandably, the decision variables of all candidate solutions are first subjected to a truncation boundary strategy to handle upper and lower bound constraints. Then, a linear backtracking strategy is applied to the processed candidate solutions to handle general inequality constraints. Finally, all feasible individuals that satisfy the constraints are selected and formed into the offspring population.

[0211] Step S44: Determine the optimal configuration of the target energy storage capacity in the temporary population based on the improved non-dominated sorting genetic strategy.

[0212] Understandably, a fast non-dominated sorting and crowding calculation are performed on the temporary population, and a new parent population is selected according to the non-dominated level and crowding. After iterating until the termination condition is met, the Pareto optimal solution set is obtained. Finally, the target energy storage capacity is optimized by screening through the ideal point method.

[0213] In one feasible implementation, step S44 may include steps S441 to S444: Step S441: Perform non-dominated sorting on the temporary population according to the improved non-dominated sorting genetic strategy to obtain the crowding degree of individuals corresponding to the non-dominated level and the non-dominated level.

[0214] It should be noted that non-dominated ranking is a core method used in multi-objective optimization to classify individuals into different levels of superiority or inferiority. Its core logic is based on the dual-objective optimization objective to determine the dominance relationship between individuals and classify individuals that do not dominate each other into the same non-dominated level.

[0215] In this embodiment, the criterion for determining the dominance relationship is that one entity is not inferior to the other entity in both optimization objectives, and is superior in at least one objective.

[0216] In addition, individual crowding is a core indicator used to measure the sparseness of individual distribution within the same non-dominated level. It is used to ensure the diversity of the solution set and avoid the final solution set being overly concentrated in local areas.

[0217] In this embodiment, the higher the individual crowding value, the sparser the individuals at the same level around that individual are, and the higher its retention priority.

[0218] Understandably, based on the dual-objective optimization goal, a fast non-dominated sort is first performed on all individuals in the temporary population to determine the dominance relationship between individuals one by one and divide all individuals into different non-dominated levels. Then, for all individuals in each non-dominated level, the corresponding individual crowding degree is calculated based on the numerical values ​​of the two optimization goals to obtain the complete non-dominated level division result and the individual crowding degree corresponding to each non-dominated level.

[0219] Step S442: According to the order of the non-dominant hierarchy, individuals are added to the preset parent population in sequence to obtain the target population size.

[0220] It should be noted that the preset parent population is a new parent population reserved for the next iteration. Its preset size is exactly the same as the initial parent population size, and it is the target carrier for selecting elite individuals in this iteration.

[0221] Step S443: When the target population size is greater than a preset size threshold, determine the individuals to be retained based on the individual crowding and determine the target population based on the retained individuals.

[0222] It should be noted that the preset size threshold is the maximum number of individuals in the parent population set in advance by the algorithm. It is a fixed constraint value for the population size during the iteration process and also a critical value for determining whether crowding screening is necessary.

[0223] Additionally, the retained individuals are those that have undergone crowding screening and are ultimately retained in the new parent population. The screening rule is to prioritize retaining individuals with higher crowding within the same non-dominant level until the population size reaches a preset size threshold.

[0224] In addition, the target population is the new parent population that is obtained after non-dominated hierarchy screening and crowding screening, and whose size is exactly equal to the preset size threshold. It is the initial parent population for the next iteration.

[0225] Understandably, this step first determines whether the target population size after being added to the current non-dominated level is greater than the preset size threshold. If it is, the addition of individuals to subsequent levels is stopped. Within the current non-dominated level, individuals are sorted in descending order of crowding and selected to be added to the preset parent population in turn until the population size is exactly equal to the preset size threshold. All selected individuals together form the target population.

[0226] Step S444: When the target population meets the preset iteration termination condition, determine the Pareto solution set, and determine the optimal configuration of the target energy storage capacity based on the Pareto solution set.

[0227] It should be noted that the preset iteration termination condition is a criterion set in advance by the algorithm to stop the iteration loop. It usually includes two types: reaching the preset maximum number of iterations and the convergence accuracy threshold when there is no significant change in the Pareto front for multiple consecutive generations.

[0228] Furthermore, the Pareto solution set is the set of non-dominated optimal solutions obtained after multiple rounds of iterative convergence, also known as the Pareto optimal front. Each solution in the set cannot be dominated by any other solution in either of the two optimization objectives, and each solution corresponds to a set of energy storage capacity configuration schemes that balance economy and stability. In this embodiment, this solution set provides diverse decision options for engineering design.

[0229] Understandably, the process first determines whether the target population meets the preset iteration termination condition. If it does not meet the condition, the target population is used as the initial parent population for the next iteration, and the iteration process is returned. If it meets the condition, the Pareto solution set obtained in the current iteration is output. Then, the ideal point method is used to select the balanced optimal solution that takes into account both objectives from the Pareto solution set, and the target energy storage capacity optimization configuration is obtained.

[0230] For example, a fast non-dominated sort is performed on a temporary population Rt. Based on the dominance relationship between individuals, all individuals are divided into different non-dominated levels F1, F2, ..., where F1 is the first level of non-dominated level (Pareto optimal front), F2 is the second level of non-dominated level, and so on.

[0231] For each non-dominated level Fi, the crowding degree of all individuals in that level is calculated to measure the sparseness of the distribution of individuals in the same non-dominated level, thus ensuring the diversity of the solution set.

[0232] Individuals are added to the new parent population Pt in descending order of non-dominant hierarchy. If the population size exceeds the preset value after adding a certain layer Fi, individuals are selected based on crowding within that layer, prioritizing individuals with high crowding (sparsely distributed areas) until the population size reaches the preset value.

[0233] Determine if the maximum number of iterations or convergence accuracy has been reached. If the termination condition is not met, return to the binary tournament selection strategy to select superior individuals from the parent population Pt as evolutionary parents. Then, perform the SBX operation on the selected individuals to generate new individual gene combinations; finally, perform the nonlinear adaptive feasible mutation operation on the crossover individuals to continue the next generation of evolution; if the termination condition is met, output the final Pareto front solution set, which is the set of nondominated optimal solutions to the multi-objective optimization problem.

[0234] It should be understood that the convergence of the algorithm is guaranteed by the preset termination conditions, which avoids invalid iterative calculations. At the same time, the ideal point method is used to select the balanced optimal solution from the Pareto solution set that takes into account the two core requirements. This not only provides diversified decision options for engineering design, but also can directly output feasible energy storage capacity configuration schemes, which greatly improves the engineering practicality and feasibility of the method.

[0235] Reference Figure 3 , Figure 3 This is a schematic diagram illustrating the process of determining the target energy storage capacity optimization configuration in the second embodiment of the off-grid hydrogen production system energy storage capacity optimization method of this application.

[0236] like Figure 3As shown, the process begins in the top start box, followed by the step of generating the initial parent population Pt. This step employs the Logistic chaotic mapping initialization method, generating an initial population with high randomness and ergodicity through nonlinear difference equations, thus solving the solution set clustering problem that is easily caused by traditional random initialization. Next, the process proceeds to the step of generating the offspring population Qt through selection, crossover, and mutation. This step includes three parallel sub-processes: a binary tournament selection strategy, an SBX crossover operation, and an adaptive feasible mutation operation. The binary tournament selection strategy is used to select superior individuals from the parent population as evolutionary parents. The SBX crossover operation is used to generate new individual gene combinations. The adaptive feasible mutation operation dynamically adjusts the mutation distribution exponent through a power function to achieve an adaptive strategy of large-step global search in the early stage and small-step local development in the later stage. The mutation probability is set to 0.05 to ensure randomness and effectiveness. The generated offspring population Qt is merged with the parent population Pt to form a temporary population Rt. At this point, Rt is equal to the union of Pt and Qt, and the size of the temporary population is expanded to twice that of the original population. Then, a fast non-dominated sort is performed on Rt, constructing non-dominated layers F1, F2, etc. This step divides all individuals into different non-dominated layers based on the dominance relationships between individuals, where F1 is the first-level non-dominated layer, i.e., the Pareto optimal front, F2 is the second-level non-dominated layer, and so on. Next, the crowding degree of all solutions in each non-dominated layer is calculated. The crowding degree is used to measure the sparseness of the distribution of individuals within the same non-dominated layer to ensure the diversity of the solution set. Then, a new parent population Pt is generated based on the dominance relationships and crowding degree. In this step, individuals are added to the new parent population in descending order of non-dominated layer. If the population size exceeds a preset value after adding a certain layer, individuals within that layer are selected based on crowding degree, and individuals in the sparsely distributed regions with high crowding degree are retained until the population size reaches the preset value. At this point, it is determined whether the termination condition has been met. If not, the process returns to the Qt step of selecting crossover and mutation to generate offspring population and continues to iterate. If the condition has been met, the Pareto front is output. This step outputs the final Pareto front solution set, which is the set of non-dominated optimal solutions to the multi-objective optimization problem. Finally, the process terminates at the end box.

[0237] This embodiment provides a method for optimizing the energy storage capacity of an off-grid hydrogen production system. Through an improved non-dominated sorting genetic strategy, it can efficiently solve the dual-objective optimization model, obtain a Pareto optimal solution set with uniform distribution and good diversity, and accurately select the optimal energy storage configuration scheme that takes into account both economy and voltage stability. It is fully adapted to the actual engineering needs of off-grid hydrogen production systems without the support of a large power grid.

[0238] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the energy storage capacity optimization method of the off-grid hydrogen production system of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0239] This application also provides a device for optimizing the energy storage capacity of an off-grid hydrogen production system. Please refer to [reference needed]. Figure 4 The off-grid hydrogen production system energy storage capacity optimization device includes: The output parameter calculation module 10 is used to input the target weather data and the station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy and hydrogen production rate. The off-grid hydrogen production system output model includes wind farm output model, photovoltaic farm output model, energy storage farm output model and electrolyzer output model. The voltage support coefficient calculation module 20 is used to calculate the voltage support coefficient based on wind power, photovoltaic power, energy storage potential and AC bus parameters; The optimization objective determination module 30 is used to determine the dual-objective optimization objective based on energy storage energy, hydrogen production rate, voltage support coefficient, and cost parameters. The optimization configuration solution module 40 is used to determine the target energy storage capacity optimization configuration based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy.

[0240] The off-grid hydrogen production system energy storage capacity optimization device provided in this application adopts the off-grid hydrogen production system energy storage capacity optimization method in the above embodiments, which can solve the technical problem of how to balance economy and voltage stability in the process of configuring energy storage capacity in off-grid hydrogen production systems. Compared with the prior art, the beneficial effects of the off-grid hydrogen production system energy storage capacity optimization device provided in this application are the same as the beneficial effects of the off-grid hydrogen production system energy storage capacity optimization method provided in the above embodiments, and other technical features in the off-grid hydrogen production system energy storage capacity optimization device are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0241] In one embodiment, the optimization configuration solving module 40 is further configured to determine decision variables based on the dual-objective optimization objective; An initial population is constructed based on chaotic initialization, and the initial population is mapped to the decision variable space of the decision variables based on chaotic mapping to obtain the initial parent population. The target offspring population is determined from the initial parent population based on the adaptive feasible mutation strategy, and a temporary population is determined based on the initial parent population and the target offspring population. The target energy storage capacity is optimally configured in a temporary population based on an improved non-dominated sorting genetic strategy.

[0242] In one embodiment, the optimization configuration solver module 40 is further configured to calculate the variation distribution index based on the iteration parameters and distribution index of the initial parent population; Calculate the variation disturbance term based on the variation distribution index; The initial parent population is mutated according to the adaptive feasible mutation strategy and the mutation perturbation term to generate candidate solutions. By using a truncation boundary strategy and a linear backtracking strategy to handle the decision variable boundary constraints and general inequality constraints of the candidate solutions, respectively, the offspring population is obtained.

[0243] In one embodiment, the optimization configuration solution module 40 is further used to perform non-dominated sorting on the temporary population according to the improved non-dominated sorting genetic strategy to obtain the individual crowding degree corresponding to the non-dominated level and the non-dominated level. Individuals are added to the preset parent population in sequence according to the order of the non-dominant hierarchy to obtain the target population size. When the target population size exceeds a preset size threshold, individuals are selected to be retained based on their crowding levels, and the target population is determined based on the retained individuals. When the target population meets the preset iteration termination condition, the Pareto solution set is determined, and the optimal configuration of the target energy storage capacity is determined based on the Pareto solution set.

[0244] In one embodiment, the cost parameters include the discount rate, investment period, energy storage unit energy investment cost, equipment investment capacity, equipment investment cost, and equipment operation and maintenance cost; the optimization target determination module 30 is also used to calculate the annualized investment cost based on the discount rate, investment period, energy storage energy, energy storage unit energy investment cost, equipment investment capacity, and equipment investment cost; Calculate the annualized operation and maintenance cost based on the equipment investment capacity and equipment operation and maintenance cost; Calculate the annualized total cost based on the annualized investment cost and annualized operation and maintenance cost; Calculate the levelized cost of hydrogen production target based on the annualized total cost and hydrogen production rate; The voltage stability target is determined based on the voltage support coefficient. The dual-objective optimization objective is determined based on the levelized hydrogen production cost objective and the voltage stability objective.

[0245] In one embodiment, the AC bus parameters include AC equivalent impedance, short-circuit capacity, bus nominal voltage, AC equivalent impedance, and bus nominal voltage; the voltage support coefficient calculation module 20 is also used to calculate the short-circuit current of the AC bus based on the energy storage potential and AC equivalent impedance; Calculate the short-circuit capacity of grid-type energy storage at the AC bus based on the short-circuit current and the bus nominal voltage. Calculate the total power generated by wind and solar power based on wind power and solar power; The voltage support factor is calculated based on the short-circuit capacity and the total power generated by wind and solar power.

[0246] In one embodiment, the output parameter calculation module 10 is also used to acquire the raw meteorological data of the off-grid hydrogen production system, wherein the raw meteorological data includes wind speed data, light intensity data and ambient temperature data. The raw meteorological data is preprocessed to obtain the target weather data; Based on the dynamic balance strategy of supply and demand, the collaborative operation logic of wind and solar turbines, energy storage systems and electrolyzers in the off-grid hydrogen production system is formulated to obtain the site operation rules.

[0247] This application provides an off-grid hydrogen production system energy storage capacity optimization device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the off-grid hydrogen production system energy storage capacity optimization method in the above embodiment 1.

[0248] The following is for reference. Figure 5 The diagram illustrates a structural schematic of an off-grid hydrogen production system energy storage capacity optimization device suitable for implementing embodiments of this application. The off-grid hydrogen production system energy storage capacity optimization device in this application embodiment may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The off-grid hydrogen production system energy storage capacity optimization device shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0249] like Figure 5As shown, the off-grid hydrogen production system energy storage capacity optimization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the off-grid hydrogen production system energy storage capacity optimization device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the off-grid hydrogen production system energy storage capacity optimization equipment to exchange data with other devices wirelessly or via wired communication. Although the figure shows an off-grid hydrogen production system energy storage capacity optimization equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0250] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0251] The off-grid hydrogen production system energy storage capacity optimization device provided in this application adopts the off-grid hydrogen production system energy storage capacity optimization method in the above embodiments, which can solve the technical problem of how to balance economy and voltage stability in the process of configuring energy storage capacity in off-grid hydrogen production systems. Compared with the prior art, the beneficial effects of the off-grid hydrogen production system energy storage capacity optimization device provided in this application are the same as the beneficial effects of the off-grid hydrogen production system energy storage capacity optimization method provided in the above embodiments, and other technical features in the off-grid hydrogen production system energy storage capacity optimization device are the same as the features disclosed in the method of the previous embodiment, and will not be repeated here.

[0252] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0253] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0254] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the off-grid hydrogen production system energy storage capacity optimization method in the above embodiments.

[0255] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), Erasable Programmable Read Only Memory (EPROM), optical fiber, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0256] The aforementioned computer-readable storage medium may be included in the off-grid hydrogen production system energy storage capacity optimization device; or it may exist independently and not be installed in the off-grid hydrogen production system energy storage capacity optimization device.

[0257] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the off-grid hydrogen production system energy storage capacity optimization device, the device performs the following actions: inputs target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy, and hydrogen production rate. The off-grid hydrogen production system output model includes a wind farm output model, a photovoltaic farm output model, an energy storage farm output model, and an electrolyzer output model. It then calculates the voltage support coefficient based on the wind power, photovoltaic power, energy storage potential, and AC bus parameters. Finally, it determines a dual-objective optimization target based on the energy storage energy, the hydrogen production rate, the voltage support coefficient, and cost parameters. Finally, it determines the target energy storage capacity optimization configuration based on the dual-objective optimization target and an improved non-dominated sorting genetic strategy.

[0258] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0259] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0260] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0261] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described off-grid hydrogen production system energy storage capacity optimization method. This solves the technical problem of balancing economy and voltage stability during energy storage capacity configuration in an off-grid hydrogen production system. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the off-grid hydrogen production system energy storage capacity optimization method provided in the above embodiments, and will not be elaborated upon here.

[0262] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the off-grid hydrogen production system energy storage capacity optimization method described above.

[0263] The computer program product provided in this application can solve the technical problem of balancing economy and voltage stability in the energy storage capacity configuration process of off-grid hydrogen production systems. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the off-grid hydrogen production system energy storage capacity optimization method provided in the above embodiments, and will not be repeated here.

[0264] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for optimizing the energy storage capacity of an off-grid hydrogen production system, characterized in that, The method includes: The target weather data and station operation rules are input into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy and hydrogen production rate. The off-grid hydrogen production system output model includes wind farm output model, photovoltaic farm output model, energy storage farm output model and electrolyzer output model. The voltage support coefficient is calculated based on the wind power, the photovoltaic power, the energy storage potential, and the AC bus parameters. The dual-objective optimization objective is determined based on the energy storage capacity, the hydrogen production rate, the voltage support coefficient, and the cost parameters. The optimal configuration of target energy storage capacity is determined based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy.

2. The method of claim 1, wherein, The step of determining the optimal configuration of the target energy storage capacity based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy includes: Decision variables are determined based on the aforementioned dual-objective optimization objective; An initial population is constructed based on chaotic initialization, and the initial population is mapped to the decision variable space of the decision variables according to chaotic mapping to obtain the initial parent population; A target offspring population is determined from the initial parent population according to an adaptive feasible mutation strategy, and a temporary population is determined based on the initial parent population and the target offspring population. The target energy storage capacity optimization configuration is determined in the temporary population based on an improved non-dominated sorting genetic strategy.

3. The method of claim 2, wherein, The steps for determining the offspring population from the parent population according to the adaptive feasible mutation strategy include: The variation distribution index is calculated based on the iteration parameters and distribution index of the initial parent population; The variation disturbance term is calculated based on the variation distribution index; The initial parent population is mutated according to the adaptive feasible mutation strategy and the mutation perturbation term to generate candidate solutions. The decision variable boundary constraints and general inequality constraints of the candidate solutions are processed by the truncation boundary strategy and the linear backtracking strategy, respectively, to obtain the offspring population.

4. The method as described in claim 2, characterized in that, The step of determining the optimal configuration of the target energy storage capacity in the temporary population according to the improved non-dominated sorting genetic strategy includes: The temporary population is subjected to non-dominated sorting according to the improved non-dominated sorting genetic strategy to obtain the non-dominated hierarchy and the crowding degree of the individuals corresponding to the non-dominated hierarchy. Individuals are added to the preset parent population in sequence according to the order of the non-dominant hierarchy to obtain the target population size. When the target population size is greater than a preset size threshold, individuals are determined to be retained based on the individual crowding, and the target population is determined based on the retained individuals. When the target population meets the preset iteration termination condition, the Pareto solution set is determined, and the target energy storage capacity optimization configuration is determined based on the Pareto solution set.

5. The method as described in claim 1, characterized in that, Cost parameters include discount rate, investment period, unit energy investment cost of energy storage, equipment investment capacity, equipment investment cost, and equipment operation and maintenance cost; The step of determining the dual-objective optimization objective based on the energy storage capacity, the hydrogen production rate, the voltage support coefficient, and the cost parameters includes: The annualized investment cost is calculated based on the discount rate, the investment period, the energy storage capacity, the energy storage unit energy investment cost, the equipment investment capacity, and the equipment investment cost. Calculate the annualized operation and maintenance cost based on the equipment investment capacity and the equipment operation and maintenance cost; Calculate the annualized total cost based on the annualized investment cost and the annualized operation and maintenance cost; Calculate the levelized cost of hydrogen production target based on the annualized total cost and the hydrogen production rate; The voltage stability target is determined based on the voltage support coefficient. The dual-objective optimization objective is determined based on the levelized hydrogen production cost objective and the voltage stability objective.

6. The method as described in claim 1, characterized in that, AC bus parameters include AC equivalent impedance, short-circuit capacity, bus nominal voltage, AC equivalent impedance, and bus nominal voltage; The step of calculating the voltage support coefficient based on the wind power, the photovoltaic power, the energy storage potential, and the AC bus parameters includes: Calculate the short-circuit current of the AC bus based on the energy storage potential and the AC equivalent impedance; Calculate the short-circuit capacity of the grid-type energy storage at the AC bus based on the short-circuit current and the nominal bus voltage. Calculate the total wind and solar power generation based on the wind power and photovoltaic power; The voltage support coefficient is calculated based on the short-circuit capacity and the total power generated by wind and solar power.

7. The method as described in claim 1, characterized in that, Before the step of inputting target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy, and hydrogen production rate, the method further includes: Acquire raw meteorological data from the off-grid hydrogen production system, wherein the raw meteorological data includes wind speed data, light intensity data, and ambient temperature data; The raw meteorological data is preprocessed to obtain the target weather data; Based on the dynamic balance strategy of supply and demand, the collaborative operation logic of wind and solar turbines, energy storage systems and electrolyzers in the off-grid hydrogen production system is formulated to obtain the site operation rules.

8. A device for optimizing the energy storage capacity of an off-grid hydrogen production system, characterized in that, The device includes: The output parameter calculation module is used to input target weather data and station operation rules into the off-grid hydrogen production system output model to obtain wind power, photovoltaic power, energy storage potential, energy storage energy and hydrogen production rate. The off-grid hydrogen production system output model includes wind farm output model, photovoltaic farm output model, energy storage farm output model and electrolyzer output model. The voltage support coefficient calculation module is used to calculate the voltage support coefficient based on the wind power, the photovoltaic power, the energy storage potential, and the AC bus parameters. An optimization objective determination module is used to determine a dual-objective optimization objective based on the energy storage energy, the hydrogen production rate, the voltage support coefficient, and cost parameters. The optimization configuration solution module is used to determine the optimal configuration of the target energy storage capacity based on the dual-objective optimization objective and the improved non-dominated sorting genetic strategy.

9. An energy storage capacity optimization device for an off-grid hydrogen production system, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the method for optimizing the energy storage capacity of an off-grid hydrogen production system as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the steps of the off-grid hydrogen production system energy storage capacity optimization method as described in any one of claims 1 to 7.

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