Electro-hydrogen system whole life cycle planning method and system considering diversified applications

Abstract

The invention discloses an electro-hydrogen system whole life cycle planning method and system considering diversified applications, and the method comprises the steps: considering the links of hydrogen energy production, conversion, storage and application, and constructing an electro-hydrogen coupling system; constructing an upper-layer capacity configuration optimization model by taking the maximum net profit of the whole life cycle and the minimum scene reconstruction error as the target, and constructing a lower-layer operation optimization model by taking the minimum total operation cost in the actual operation process as the target; and solving the double-layer optimization model, evaluating the whole life cycle planning net profit in the solving process, and finally obtaining the optimal capacity configuration of the electro-hydrogen coupling system and the optimal configuration result of the long and short cycle hydrogen energy storage energy. According to the method, links of hydrogen energy production, conversion, storage, application and the like are considered, the double-layer multi-stage optimal configuration model of the electricity-hydrogen coupling system is established based on full-life-cycle planning benefits, and economic benefits of the electricity-hydrogen coupling system are maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of electric-hydrogen coupling system planning, and in particular to a method and system for full life cycle planning of an electric-hydrogen system taking into account diversified applications. Background Art

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

[0003] Hydrogen energy has the advantages of high energy density, green and low carbon, and low long-term storage loss. It can play its advantages in the multi-time scale energy balance scenario of the electric-hydrogen coupling system and reduce the risk of imbalance between supply and demand in the system operation. At the same time, hydrogen energy has great potential for application as a terminal energy. On the basis of the full life cycle planning benefit analysis, it combines long-term and short-term electric-hydrogen energy storage with the multi-application mode of hydrogen energy, and gives full play to the multi-application value of hydrogen energy through pressure conversion links, hydrogen blending systems, electric-hydrogen conversion equipment, etc., which can further improve the energy conversion and utilization efficiency of the electric-hydrogen coupling system, and then comprehensively evaluate the planning benefits while ensuring the reliable operation of the electric-hydrogen coupling system, and improve the economic and reliability of the planning operation.

[0004] However, the current hydrogen application model in the planning of electric-hydrogen coupling systems considering long- and short-cycle electric-hydrogen energy storage is simple and limited to fuel cell power generation and hydrogen storage applications. It fails to combine the terminal energy application potential of hydrogen with the potential for flexible balance of multi-time scale supply and demand risks of long- and short-cycle electric-hydrogen energy storage. This makes the electric-hydrogen hybrid energy storage planning scheme relatively conservative, and lacks a reasonable assessment of the full life cycle planning benefits for multiple planning years, which has a certain impact on the economy and reliability of the planned operation of the electric-hydrogen coupling system.

[0005] The prior art discloses a double-layer optimization method for an electric-hydrogen integrated energy system based on the entire life cycle. Based on the theory of the entire life cycle, the cost and benefit of the electric-hydrogen integrated energy system are analyzed, and a cost-benefit mathematical model is established. Based on the double-layer optimization theory, the operating output optimization of the hybrid energy storage system is determined to form an optimal operating mode, and the optimal output is fed back to the upper model. The two-layer optimization models are coupled to establish a double-layer optimization model for the electric-hydrogen integrated energy system. However, this scheme only selects typical days for optimization and only considers intra-day energy transfer. The constructed electric energy storage and hydrogen energy storage models are both short-cycle energy storage, which cannot meet the energy regulation needs of long time scales. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a full life cycle planning method and system for an electric-hydrogen system taking into account diversified applications, taking into account the production, conversion, storage, and application of hydrogen energy, and constructing an electric-hydrogen coupling system that takes into account both the diversified application needs of hydrogen energy and long- and short-cycle electric-hydrogen hybrid energy storage. Based on the full life cycle planning benefits, a two-layer and multi-stage optimization configuration model for the electric-hydrogen coupling system is established to maximize the economic benefits of the electric-hydrogen coupling system.

[0007] In some embodiments, the following technical solutions are adopted: A full life cycle planning method for electric hydrogen systems considering diverse applications, including: Considering the production, conversion, storage and application of hydrogen energy, an electric-hydrogen coupling system is constructed, including renewable energy power generation equipment, electric energy storage equipment, electric-hydrogen conversion equipment, low-pressure hydrogen storage tank equipment, high-pressure hydrogen storage tank equipment, pressure conversion equipment and hydrogen blending system equipment; The upper-layer capacity configuration optimization model is constructed with the goal of maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error. The lower-layer operation optimization model is constructed with the goal of minimizing the total operating cost during actual operation. The two-layer optimization model is solved, and the net profit of the full life cycle planning is evaluated during the solution process, ultimately obtaining the optimal capacity configuration of the electric-hydrogen coupling system and the long- and short-cycle hydrogen energy storage energy optimization configuration results.

[0008] As a further solution, the upper layer capacity configuration optimization model is specifically: ; in, represents the scene reconstruction error cost, It represents the average value of net profit over the entire life cycle of multiple planning years.

[0009] The average value of the net profit over the life cycle of the multi-planning years is specifically: ; ; In the formula, Indicates the planning period corresponding to when the cumulative net profit over the entire life cycle is positive, represents the annual life cycle net profit, , They represent the initial investment cost and operation and maintenance cost of the system respectively. Indicates the net profit of system operation.

[0010] The scenario reconstruction error cost includes the reconstruction error of the system net output data for 8760 hours throughout the year and the reconstruction error of the hydrogen storage capacity of the long-term low-pressure hydrogen storage tank, specifically: ; ; in, represents the reconstruction error penalty coefficient, and They represent the reconstructed data and original data of the system net output at time t, and They represent the reconstructed data and original data of the hydrogen storage capacity of the low-pressure hydrogen storage tank on a typical day respectively; , , Respectively t The original data of photovoltaic output, wind turbine output and load demand at all times; , and Respectively t Reconstructed data of photovoltaic output, wind turbine output, and load demand at all times.

[0011] The lower layer operation optimization model is specifically: ; ; in, is the total operating cost during actual operation, is the number of scene clusters, Reconstructing a typical day for scene clustering The number of Typical day for the system Running costs, , , , , , , and They are the system electricity purchase cost, hydrogen purchase cost, carbon emission penalty cost, supply and demand imbalance risk penalty cost, wind and solar power abandonment penalty cost, compressor electricity cost, hydrogen sales income and hydrogen blending system carbon emission income.

[0012] As a further solution, the two-layer optimization model is solved. The specific process is as follows: In the upper capacity configuration layer, the particle swarm algorithm is used to randomly generate the initial capacity of system equipment configuration and the number of subsequent scenario clustering clusters; Based on the generated system equipment configuration capacity and cluster number, the energy optimization of long-term hydrogen energy storage is carried out in the upper-level capacity configuration optimization stage to obtain the total daily hydrogen charging and discharging volume of the low-pressure hydrogen storage tank; The total amount of hydrogen charged and discharged per day in the low-pressure hydrogen storage tank is used together with the system wind turbine output, photovoltaic output and load demand data as the original data for time series scenario clustering. The kmeans clustering method is used to obtain typical days and reconstruct them to restore the whole year scenario. The repurchase data is verified with the original data, and the typical days with reconstruction errors greater than the set threshold are added to the typical scenario. Conduct short-term electricity-hydrogen energy storage collaborative optimization for typical scenarios, and reconstruct the optimization results into a continuous time series throughout the year to obtain the objective function value of the underlying operation optimization model; Determine whether the full life cycle net profit in the optimization operation objective function of the current planning year under the current equipment configuration capacity is positive. If it is negative, it indicates that the system is in a loss state, and then continue to calculate the full life cycle net profit of the next planning year, and accumulate the current multi-planning year full life cycle net profit to obtain the total benefit, until the system full life cycle multi-planning year total benefit is positive, output the planning years and the accumulated total benefit, calculate the annual average net profit, and return it to the upper capacity configuration layer, update the objective function of the upper capacity configuration optimization model, and use the updated upper capacity configuration optimization model as the fitness function of the particle swarm for further iterative optimization; Find the configuration scheme with the best particle swarm fitness, the number of scenario clustering clusters and the system optimization operation results, and obtain the system optimal capacity optimization configuration results that take into account the diversified application needs of hydrogen energy and the benefits of full life cycle planning.

[0013] In other embodiments, the following technical solutions are adopted: A full life cycle planning system for electric hydrogen systems considering diverse applications, including: System building module, which is used to consider the production, conversion, storage and application of hydrogen energy, and to build an electric-hydrogen coupling system including renewable energy power generation equipment, electric energy storage equipment, electric-hydrogen conversion equipment, low-pressure hydrogen storage tank equipment, high-pressure hydrogen storage tank equipment, pressure conversion equipment and hydrogen blending system equipment; The optimization model building module is used to build an upper-layer capacity configuration optimization model with the goal of maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error, and to build a lower-layer operation optimization model with the goal of minimizing the total operating cost during the actual operation process; The model solving module is used to solve the two-layer optimization model. During the solving process, the net profit of the whole life cycle planning is evaluated, and finally the optimal capacity configuration of the electric-hydrogen coupling system and the long- and short-cycle hydrogen energy storage energy optimization configuration results are obtained.

[0014] In other embodiments, the following technical solution is adopted: a terminal device includes a processor and a memory, the processor is used to implement instructions; the memory is used to store multiple instructions, and the instructions are suitable for being loaded by the processor and executing the above-mentioned full life cycle planning method of the electric hydrogen system considering diversified applications.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention takes into account the production, conversion, storage and application of hydrogen energy, and constructs an electric-hydrogen coupled energy system that takes into account both the diversified application needs of hydrogen energy and the long- and short-cycle electric-hydrogen hybrid energy storage; it not only meets the basic energy regulation needs, but also meets the needs of hydrogen refueling stations and hydrogen blending systems, giving full play to the terminal application value of hydrogen energy.

[0016] Electric energy storage is used as short-cycle energy storage to balance short-term power fluctuations and meet intraday regulation needs, while hydrogen energy storage is used as long-cycle energy storage to achieve diurnal energy transfer and balance long-cycle energy fluctuations. Short-cycle battery energy storage and long-cycle hydrogen energy storage are coordinated to achieve diurnal and intraday energy transfer, so that they can play the greatest role in different time periods, meet multi-time scale energy regulation needs, reduce the risk of system supply and demand imbalance, and improve system operation reliability.

[0017] The present invention maximizes the benefits of the electric-hydrogen coupling system by meeting the needs of diversified hydrogen energy applications, while giving full play to the potential of long- and short-cycle electric-hydrogen energy storage to reduce the risk of supply and demand imbalance on multiple time scales, further improving the energy utilization and conversion efficiency of the electric-hydrogen coupling system, and paying attention to the investment returns over the entire life cycle, thereby improving the economy, flexibility and reliability of the planning scheme.

[0018] (2) Considering the problem that hydrogen is easy to expand under high pressure, it is difficult to accurately describe the forces between hydrogen molecules using the ideal gas state equation. The present invention constructs a nonlinear relationship between the pressure of the high-pressure hydrogen storage tank and its influencing factors, and then uses an incremental piecewise linearization processing method to obtain a linear relationship between the gas pressure and the gas mass in the high-pressure hydrogen storage tank; on the one hand, the expansion effect and intermolecular forces of hydrogen under high pressure make it no longer strictly follow the ideal gas state equation; through nonlinear relationship modeling, the real behavior of hydrogen under high pressure conditions can be more accurately captured, avoiding the errors caused by the ideal gas assumption; on the other hand, directly using complex nonlinear equations for optimization calculations usually increases the difficulty of calculations and the solution time, especially in optimization scheduling or control systems. Incremental piecewise linearization is used to approximate the nonlinear relationship into linear equations, so that the problem can be converted into a linear optimization problem, improving the calculation efficiency.

[0019] (3) The present invention constructs a two-layer optimization model, in which the upper optimization model introduces the scenario reconstruction error cost and the average value of the full life cycle net profit of multiple planning years, while considering the maximization of the full life cycle net profit and satisfying the minimum scenario reconstruction error; on the one hand, the full life cycle net profit is maximized to ensure long-term investment returns, so that the planning and operation of the entire system can achieve maximum benefits in a long period; on the other hand, the scenario reconstruction error is minimized to ensure the rationality of short-term optimization decisions, so that the clustering and reconstruction errors in different scenarios are minimized, and the stability and reliability of the optimization scheme are improved.

[0020] (4) The lower-level optimization model of the present invention aims to minimize the total operating cost during the actual operation of the system. In the lower-level model, the terminal energy application potential of hydrogen energy, the full life cycle economic evaluation, and the flexible balance of multi-time scale supply and demand risk potential of long- and short-cycle electric hydrogen energy storage are combined to improve the economy and reliability of system operation.

[0021] Other features and advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of the structure of an electric-hydrogen coupling system considering diversified applications in an embodiment of the present invention; Figure 2 Schematic diagram of the connection structure of the linear displacement sensor in the embodiment of the present invention; Figure 3 It is a schematic diagram of the data collection process of installing an elevator settlement in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] It should be noted that the following detailed descriptions are illustrative and are intended to provide further explanation of the present application. Unless otherwise specified, all technical and scientific terms used in the present invention have the same meanings as those commonly understood by those skilled in the art to which the present application belongs.

[0024] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0025] Embodiment 1 In one or more embodiments, a method for planning the entire life cycle of an electric hydrogen system considering diversified applications is disclosed, which specifically includes the following process: S101: Considering the production, conversion, storage and application of hydrogen energy, construct an electric-hydrogen coupling system including renewable energy power generation equipment, electric energy storage equipment, electric-hydrogen conversion equipment, low-pressure hydrogen storage tank equipment, high-pressure hydrogen storage tank equipment, pressure conversion equipment and hydrogen blending system equipment.

[0026] In this embodiment, the hydrogen energy production link refers to the generation of electricity from renewable energy such as photovoltaics and wind turbines and the conversion of electrical energy into hydrogen energy with the help of an electrolyzer; the hydrogen energy conversion link includes supplying hydrogen to a fuel cell for power generation after passing through a pressure reducing valve, pressurizing the hydrogen through a compressor and injecting it into a high-pressure hydrogen storage tank and a low-pressure hydrogen storage tank for storage, and injecting it into a natural gas system through a hydrogen blending system; the hydrogen energy storage link includes storing hydrogen energy in a high-pressure hydrogen storage tank and a low-pressure hydrogen storage tank; the hydrogen energy application link includes supplying hydrogen fuel electric vehicles through a hydrogen refueling station, converting hydrogen energy into electrical energy through a hydrogen fuel cell, and meeting the gas load demand through a hydrogen blending system.

[0027] The basic framework diagram of energy conversion considering the diversified application needs of hydrogen energy is shown in the attached figure. Figure 1 As shown in the figure, hydrogen production, conversion, storage, and application together realize four application modes of hydrogen energy: Application mode 1: Use renewable energy sources such as photovoltaics and wind turbines to generate electricity to supply electrolyzers to achieve the conversion of electricity into hydrogen energy, and the resulting hydrogen energy is supplied to hydrogen fuel cells for power generation.

[0028] Application mode 2: The conversion of electricity into hydrogen energy is achieved by supplying electrolyzers with electricity generated by renewable energy such as photovoltaics and wind turbines. The resulting hydrogen energy is pressurized by pressurizing equipment and stored in high-pressure hydrogen storage tanks, which are then pressurized again and supplied to hydrogen filling stations to meet the hydrogen load needs of hydrogen fuel cell electric vehicles.

[0029] Application mode 3: The conversion of electricity to hydrogen is achieved by supplying electrolyzers with electricity generated by renewable energy such as photovoltaics and wind turbines. The resulting hydrogen is pressurized by a pressurizing device and stored in a low-pressure hydrogen storage tank for long-term hydrogen storage. When renewable energy generation cannot meet the electricity demand, it is supplied to hydrogen fuel cells to achieve the conversion of hydrogen to electricity and reduce the risk of imbalance between supply and demand in the electric-hydrogen coupling system.

[0030] Application mode 4: The conversion of electricity into hydrogen is achieved by supplying electrolyzers with electricity generated by renewable energy such as photovoltaics and wind turbines. The resulting hydrogen is supplied to gas load users through a hydrogen blending system to reduce carbon emissions and promote the development of sustainable energy.

[0031] This embodiment comprehensively considers the aspects of hydrogen energy production, conversion, storage and application, and combines the terminal energy application potential of hydrogen energy with the potential for flexible balance of multi-time scale supply and demand risks of long- and short-cycle electric hydrogen energy storage. It can maximize the benefits of the electric-hydrogen coupling system by meeting the diversified application needs of hydrogen energy.

[0032] Combination Figure 1 The basic equipment of the electric hydrogen coupling system constructed in this embodiment includes a fan, photovoltaic, electric energy storage, a high-pressure hydrogen storage tank, a low-pressure hydrogen storage tank, an electrolyzer, a fuel cell, a pressure conversion device, and a hydrogen blending system. Among them, hydrogen energy production is achieved through the electrolyzer, hydrogen energy storage is achieved through the high-pressure hydrogen storage tank and the low-pressure hydrogen storage tank, and hydrogen energy conversion and application are achieved through the fuel cell, the pressure conversion device, and the hydrogen blending system.

[0033] The following is a description of the model of each device.

[0034] (1) Electric energy storage device model The electric energy storage device model needs to meet power constraints and energy constraints. Specifically, the power constraints are: (1) in, , , , are the energy storage charging and discharging state and charging and discharging power respectively. and They are the upper limits of charging and discharging power of electric energy storage respectively.

[0035] The energy constraints are as follows: (2) in, , , and are the storage capacity of the initial period, the final period, the t period and the t+1 period of the energy storage, respectively. and are the upper and lower limits of the energy storage capacity, and are the energy storage charging and discharging efficiency respectively.

[0036] (2) Electricity-hydrogen conversion equipment model The electric-hydrogen conversion equipment includes electrolyzers and fuel cells, which must meet output constraints, start-stop constraints, power and climbing constraints.

[0037] The specific output constraints of the electric-hydrogen conversion equipment are: (3) in, , are the output powers of the electrolyzer and fuel cell, respectively. , Represent the hydrogen production efficiency and electricity production efficiency of the electrolyzer and fuel cell respectively. , They represent the equivalent power of hydrogen produced by the electrolyzer and the equivalent power of electricity produced by the fuel cell respectively.

[0038] Furthermore, with the help of the calorific value of hydrogen Convert hydrogen production equivalent power into hydrogen production mass: (4) in, , are the equivalent mass and power of hydrogen produced by the electrolyzer, respectively.

[0039] The specific start-stop constraints for electric-hydrogen conversion equipment are: (5) in, , The electricity-hydrogen conversion equipment is Start-up and shutdown status at all times; is a 0-1 variable, indicating the working status of the electric-hydrogen conversion device; and They are the upper limits of the number of startups and shutdowns of the electric-hydrogen conversion equipment in a single day.

[0040] The power and ramp constraints of the electric-hydrogen conversion equipment are as follows: (6) (7) in, The working power of the electricity-hydrogen conversion equipment; and It is the upper and lower limits of the working power of the electric-hydrogen conversion equipment when it is turned on; It is the maximum climbing power per unit time period of the electric-hydrogen conversion equipment when it is in the on state. , Respectively The working state variables of the electric-hydrogen conversion equipment at the moment and the power of the electric-hydrogen conversion equipment at the previous moment.

[0041] (3) Low-pressure hydrogen storage tank equipment model: First, the ideal gas state equation is used to describe the relationship between the pressure in the low-pressure hydrogen storage tank and its influencing factors: (8) (9) in, , are the gas pressure and mass in the low-pressure hydrogen storage tank at time t respectively; , are the volume and temperature of the low-pressure hydrogen storage tank, is the ideal gas constant, is the relative molecular mass of hydrogen, It is the amount of hydrogen in the low-pressure hydrogen storage tank at time t.

[0042] Furthermore, the relationship between pressure and mass in the low-pressure hydrogen storage tank is obtained as follows: (10) Since low-pressure hydrogen storage tanks can realize multi-time scale energy transfer, their hydrogen storage state must meet the intra-day and inter-day time series coupling constraints, including hydrogen storage pressure constraints and hydrogen charging and discharging constraints. Long-term energy optimization of low-pressure hydrogen storage tanks is performed to realize inter-day energy transfer: (11) (12) (13) in, is the hydrogen storage capacity of the low-pressure hydrogen storage tank on day d, The efficiency of charging and discharging hydrogen in low-pressure hydrogen storage tanks. and They are the initial hydrogen storage capacity and the final hydrogen storage capacity of the hydrogen storage tank respectively; and are the working state variables of hydrogen filling and hydrogen discharge of the low-pressure hydrogen storage tank on day d, Configure capacity for low-pressure hydrogen storage tanks, is the conversion coefficient, take 0.2778, is the mass energy density of hydrogen, which is taken as 120MJ / kg.

[0043] , , Typical daily life of low-pressure hydrogen storage tanks The allowable lower pressure limit, upper pressure limit and actual pressure; , , , Represents the typical daily Hydrogen charging amount, hydrogen charging upper limit, hydrogen degassing amount and hydrogen degassing upper limit.

[0044] Furthermore, after obtaining the total mass of hydrogen charged and discharged every day, short-cycle electricity-hydrogen energy storage synergy optimization is carried out to achieve energy transfer within the day: (14) (15) (16) (17) in, is the difference between the hydrogen storage capacity of the low-pressure hydrogen storage tank on day d and day d-1; , Typical daily life of low-pressure hydrogen storage tanks The amount of hydrogen charged and discharged at all times; and Typical daily life of low-pressure hydrogen storage tanks The working state variables of hydrogen charging and dehydrogenating at all times.

[0045] , Typical daily life of low-pressure hydrogen storage tanks The upper limit of hydrogen charging and decharging at time t; , , Typical daily life of low-pressure hydrogen storage tanks The lower limit of pressure, upper limit of pressure and actual pressure allowed at time t.

[0046] (4) High-pressure hydrogen storage tank equipment model Considering that hydrogen has a relatively small molecular mass, it is easier to expand under high pressure. At this time, it is difficult to accurately describe the forces between hydrogen molecules using the ideal gas state equation. This embodiment constructs the following description of the relationship between the pressure of the high-pressure hydrogen storage tank and its influencing factors, which can more accurately reflect the physical properties of hydrogen under real high-pressure conditions.

[0047] (18) in, , are the gas pressure and mass in the low-pressure hydrogen storage tank at time t respectively; , are the volume and temperature of the low-pressure hydrogen storage tank, is the ideal gas constant, is the relative molecular mass of hydrogen, , They respectively measure the attraction between hydrogen molecules and the finite volume effect of hydrogen molecules.

[0048] The above formula takes into account the forces between hydrogen molecules (a) and the finite volume effect (b), overcoming the error of the ideal gas equation under high pressure conditions, making the calculated gas pressure of the high-pressure hydrogen storage tank more accurate. This method can better describe the real physical properties of hydrogen under high pressure and has important application value in the fields of high-pressure hydrogen storage, fuel cell systems, and hydrogen energy storage.

[0049] Since the gas pressure in the high-pressure hydrogen storage tank and the gas mass are nonlinear in the above equation, in order to facilitate subsequent calculations, it is linearized and the incremental piecewise linearization method is used to obtain the relationship between the gas pressure in the high-pressure hydrogen storage tank and the gas mass as follows: (19) (20) (twenty one) (twenty two) in, , are the gas pressure and mass in the low-pressure hydrogen storage tank at time t respectively; represents the number of linearization segments, Indicates the segmentation point, is a binary variable, and Ensure segment interval continuity; , , Respectively represent the first and , Gas quality of low-pressure hydrogen storage tank at each segment point, , Respectively represent the gas mass of the low-pressure hydrogen storage tank in the and The position of each segment point is used to describe the weight factor of the interval where the current quality is located.

[0050] After obtaining the calculation equation for the pressure in the high-pressure hydrogen storage tank, the hydrogen storage pressure and the amount of hydrogen charged and discharged in the high-pressure hydrogen storage tank are further restricted.

[0051] (twenty three) in, , Respectively The amount of hydrogen charged and released in the high-pressure hydrogen storage tank at all times; , Respectively The maximum mass of hydrogen that can be charged into and released from the high-pressure hydrogen storage tank at any given moment; , Respectively The state variable of whether the high-pressure hydrogen storage tank is filled with and released hydrogen at any moment.

[0052] (5) Pressure conversion device model The pressure conversion equipment pressurizes the hydrogen produced by the electrolyzer and injects it into the low-pressure hydrogen storage tank (low-pressure hydrogen compressor) and the high-pressure hydrogen storage tank (high-pressure hydrogen compressor). The power consumed by the compressor in this process can be expressed as: (twenty four) (25) in, , Respectively represent the power consumed by the low-pressure hydrogen compressor and the high-pressure hydrogen compressor, express The electrolyzer produces hydrogen at all times. , represents the dissipation rate transferred from the electrolyzer equipment to the low-pressure hydrogen compressor and the high-pressure hydrogen compressor, respectively. , are the reference working pressure and the power consumed by the compressor under the reference working pressure, , They are the hydrogen pressure of the low-pressure hydrogen storage tank and the high-pressure hydrogen storage tank, is standard atmospheric pressure.

[0053] The prior art rarely considers pressure conversion equipment when modeling the electric-hydrogen coupling system. The present embodiment considers pressure conversion to refine the existing hydrogen energy storage model and thus expand the application of hydrogen energy storage. After pressure conversion, hydrogen is injected into low-pressure hydrogen storage tanks and high-pressure hydrogen storage tanks respectively. The low-pressure hydrogen storage tank is long-cycle hydrogen energy storage to achieve daytime energy transfer. The hydrogen in the high-pressure hydrogen storage tank is mainly supplied to hydrogen refueling stations and does not participate in power regulation. By considering pressure conversion, various application needs of hydrogen energy can be met and the terminal regulation potential of hydrogen energy can be fully utilized.

[0054] (6) Hydrogen doping system model Mixing hydrogen with traditional gases (such as natural gas) can reduce carbon emissions and promote the development of sustainable energy without increasing additional investment costs. However, it is necessary to control the hydrogen blending ratio to avoid the impact of high-concentration hydrogen blending on the performance of natural gas pipelines: (26) in, The hydrogen content in natural gas pipelines on a typical day must not exceed 20%. , Respectively The hydrogen flow rate injected into the natural gas pipeline at all times and the original gas flow rate in the natural gas pipeline.

[0055] The electric-hydrogen coupling system of this embodiment takes into account both the diversified application needs of hydrogen energy and the long- and short-cycle electric-hydrogen hybrid energy storage. It can maximize the benefits of the electric-hydrogen coupling system while giving full play to the potential of long- and short-cycle electric-hydrogen energy storage to reduce the risk of supply and demand imbalance on multiple time scales, thereby further improving the energy utilization and conversion efficiency of the electric-hydrogen coupling system.

[0056] S102: With the goal of maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error, an upper-level capacity configuration optimization model is constructed, and with the goal of minimizing the total operating cost during actual operation, a lower-level operation optimization model is constructed.

[0057] This embodiment constructs a two-level optimization model for the electric-hydrogen coupling system that takes into account the diversified application needs of hydrogen energy and the benefits of full life cycle planning. Figure 2The model includes an upper capacity configuration layer and a lower optimization operation layer. The capacity configuration layer considers the planning benefits of the entire life cycle, and the optimization operation layer sequentially performs long-term energy optimization of hydrogen energy storage, clustering and reconstruction of time series scenarios, and short-term coordinated optimization of electric and hydrogen energy storage. In the short-term coordinated optimization of electric and hydrogen energy storage, the diversified application needs of hydrogen energy are considered to give full play to the terminal application potential of hydrogen energy.

[0058] Specifically, the objective function of the capacity configuration layer considers maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error. The upper-layer capacity configuration optimization model is specifically as follows: (27) represents the scene reconstruction error cost, It represents the average value of net profit over the whole life cycle of multiple planning years. It represents the upper layer capacity configuration optimization objective function, that is, the particle swarm fitness function.

[0059] The average value of the full life cycle net profit of multiple planning years is obtained by accumulating the full life cycle net profit of each year and taking the average value when the cumulative value is positive: (28) in, represents the annual life cycle net profit, Indicates the planning years corresponding to when the cumulative full life cycle net profit is positive.

[0060] The annual life cycle net profit can be calculated as the difference between the net operating income of the system during its life cycle and the total system cost: (29) in, , They represent the initial investment cost and operation and maintenance cost of the system respectively. Indicates the net profit of system operation.

[0061] The initial investment cost of the system is as follows: (30) (31) in, , , , , and Respectively represent the one-time investment costs of system equipment such as electrolyzer, fuel cell, low-pressure hydrogen storage tank, high-pressure hydrogen storage tank, low-pressure hydrogen compressor and high-pressure hydrogen compressor; , , , and Respectively represent the unit capacity investment cost of the corresponding equipment; , , , , and Respectively represent the configuration capacity of the corresponding device.

[0062] The system operation and maintenance costs are as follows: Based on the initial investment cost of the system, the corresponding percentage is used to estimate the system operation and maintenance cost: (32) (33) Wherein, a, b, c, d, e, and f represent the ratio of initial investment of electrolyzer, fuel cell, low-pressure hydrogen storage tank, high-pressure hydrogen storage tank, low-pressure hydrogen compressor, high-pressure hydrogen compressor and their corresponding equipment respectively; is the discount rate, take 5%; Plan the years for the entire life cycle of the electric-hydrogen coupling system.

[0063] Net profit of system operation over its entire life cycle : (34) in, is the total operating cost of the system, that is, the objective function of the optimized operation layer .

[0064] At the same time, in order to ensure the accuracy of time series scene clustering and reconstruction and the comprehensiveness and authenticity of the generated scenes, the scene reconstruction error cost is introduced, including the reconstruction error of the system net output data for 8760 hours throughout the year and the reconstruction error of the hydrogen storage capacity of the long-term low-pressure hydrogen storage tank: (35) (36) in, represents the reconstruction error penalty coefficient, and Respectively The net output of the system at each moment is reconstructed and compared with the original data. and Represents a typical day Reconstructed data and original data of hydrogen storage capacity in low-pressure hydrogen storage tanks. , , , , , Respectively The original data of photovoltaic output, wind turbine output, load demand, photovoltaic output reconstruction data, wind turbine output reconstruction data, and load demand reconstruction data at each moment.

[0065] This embodiment obtains the optimization results of device capacity and the number of clusters by optimizing the upper capacity configuration layer.

[0066] In this embodiment, the objective function of the optimized operation layer considers the minimum total operation cost during the actual operation of the system, and the lower layer operation optimization model is specifically: (37) (38) in, is the number of scene clusters, Reconstructing a typical day for scene clustering The number of Typical day for the system Running costs, , , , , , , and They are the system electricity purchase cost, hydrogen purchase cost, carbon emission penalty cost, supply and demand imbalance risk penalty cost, wind and solar power abandonment penalty cost, compressor electricity cost, hydrogen sales income, and hydrogen blending system carbon emission income.

[0067] (39) in, , , , , , They respectively represent the unit electricity purchase cost, unit hydrogen purchase cost, carbon emission penalty coefficient, load shedding penalty coefficient, unit hydrogen sales revenue, and supply and demand imbalance penalty coefficient. , , , , , , , They respectively represent the system's hydrogen purchase volume, electricity purchase volume, air abandonment volume, light abandonment volume, power consumption of the low-pressure hydrogen compressor, power consumption of the high-pressure hydrogen compressor, hydrogen sales volume, and hydrogen blending volume in the hydrogen blending system.

[0068] , , , , They respectively represent the system net output power (the difference between wind and solar output and load demand), fuel cell power generation power, electrolyzer power consumption power, energy storage discharge power, and energy storage charging power.

[0069] Furthermore, the capacity configuration layer constraints are: The capacity configuration layer must ensure that the capacity of system equipment such as electrolyzers, fuel cells, electric energy storage, low-pressure hydrogen storage tanks, high-pressure hydrogen storage tanks, and the number of scenario clusters are within a reasonable range: (40) in, , , , , , They represent the electric energy storage capacity, electrolyzer capacity, fuel cell capacity, low-pressure hydrogen storage tank capacity, high-pressure hydrogen storage tank capacity, and the number of scenario clusters respectively; , , , , , They represent the electric energy storage capacity, electrolyzer capacity, fuel cell capacity, low-pressure hydrogen storage tank capacity, high-pressure hydrogen storage tank capacity, and the upper limit of the number of scenario clusters respectively; , , , , , They respectively represent the electric energy storage capacity, electrolyzer capacity, fuel cell capacity, low-pressure hydrogen storage tank capacity, high-pressure hydrogen storage tank capacity, and the lower limit of the number of scenario clustering clusters.

[0070] The constraints of the optimization operation layer are: The optimized operation layer needs to meet the operation constraints of each device in the system in S101, and also needs to meet the corresponding energy balance of system electricity and hydrogen energy: (41) (42) in, , , , , , , , , , They respectively represent the system net output power (the difference between wind and solar power output and load demand), abandoned wind and solar power, electric energy storage discharge power, electric energy storage charging power, system power purchase power, system power sales power, fuel cell power generation power, electrolyzer power consumption, low-pressure hydrogen compressor power consumption, and high-pressure hydrogen compressor power consumption; , , , , , , , , They respectively represent the hydrogen consumption of the fuel cell, the hydrogen blending amount in the hydrogen blending system, the hydrogen filling amount of the high-pressure hydrogen storage tank, the hydrogen filling amount of the low-pressure hydrogen storage tank, the hydrogen sales amount, the hydrogen purchase amount, the hydrogen discharge amount of the high-pressure hydrogen storage tank, the hydrogen discharge amount of the low-pressure hydrogen storage tank, and the hydrogen production of the electrolyzer.

[0071] This embodiment optimizes the lower-level operation optimization model and uses the equipment capacity optimization results to perform long-term hydrogen energy storage energy optimization to obtain the optimization results of hydrogen charging and discharging volume. Based on the clustering number and the optimization results of hydrogen charging and discharging volume, typical scenarios are obtained through scenario clustering and verification. Based on the equipment capacity under the typical scenario, the optimal operating cost is obtained through short-term electric-hydrogen energy storage collaborative optimization.

[0072] S103: An improved particle swarm algorithm is used to perform multi-stage solutions to the two-layer optimization model. During the solution process, the net profit of the full life cycle planning is evaluated, and ultimately the optimal capacity configuration of the electric-hydrogen coupling system and the long- and short-cycle hydrogen energy storage energy optimization configuration results are obtained.

[0073] The multi-stage solution of this embodiment includes two stages: a two-layer optimization configuration of the electric-hydrogen coupling system that considers the diversified application needs of hydrogen energy and the benefits of full life cycle planning, and a net profit evaluation of the full life cycle planning. The two-layer optimization configuration stage includes capacity optimization configuration, long-term hydrogen energy storage energy optimization, scenario clustering and reduction, and short-term electric-hydrogen energy storage collaborative optimization.

[0074] Combination Figure 3 The multi-stage solution process of the improved particle swarm algorithm in this embodiment is as follows: S1031: In the upper capacity configuration layer, a particle swarm algorithm is used to randomly generate system equipment configuration capacity and the number of subsequent scenario clustering clusters.

[0075] S1032: Based on the generated system equipment configuration capacity and the number of clusters, energy optimization of long-term hydrogen energy storage is first performed in the lower optimization operation layer to obtain the total daily hydrogen charging and discharging volume of the low-pressure hydrogen storage tank.

[0076] The specific optimization process is: taking day as the minimum optimization time unit, on the premise of satisfying the system's annual electrical energy balance, the gurobi solver is used for optimization with the goal of optimal economic efficiency to obtain the total daily hydrogen charging and discharging volume of hydrogen energy storage.

[0077] S1033: The total amount of hydrogen charged and discharged per day in the low-pressure hydrogen storage tank obtained based on the above steps is used together with the system wind turbine output, photovoltaic output, load demand and other data as the original data for time series scene clustering; The kmeans clustering method is used to obtain typical days and reconstruct them into full-year scenarios. The data are verified with the original data, and typical days with large reconstruction errors are added to the typical scenarios.

[0078] Specifically, firstly, the annual wind and solar load data and the daily total amount of hydrogen charging and discharging are used as features (a total of four features) and clustered using the kmeans clustering method to obtain typical days. Each typical day corresponds to the number of the cluster to which it belongs, that is, all the data for the whole year are classified, and each category is represented by its own typical day. These typical days are used to reconstruct and restore the annual data according to the numbers, and the four features of wind turbine output, photovoltaic output, load demand, and daily total amount of hydrogen charging and discharging of hydrogen energy storage are verified with the original data. Scenarios with large errors (the error value can be selected to be greater than the set threshold) are screened out and added to the typical scenarios.

[0079] S1034: Perform short-term electricity-hydrogen energy storage collaborative optimization for typical scenarios (typical days), and reconstruct the optimization results into a continuous time series throughout the year to obtain the objective function value of the lower optimization operation layer (i.e., the net benefit of full life cycle operation).

[0080] Short-cycle electric and hydrogen energy storage coordinated optimization means that in the daily optimization operation, hydrogen energy storage and electric energy storage work together to meet the daily power regulation needs. The only difference is that the total daily charge and discharge volume of the electric energy storage is required to be 0, while the total daily charge and discharge volume of the hydrogen energy storage is the result obtained in the long-cycle hydrogen energy storage optimization that was carried out first.

[0081] S1035: Determine whether the full life cycle net profit in the optimization operation objective function of the current planning year under the equipment configuration capacity is positive. If it is negative, it indicates that the system is in a loss state, and then continue to calculate the full life cycle net profit of the next planning year, and accumulate the current multi-planning year full life cycle net profit to obtain the total benefit, until the system full life cycle multi-planning year total benefit is positive, indicating that the system investment is profitable. At this time, the planning years and the accumulated total benefit are output, the annual average net profit is calculated, and it is returned to the upper capacity configuration layer, and the objective function of the upper capacity configuration optimization model is updated. The updated upper capacity configuration optimization model objective function is used as the fitness function of the particle swarm for further iterative optimization.

[0082] S1036: Iterate in a loop to find the configuration scheme with the best fitness of the particle swarm, the number of scenario clustering clusters and the system optimization operation results, and obtain the system optimal capacity optimization configuration results that take into account the diversified application needs of hydrogen energy and the benefits of full life cycle planning.

[0083] This embodiment maximizes the benefits of the electric-hydrogen coupling system by meeting the needs of diversified hydrogen energy applications, while leveraging the potential of long- and short-cycle electric-hydrogen energy storage to reduce the risk of supply and demand imbalance on multiple time scales, further improving the energy utilization and conversion efficiency of the electric-hydrogen coupling system, and focusing on the investment returns over the entire life cycle, thereby improving the economy, flexibility, and reliability of the planning scheme.

[0084] Embodiment 2 In one or more embodiments, a full life cycle planning system for an electric hydrogen system considering diversified applications is disclosed, comprising: System building module, which is used to consider the production, conversion, storage and application of hydrogen energy, and to build an electric-hydrogen coupling system including renewable energy power generation equipment, electric energy storage equipment, electric-hydrogen conversion equipment, low-pressure hydrogen storage tank equipment, high-pressure hydrogen storage tank equipment, pressure conversion equipment and hydrogen blending system equipment; The optimization model building module is used to build an upper-layer capacity configuration optimization model with the goal of maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error, and to build a lower-layer operation optimization model with the goal of minimizing the total operating cost during the actual operation process; The model solving module is used to solve the two-layer optimization model. During the solving process, the net profit of the whole life cycle planning is evaluated, and finally the optimal capacity configuration of the electric-hydrogen coupling system and the long- and short-cycle hydrogen energy storage energy optimization configuration results are obtained.

[0085] It should be noted that the specific implementation method of the above modules is the same as that in Example 1 and will not be described in detail.

[0086] Embodiment 3 In one or more embodiments, a terminal device is disclosed, which includes a processor and a memory, the processor being used to implement instructions; the memory being used to store multiple instructions, wherein the instructions are suitable for being loaded by the processor and executing the full life cycle planning method for an electric hydrogen system considering diversified applications described in Example 1.

[0087] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it 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 on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A method for planning the entire life cycle of an electric hydrogen system considering diversified applications, characterized in that: include: Considering the production, conversion, storage and application of hydrogen energy, an electric-hydrogen coupling system is constructed, including renewable energy power generation equipment, electric energy storage equipment, electric-hydrogen conversion equipment, low-pressure hydrogen storage tank equipment, high-pressure hydrogen storage tank equipment, pressure conversion equipment and hydrogen blending system equipment; The upper-layer capacity configuration optimization model is constructed with the goal of maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error. The lower-layer operation optimization model is constructed with the goal of minimizing the total operating cost during actual operation. The two-layer optimization model is solved, and the net profit of the full life cycle planning is evaluated during the solution process, ultimately obtaining the optimal capacity configuration of the electric-hydrogen coupling system and the long- and short-cycle hydrogen energy storage energy optimization configuration results.

2. A method for planning the entire life cycle of an electric hydrogen system taking into account diversified applications as claimed in claim 1, characterized in that: Consider the production, conversion, storage and application of hydrogen energy, specifically: The hydrogen energy production process includes: generating electricity through renewable energy and converting electricity into hydrogen energy; The hydrogen energy conversion process includes: hydrogen energy is supplied to hydrogen fuel cells for power generation through a pressure reducing valve, is pressurized by a compressor and injected into high-pressure hydrogen storage tanks and low-pressure hydrogen storage tanks for storage, and is injected into the natural gas system through a hydrogen blending system; The hydrogen energy storage link includes: storing hydrogen energy in a high-pressure hydrogen storage tank and / or a low-pressure hydrogen storage tank; The hydrogen energy application process includes: supplying hydrogen fuel to electric vehicles through hydrogen refueling stations, converting hydrogen energy into electrical energy through hydrogen fuel cells, and meeting gas load requirements through hydrogen blending systems.

3. A method for planning the entire life cycle of an electric hydrogen system taking into account diversified applications as claimed in claim 1, characterized in that: The low-pressure hydrogen storage tank equipment model is specifically: The relationship between pressure and mass in a low-pressure hydrogen storage tank is as follows: ； in, , are the gas pressure and mass in the low-pressure hydrogen storage tank at time t respectively; , are the volume and temperature of the low-pressure hydrogen storage tank, is the ideal gas constant, is the relative molecular mass of hydrogen; The hydrogen storage state of the low-pressure hydrogen storage tank must meet the intra-day and inter-day time series coupling constraints, including hydrogen storage pressure constraints and hydrogen charging and discharging constraints; Carry out long-term energy optimization of low-pressure hydrogen storage tanks to achieve daytime energy transfer. After obtaining the total mass of hydrogen charged and discharged every day, carry out short-term electric and hydrogen energy storage coordinated optimization to achieve intraday energy transfer.

4. A method for planning the entire life cycle of an electric hydrogen system taking into account diversified applications as claimed in claim 1, characterized in that: The high-pressure hydrogen storage tank equipment model is specifically: Establish the relationship between the pressure of the high-pressure hydrogen storage tank and its influencing factors, specifically: ； The incremental piecewise linearization method is used to obtain the relationship between the gas pressure and gas mass in the high-pressure hydrogen storage tank, as follows: ； ； ； ； in, , are the gas pressure and mass in the low-pressure hydrogen storage tank at time t respectively; represents the number of linearization segments, Indicates the segmentation point, is a binary variable, and Ensure segment interval continuity; , , Respectively represent the first and , Gas quality of low-pressure hydrogen storage tank at each segment point, , Respectively represent the gas mass of the low-pressure hydrogen storage tank in the and The position of each segment point is used to describe the weight factor of the interval where the current quality is located.

5. A method for planning the entire life cycle of an electric hydrogen system taking into account diversified applications as claimed in claim 1, characterized in that: The upper layer capacity configuration optimization model is specifically: ； in, represents the scene reconstruction error cost, It represents the average value of net profit over the entire life cycle of multiple planning years.

6. A method for planning the entire life cycle of an electric hydrogen system taking into account diversified applications as claimed in claim 5, characterized in that: The average value of the net profit over the life cycle of the multi-planning years is specifically: ； ； In the formula, Indicates the planning period corresponding to when the cumulative net profit over the entire life cycle is positive, represents the annual life cycle net profit, , They represent the initial investment cost and operation and maintenance cost of the system respectively. It represents the net profit of system operation; The scenario reconstruction error cost includes the reconstruction error of the annual system net output data and the reconstruction error of the hydrogen storage capacity of the long-period low-pressure hydrogen storage tank.

7. A method for planning the entire life cycle of an electric hydrogen system considering diversified applications as claimed in claim 1, characterized in that: The lower layer operation optimization model is specifically: ； ； in, is the total operating cost during actual operation, is the number of scene clusters, Reconstructing a typical day for scene clustering The number of Typical day for the system Running costs, , , , , , , and They are the system electricity purchase cost, hydrogen purchase cost, carbon emission penalty cost, supply and demand imbalance risk penalty cost, wind and solar power abandonment penalty cost, compressor electricity cost, hydrogen sales income and hydrogen blending system carbon emission income.

8. A method for planning the entire life cycle of an electric hydrogen system taking into account diversified applications as claimed in claim 1, characterized in that: Solve the two-layer optimization model. The specific process is as follows: In the upper-layer capacity configuration optimization stage, energy optimization of long-term hydrogen energy storage is performed to obtain the total amount of hydrogen charged and discharged per day in the low-pressure hydrogen storage tank; the total amount of hydrogen charged and discharged per day in the low-pressure hydrogen storage tank is used together with the system wind turbine output, photovoltaic output and load demand data as the original data for time series scene clustering; Typical days are obtained through clustering and reconstructed to restore the whole year scenario. The repurchase data is verified with the original data, and typical days with reconstruction errors greater than the set threshold are added to the typical scenario. Conduct short-term electricity-hydrogen energy storage collaborative optimization for typical scenarios, and reconstruct the optimization results into a continuous time series throughout the year to obtain the objective function value of the underlying operation optimization model; The total income is obtained by accumulating the net profit of the current multi-planning year and the whole life cycle until the total income of the multi-planning year of the whole life cycle of the system is positive. The annual average net profit is calculated and returned to the upper capacity configuration layer, and the objective function of the upper capacity configuration optimization model is updated. The updated objective function of the upper capacity configuration optimization model is used as the fitness function of the particle swarm for further iterative optimization. Find the configuration scheme with the best particle swarm fitness, the number of scenario clustering clusters and the system optimization operation results, and obtain the system optimal capacity optimization configuration results that take into account the diversified application needs of hydrogen energy and the benefits of full life cycle planning.

9. A full life cycle planning system for an electric hydrogen system considering diversified applications, characterized in that: include: System building module, which is used to consider the production, conversion, storage and application of hydrogen energy, and to build an electric-hydrogen coupling system including renewable energy power generation equipment, electric energy storage equipment, electric-hydrogen conversion equipment, low-pressure hydrogen storage tank equipment, high-pressure hydrogen storage tank equipment, pressure conversion equipment and hydrogen blending system equipment; The optimization model building module is used to build an upper-layer capacity configuration optimization model with the goal of maximizing the net profit over the entire life cycle while minimizing the scenario reconstruction error, and to build a lower-layer operation optimization model with the goal of minimizing the total operating cost during the actual operation process; The model solving module is used to solve the two-layer optimization model. During the solving process, the net profit of the whole life cycle planning is evaluated, and finally the optimal capacity configuration of the electric-hydrogen coupling system and the long- and short-cycle hydrogen energy storage energy optimization configuration results are obtained.

10. A terminal device, comprising a processor and a memory, wherein the processor is used to implement instructions; and the memory is used to store multiple instructions, characterized in that: The instructions are suitable for being loaded by a processor and executing the method for full life cycle planning of an electric hydrogen system taking into account diversified applications as described in any one of claims 1-8.

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