Method and system for operation optimization of electric-hydrogen-ammonia hybrid energy storage system based on multi-scale load

Abstract

The application belongs to the field of energy storage system optimization, and provides a multi-scale load-based electricity-hydrogen-ammonia hybrid energy storage system operation optimization method and system. The annual net load sequence is calculated according to the original source load data, the power spectrum density analysis is performed on the annual net load sequence, and the system significant period is determined. The annual net load sequence is decomposed into time series with the system significant period as the period length, and the trend component, the periodic component and the residual component of the whole year are obtained. Based on the trend component and the periodic component of the whole year, the hydrogen-ammonia energy storage long-time scale energy optimization model is used for optimization, and the hydrogen-ammonia energy storage state change quantity of each system significant period is obtained. The electricity-hydrogen-ammonia hybrid energy storage collaborative optimization operation in multiple system significant periods is performed with the hydrogen-ammonia energy storage state change quantity of each system significant period as the boundary condition, and the hourly operation output of the electricity-hydrogen-ammonia hybrid energy storage system is obtained. The application can reduce the calculation complexity of the annual hybrid integer optimization problem.

Description

Technical Field

[0001] This invention belongs to the field of energy storage system optimization technology, specifically relating to an operation optimization method and system for an electric-hydrogen-ammonia hybrid energy storage system based on multi-scale load. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the increasing penetration of renewable energy sources such as wind and solar power, the power system exhibits multi-timescale fluctuation characteristics, including long-term energy mismatches on a seasonal scale, periodic fluctuations on a weekly scale, and random disturbances on an hourly scale. Traditional operation modes that rely solely on short-cycle energy storage are insufficient to balance economic efficiency and reliability. Hybrid energy storage systems using hydrogen electrolysis, ammonia synthesis, and storage enable the conversion and transfer of electrical energy across different time scales and energy carriers. Hydrogen storage offers rapid response and is suitable for medium-cycle regulation, while ammonia storage boasts high energy density and a long storage period, making it suitable for long-term cross-seasonal regulation. Therefore, this technology has become a crucial pathway to support the high-proportion consumption of renewable energy and long-term energy balance.

[0004] However, existing research and engineering applications mostly target single-cycle energy storage or electro-hydrogen energy storage, with operational optimization primarily based on a single hourly scale. They distinguish between long- and short-cycle energy storage only by the energy storage state regression cycle, failing to accurately reflect the energy balance across multiple time scales throughout the year. This leads to a blurred division of labor between hydrogen and ammonia energy storage, with long- and medium-duration energy storage frequently participating in short-term intraday charging and discharging, failing to fully leverage the multi-time scale characteristics of hybrid energy storage and lacking an effective multi-time scale coordination mechanism. Furthermore, existing technologies often employ decomposition methods such as STL, relying on artificially preset fixed periods, such as 24 hours as a daily period or 168 hours as a weekly period. In microgrids with complex and variable source-load structures, such rigid decompositions based on prior experience are prone to distortion, failing to accurately identify the true cycle of actual energy fluctuations, causing subsequent energy storage configuration and operational optimization to deviate from physical characteristics. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes an operation optimization method and system for a hybrid electric-hydrogen-ammonia energy storage system based on multi-scale loads. This method can optimize operation while meeting the energy balance requirements of the system across multiple time scales, thereby improving the system's multi-time scale energy balance capability and energy storage utilization rate.

[0006] According to some embodiments, the first aspect of the present invention provides an operation optimization method for an electro-hydrogen-ammonia hybrid energy storage system based on multi-scale load, employing the following technical solution: An operation optimization method for a hybrid electric-hydrogen-ammonia energy storage system based on multi-scale loads includes: The annual net load sequence is calculated based on the original source load data. Power spectral density analysis is performed on the annual net load sequence to determine the significant period of the system. The annual net load series is decomposed using the system's significant period as the period length to obtain the annual trend component, periodic component, and residual component. Based on the trend and periodic components throughout the year, the hydrogen-ammonia energy storage long-term energy optimization model is used for optimization to obtain the hydrogen-ammonia energy storage state change for each system in a significant period. Using the change in the state of hydrogen-ammonia energy storage during each significant period of the system as the boundary condition, the collaborative optimization operation of the hybrid electric-hydrogen-ammonia energy storage system within the significant periods of multiple systems is carried out to obtain the hourly operating output of the hybrid electric-hydrogen-ammonia energy storage system.

[0007] Furthermore, the step of calculating the annual net load sequence based on the original source-load data, performing power spectral density analysis on the annual net load sequence, and determining the system's significant period includes: The net load for each hour is calculated based on the original source load data, and the combined data yields the annual net load sequence. Power spectral density analysis was performed on the annual net load sequence to obtain the power spectral density; a set of periods was constructed based on the power spectral density, and the time period with the largest energy proportion was selected as the significant period of the system.

[0008] Furthermore, the hydrogen-ammonia energy storage long-term energy optimization model, based on the annual trend and periodic components, is used to obtain the hydrogen-ammonia energy storage state change for each significant period of the system, including: Based on the trend component and periodic component throughout the year, the summation is performed on all hours within each system's significant period to obtain the trend component and seasonal component of each system's significant period. Using an annual cycle and the system's significant cycle as the time step, and constrained by the trend and seasonal components of each system's significant cycle, the hydrogen-ammonia energy storage long-term energy optimization model is optimized to obtain the state change of hydrogen-ammonia energy storage within each system's significant cycle.

[0009] Furthermore, the long-term energy optimization model for hydrogen-ammonia energy storage includes a long-term energy optimization objective function for hydrogen energy storage and a long-term energy optimization objective function for ammonia energy storage. The long-term energy optimization objective function for hydrogen energy storage aims to minimize the seasonal imbalance throughout the year. The objective function for long-term energy optimization of ammonia energy storage aims to minimize the imbalance of the annual trend component.

[0010] Furthermore, the method of using the change in the state of hydrogen-ammonia energy storage during each significant cycle of the system as boundary conditions to perform coordinated optimization operation of the hybrid hydrogen-ammonia energy storage across multiple significant cycles includes: The change in the hydrogen and ammonia energy storage state during each significant period of the system is used as the boundary condition, the significant period is used as the total operating period, and the time step is used as the hour. The collaborative optimization operation model of the hybrid energy storage system (electricity, hydrogen, and ammonia) was used to optimize the operation of the hybrid energy storage system over a significant period of time for multiple systems, and the hourly operating output of the hybrid energy storage system was obtained.

[0011] Furthermore, the proposed hybrid energy storage synergistic optimization operation model aims to minimize operating costs over a significant period of the system's lifecycle. The constraints include system equipment model constraints, electrical power balance constraints, hydrogen power balance constraints, ammonia power balance constraints, purchased and sold electrical power constraints, and hydrogen and ammonia energy storage state constraints.

[0012] According to some embodiments, the second aspect of the present invention provides an operation optimization system for a hybrid energy storage system based on multi-scale load, employing the following technical solution: An operation optimization system for a hybrid electric-hydrogen-ammonia energy storage system based on multi-scale loads includes: The net load sequence analysis module is used to calculate the annual net load sequence based on the original source load data, perform power spectral density analysis on the annual net load sequence, and determine the significant periodicity of the system. The time series decomposition module is used to decompose the annual net load series using the system's significant period as the period length, to obtain the annual trend component, periodic component, and residual component. The long-term energy optimization module is used to optimize the hydrogen-ammonia energy storage long-term energy optimization model based on the annual trend component and periodic component, and obtain the hydrogen-ammonia energy storage state change of each system for a significant period. The cycle-coordinated optimization operation module is used to perform coordinated optimization operation of the hybrid electric-hydrogen-ammonia energy storage system within multiple significant cycles, using the change in the state of hydrogen-ammonia energy storage in each significant cycle as the boundary condition, and to obtain the hourly operating output of the hybrid electric-hydrogen-ammonia energy storage system.

[0013] According to some embodiments, a third aspect of the present invention provides a computer-readable storage medium.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the operation optimization method for an electro-hydrogen-ammonia hybrid energy storage system based on multi-scale loads as described in the first embodiment above.

[0015] According to some embodiments, a fourth aspect of the present invention provides a computer device.

[0016] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the operation optimization method for a multi-scale load-based hybrid energy storage system of hydrogen and ammonia as described in the first embodiment above.

[0017] According to some embodiments, a fifth aspect of the present invention provides a computer program product or computer program.

[0018] A computer program product or computer program includes computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the operation optimization method for a multi-scale load-based hybrid energy storage system as described in the first embodiment above.

[0019] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention addresses the technical challenge of existing single-scale optimization techniques, which struggle to adapt to energy fluctuations across multiple time scales throughout the year. Compared to conventional time series decomposition techniques that rely on manually preset fixed time periods and are prone to distortion, this application identifies significant periodic components of the system through power spectral density analysis. By combining these with time series decomposition to obtain trend components, periodic components, and residual components, it explicitly characterizes the energy fluctuation characteristics of the system at seasonal, weekly, and hourly scales. This breaks through the limitations of existing technologies that rely solely on hourly-level single-scale optimization and cannot reflect the energy balance relationship across multiple time scales throughout the year. It achieves complete preservation of the long-term and medium-term energy fluctuation characteristics of the original source-load data, laying a data foundation for the rational division of labor in hybrid energy storage. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0021] Figure 1 This is a schematic diagram of the structure of the electro-hydrogen-ammonia hybrid energy storage system in an embodiment of the present invention; Figure 2 This is a flowchart of the operation optimization method for a hybrid energy storage system based on multi-scale load in an embodiment of the present invention. Detailed Implementation

[0022] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0024] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0025] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0026] Example 1 like Figure 2 As shown, this embodiment provides an operation optimization method for a hybrid energy storage system based on multi-scale loads, using a server as an example. It is understood that this method can also be applied to terminals, and can be applied to systems including terminals, servers, and other components, and implemented through interaction between the terminals and servers. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, CDN security services, and big data and artificial intelligence platforms. The terminal can be a smartphone, tablet, laptop, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein. In this embodiment, the method includes the following steps: Step S1: Calculate the annual net load sequence based on the original source load data, perform power spectral density analysis on the annual net load sequence, and determine the significant period of the system; Step S2: Decompose the annual net load series using the system's significant period as the period length to obtain the annual trend component, periodic component, and residual component. Step S3: Based on the trend and periodic components throughout the year, optimize using the long-term energy optimization model for hydrogen-ammonia energy storage to obtain the state change of hydrogen-ammonia energy storage for each system in a significant period. Step S4: Using the change in the state of hydrogen-ammonia energy storage during each significant period of the system as the boundary condition, perform coordinated optimization operation of the hybrid electric-hydrogen-ammonia energy storage system during the significant periods of multiple systems to obtain the hourly operating output of the hybrid electric-hydrogen-ammonia energy storage system.

[0027] This invention utilizes multi-scale load decomposition technology to explicitly characterize the energy fluctuation characteristics of a system at different time scales and establishes an energy mapping relationship between multi-scale load components and hydrogen and ammonia energy storage. It preserves the system's multi-time-scale fluctuation characteristics and fully leverages the long-term and medium-term energy transfer characteristics of hybrid energy storage. Simultaneously, while ensuring energy balance requirements across multiple time scales throughout the year, it conducts short-cycle refined operation optimization, reducing the computational complexity of the annual mixed integer optimization problem, improving solution efficiency and engineering applicability. It solves the problem of unclear division of labor in single-scale operation optimization of electric-hydrogen-ammonia energy storage, and leverages the energy storage characteristics of various energy types at different time scales. It possesses strong versatility and scalability, and can be widely applied to power systems with a high proportion of renewable energy grid connection and different data fluctuation characteristics. It is expected to further promote the application of electric-hydrogen-ammonia hybrid energy storage systems in the energy field and provide strong support for the further development of renewable energy technologies.

[0028] Specifically, the method described in this embodiment includes the following process: In step S1, the annual net load sequence is calculated based on the original source-load data. Power spectral density analysis is then performed on the annual net load sequence to determine the system's significant period, including: Step S1.1: Calculate the data for each hour based on the original source load data. The net load is combined to obtain the annual net load sequence; where each hour The net load is calculated as follows: (1); in: It is every hour Photovoltaic power output; It is every hour The output of the wind turbine; It is every hour The load; It is every hour Net load; It is an hourly index for the entire year.

[0029] Step S1.2: Perform power spectral density analysis on the annual net load sequence to obtain the power spectral density; construct a period set based on the power spectral density, and select the time period with the largest energy proportion from the period set as the significant period of the system; Power spectral density analysis involves frequency domain decomposition of the annual net load sequence at hourly intervals. By quantifying the contribution of different frequency components to the total energy, it identifies several significant periodic components that have the most substantial impact on system operation. By mapping multiple dominant frequencies in the power spectrum to their corresponding time periods, representative significant periods of the system are systematically extracted from the net load sequence.

[0030] Step S1.2.1: Using hours as the sampling interval, perform frequency domain decomposition on the annual net load sequence, that is, perform Discrete Fourier Transform on the annual net load sequence to obtain the annual Discrete Fourier Transform result, as follows: (2); in: Represents the frequency domain of the corresponding discrete frequency index. Complex coefficients; The number of sampling points in this invention ; This is the sampling point index, corresponding to the net load sequence. .

[0031] Step S1.2.2: Based on the discrete Fourier transform results for the whole year and the number of sampling points, the power spectral density within a unit frequency interval is obtained, as shown in the following formula: (3); in: The frequency is expressed in units of times per hour. It represents the energy contribution within a unit frequency range, i.e., the power spectral density.

[0032] Power spectral density reflects the distribution of net load sequence energy along the frequency axis. The position of the spectral peak corresponds to a significant periodic component, while the amplitude of the peak reflects the importance of that period in the annual net load fluctuations. In other words, it quantifies the contribution of different frequency components to the total energy.

[0033] Step S1.2.3: Based on the relationship between unit frequency and time period, select the time period according to the power spectral density in each unit frequency interval according to the relative threshold criterion to obtain the period set. ,for: (4); in, This represents the maximum value of the power spectrum. This represents the threshold coefficient. Regarding the relationship between unit frequency and time period, mapping unit frequency to time period, under a sampling interval of 1 hour, the following relationship exists between unit frequency and time period: (5); in, Represents unit frequency The corresponding period length is in hours. This mapping transforms unit frequencies in the power spectrum into time scales with clear physical meaning.

[0034] Step S1.2.4: Calculate the energy proportion of each time period based on the power spectral density of each time period and all power spectral densities within the period set, and take the time period with the largest energy proportion as the significant period of the system. To quantitatively describe the importance of different periodic components, the periodic energy percentage index is defined as: (6); Energy proportion analysis was performed on the obtained period set. Periodic components with larger energy proportions were considered to be the main time scales affecting the change in system net load. The periodic component with the largest energy proportion was the significant system period obtained through system power spectral density analysis, denoted as . .

[0035] In step S2, the annual net load sequence is decomposed into a time series using the system's significant period as the period length to obtain the annual trend component, periodic component, and residual component. The aforementioned time series decomposition, i.e., performing time series (STL) decomposition on the annual net load series under a given period length, refers to the system's significant period in this step. The decomposition result is as follows: (7); in, This is a periodic component for the entire year; This represents the trend component for the entire year. This represents the remaining non-periodic random fluctuation residual components, which are the residual components for the entire year.

[0036] In step S3, based on the trend component and periodic component throughout the year, the hydrogen-ammonia energy storage long-term energy optimization model is used for optimization to obtain the hydrogen-ammonia energy storage state change for each system in a significant period. The trend component and the periodic component are summed within the system's significant period, with the year as the period and the obtained significant period as the time step, to perform long-term energy optimization of hydrogen-ammonia energy storage and obtain the change in the state of hydrogen-ammonia energy storage within each system's significant period. Step S3.1: Based on the trend component and periodic component throughout the year, sum them up for all hours within each system's significant period to obtain the trend component and seasonal component for each system's significant period; The trend component and the periodic component are summed over each significant period of the system, i.e., for each significant period of the annual net load. The trend components of each system's significant period are obtained by summing the results hourly. and seasonal portion The formula is as follows: (8); (9); in, A periodic index representing a significant period of the system; This is an hourly index within a significant period of the system. The system's significant period is defined as the total number of time periods within each significant period. and The first Within the first significant period The trend component and seasonal component of time; For the first The total energy of the trend components of a system's significant periodicity; For the first The total energy of the seasonal components of a system's significant periodicity. The number of significant systemic periods throughout the year is calculated using the following formula: (10); in, It is the number of sampling points; Step S3.2: Using an annual cycle and the system's significant cycle as the time step, optimize based on the long-term energy optimization model for hydrogen-ammonia energy storage under the constraints of the trend component and seasonal component of each system's significant cycle, and obtain the change in the state of hydrogen-ammonia energy storage within each system's significant cycle. The long-term energy optimization model for hydrogen-ammonia energy storage includes the long-term energy optimization objective function for hydrogen energy storage and the long-term energy optimization objective function for ammonia energy storage. Long-term energy optimization objective function for hydrogen energy storage The objective is to minimize the seasonal imbalance throughout the year, as shown in the following formula: (11); in: This refers to the seasonal imbalance throughout the year; The seasonal component imbalance for each system's significant period.

[0037] The constraints are: (12); in: and These are the energy generated by electricity and the energy generated by hydrogen production, respectively. and These are the state variables for hydrogen production by electricity and the state variables for electricity production by hydrogen, respectively. This refers to the capacity of the electrolytic cell; This refers to the capacity of the hydrogen fuel cell.

[0038] (13); in: and These are the equivalent energy for hydrogen storage and the equivalent energy for hydrogen release, respectively. and These are the efficiencies of hydrogen production by electricity and hydrogen production by electricity, respectively. and These are the hydrogen storage state variables and the hydrogen release state variables, respectively. For the first The amount of hydrogen storage state change in a system with significant periods; and These are hydrogen storage power and hydrogen release power, respectively. and These are the efficiency of hydrogen storage tanks for storing hydrogen and the efficiency of hydrogen release, respectively. The amount of hydrogen stored in the last significant cycle of the system; This represents the initial hydrogen storage capacity of the hydrogen storage tank.

[0039] Under the constraint of the seasonal component of each significant period of the system, the long-term energy optimization objective function for hydrogen storage is solved to obtain the hydrogen storage state change during each significant period of the system. ; Long-term energy optimization objective function for ammonia energy storage The objective is to minimize the imbalance of trend components throughout the year, as shown in the following formula: (14); in: This represents the unbalanced component of the annual trend. The trend component imbalance for each system's significant period.

[0040] The constraints are: (15); in, and These are the energy generated by electro-ammonia production and the energy generated by ammonia production; and These are the state variables for electro-ammonia production and the state variables for ammonia-to-electricity production, respectively. This refers to the capacity of the electrolytic cell; This refers to the capacity of the ammonia fuel cell.

[0041] (16); in, and These are the equivalent energy for ammonia storage and the equivalent energy for ammonia release, respectively. and These are the efficiencies of electro-ammonia production and ammonia-to-electricity production, respectively. and These are the ammonia storage state variables and the ammonia release state variables, respectively. For the first The changes in the ammonia storage state of a system over a significant period; and These are ammonia storage power and ammonia release power, respectively. and These are the efficiency of ammonia storage tanks for storing ammonia and the efficiency for releasing ammonia, respectively. The amount of ammonia stored in the last significant cycle of the system; This represents the initial ammonia storage capacity of the ammonia storage tank.

[0042] The amount of hydrogen storage state change with significant periodicity in the system and changes in the state of stored ammonia , which represents the change in the hydrogen and ammonia energy storage state during each significant cycle of the system.

[0043] In step S4, the change in the hydrogen-ammonia energy storage state during each significant cycle of the system is used as the boundary condition to perform coordinated optimization operation of the hybrid electric-hydrogen-ammonia energy storage system within multiple significant cycles, thereby obtaining the hourly operating output of the hybrid electric-hydrogen-ammonia energy storage system.

[0044] Using the change in hydrogen-ammonia energy storage state in each system's significant cycle as the boundary condition, the significant cycle as the total operating cycle, and the hour as the time step, we conduct collaborative optimization of the hybrid energy storage system for multiple systems within their significant cycles to obtain the hourly operating output of the hybrid energy storage system.

[0045] The system framework of the electro-hydrogen-ammonia hybrid energy storage system is shown in the attached figure. Figure 1 As shown. The components of the hybrid energy storage system (electricity, hydrogen, and ammonia) include a battery, an electrolyzer, a pressure swing adsorption (PSA) unit, an ammonia synthesis unit, a hydrogen storage tank, an ammonia storage tank, a hydrogen fuel cell, and an ammonia fuel cell. The equipment model of the hybrid energy storage system includes an energy conversion equipment model, an ammonia production model, and an energy storage model.

[0046] Energy conversion equipment includes electrolyzers, hydrogen fuel cells, and ammonia fuel cells. The energy conversion equipment model and formula are as follows: (17); Among them, superscript This refers to a collection of energy conversion devices. EC, HFC, and AFC represent an electrolyzer, a hydrogen fuel cell, and an ammonia fuel cell, respectively. The operating power of the energy conversion equipment; and The upper and lower limits of the operating power of the energy conversion equipment; This refers to the maximum ramping power of the energy conversion equipment per unit time period; The output power of the energy conversion device; The conversion efficiency of energy conversion equipment; The ammonia production model includes a pressure swing adsorption (PSA) unit model, an ammonia synthesis equipment model, and a gas mass model, with the following formulas: (18); in: This is the capacity of the pressure swing adsorption (PSA) device, and also the upper limit mass of nitrogen gas during the PSA process. This represents the mass of nitrogen obtained during the pressure swing adsorption process. This refers to the electrical power consumed in the pressure swing adsorption process. The unit time interval; The amount of electricity consumed to obtain 1 kg of nitrogen; This represents the upper limit of the ammonia mass produced during the ammonia synthesis process. The mass of synthesized ammonia; This refers to the electrical power consumed during the ammonia synthesis process. The amount of electricity consumed to synthesize 1 kg of ammonia; (19); in: and These represent the proportions of nitrogen and hydrogen atoms per unit mass of ammonia, and their values ​​are respectively... and ; The mass of hydrogen used in the synthesis of ammonia; and These are the equivalent power for hydrogen and ammonia, respectively. and These are the calorific value equivalence coefficients for hydrogen and ammonia, respectively, representing the equivalent amount of electricity produced by 1 kg of hydrogen and 1 kg of ammonia. Energy storage includes batteries, hydrogen storage tanks, and ammonia storage tanks. The energy storage model is as follows: (20); Among them, superscript This represents an energy storage system, where bat, HST, and AST represent a storage battery, a hydrogen storage tank, and an ammonia storage tank, respectively. and These represent the energy storage charging and discharging states, respectively. and These are the energy storage charging and discharging power, respectively. and These are the upper limits of energy storage charging and discharging power; (twenty one); in: , and These represent the stored energy during the initial, final, and time periods t, respectively. The energy balance period for different energy storage systems indicates that the stored energy is equal from the initial state to the end of its balance period. and These are the upper and lower limits of the energy storage capacity; and These are the energy storage charging and discharging efficiencies, respectively. The collaborative optimization operation variables of the hybrid energy storage system with multiple system cycles include the hourly operating power of system equipment, the power purchased and sold, the power abandoned, and the power shedding.

[0047] Synergistic Optimization Operation Model of Hybrid Energy Storage of Electricity, Hydrogen and Ammonia The objective is to minimize operating costs over a significant period of the system's lifecycle, as shown in the following formula: (twenty two); (twenty three); in, Weekly operating costs; To reduce transaction costs with the upper-level power grid, To incur penalties, For energy storage operating costs, It is the operating cost of the ammonia storage tank. It is the operating cost of the hydrogen storage tank. It is the operating cost of the battery; (twenty four); in, For unit time intervals, The total number of time periods for each system's significant cycles; It is the unit electricity purchase cost coefficient. It is the unit electricity sales revenue coefficient. It is the unit power curtailment cost coefficient. It is the unit power shortage cost coefficient. It is the unit energy storage operation and maintenance cost coefficient; It is the cost of purchasing electricity. It's the revenue from selling electricity. It is the cost of power curtailment. The cost is due to insufficient power supply. It is the cost of energy storage operation and maintenance.

[0048] The constraints include system equipment model constraints, electrical power balance constraints, hydrogen power balance constraints, ammonia power balance constraints, purchased and sold electrical power constraints, and hydrogen and ammonia energy storage state constraints.

[0049] Electric power balance constraints: (25); (26); (27); in, , and These are data for photovoltaic power output, wind turbine power output, and electrical load, respectively. and These are respectively the power of abandoned power and the power of insufficient power supply; and These are charging power and discharging power, respectively. and These are the power purchased and the power sold, respectively. The power input to the electrolytic cell; This refers to the electrical power output of the hydrogen fuel cell; This refers to the electrical power output of the ammonia fuel cell; The power consumed in producing nitrogen; The power consumed in the synthesis of ammonia.

[0050] Hydrogen power balance constraint: (28); in, The equivalent power input to the hydrogen fuel cell; The equivalent power of hydrogen used in ammonia synthesis; This provides the equivalent power output of the electrolytic cell. and These are the equivalent power for charging and discharging hydrogen, respectively.

[0051] Ammonia power balance constraints: (29); in, The equivalent power input to the ammonia fuel cell; The equivalent power of the synthesized ammonia gas; and These are the equivalent power for ammonia charging and the equivalent power for ammonia discharging, respectively.

[0052] Power purchase and sale constraints: (30); (31); (32); in, and These are the electricity purchase status variables and the electricity sales status variables, respectively. It is the upper limit of the power exchange between the system and the external power grid.

[0053] The hydrogen-ammonia energy storage state constraint uses the change in the hydrogen-ammonia energy storage state within each significant period of the system as the operating boundary for the coordinated optimization of the hybrid energy storage system, as follows: (33); in: and These represent the initial and final states of hydrogen energy storage during each significant cycle of the system. and These represent the initial and final states of ammonia storage during each significant cycle of the system. and These represent the changes in hydrogen storage state and ammonia storage state during each significant period of the system, respectively.

[0054] This embodiment establishes a precise energy mapping relationship between multi-scale load components and hydrogen and ammonia energy storage, clarifies the functional positioning of different energy storage systems, and effectively avoids the problems of frequent short-term intraday charging and discharging and unclear division of labor between long-term and medium-term energy storage in existing technologies. It fully leverages the inherent advantages of ammonia energy storage (high energy density, long storage period) and hydrogen energy storage (fast response speed, suitable for medium-cycle regulation), maximizing the multi-timescale energy storage potential of the hybrid electric-hydrogen-ammonia energy storage system. Simultaneously, it enhances the system's ability to cope with multi-timescale fluctuations, ensuring the stability and reliability of the power system's year-round operation.

[0055] Example 2 This embodiment provides an operation optimization system for a hybrid electric-hydrogen-ammonia energy storage system based on multi-scale loads, including: The net load sequence analysis module is used to calculate the annual net load sequence based on the original source load data, perform power spectral density analysis on the annual net load sequence, and determine the significant periodicity of the system. The time series decomposition module is used to decompose the annual net load series using the system's significant period as the period length, to obtain the annual trend component, periodic component, and residual component. The long-term energy optimization module is used to optimize the hydrogen-ammonia energy storage long-term energy optimization model based on the annual trend component and periodic component, and obtain the hydrogen-ammonia energy storage state change of each system for a significant period. The cycle-coordinated optimization operation module is used to perform coordinated optimization operation of the hybrid electric-hydrogen-ammonia energy storage system within multiple significant cycles, using the change in the state of hydrogen-ammonia energy storage in each significant cycle as the boundary condition, and to obtain the hourly operating output of the hybrid electric-hydrogen-ammonia energy storage system.

[0056] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1 above. It should be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0057] The descriptions of each embodiment in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0058] The proposed system can be implemented in other ways. For example, the system embodiments described above are merely illustrative, and the division of modules described above is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed.

[0059] Example 3 This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in the operation optimization method for a multi-scale load-based hybrid energy storage system of hydrogen and ammonia as described in Embodiment 1 above.

[0060] Example 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the operation optimization method for a multi-scale load-based hybrid energy storage system of hydrogen and ammonia as described in Embodiment 1 above.

[0061] Example 5 This embodiment provides a computer program product or computer program, including computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the steps in the operation optimization method of the electro-hydrogen-ammonia hybrid energy storage system based on multi-scale load described in Embodiment 1 above.

[0062] Those skilled in the art will understand that embodiments of the present invention can provide methods, systems, or computer program products. Therefore, the present invention can take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0063] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, as well as combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0067] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. An operation optimization method for a hybrid energy storage system based on multi-scale loads, characterized in that, include: The annual net load sequence was calculated based on the original source-load data. Power spectral density analysis was performed on the annual net load sequence to determine the system's significant periods, including: The net load for each hour is calculated based on the original source load data, and the combined data yields the annual net load sequence. Power spectral density analysis was performed on the annual net load sequence to obtain the power spectral density; a set of periods was constructed based on the power spectral density, and the time period with the largest energy proportion was selected as the significant period of the system from the set of periods; The annual net load series is decomposed using the system's significant period as the period length to obtain the annual trend component, periodic component, and residual component. Based on the trend and periodic components throughout the year, the hydrogen-ammonia energy storage long-term energy optimization model is used for optimization to obtain the hydrogen-ammonia energy storage state change for each system in a significant period; the hydrogen-ammonia energy storage long-term energy optimization model includes a hydrogen energy storage long-term energy optimization objective function and an ammonia energy storage long-term energy optimization objective function. The long-term energy optimization objective function for hydrogen energy storage aims to minimize the seasonal imbalance throughout the year. The objective function for long-term energy optimization of ammonia energy storage aims to minimize the imbalance of the annual trend component. Using the state change of hydrogen-ammonia energy storage in each significant cycle as boundary conditions, collaborative optimization operation of the hybrid electric-hydrogen-ammonia energy storage system is performed within multiple significant cycles to obtain the hourly operating output of the hybrid electric-hydrogen-ammonia energy storage system, including: The change in the hydrogen and ammonia energy storage state during each significant period of the system is used as the boundary condition, the significant period is used as the total operating period, and the time step is used as the hour. The collaborative optimization operation model of the hybrid energy storage system (electric, hydrogen, and ammonia) was used to optimize the operation of the hybrid energy storage system over a significant period of multiple systems, and the hourly operating output of the hybrid energy storage system was obtained. The proposed hybrid energy storage model for electricity, hydrogen, and ammonia aims to minimize operating costs over a significant period of the system's lifecycle. The constraints include system equipment model constraints, electrical power balance constraints, hydrogen power balance constraints, ammonia power balance constraints, purchased and sold electrical power constraints, and hydrogen and ammonia energy storage state constraints.

2. The method for optimizing the operation of a hybrid energy storage system based on multi-scale loads using an electro-hydrogen-ammonia system as described in claim 1, characterized in that, The hydrogen-ammonia energy storage long-term energy optimization model, based on the annual trend and periodic components, is used to obtain the hydrogen-ammonia energy storage state changes for each significant period of the system, including: Based on the trend component and periodic component throughout the year, the summation is performed on all hours within each system's significant period to obtain the trend component and seasonal component of each system's significant period. Using an annual cycle and the system's significant cycle as the time step, and constrained by the trend and seasonal components of each system's significant cycle, the hydrogen-ammonia energy storage long-term energy optimization model is optimized to obtain the state change of hydrogen-ammonia energy storage within each system's significant cycle.

3. An operation optimization system for a multi-scale load-based hybrid energy storage system of electricity, hydrogen, and ammonia, characterized in that, include: The net load sequence analysis module is used to calculate the annual net load sequence based on the original source load data, perform power spectral density analysis on the annual net load sequence, and determine the significant periodicity of the system, including: The net load for each hour is calculated based on the original source load data, and the combined data yields the annual net load sequence. Power spectral density analysis was performed on the annual net load sequence to obtain the power spectral density; a set of periods was constructed based on the power spectral density, and the time period with the largest energy proportion was selected as the significant period of the system from the set of periods; The time series decomposition module is used to decompose the annual net load series using the system's significant period as the period length, to obtain the annual trend component, periodic component, and residual component. The long-term energy optimization module is used to optimize the hydrogen-ammonia energy storage long-term energy optimization model based on the annual trend component and periodic component, and obtain the hydrogen-ammonia energy storage state change for each system in a significant period; the hydrogen-ammonia energy storage long-term energy optimization model includes a hydrogen energy storage long-term energy optimization objective function and an ammonia energy storage long-term energy optimization objective function. The long-term energy optimization objective function for hydrogen energy storage aims to minimize the seasonal imbalance throughout the year. The objective function for long-term energy optimization of ammonia energy storage aims to minimize the imbalance of the annual trend component. The periodic collaborative optimization operation module is used to perform collaborative optimization operation of the hybrid electric-hydrogen-ammonia energy storage system within multiple significant periods of each system, using the change in the state of hydrogen-ammonia energy storage in each significant period of the system as boundary conditions. This allows for the acquisition of hourly operational output data for the hybrid electric-hydrogen-ammonia energy storage system, including: The change in the hydrogen and ammonia energy storage state during each significant period of the system is used as the boundary condition, the significant period is used as the total operating period, and the time step is used as the hour. The collaborative optimization operation model of the hybrid energy storage system (electric, hydrogen, and ammonia) was used to optimize the operation of the hybrid energy storage system over a significant period of multiple systems, and the hourly operating output of the hybrid energy storage system was obtained. The proposed hybrid energy storage model for electricity, hydrogen, and ammonia aims to minimize operating costs over a significant period of the system's lifecycle. The constraints include system equipment model constraints, electrical power balance constraints, hydrogen power balance constraints, ammonia power balance constraints, purchased and sold electrical power constraints, and hydrogen and ammonia energy storage state constraints.

4. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the method for optimizing the operation of a hybrid energy storage system based on multi-scale load as described in any one of claims 1-2.

5. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the operation optimization method of the electro-hydrogen-ammonia hybrid energy storage system based on multi-scale load as described in any one of claims 1-2.

6. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps in the method for optimizing the operation of a hybrid energy storage system based on multi-scale loads as described in any one of claims 1-2.

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