Optimal configuration method and device for hybrid energy storage system based on wind power hydrogen production

Abstract

The invention relates to an optimal configuration method and device for a hybrid energy storage system based on wind power hydrogen production, and the method comprises the steps: integrating a wind turbine generator, an alkaline electrolytic bath, a hydrogen storage tank, a fuel cell and a storage battery, and carrying out the mathematical modeling of the components, so as to analyze the energy flow conversion relation of each assembly; the wind power distribution is optimized by adopting a VMD algorithm, and the grid-connected stability is ensured; in combination with an improved NSGA-II algorithm, multi-objective optimization is carried out on the capacity of the energy storage system, and economical efficiency and reliability are balanced; and an energy management control strategy is constructed, and efficient operation of the system under different working conditions is realized, so that the wind power utilization rate is improved, and an innovative capacity configuration and operation strategy is provided for the wind power hydrogen production system. Therefore, the problems that the power grid stability is poor due to intermittency and uncertainty of wind power output, and large-scale application of the wind power hydrogen production technology is greatly affected due to the fact that an existing wind power hydrogen production system is unreasonable in system capacity configuration, poor in operation strategy flexibility, one-sided in economical efficiency analysis and the like are solved.

Description

Technical Field

[0001] The present application relates to the field of new energy technologies, and in particular to a method and device for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production. Background Art

[0002] As an important component of clean and renewable energy, wind power is developing rapidly. The installed capacity and power generation of wind power are increasing year by year, and it has become the main body of new power installed capacity.

[0003] However, the intermittent and random characteristics of wind power make it easy to have reverse peak load regulation and wind abandonment when it is connected to the grid, which limits the further development of the wind power industry. To solve these problems, wind power hydrogen production technology came into being. By converting unstable wind power into hydrogen energy storage, it can not only effectively utilize the abandoned wind power, but also improve the peak load regulation capacity and energy utilization efficiency of the power grid. At present, the research on wind power hydrogen production system mainly focuses on improving the energy conversion efficiency of the system, reducing costs, optimizing system configuration, and enhancing the economy and reliability of the system. The wind power hydrogen production system converts wind power into hydrogen storage through water electrolysis hydrogen production equipment, and then converts hydrogen energy back to electrical energy through fuel cells and other equipment, realizing the mutual conversion of electrical energy and hydrogen energy, providing an effective means to solve the problem of wind power access to the grid; in addition, hydrogen energy, as a green energy, has the advantages of high energy density and clean and pollution-free, and is regarded as an important part of the future energy structure.

[0004] However, wind power hydrogen production systems face many challenges in actual operation, such as unreasonable system capacity configuration, inflexible operation strategies, and incomplete economic analysis, which result in the system being unable to fully realize its potential and affect the large-scale application of wind power hydrogen production technology, which needs to be urgently resolved. Summary of the invention

[0005] The present application provides a method and device for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production, in order to solve the problems that the intermittent and uncertain wind power output leads to poor grid stability, and the existing wind power hydrogen production system has unreasonable system capacity configuration, poor operating strategy flexibility, and one-sided economic analysis, which greatly affects the large-scale application of wind power hydrogen production technology.

[0006] The first aspect of the present application provides a method for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production, comprising the following steps: mathematically modeling each system component in a pre-constructed hybrid energy storage system to obtain a mathematical model corresponding to each system component; collecting real-time wind speed and power data of a target wind farm, and generating wind power distribution information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm; constructing a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and the wind power distribution information, and solving the multi-objective optimization model through a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, and optimizing the capacity configuration result according to a preset energy management and control strategy to obtain capacity configuration optimization results under different working conditions, and optimizing the hybrid energy storage system using the capacity configuration optimization result.

[0007] Optionally, in one embodiment of the present application, mathematical modeling is performed on each system component in the pre-constructed hybrid energy storage system to obtain a mathematical model corresponding to each system component, including: constructing the hybrid energy storage system through a preset wind turbine, an alkaline electrolyzer, a high-pressure gaseous hydrogen storage tank, a proton exchange membrane fuel cell and a battery, and determining specification information and parameter information of each system component; based on the specification information and the parameter information, establishing a mathematical model of each system component.

[0008] Optionally, in one embodiment of the present application, the wind power distribution information corresponding to the hybrid energy storage system is generated based on the real-time wind speed and power data and a preset variational mode decomposition algorithm, including: decomposing the real-time wind speed and power data through the variational mode decomposition algorithm to generate low-frequency components and high-frequency components that meet the preset grid connection requirements; based on the low-frequency component, the high-frequency component and the preset grid-connected power fluctuation limit, determining the corresponding absorption power or release power of the hybrid energy storage system, and generating the wind power distribution information corresponding to the hybrid energy storage system according to the absorption power or the release power.

[0009] Optionally, in one embodiment of the present application, based on the mathematical model and the wind power distribution information, a multi-objective optimization model corresponding to the hybrid energy storage system is constructed, and the multi-objective optimization model is solved by a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, including: iteratively initializing the target population through the NSGA-II algorithm, and calculating the fitness of each individual in the initialized target population, and generating a new population according to the fitness and preset selection, crossover and mutation operation strategies; based on a preset elite strategy, retaining excellent individuals in the new population that meet preset excellent conditions, and iteratively executing the operation of generating a new population and excellent individuals until the NSGA-II algorithm meets the preset number of iterations or convergence criteria to obtain the capacity configuration result corresponding to the hybrid energy storage system.

[0010] Optionally, in one embodiment of the present application, the capacity configuration result is optimized according to a preset energy management and control strategy to obtain capacity configuration optimization results under different operating conditions, including: determining the hydrogen production efficiency characteristics of the alkaline electrolyzer and the charge and discharge characteristics of the battery, and constructing an energy management and control strategy for the hybrid energy storage system based on the hydrogen production efficiency characteristics and the charge and discharge characteristics; determining the target operating range of the alkaline electrolyzer and the charge and discharge power limit information of the fuel cell and the battery, and based on the energy management and control strategy, the actual grid demand of the target wind farm, the target operating range and the charge and discharge power limit information, dynamically adjusting the working states of the alkaline electrolyzer, the fuel cell and the battery to obtain capacity configuration optimization results under the different operating conditions.

[0011] The second aspect of the present application provides an optimization configuration device for a hybrid energy storage system based on wind power hydrogen production, including: a modeling module, used to perform mathematical modeling on each system component in a pre-constructed hybrid energy storage system to obtain a mathematical model corresponding to each system component; a collection module, used to collect real-time wind speed and power data of a target wind farm, and generate wind power distribution information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm; an optimization configuration module, used to construct a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and the wind power distribution information, and solve the multi-objective optimization model through a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, and optimize the capacity configuration result according to a preset energy management and control strategy to obtain a capacity configuration optimization result under different working conditions, and use the capacity configuration optimization result to optimize the hybrid energy storage system.

[0012] Optionally, in one embodiment of the present application, the modeling module includes: a construction unit, used to construct the hybrid energy storage system through a preset wind turbine, an alkaline electrolyzer, a high-pressure gaseous hydrogen storage tank, a proton exchange membrane fuel cell and a battery, and determine the specification information and parameter information of each system component; an establishment unit, used to establish a mathematical model of each system component based on the specification information and the parameter information.

[0013] Optionally, in one embodiment of the present application, the acquisition module includes: a decomposition unit, used to decompose the real-time wind speed and power data through the variational mode decomposition algorithm to generate low-frequency components and high-frequency components that meet the preset grid connection requirements; a first determination unit, used to determine the corresponding absorption power or release power of the hybrid energy storage system based on the low-frequency component, the high-frequency component and the preset grid-connected power fluctuation limit, and generate the wind power distribution information corresponding to the hybrid energy storage system according to the absorption power or the release power.

[0014] Optionally, in one embodiment of the present application, the optimization configuration module includes: a calculation unit, which is used to iteratively initialize the target population through the NSGA-II algorithm, calculate the fitness of each individual in the initialized target population, and generate a new population based on the fitness and preset selection, crossover and mutation operation strategies; an iteration unit, which is used to retain excellent individuals in the new population that meet preset excellent conditions based on a preset elite strategy, and iteratively execute the operation of generating a new population and excellent individuals until the NSGA-II algorithm meets the preset number of iterations or convergence criteria, so as to obtain the capacity configuration result corresponding to the hybrid energy storage system.

[0015] Optionally, in one embodiment of the present application, the optimization configuration module also includes: a second determination unit, used to determine the hydrogen production efficiency characteristics of the alkaline electrolyzer and the charge and discharge characteristics of the battery, and construct an energy management and control strategy for the hybrid energy storage system based on the hydrogen production efficiency characteristics and the charge and discharge characteristics; a dynamic adjustment unit, used to determine the target operating range of the alkaline electrolyzer and the charge and discharge power limit information of the fuel cell and the battery, and based on the energy management and control strategy, the actual grid demand of the target wind farm, the target operating range and the charge and discharge power limit information, dynamically adjust the working states of the alkaline electrolyzer, the fuel cell and the battery to obtain the capacity configuration optimization results under the different operating conditions.

[0016] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production as described in the above embodiment.

[0017] The fourth aspect of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production.

[0018] The fifth aspect of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production.

[0019] Therefore, the embodiments of the present application have the following beneficial effects:

[0020] The embodiments of the present application can mathematically model each system component in the pre-built hybrid energy storage system to obtain the mathematical model corresponding to each system component; collect the real-time wind speed and power data of the target wind farm, and generate the wind power distribution information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and the preset variational mode decomposition algorithm; construct a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and the wind power distribution information, and solve the multi-objective optimization model through the preset NSGA-II algorithm to obtain the capacity configuration result corresponding to the hybrid energy storage system, and optimize the capacity configuration result according to the preset energy management and control strategy to obtain the capacity configuration optimization result under different working conditions, and use the capacity configuration optimization result to optimize the hybrid energy storage system. The present application comprehensively considers the power generation characteristics of the wind farm and the advantages of hydrogen energy storage to improve the wind power absorption rate, reduce the wind abandonment phenomenon caused by wind power fluctuations, and ultimately achieve efficient conversion and utilization between wind power and hydrogen energy. This solves the problems that the intermittent and uncertain nature of wind power output leads to poor grid stability, and that the existing wind power hydrogen production system has unreasonable system capacity configuration, poor operating strategy flexibility, and one-sided economic analysis, which greatly affect the large-scale application of wind power hydrogen production technology.

[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0023] Figure 1 A flowchart of a method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production provided according to an embodiment of the present application;

[0024] Figure 2A schematic diagram of the logical architecture of a hybrid energy storage system optimization configuration method based on wind power hydrogen production provided for one embodiment of the present application;

[0025] Figure 3 This is an example diagram of an optimization configuration device for a hybrid energy storage system based on wind power hydrogen production according to an embodiment of the present application;

[0026] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0027] Among them, 10-a hybrid energy storage system optimization configuration device based on wind power hydrogen production; 100-a modeling module, 200-a collection module, 300-an optimization configuration module; 401-a memory, 402-a processor, 403-a communication interface. DETAILED DESCRIPTION

[0028] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0029] The following describes the method and device for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production according to an embodiment of the present application with reference to the accompanying drawings. In response to the problems mentioned in the above background technology, the present application provides a method for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production, in which a mathematical model corresponding to each system component in a pre-built hybrid energy storage system is mathematically modeled; real-time wind speed and power data of the target wind farm are collected, and based on the real-time wind speed and power data and a preset variational mode decomposition algorithm, wind power distribution information corresponding to the hybrid energy storage system is generated; based on the mathematical model and the wind power distribution information, a multi-objective optimization model corresponding to the hybrid energy storage system is constructed, and the multi-objective optimization model is solved by a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, and the capacity configuration result is optimized according to a preset energy management and control strategy to obtain a capacity configuration optimization result under different working conditions, and the capacity configuration optimization result is used to optimize the hybrid energy storage system. This application comprehensively considers the power generation characteristics of wind farms and the advantages of hydrogen energy storage to improve the absorption rate of wind power, reduce the phenomenon of wind abandonment caused by wind power fluctuations, and ultimately achieve efficient conversion and utilization between wind power and hydrogen energy. In this way, the intermittent and uncertain output of wind power leads to poor grid stability, and the existing wind power hydrogen production system has unreasonable system capacity configuration, poor operating strategy flexibility, and one-sided economic analysis, which greatly affects the large-scale application of wind power hydrogen production technology.

[0030] Specifically, Figure 1 A flow chart of a method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production provided in an embodiment of the present application.

[0031] like Figure 1 As shown, the hybrid energy storage system optimization configuration method based on wind power hydrogen production includes the following steps:

[0032] In step S101, mathematical modeling is performed on each system component in the pre-built hybrid energy storage system to obtain a mathematical model corresponding to each system component.

[0033] The embodiments of the present application can first integrate key components such as wind turbines, alkaline electrolyzers, high-pressure gaseous hydrogen storage tanks, proton exchange membrane fuel cells and batteries into a hybrid energy storage system. Each component is carefully designed to ensure efficient energy conversion and storage in the entire system.

[0034] Wind turbines are responsible for converting wind energy into electrical energy, alkaline electrolyzers use this electricity to produce hydrogen by electrolyzing water, high-pressure gaseous hydrogen storage tanks are used to store the produced hydrogen, proton exchange membrane fuel cells convert the stored hydrogen back into electrical energy for use, and batteries provide the necessary power buffer to cope with the volatility of wind power.

[0035] To ensure the efficient operation of the hybrid energy storage system, the embodiment of the present application establishes a set of detailed mathematical models, including a power output model of the wind turbine, a hydrogen production efficiency model of the electrolyzer, a storage model of the hydrogen storage tank, a power generation model of the fuel cell, and a charge and discharge model of the battery. The above models not only consider the physical properties of each component, but also their operating characteristics and the energy flow conversion relationship between each other, providing a solid theoretical basis for subsequent system optimization configuration and operation strategies.

[0036] Optionally, in one embodiment of the present application, mathematical modeling is performed on each system component in a pre-constructed hybrid energy storage system to obtain a mathematical model corresponding to each system component, including: constructing a hybrid energy storage system through a preset wind turbine, an alkaline electrolyzer, a high-pressure gaseous hydrogen storage tank, a proton exchange membrane fuel cell and a battery, and determining the specification information and parameter information of each system component; based on the specification information and parameter information, establishing a mathematical model of each system component.

[0037] It should be noted that the design of a hybrid energy storage system based on wind power hydrogen production involves the integration of wind turbines, alkaline electrolyzers, high-pressure gaseous hydrogen storage tanks, proton exchange membrane fuel cells and batteries.

[0038] In the actual implementation process, the embodiments of the present application first need to determine the specifications and parameters of each component, such as the rated power of the wind turbine, the electrolysis efficiency of the electrolyzer, the capacity and pressure level of the hydrogen storage tank, the power generation efficiency of the fuel cell, and the charge and discharge characteristics of the battery; secondly, establish a mathematical model of each component, including the relationship between wind speed and power output, the relationship between the current density of the electrolyzer and the hydrogen production, the relationship between the pressure and capacity of the hydrogen storage tank, the relationship between the hydrogen consumption and the power output of the fuel cell, and the relationship between the charge state of the battery and the charge and discharge power. The above model is used to simulate the performance of the system under different working conditions and provide a theoretical basis for subsequent optimization configuration and control strategies.

[0039] In addition, the embodiment of the present application can also verify and adjust the model through the simulation software MATLAB / Simulink to ensure the accuracy and reliability of the model; at the same time, considering the randomness and volatility of wind power, the model also includes a probability distribution of wind speed and a prediction model of wind power, so as to more accurately simulate the output characteristics of the wind farm.

[0040] Therefore, the embodiments of the present application construct a hybrid energy storage system integrating a wind turbine, an alkaline electrolyzer, a high-pressure gaseous hydrogen storage tank, a proton exchange membrane fuel cell and a battery, and establish a detailed mathematical model of each component, thereby achieving an in-depth analysis of the energy flow conversion relationship of the system, and providing a solid theoretical basis for optimizing configuration and operation strategies.

[0041] In step S102, real-time wind speed and power data of the target wind farm are collected, and based on the real-time wind speed and power data and a preset variational mode decomposition algorithm, wind power allocation information corresponding to the hybrid energy storage system is generated.

[0042] Furthermore, if Figure 2 As shown, the embodiment of the present application can also perform a wind power allocation method based on a variational mode decomposition (VMD) algorithm.

[0043] It should be noted that the VMD algorithm can decompose the wind power signal into multiple modal components with different center frequencies, thereby effectively separating the power component that meets the grid connection requirements and the power fluctuation that needs to be smoothed by the energy storage system.

[0044] By comparing the performance of the VMD algorithm with the Empirical Mode Decomposition (EMD) algorithm, the advantages of the VMD algorithm in the embodiment of the present application in wind power smoothing are effectively verified, especially in avoiding spectrum aliasing and improving decomposition accuracy, so that the power allocation strategy can ensure the stability of wind power grid-connected power, while providing a clear power smoothing task for the hybrid energy storage system.

[0045] Optionally, in one embodiment of the present application, based on real-time wind speed and power data and a preset variational mode decomposition algorithm, wind power distribution information corresponding to the hybrid energy storage system is generated, including: decomposing the real-time wind speed and power data through a variational mode decomposition algorithm to generate low-frequency components and high-frequency components that meet preset grid connection requirements; based on the low-frequency components, high-frequency components and a preset grid-connected power fluctuation limit, determining the corresponding absorption power or release power of the hybrid energy storage system, and generating the wind power distribution information corresponding to the hybrid energy storage system according to the absorption power or release power.

[0046] As an achievable manner, the embodiment of the present application may implement a wind power allocation strategy based on a variational modal decomposition algorithm, which can decompose a wind power signal into multiple modal components with different frequencies.

[0047] Specifically, the embodiments of the present application can first collect real-time wind speed and power data of the wind farm, and then apply the VMD algorithm to decompose the wind power signal to identify the low-frequency components that meet the grid connection requirements and the high-frequency components that need to be smoothed by the energy storage system; secondly, the embodiments of the present application determine the power that needs to be absorbed or released by the energy storage system by setting a reasonable grid-connected power fluctuation limit.

[0048] It can be understood that by comparing the performance of the VMD algorithm and the EMD, the embodiments of the present application can further optimize the algorithm parameters and improve the accuracy and efficiency of power allocation; in the specific implementation process, the embodiments of the present application also need to consider the real-time changes in wind power and the scheduling requirements of the power grid to ensure the stability and economy of wind power grid connection, so as to maximize the use of wind power resources while reducing the impact on the power grid.

[0049] Therefore, the embodiment of the present application can effectively avoid spectrum aliasing and improve the accuracy of power decomposition through a wind power allocation method based on a variational mode decomposition algorithm, thereby achieving smooth access to the grid for wind power and improving the friendliness of wind power grid connection.

[0050] In step S103, based on the mathematical model and wind power distribution information, a multi-objective optimization model corresponding to the hybrid energy storage system is constructed, and the multi-objective optimization model is solved by the preset NSGA-II algorithm to obtain the capacity configuration result corresponding to the hybrid energy storage system, and the capacity configuration result is optimized according to the preset energy management and control strategy to obtain the capacity configuration optimization results under different working conditions, and the hybrid energy storage system is optimized using the capacity configuration optimization results.

[0051] Furthermore, the embodiments of the present application also need to establish a multi-objective optimization model to achieve the goals of minimizing the overall system cost and maximizing the system reliability. The model comprehensively considers the conditions of the wind farm, the requirements of the power grid, and the characteristics of the energy storage system, and optimizes the capacity of the energy storage system by adopting an improved non-dominated sorting genetic algorithm (NSGA-II).

[0052] It should be noted that the improved NSGA-II algorithm can improve the algorithm's search capability and convergence speed by introducing new selection mechanisms, crossover strategies and mutation strategies, thereby finding the optimal balance between economy and reliability; in addition, the embodiments of the present application also take into account the uncertainty and volatility of wind power, and adapt to different wind farm conditions and grid requirements by optimizing the configuration model to ensure that the system can operate stably under various conditions.

[0053] Afterwards, in order to achieve optimal operation of the wind power hydrogen production hybrid energy storage system under different working conditions, the embodiment of the present application formulates a detailed energy management control strategy based on the hydrogen production efficiency characteristics of the electrolyzer and the charging and discharging characteristics of the battery to optimize the power distribution of the hybrid energy storage unit.

[0054] During the actual implementation process, by real-time monitoring of the system's operating status, the control strategy can quickly respond to changes in wind power, reasonably allocate the workload of the electrolyzer and the battery, and ensure stable power supply to the system; in addition, the embodiments of the present application take into account the real-time response capability and long-term stability of the system, and through sophisticated energy management, ensure the safe and efficient operation of the system under various conditions, while also effectively improving the economy of the system.

[0055] Optionally, in one embodiment of the present application, a multi-objective optimization model corresponding to the hybrid energy storage system is constructed based on the mathematical model and wind power distribution information, and the multi-objective optimization model is solved by a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, including: iteratively initializing the target population through the NSGA-II algorithm, and calculating the fitness of each individual in the initialized target population, and generating a new population based on the fitness and preset selection, crossover and mutation operation strategies; based on the preset elite strategy, retaining excellent individuals in the new population that meet preset excellent conditions, and iteratively executing the operation of generating a new population and excellent individuals until the NSGA-II algorithm meets the preset number of iterations or convergence criteria to obtain the capacity configuration result corresponding to the hybrid energy storage system.

[0056] It should be noted that the implementation of capacity configuration optimization of hybrid energy storage systems involves the establishment of a multi-objective optimization model, including the system's initial investment cost, operation and maintenance cost, wind curtailment cost, and carbon emission cost.

[0057] The embodiment of the present application may use an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the model. Specifically, the present application may first initialize the population and calculate the fitness of each individual in the population; then perform selection, crossover and mutation operations to generate a new population; secondly, the embodiment of the present application may apply an elite strategy to retain excellent individuals; thereafter, the embodiment of the present application may repeat the above process until the number of iterations or convergence criteria are met.

[0058] In the solution process, the embodiment of the present application needs to consider the uncertainty and volatility of wind power, as well as the dynamic response characteristics of the energy storage system. Through optimization, the Pareto optimal solution set of different energy storage configuration schemes can be obtained, providing decision makers with multiple options. In the specific implementation process, the embodiment of the present application also includes a sensitivity analysis of the optimization results to evaluate the impact of different parameter changes on system performance.

[0059] Therefore, the embodiments of the present application innovatively apply an improved non-dominated sorting genetic algorithm (NSGA-II) to optimize the capacity of the energy storage system and take into account the economy and reliability of the system, thereby achieving adaptive configuration under different wind farm conditions and grid requirements.

[0060] Optionally, in one embodiment of the present application, the capacity configuration results are optimized according to a preset energy management and control strategy to obtain capacity configuration optimization results under different operating conditions, including: determining the hydrogen production efficiency characteristics of the alkaline electrolyzer and the charge and discharge characteristics of the battery, and constructing an energy management and control strategy for the hybrid energy storage system based on the hydrogen production efficiency characteristics and the charge and discharge characteristics; determining the target operating range of the alkaline electrolyzer and the charge and discharge power limit information of the fuel cell and the battery, and dynamically adjusting the working states of the alkaline electrolyzer, the fuel cell and the battery based on the energy management and control strategy, the actual grid demand of the target wind farm, the target operating range and the charge and discharge power limit information to obtain capacity configuration optimization results under different operating conditions.

[0061] It should be noted that the implementation of the energy management and control strategy in the embodiments of the present application is intended to ensure the optimal operation of the wind power hydrogen production system under different operating conditions.

[0062] During the specific implementation process, the embodiment of the present application can formulate a power allocation strategy for the hybrid energy storage unit according to the hydrogen production efficiency characteristics of the alkaline electrolyzer and the charge and discharge characteristics of the battery, and determine the optimal working range of the electrolyzer and the charge and discharge power limit of the battery.

[0063] The embodiments of the present application can dynamically adjust the working status of the electrolyzer, fuel cell and battery according to the real-time changes of wind power and the demand of the power grid by designing an energy management control strategy. When the wind power is in excess, the hydrogen production power of the electrolyzer is increased; when the wind power is insufficient, the fuel cell is started to discharge or the battery is started to supplement the power. In the actual implementation process, the embodiments of the present application need to monitor the system status in real time, such as the hydrogen storage capacity of the hydrogen storage tank, the SOC of the battery, etc., to ensure the safety and stable operation of the system; therefore, the embodiments of the present application can maximize the utilization rate of wind power, reduce wind abandonment and ensure the power supply quality of the power grid through dynamic energy management.

[0064] Therefore, the embodiments of the present application develop a hybrid energy storage unit power allocation strategy based on the hydrogen production efficiency characteristics of the electrolyzer and the charging and discharging characteristics of the battery, and perform real-time monitoring and dynamic adjustment to optimize the operation of the system under different operating conditions, thereby improving the economy and power supply reliability of the system.

[0065] According to the hybrid energy storage system optimization configuration method based on wind power hydrogen production proposed in the embodiment of the present application, by integrating wind turbines, alkaline electrolyzers, hydrogen storage tanks, fuel cells and batteries, and mathematically modeling them, the energy flow conversion relationship of each component is analyzed; the VMD algorithm is used to optimize wind power distribution to ensure grid-connected stability; combined with the improved NSGA-II algorithm, the capacity of the energy storage system is optimized for multiple objectives to balance economy and reliability; an energy management control strategy is constructed to achieve efficient operation of the system under different working conditions, thereby improving wind power utilization and providing innovative capacity configuration and operation strategies for wind power hydrogen production systems.

[0066] Secondly, the optimized configuration device of the hybrid energy storage system based on wind power hydrogen production proposed in the embodiment of the present application is described with reference to the accompanying drawings.

[0067] Figure 3 It is a block diagram of an optimized configuration device of a hybrid energy storage system based on wind power hydrogen production according to an embodiment of the present application.

[0068] like Figure 3 As shown, the hybrid energy storage system optimization configuration device 10 based on wind power hydrogen production includes: a modeling module 100, a collection module 200 and an optimization configuration module 300.

[0069] The modeling module 100 is used to perform mathematical modeling on each system component in the pre-built hybrid energy storage system to obtain a mathematical model corresponding to each system component.

[0070] The acquisition module 200 is used to acquire the real-time wind speed and power data of the target wind farm, and generate wind power allocation information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm.

[0071] The optimization configuration module 300 is used to construct a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and wind power distribution information, and solve the multi-objective optimization model through the preset NSGA-II algorithm to obtain the capacity configuration result corresponding to the hybrid energy storage system, and optimize the capacity configuration result according to the preset energy management and control strategy to obtain the capacity configuration optimization result under different working conditions, and use the capacity configuration optimization result to optimize the configuration of the hybrid energy storage system.

[0072] Optionally, in one embodiment of the present application, the modeling module 100 includes: a construction unit and a creation unit.

[0073] Among them, the construction unit is used to build a hybrid energy storage system through preset wind turbines, alkaline electrolyzers, high-pressure gaseous hydrogen storage tanks, proton exchange membrane fuel cells and batteries, and determine the specification information and parameter information of each system component.

[0074] The building unit is used to build a mathematical model of each system component based on the specification information and the parameter information.

[0075] Optionally, in one embodiment of the present application, the acquisition module 200 includes: a decomposition unit and a first determination unit.

[0076] Among them, the decomposition unit is used to decompose the real-time wind speed and power data through a variational mode decomposition algorithm to generate low-frequency components and high-frequency components that meet the preset grid connection requirements.

[0077] The first determination unit is used to determine the absorption power or release power corresponding to the hybrid energy storage system based on the low-frequency component, the high-frequency component and the preset grid-connected power fluctuation limit, and generate wind power allocation information corresponding to the hybrid energy storage system according to the absorption power or the release power.

[0078] Optionally, in one embodiment of the present application, the optimization configuration module 300 includes: a calculation unit and an iteration unit.

[0079] The calculation unit is used to iteratively initialize the target population through the NSGA-II algorithm, calculate the fitness of each individual in the initialized target population, and generate a new population according to the fitness and preset selection, crossover and mutation operation strategies.

[0080] The iteration unit is used to retain the excellent individuals that meet the preset excellent conditions in the new population based on the preset elite strategy, and iteratively execute the operation of generating new populations and excellent individuals until the NSGA-II algorithm meets the preset number of iterations or convergence criteria, so as to obtain the capacity configuration result corresponding to the hybrid energy storage system.

[0081] Optionally, in one embodiment of the present application, the optimization configuration module 300 further includes: a second determination unit and a dynamic adjustment unit.

[0082] Among them, the second determination unit is used to determine the hydrogen production efficiency characteristics of the alkaline electrolyzer and the charge and discharge characteristics of the battery, and to construct an energy management and control strategy for the hybrid energy storage system based on the hydrogen production efficiency characteristics and the charge and discharge characteristics.

[0083] The dynamic adjustment unit is used to determine the target working range of the alkaline electrolyzer and the charge and discharge power limit information of the fuel cell and the battery, and dynamically adjust the working states of the alkaline electrolyzer, the fuel cell and the battery based on the energy management and control strategy, the actual grid demand of the target wind farm, the target working range and the charge and discharge power limit information to obtain the capacity configuration optimization results under different working conditions.

[0084] It should be noted that the aforementioned explanation of the embodiment of the method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production is also applicable to the device for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production in this embodiment, and will not be repeated here.

[0085] According to the embodiment of the present application, the hybrid energy storage system optimization configuration device based on wind power hydrogen production includes a modeling module for mathematically modeling each system component in the pre-built hybrid energy storage system to obtain a mathematical model corresponding to each system component; a collection module for collecting real-time wind speed and power data of the target wind farm, and generating wind power distribution information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm; an optimization configuration module for constructing a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and the wind power distribution information, and solving the multi-objective optimization model through a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, and optimizing the capacity configuration result according to a preset energy management and control strategy to obtain a capacity configuration optimization result under different working conditions, and using the capacity configuration optimization result to optimize the hybrid energy storage system. The present application comprehensively considers the power generation characteristics of the wind farm and the advantages of hydrogen energy storage to improve the wind power consumption rate, reduce the wind abandonment phenomenon caused by wind power fluctuations, and ultimately achieve efficient conversion and utilization between wind power and hydrogen energy.

[0086] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0087] Memory 401 , processor 402 , and a computer program stored in the memory 401 and executable on the processor 402 .

[0088] When the processor 402 executes the program, the method for optimizing configuration of the hybrid energy storage system based on wind power hydrogen production provided in the above embodiment is implemented.

[0089] Furthermore, the electronic device further comprises:

[0090] The communication interface 403 is used for communication between the memory 401 and the processor 402 .

[0091] The memory 401 is used to store computer programs that can be executed on the processor 402 .

[0092] The memory 401 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0093] If the memory 401, the processor 402 and the communication interface 403 are implemented independently, the communication interface 403, the memory 401 and the processor 402 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 4 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0094] Optionally, in a specific implementation, if the memory 401, the processor 402 and the communication interface 403 are integrated on a chip, the memory 401, the processor 402 and the communication interface 403 can communicate with each other through an internal interface.

[0095] The processor 402 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0096] The embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production.

[0097] The embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production.

[0098] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0099] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0100] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0101] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0102] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0103] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0105] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for optimizing the configuration of a hybrid energy storage system based on wind power hydrogen production, characterized in that: The following steps are involved: Performing mathematical modeling on each system component in the pre-built hybrid energy storage system to obtain a mathematical model corresponding to each system component; Collecting real-time wind speed and power data of the target wind farm, and generating wind power allocation information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm; Based on the mathematical model and the wind power distribution information, a multi-objective optimization model corresponding to the hybrid energy storage system is constructed, and the multi-objective optimization model is solved by a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, and the capacity configuration result is optimized according to a preset energy management and control strategy to obtain capacity configuration optimization results under different working conditions, and the hybrid energy storage system is optimized using the capacity configuration optimization result.

2. The method according to claim 1, characterized in that The mathematical modeling of each system component in the pre-built hybrid energy storage system to obtain a mathematical model corresponding to each system component includes: Constructing the hybrid energy storage system through a preset wind turbine, an alkaline electrolyzer, a high-pressure gaseous hydrogen storage tank, a proton exchange membrane fuel cell and a battery, and determining specification information and parameter information of each system component; A mathematical model of each system component is established based on the specification information and the parameter information.

3. The method according to claim 2, characterized in that The generating of wind power distribution information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm includes: Decomposing the real-time wind speed and power data by the variational mode decomposition algorithm to generate low-frequency components and high-frequency components that meet the preset grid connection requirements; Based on the low-frequency component, the high-frequency component and the preset grid-connected power fluctuation limit, the corresponding absorption power or release power of the hybrid energy storage system is determined, and the wind power allocation information corresponding to the hybrid energy storage system is generated according to the absorption power or the release power.

4. The method according to claim 3, characterized in that The method of constructing a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and the wind power distribution information, and solving the multi-objective optimization model by a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system includes: Iteratively initialize the target population through the NSGA-II algorithm, calculate the fitness of each individual in the initialized target population, and generate a new population according to the fitness and preset selection, crossover and mutation operation strategies; Based on the preset elite strategy, the excellent individuals in the new population that meet the preset excellent conditions are retained, and the operation of generating a new population and excellent individuals is iteratively executed until the NSGA-II algorithm meets the preset number of iterations or convergence criteria, so as to obtain the capacity configuration result corresponding to the hybrid energy storage system.

5. The method according to claim 4, characterized in that Optimizing the capacity configuration result according to the preset energy management and control strategy to obtain the capacity configuration optimization results under different working conditions includes: Determining the hydrogen production efficiency characteristics of the alkaline electrolyzer and the charge and discharge characteristics of the battery, and constructing an energy management and control strategy for the hybrid energy storage system based on the hydrogen production efficiency characteristics and the charge and discharge characteristics; The target operating range of the alkaline electrolyzer and the charge and discharge power limit information of the fuel cell and the battery are determined, and based on the energy management and control strategy, the actual grid demand of the target wind farm, the target operating range and the charge and discharge power limit information, the operating states of the alkaline electrolyzer, the fuel cell and the battery are dynamically adjusted to obtain capacity configuration optimization results under the different operating conditions.

6. A hybrid energy storage system optimization configuration device based on wind power hydrogen production, characterized in that: include: A modeling module, used for mathematically modeling each system component in the pre-built hybrid energy storage system to obtain a mathematical model corresponding to each system component; A collection module, used to collect real-time wind speed and power data of the target wind farm, and generate wind power allocation information corresponding to the hybrid energy storage system based on the real-time wind speed and power data and a preset variational mode decomposition algorithm; An optimization configuration module is used to construct a multi-objective optimization model corresponding to the hybrid energy storage system based on the mathematical model and the wind power distribution information, and solve the multi-objective optimization model through a preset NSGA-II algorithm to obtain a capacity configuration result corresponding to the hybrid energy storage system, and optimize the capacity configuration result according to a preset energy management and control strategy to obtain capacity configuration optimization results under different working conditions, and use the capacity configuration optimization result to optimize the configuration of the hybrid energy storage system.

7. The device according to claim 6, characterized in that The modeling module includes: A construction unit, used to construct the hybrid energy storage system through a preset wind turbine, an alkaline electrolyzer, a high-pressure gaseous hydrogen storage tank, a proton exchange membrane fuel cell and a battery, and determine specification information and parameter information of each system component; A building unit is used to build a mathematical model of each system component based on the specification information and the parameter information.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production as described in any one of claims 1 to 5.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production as described in any one of claims 1 to 5.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for optimizing configuration of a hybrid energy storage system based on wind power hydrogen production as described in any one of claims 1 to 5.

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