Distributed electricity-hydrogen coupling system coordination control method and device based on edge calculation and upper computer

Abstract

The invention discloses a distributed electricity-hydrogen coupling system coordination control method and device based on edge calculation and an upper computer. The method comprises the following steps: firstly, acquiring electric quantity information and hydrogen energy data of each energy device in the distributed electricity-hydrogen coupling system; predicting the generating capacity of various distributed power generation devices and the power consumption of various loads; then selecting a target operation mode of the distributed electricity-hydrogen coupling system; and according to the optimization objective function and the constraint condition of the target operation mode, obtaining an optimal operation strategy of the target operation mode, and finally, the optimal operation strategy is sent to each energy device in the distributed electricity-hydrogen coupling system, so that the energy devices operate according to the optimal operation strategy, and distributed electricity-hydrogen coupling system coordination control based on edge calculation is completed. According to the method, the control speed of the distributed electricity-hydrogen coupling system can be remarkably improved, so that the overall operation efficiency is improved, and meanwhile, the reliability and the expandability of the system are enhanced.

Description

Technical Field

[0001] The present invention belongs to the field of energy technology, and in particular relates to a coordinated control method, device and host computer of a distributed electric-hydrogen coupling system based on edge computing. Background Art

[0002] As the global climate change problem intensifies, countries have set goals to reduce carbon emissions. Against this backdrop, hydrogen energy, as a clean alternative energy source, has become increasingly important. It can not only effectively reduce greenhouse gas emissions, but also play a key role in energy transformation. In recent years, the progress of water electrolysis hydrogen production and hydrogen fuel cell technology has been remarkable, with costs gradually decreasing and efficiency continuously improving, which has laid a solid foundation for the widespread application of electric hydrogen coupling systems. The electric hydrogen coupling system can effectively integrate renewable energy such as wind and solar energy, and convert excess electricity into hydrogen energy for storage through water electrolysis technology, thereby solving the instability problem of renewable energy generation.

[0003] With the introduction of distributed electric-hydrogen coupling systems, the complexity of the system has increased significantly, and the operation modes have become diversified. This makes it impossible to meet the operation requirements of the system by manual maintenance alone. Therefore, a comprehensive coordinated control system is urgently needed to ensure the safe and reliable operation of the system.

[0004] However, in the prior art, the integrated coordination control system usually uses a centralized control system for monitoring and control. However, the application of centralized control systems in distributed energy systems has many defects, mainly reflected in the slow response speed. This is because all data and decisions are concentrated in the core nodes, resulting in delays in information processing. In addition, the centralized control architecture also faces the risk of single point failure. Once the core node fails, the entire system will be paralyzed. At the same time, the centralized control system has poor scalability and is difficult to adapt to the increase in the number of devices, which limits the flexibility and emergency response capabilities of the system. In addition, information overload and data bottleneck phenomena often affect the processing efficiency of real-time data and increase the safety risks of the system. Summary of the invention

[0005] The technical problem to be solved by the present invention is to address the above-mentioned deficiencies in the prior art and propose a coordinated control method, device and host computer for a distributed electric-hydrogen coupling system based on edge computing. The method adopts a comprehensive coordinated control system, which can significantly improve the control speed of the distributed electric-hydrogen coupling system, thereby improving the overall operating efficiency. At the same time, the method also enhances the reliability and scalability of the distributed electric-hydrogen coupling system, enabling it to better adapt to the needs of different scales and application scenarios, thereby providing a more efficient, safe, economical and reliable operating environment for the distributed electric-hydrogen coupling system.

[0006] In a first aspect, the present invention provides a coordinated control method for a distributed electric-hydrogen coupling system based on edge computing, the method comprising the following steps:

[0007] Step S1: Obtaining power information and hydrogen energy data of each energy device in the distributed electricity-hydrogen coupling system;

[0008] Step S2: predicting the power generation of various distributed power generation equipment and the power consumption of various loads according to the power information and the hydrogen energy data;

[0009] Step S3: selecting a target operation mode of the distributed electricity-hydrogen coupling system according to the power generation of the distributed power generation equipment and the power consumption of various loads;

[0010] Step S4: obtaining the optimal operation strategy of the target operation mode according to the optimization objective function and constraint conditions of the target operation mode;

[0011] Step S5: Send the optimal operation strategy to each energy device in the distributed electricity-hydrogen coupling system, so that the energy device operates according to the optimal operation strategy, thereby completing the coordinated control of the distributed electricity-hydrogen coupling system based on edge computing.

[0012] Furthermore, before the step S1, the method further includes a step S0;

[0013] Step S0: Designing a distributed electricity-hydrogen coupling system based on edge computing; the distributed electricity-hydrogen coupling system is designed in a distributed manner based on edge computing;

[0014] The step S0 specifically includes the following steps:

[0015] Step S01: Design a first distributed electricity-hydrogen coupling system consisting of a cloud and an edge;

[0016] Step S02: designing a second distributed electricity-hydrogen coupling system consisting of an edge terminal and an edge terminal;

[0017] Step S03: Combine the first distributed electricity-hydrogen coupling system and the second distributed electricity-hydrogen coupling system to obtain a distributed electricity-hydrogen coupling system with cloud control and edge autonomy capabilities.

[0018] Furthermore, the step S01 specifically includes the following steps:

[0019] Step S011: designing a first architecture consisting of a cloud control center, edge computing nodes, and energy devices;

[0020] Step S012: determining a first sensor corresponding to the first architecture;

[0021] Step S013: designing a first data collection process so that the first sensor collects data of the energy device in real time according to the first data collection process, and transmits the data to the edge computing node and the cloud control center for analysis and processing;

[0022] Step S014: developing a first scheduling algorithm, and combining the central control of the cloud control center with the local control of the edge computing node according to the first scheduling algorithm, thereby forming a design of a first distributed electric-hydrogen coupling system consisting of the cloud and the edge;

[0023] The step S02 specifically includes the following steps:

[0024] Step S021: designing a second architecture consisting of edge computing nodes and energy devices; the edge computing nodes communicate with each other through point-to-point communication;

[0025] Step S022: determining a second sensor corresponding to the second architecture;

[0026] Step S023: designing a second data collection process so that the second sensor collects data of the energy device in real time according to the second data collection process, and transmits the data to the edge computing node for analysis and processing;

[0027] Step S024: Develop a second scheduling algorithm, and combine the local control of the edge computing nodes according to the second scheduling algorithm to form a design of a second distributed electric-hydrogen coupling system consisting of edge terminals and edge nodes.

[0028] Further, the target operation model in step S3 is specifically one of a strong grid-connected mode, a weak grid-connected mode, an island mode, or a virtual power plant mode;

[0029] In step S4, according to the optimization objective function and constraint conditions of the target operation mode, the optimal operation strategy of the target operation mode is obtained, which specifically includes the following steps:

[0030] According to the optimization objective function and constraint conditions of the strong grid-connected mode, the optimal operation strategy of the strong grid-connected mode is obtained; or,

[0031] According to the optimization objective function and constraint conditions of the weak grid-connected mode, the optimal operation strategy of the weak grid-connected mode is obtained; or,

[0032] According to the optimization objective function and constraint conditions of the island mode, the optimal operation strategy of the island mode is obtained; or,

[0033] According to the optimization objective function and constraints of the virtual power plant model, the optimal operation strategy of the virtual power plant model is obtained.

[0034] Furthermore, the optimal operation strategy of the strong grid-connected mode is obtained according to the optimization objective function and constraint conditions of the strong grid-connected mode, which specifically includes the following steps:

[0035] Step A1: setting the minimum operating cost of the strong grid-connected mode as the optimization target; and setting the equality constraint condition of the strong grid-connected mode and the inequality constraint condition of the strong grid-connected mode;

[0036] The calculation formula of the minimum operation cost of the strong grid-connected mode is as follows:

[0037]

[0038] In formula (1), f 强并网 represents the minimum operating cost of the strong grid-connected mode, F 强并网 represents the operating cost of the strong grid-connected mode;

[0039] E gd，t and E gu，t are the off-grid power and on-grid power at time t, respectively, gd，t and P gu，t are the off-grid electricity price and on-grid electricity price at time t, respectively, t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price;

[0040] The equation constraints of the strong grid connection mode include a first total electricity balance equation constraint, a first battery energy storage balance equation constraint, and a first hydrogen storage tank hydrogen storage balance equation constraint;

[0041] The equation constrained by the first total power balance equation is as follows:

[0042] E gd，t +E w，t +E p，t =E gu，t +E b，t +E l1，t +E l2，t +E l3，t +E h，t (2)

[0044] In formula (2), E gd，t and E gu，t are the off-grid power and on-grid power at time t, respectively, w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and El3，t They are the power consumption of the first type of load, the second type of load and the third type of load at time t respectively;

[0045] The equation constrained by the first battery energy storage balance equation is as follows:

[0046] E b0，t+1 =E b0，t +E b，t (3)

[0047] In formula (3), E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0048] The equation constrained by the hydrogen storage balance equation of the first hydrogen storage tank is as follows:

[0049] H s，t+1 =H s，t +E h，t *α-H truck，t (4)

[0050] In formula (4), H s，t , H s，t+1 are the amount of hydrogen stored in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the amount of hydrogen added to the hydrogen heavy truck at time t;

[0051] The inequality constraint condition of the strong grid-connected mode is the upper and lower limits of the operation of each energy device in the strong grid-connected mode;

[0052] Step A2: according to the minimum operation cost of the strong grid-connected mode, the equality constraint condition of the strong grid-connected mode, and the inequality constraint condition of the strong grid-connected mode, solving and obtaining the optimal operation plan of the strong grid-connected mode;

[0053] Step A3: According to the optimal operation plan of the strong grid-connected mode, an optimal operation strategy under the strong grid-connected mode is obtained.

[0054] Furthermore, the optimal operation strategy of the weak grid-connected mode is obtained according to the optimization objective function and constraint conditions of the weak grid-connected mode, which specifically includes the following steps:

[0055] Step B1: setting the minimum operating cost of the weak grid-connected mode as the optimization target; and setting the equality constraint condition of the weak grid-connected mode and the inequality constraint condition of the weak grid-connected mode;

[0056] The calculation formula of the minimum operating cost of the weak grid-connected mode is as follows:

[0057]

[0058] In formula (5), f 弱并网 represents the minimum operating cost of the weak grid-connected mode, F 弱并网 represents the operating cost of the weak grid-connected mode;

[0059] E gd，t is the off-grid power at time t, P gd，t is the off-grid electricity price at time t, H t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E l3s，t is the third type of load reduction, P l，t Compensation price for the third category of load reduction;

[0060] The equation constraints of the weak grid-connected mode include a second total electricity balance equation constraint, a second battery energy storage balance equation constraint, and a second hydrogen storage tank hydrogen storage balance equation constraint;

[0061] The equation constraining the second total power balance equation is as follows:

[0062] E gd，t +E w，t +E p，t =E b，t +E l1，t +E l2，t +E l3，t -E l3s，t +E h，t (6);

[0063] In formula (6), E gd，t is the off-grid power at time t, E w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and E l3，t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. l3s，t The load reduction for the third category;

[0064] The equation for the second battery energy storage balance equation constraint is as follows:

[0065] E b0，t+1 =E b0，t +E b，t (7)

[0066] In formula (7), E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0067] The equation constrained by the hydrogen storage balance equation of the second hydrogen storage tank is as follows (8):

[0068] H s，t+1 =H s，t +E h，t *α-H truck，t (8)

[0069] In formula (8), H s，t , H s，t+1 are the hydrogen storage capacity in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the amount of hydrogen added to the hydrogen heavy truck at time t;

[0070] The inequality constraint condition of the weak grid-connected mode is the upper and lower limits of the operation of each energy device in the weak grid-connected mode;

[0071] Step B2: solving the optimal operation plan of the weak grid-connected mode according to the minimum operation cost of the weak grid-connected mode, the equality constraint condition of the weak grid-connected mode, and the inequality constraint condition of the weak grid-connected mode;

[0072] Step B3: according to the optimal operation scheme of the weak grid-connected mode, an optimal operation strategy in the weak grid-connected mode is obtained.

[0073] Furthermore, the optimal operation strategy of the island mode is obtained according to the optimization objective function and constraint conditions of the island mode, which specifically includes the following steps:

[0074] Step C1: setting the minimum operating cost of the island mode as the optimization target; and setting the equality constraint condition of the island mode and the inequality constraint condition of the island mode;

[0075] The calculation formula of the minimum operating cost of the island mode is as follows:

[0076]

[0077] In formula (9), f 孤岛 represents the minimum operating cost of the weak grid-connected mode, F 孤岛 represents the operating cost of the weak grid-connected mode;

[0078] H t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, El2s，t and E l3s，t are the second type of load reduction and the third type of load reduction, P l，t is the reduction compensation price for the second type of load; the reduction compensation price for the third type of load is equal to the reduction compensation price for the second type of load;

[0079] The equation constraints of the island mode include a third total electricity balance equation constraint, a third battery energy storage balance equation constraint, and a third hydrogen storage tank hydrogen storage balance equation constraint;

[0080] The third total power balance equation constraint is expressed as equation (10):

[0081] E fc，t +E w，t +E p，t =E b，t +E l1，t +E l2，t -E l2s，t +E l3，t -E l3s，t +E h，t (10)

[0082] In formula (10), E fc，t is the power generation of the fuel cell at time t, E w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and E l3，t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. l2s，t and E l3s，t They are the second category load reduction and the third category load reduction respectively;

[0083] The equation constrained by the third battery energy storage balance equation is as follows:

[0084] E b0，t+1 =E b0，t +E b，t (11)

[0085] In formula (11), E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0086] The equation constrained by the hydrogen storage balance equation of the third hydrogen storage tank is as follows (12):

[0087] H s，t+1 =H s，t +E h，t *α-H truck，t -E fc，t *β (12)

[0088] In formula (12), H s，t , H s，t+1 are the hydrogen storage capacity in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the hydrogenation amount of the hydrogen heavy truck at time t, E fc，t is the power generation of the fuel cell at time t, β is the power generation efficiency of the fuel cell;

[0089] The inequality constraints of the island mode are the upper and lower limits of the operation of each energy device in the island mode;

[0090] Step C2: solving the optimal operation plan of the island mode according to the minimum operation cost of the island mode, the equality constraint condition of the island mode, and the inequality constraint condition of the island mode;

[0091] Step C3: according to the optimal operation scheme of the island mode, obtain the optimal operation strategy in the island mode.

[0092] Furthermore, the optimal operation strategy of the virtual power plant mode is obtained according to the optimization objective function and constraint conditions of the virtual power plant mode, which specifically includes the following steps:

[0093] Step D1: setting the minimum operating cost of the virtual power plant mode as the optimization target; and setting the equality constraint condition of the virtual power plant mode and the inequality constraint condition of the virtual power plant mode;

[0094] The calculation formula of the minimum operating cost of the virtual power plant mode is as follows:

[0095]

[0096] In formula (13), f 虚拟电厂 represents the minimum operating cost of the weak grid-connected mode, F 虚拟电厂 represents the operating cost of the weak grid-connected mode;

[0097] E gd，t and E gu，t are the off-grid power and on-grid power at time t, respectively, gd，t and P gu，t are the off-grid electricity price and on-grid electricity price at time t, respectively, t is the hydrogenation amount of the hydrogen heavy truck at time t, Ph is the hydrogenation price, E s，t is the total system load reduction, E l2s，t and E l3s，t are the second type of load reduction and the third type of load reduction, P l，t Compensation price for load reduction;

[0098] The equation constraints of the virtual power plant mode include a fourth total electricity balance equation constraint, a fourth battery energy storage balance equation constraint, and a fourth hydrogen storage tank hydrogen storage balance equation constraint;

[0099] The fourth total power balance equation constraint is expressed as equation (14):

[0100] E fc，t +E w，t +E p，t =E b，t +E l1，t +E l2，t +E l3，t -E s，t +E h，t (14)

[0102] In formula (14), E fc，t is the power generation of the fuel cell at time t, E w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and E l3，t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. s，tt is the total system load reduction;

[0103] The equation of the fourth battery energy storage balance equation constraint is as follows:

[0104] E b0，t+1 =E b0，t +E b，t (15)

[0105] In formula (15), E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0106] The equation constrained by the hydrogen storage balance equation of the fourth hydrogen storage tank is as follows (16):

[0107] H s，t+1 =H s，t +E h，t *α-H truck，t -E fc，t *β (16)

[0108] In formula (16), H s，t , H s，t+1 are the hydrogen storage capacity in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the hydrogenation amount of the hydrogen heavy truck at time t, E fc，t is the power generation of the fuel cell at time t, β is the power generation efficiency of the fuel cell;

[0109] The inequality constraints of the virtual power plant mode are the upper and lower limits of the operation of each energy device in the virtual power plant mode;

[0110] Step D2: solving the optimal operation plan of the virtual power plant mode according to the minimum operation cost of the virtual power plant mode, the equality constraint condition of the virtual power plant mode, and the inequality constraint condition of the virtual power plant mode;

[0111] Step D3: According to the optimal operation plan of the virtual power plant mode, the optimal operation strategy under the virtual power plant mode is obtained.

[0112] In a second aspect, the present invention provides a coordinated control device for a distributed electric-hydrogen coupling system based on edge computing, the device comprising:

[0113] An acquisition unit, used to acquire power information and hydrogen energy data of each energy device in the distributed electric-hydrogen coupling system;

[0114] A prediction unit, connected to the acquisition unit, for predicting the power generation of various distributed power generation equipment and the power consumption of various loads according to the power information and the hydrogen energy data;

[0115] A selection unit, connected to the prediction unit, for selecting a target operation mode of the distributed electric-hydrogen coupling system according to the power generation of the distributed power generation equipment and the power consumption of various loads;

[0116] a processing unit connected to the selection unit, and configured to obtain an optimal operation strategy for the target operation mode according to the optimization objective function and constraint conditions of the target operation mode;

[0117] A sending unit is connected to the processing unit and is used to send the optimal operation strategy to each energy device in the distributed electric-hydrogen coupling system, so that the energy device operates according to the optimal operation strategy, thereby completing the coordinated control of the distributed electric-hydrogen coupling system based on edge computing.

[0118] Furthermore, the device also includes a design unit;

[0119] The design unit is connected to the acquisition unit and is used to design a distributed electric-hydrogen coupling system based on edge computing; the distributed electric-hydrogen coupling system is designed in a distributed manner based on edge computing;

[0120] The design unit comprises:

[0121] The first design module is used to design the first distributed electricity-hydrogen coupling system consisting of the cloud and edge;

[0122] A second design module, connected to the first design module, for designing a second distributed electric-hydrogen coupling system consisting of an edge terminal and an edge terminal;

[0123] A combination module is respectively connected to the first design module and the second design module, and is used to combine the first distributed electric-hydrogen coupling system and the second distributed electric-hydrogen coupling system to obtain a distributed electric-hydrogen coupling system with cloud control and edge autonomy capabilities.

[0124] Furthermore, the processing unit includes:

[0125] A first processing module, connected to the selection unit, for obtaining an optimal operation strategy for the strong grid-connected mode according to the optimization objective function and constraint conditions of the strong grid-connected mode in the target operation model;

[0126] A second processing module, connected to the selection unit, is used to obtain an optimal operation strategy for the weak grid-connected mode according to the optimization objective function and constraint conditions of the weak grid-connected mode in the target operation model;

[0127] A third processing module, connected to the selection unit, is used to obtain an optimal operation strategy of the island mode according to the optimization objective function and constraint conditions of the island mode in the target operation model;

[0128] The fourth processing module is connected to the selection unit and is used to obtain the optimal operation strategy of the virtual power plant mode according to the optimization objective function and constraint conditions of the virtual power plant mode in the target operation model.

[0129] In a third aspect, the present invention provides a host computer of an integrated energy station, on which a computer program is stored, and is characterized in that when the computer program is executed by a processor, the coordinated control method of a distributed electric-hydrogen coupling system based on edge computing described in the first aspect is implemented.

[0130] The present invention adopts a comprehensive coordinated control system, which can significantly improve the control speed of the distributed electric-hydrogen coupling system, thereby improving the overall operating efficiency. At the same time, the method also enhances the reliability and scalability of the distributed electric-hydrogen coupling system, enabling it to better adapt to the needs of different scales and application scenarios, thereby providing a more efficient, safe, economical and reliable operating environment for the distributed electric-hydrogen coupling system. The specific effective effects are as follows:

[0131] 1. Improve operation efficiency and economy

[0132] 1.1 Optimize resource allocation: The present invention uses edge computing to analyze real-time data, optimize the production and consumption of electricity and hydrogen energy, reduce energy waste, and ensure efficient use of resources.

[0133] 1.2 Reduce operating costs: The present invention reduces dependence on centralized control systems and reduces equipment maintenance and operating costs through real-time monitoring and intelligent scheduling.

[0134] 1.3 Dynamic load management: The present invention adjusts the working state of the equipment according to real-time demand, realizes on-demand hydrogen production, and reduces unnecessary energy consumption and costs.

[0135] 2. Enhanced reliability

[0136] 2.1 Redundant design: The edge computing nodes of the present invention are scattered in different locations. Even if a node fails, other nodes can still continue to provide services to ensure the continuity of the overall operation of the system.

[0137] 2.2 Real-time monitoring and fault warning: The edge computing nodes of the present invention can continuously monitor the operating status of the equipment, identify potential faults in a timely manner and issue warnings, thereby reducing the probability of system failures.

[0138] 2.3 Adaptive control algorithm: The present invention can automatically adjust the control strategy according to the historical performance and real-time status of the equipment, thereby enhancing the system's ability to cope with emergencies.

[0139] 3. Improved security

[0140] 3.1 Reduce single point failure risk: The present invention adopts a decentralized control system, which reduces the dependence on a single control center and reduces the overall vulnerability of the system.

[0141] 3.2 Data security: The present invention utilizes edge computing to process data locally, reducing the risk of sensitive information being transmitted to the cloud and the possibility of data leakage and cyber attacks.

[0142] 3.3 Multi-level security protection: The present invention implements a multi-level security mechanism in the edge device and enhances the security of the system through identity authentication, access control and other means. BRIEF DESCRIPTION OF THE DRAWINGS

[0143] Figure 1 Schematic diagram of a coordinated control method for a distributed electric-hydrogen coupling system based on edge computing in an embodiment of the present invention;

[0144] Figure 2 It is a schematic diagram of the coordinated control flow of a distributed electric-hydrogen coupling system based on edge computing in an embodiment of the present invention;

[0145] Figure 3 This is a connection diagram of a distributed electricity-hydrogen coupling system based on edge computing in an embodiment of the present invention;

[0146] Figure 4 A schematic diagram of topological conversion between different operating modes in an embodiment of the present invention;

[0147] Figure 5 A schematic diagram of switching conditions between different operating modes in an embodiment of the present invention;

[0148] Figure 6 Schematic diagram of a typical topological structure of a distributed electric-hydrogen coupling system in an embodiment of the present invention;

[0149] Figure 7 Schematic diagram of a distributed electricity-hydrogen coupling system with cloud control and edge autonomy capabilities in an embodiment of the present invention;

[0150] Figure 8 Schematic diagram of a coordinated control device for a distributed electric-hydrogen coupling system based on edge computing in an embodiment of the present invention;

[0151] Among them, the figure numbers are: 10, acquisition unit, 20, prediction unit, 30, selection unit, 40, processing unit, 50, sending unit. DETAILED DESCRIPTION

[0152] In order to enable those skilled in the art to better understand the technical solution of the present invention, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0153] It should be understood that the specific embodiments and drawings described herein are only used to explain the present invention rather than to limit the present invention.

[0154] It can be understood that, in the absence of conflict, the various embodiments of the present invention and the various features in the embodiments can be combined with each other.

[0155] It can be understood that, for the convenience of description, the drawings of the present invention only show the parts related to the present invention, while the parts irrelevant to the present invention are not shown in the drawings.

[0156] It can be understood that each unit and module involved in the embodiments of the present invention may correspond to only one physical structure, or may be composed of multiple physical structures, or multiple units and modules may be integrated into one physical structure.

[0157] It can be understood that, without conflict, the functions and steps marked in the flowcharts and block diagrams of the present invention may occur in an order different from that marked in the drawings.

[0158] It is understood that the flowcharts and block diagrams of the present invention illustrate the possible architectures, functions, and operations of the systems, devices, equipment, and methods according to the various embodiments of the present invention. Each box in the flowchart or block diagram may represent a unit, module, program segment, or code, which contains executable instructions for implementing the specified functions. Moreover, each box or combination of boxes in the block diagram and flowchart may be implemented by a hardware-based system that implements the specified functions, or may be implemented by a combination of hardware and computer instructions.

[0159] It can be understood that the units and modules involved in the embodiments of the present invention can be implemented by software or hardware. For example, the units and modules can be located in a processor.

[0160] Embodiment 1:

[0161] The distributed electric-hydrogen coupling system is an innovative solution that applies water electrolysis hydrogen production and hydrogen fuel cell technology to distributed energy systems. It combines the flexibility of distributed energy systems with the efficient energy storage characteristics of hydrogen energy, and has the following significant features:

[0162] (1) Flexibility and reliability: Distributed electricity-hydrogen coupling systems can operate independently at different locations, reducing dependence on centralized power grids and improving the reliability of energy supply.

[0163] (2) Renewable energy integration: This system can effectively integrate renewable energy such as wind energy and solar energy, produce hydrogen through water electrolysis, and convert excess electricity into hydrogen energy for storage.

[0164] (3) Long-term energy storage: Hydrogen energy storage can achieve long-term, large-capacity energy storage, making up for the shortcomings of traditional battery energy storage.

[0165] (4) Grid peak shaving and valley filling: Use grid valley electricity and clean energy to produce hydrogen, and then use hydrogen fuel cells to generate electricity during peak hours to achieve grid peak shaving and valley filling

[0166] (5) Diversified applications: Hydrogen energy can be used in multiple fields such as power generation, transportation, and industry, providing diversified energy solutions.

[0167] This system not only helps improve energy efficiency, but also promotes the green transformation of energy structure. However, the randomness and intermittency of renewable energy, the volatility of user power load, and the power restrictions on the grid make the system operation state complex and changeable. In order to ensure the safe, reliable and economical operation of the system, the coordinated operation of various devices in the system is crucial.

[0168] In view of the characteristics of the distributed electric-hydrogen coupling system, which has a wide distribution range and a small single-unit capacity, this embodiment proposes a coordinated control method based on edge computing. First, the distributed electric-hydrogen coupling system is divided into multiple operating modes, and a corresponding optimization model is established for each mode. On this basis, these optimization models are used to calculate the coordinated control strategy of each mode and implement it in the edge computing device. In addition, this embodiment also proposes a coordinated control method for multiple distributed electric-hydrogen coupling systems based on edge computing to achieve more efficient system management and scheduling.

[0169] like Figure 2 As shown in the figure, in order to achieve coordinated control of the distributed electric-hydrogen coupling system, all types of energy equipment in the system are equipped with sensors to measure power information such as voltage, current, power, and electricity, as well as hydrogen usage. The edge computing device is deployed around the distributed electric-hydrogen coupling system, communicates with the sensors of all devices through the field bus, and collects the power and hydrogen energy data of each energy device in real time. Based on the collected real-time data, the system can predict the power generation capacity of various distributed power generation equipment and the power demand of various loads. At the same time, the system also determines or switches to the current operating mode in combination with the manually input data. Next, the system will optimize the objective function and constraints of each operating mode, and calculate the optimal operating strategy for each mode through the optimization algorithm. Finally, these optimal operating strategies will be sent to each energy device, and each device will make corresponding operating adjustments according to the received instructions. Through such coordinated control, the distributed electric-hydrogen coupling system can achieve efficient and stable energy management.

[0170] The following is a detailed description of the coordinated control method of the distributed electric-hydrogen coupling system based on edge computing in this embodiment. Figure 1 As shown, this embodiment provides a coordinated control method for a distributed electric-hydrogen coupling system based on edge computing, and the method comprises the following steps:

[0171] Step S0: Designing a distributed electricity-hydrogen coupling system based on edge computing; the distributed electricity-hydrogen coupling system is designed in a distributed manner based on edge computing;

[0172] The step S0 specifically includes the following steps:

[0173] Step S01: Design a first distributed electricity-hydrogen coupling system consisting of the cloud and the edge.

[0174] As a specific implementation, the step S01 specifically includes the following steps:

[0175] Step S011: designing a first architecture consisting of a cloud control center, edge computing nodes, and energy devices;

[0176] Step S012: determining a first sensor corresponding to the first architecture;

[0177] Step S013: designing a first data collection process so that the first sensor collects data of the energy device in real time according to the first data collection process, and transmits the data to the edge computing node and the cloud control center for analysis and processing;

[0178] Step S014: Develop a first scheduling algorithm, and combine the central control of the cloud control center with the local control of the edge computing node according to the first scheduling algorithm, thereby forming a design of a first distributed electric-hydrogen coupling system consisting of the cloud and the edge.

[0179] The first architecture consists of a cloud control center, multiple edge computing nodes, and various energy devices. The cloud control center is responsible for storing and analyzing data from edge computing nodes, running complex data analysis and decision support models, and monitoring the overall operating status of the system. Edge computing nodes process real-time data closer to the equipment, provide low-latency responses and preliminary analysis, and quickly feedback control instructions to energy devices. Each energy device monitors key parameters in real time through sensors and transmits data to edge computing nodes for processing and optimization. The first architecture realizes the effective combination of cloud computing and edge computing, improves the system's responsiveness and operating efficiency, and ensures the real-time and accuracy of data.

[0180] The first sensor corresponding to the first architecture has multiple functions, including environmental monitoring, equipment status monitoring and data collection capabilities. These sensors need to be able to measure key parameters such as temperature, humidity, pressure, current and vibration in real time to ensure comprehensive monitoring of energy equipment. At the same time, they should have the characteristics of high precision and fast response to support real-time data processing and analysis of edge computing nodes. In addition, the first sensor must also have wireless or wired communication capabilities to efficiently transmit the collected data to the edge computing node, realize real-time transmission and analysis of data, and thus optimize the operation of the overall system.

[0181] The first scheduling algorithm is used to efficiently coordinate the central control of the cloud control center and the local control of the edge computing nodes to achieve close cooperation between the cloud and the edge, thereby forming an efficient distributed electric-hydrogen coupling system. The algorithm collects and analyzes the equipment operation data and environmental information from the edge computing nodes in real time, evaluates the overall status and load requirements of the system, and makes centralized decisions in the cloud control center. On this basis, the algorithm will generate a reasonable scheduling strategy and send optimization instructions to each edge computing node so that they can automatically adjust the operation mode and parameters of the electric-hydrogen equipment according to the local real-time situation. In addition, the algorithm also has an adaptive adjustment mechanism that can quickly adjust the scheduling strategy according to the dynamic changes of the system, achieve load balancing and resource optimization, and ensure efficient use of energy and system stability.

[0182] The first data collection process specifically refers to the complete steps to ensure that the first sensor can effectively collect data from energy equipment in real time and transmit this data to the edge computing node and cloud control center for analysis and processing. The following are the main steps of the data collection process:

[0183] (1) Data collection:

[0184] Sensor layout: According to the working characteristics of the energy equipment, the first sensor should be reasonably arranged to ensure that it can accurately reflect the operating status and related parameters of the equipment (such as temperature, pressure, current, etc.).

[0185] Real-time monitoring: The first sensor collects the required data in real time during the operation of the equipment. The frequency of data collection should be set according to specific needs to ensure that the collected data shows the dynamic changes of the equipment.

[0186] (2) Data preprocessing:

[0187] Data cleaning: Perform preliminary cleaning on the collected data to remove noise and outliers to improve data quality.

[0188] Data formatting: Process the cleaned data according to a predetermined format to facilitate subsequent transmission and storage.

[0189] (3) Data transmission:

[0190] Edge computing node access: The processed data is transmitted to the edge computing node in real time via wireless network or wired mode to ensure minimal delay.

[0191] Data caching: A data caching mechanism is set up at edge nodes to prevent data loss due to transmission interruptions.

[0192] (4) Data analysis and processing:

[0193] Edge computing processing: Perform preliminary analysis on the received data at the edge node, extract key indicators, and make necessary local decisions or predictions.

[0194] Upload to the cloud control center: Upload the analyzed important data and original data in batches to the cloud control center for deeper data analysis and processing.

[0195] (5) Feedback mechanism:

[0196] Result feedback: The cloud analysis results or control instructions are fed back to the edge computing node, and then transmitted back to the first sensor when necessary to optimize the operating status of the equipment.

[0197] Monitoring and optimization: Through continuous monitoring and data analysis, the data collection process is continuously optimized to improve the system's response speed and data processing efficiency.

[0198] Through the above process, the first sensor can realize real-time monitoring and data transmission of energy equipment.

[0199] Step S02: Design a second distributed electric-hydrogen coupling system consisting of edge terminals and side terminals.

[0200] The step S02 specifically includes the following steps:

[0201] Step S021: designing a second architecture consisting of edge computing nodes and energy devices; the edge computing nodes communicate with each other through point-to-point communication;

[0202] Step S022: determining a second sensor corresponding to the second architecture;

[0203] Step S023: designing a second data collection process so that the second sensor collects data of the energy device in real time according to the second data collection process, and transmits the data to the edge computing node for analysis and processing;

[0204] Step S024: Develop a second scheduling algorithm, and combine the local control of the edge computing nodes according to the second scheduling algorithm to form a design of a second distributed electric-hydrogen coupling system consisting of edge terminals and edge nodes.

[0205] The second architecture consists of multiple edge computing nodes and various energy devices, and realizes direct data interaction and coordination between nodes through point-to-point communication. Each edge computing node is responsible for local data processing, real-time monitoring and control tasks, and shares information with adjacent nodes to improve the response speed and reliability of the system. Each energy device collects key parameters (such as current, temperature, pressure, etc.) in real time through sensors, and transmits the data to the corresponding edge computing node for processing and analysis. In addition, each edge computing node can quickly adjust the control strategy according to the data received from local and adjacent nodes to achieve load balancing and resource optimization. The second architecture improves the flexibility of the system, allowing each node to respond quickly to changes in itself and the surrounding environment, thereby improving the efficiency and stability of overall energy management.

[0206] The second sensor corresponding to the second architecture should have multiple functions to support efficient collaboration between edge computing nodes and energy equipment. These sensors need to be able to monitor and collect data from key links in real time, such as current sensors for measuring current loads, temperature sensors for monitoring equipment and ambient temperature, and pressure sensors for ensuring that the system operates within a safe range. In addition, the second sensor should also have high accuracy and fast response capabilities to ensure that real-time data can be transmitted to nearby edge computing nodes in a timely manner for immediate processing and analysis. At the same time, these sensors need to support multiple communication protocols (such as Modbus, CAN bus or LoRaWAN) to achieve flexible data transmission and network connection to ensure the real-time and reliability of the system.

[0207] The second scheduling algorithm is used to optimize the coordination and control between the edge and the edge to build an efficient distributed electric-hydrogen coupling system. The algorithm first collects real-time data from each edge computing node, including equipment status, load demand, and environmental conditions, and then performs local state evaluation to quickly identify potential performance bottlenecks or optimization opportunities. Based on these evaluation results, the algorithm generates a scheduling strategy for each edge node to determine the operating mode and load distribution of each energy device within a specific time period. Through point-to-point communication, these nodes can share each other's operating data and control instructions to achieve dynamic adjustment and collaborative control. In addition, the second scheduling algorithm also has adaptive capabilities, and can flexibly adjust strategies according to real-time state changes and external conditions to optimize the overall performance of the system, achieve load balancing and efficient use of resources, and ensure the stability and reliability of system operation.

[0208] The second data collection process refers to the specific steps of the second sensor collecting energy equipment data in real time and transmitting it to the edge computing node for analysis and processing. The following are the main steps of the process:

[0209] (1) Sensor installation and configuration:

[0210] Sensor selection: Select a suitable second sensor (such as vibration sensor, flow sensor, etc.) according to the characteristics of the energy equipment.

[0211] Installation and debugging: Install the second sensor at the key position of the equipment and debug it to ensure its normal operation and accuracy of data collection.

[0212] (2) Real-time data collection:

[0213] Data monitoring: The second sensor continuously monitors the operating status of the energy equipment and collects the required data (such as flow, pressure, vibration frequency, etc.) in real time.

[0214] Sampling frequency setting: According to the dynamic characteristics of the device and application requirements, set a reasonable data sampling frequency to ensure timely capture of real-time changes.

[0215] (3) Data preprocessing and organization:

[0216] Data cleaning: Clean the collected data to remove noise and outliers to improve the reliability of the data.

[0217] Data formatting: Organize the cleaned data according to a predetermined format to prepare for subsequent transmission and processing.

[0218] (4) Data transmission:

[0219] Transmit via the network: The sorted data is transmitted to the edge computing node in real time via wireless or wired networks. Communication protocols such as MQTT and HTTP can be used for data transmission.

[0220] Data encryption: To ensure data security, the transmitted data can be encrypted to prevent the data from being stolen or tampered with during transmission.

[0221] (5) Edge computing analysis and processing:

[0222] Real-time data reception: The edge computing node receives the data transmitted by the second sensor.

[0223] Preliminary analysis: Perform real-time analysis on the received data, such as data trend monitoring, anomaly detection, etc., and respond quickly.

[0224] (6) Feedback and Optimization:

[0225] Feedback mechanism: Feedback the preliminary analysis results and necessary control instructions to the second sensor to ensure that the status of the equipment can be effectively adjusted.

[0226] Continuous monitoring and optimization: Through continuous monitoring and analysis of data, the data collection process is continuously optimized to improve the performance and response efficiency of the system.

[0227] Step S03: Combine the first distributed electricity-hydrogen coupling system and the second distributed electricity-hydrogen coupling system to obtain a distributed electricity-hydrogen coupling system with cloud control and edge autonomy capabilities.

[0228] like Figure 7 As shown, this embodiment combines the first distributed electricity-hydrogen coupling system with the second distributed electricity-hydrogen coupling system to construct a distributed electricity-hydrogen coupling system with cloud control and edge autonomy capabilities.

[0229] (1) Cloud control:

[0230] Centralized management: The cloud system can be responsible for overall strategic planning and scheduling, optimizing the distribution and utilization of energy. In the cloud, big data and artificial intelligence technologies are used for trend analysis and decision support to ensure that the system can adapt to changing environments and user needs.

[0231] Data aggregation: collects real-time data from each edge, conducts comprehensive analysis and processing, and provides a basis for optimizing system operation.

[0232] (2) Edge autonomy:

[0233] Local decision-making: Edge devices can make independent real-time decisions based on data collected by local sensors and prediction results. This enhances the flexibility and responsiveness of the system and reduces dependence on the cloud.

[0234] Autonomous scheduling: Each distributed electric-hydrogen coupling system can implement autonomous scheduling according to its own operating status and power demand, optimize local energy management, and ensure stable operation in the event of cloud communication interruption or delay.

[0235] Through this combination, the distributed electric-hydrogen coupling system not only improves the overall operational efficiency, but also enhances the system's anti-interference ability and achieves a higher level of intelligent management.

[0236] The connection of distributed electric-hydrogen coupling systems with cloud control and edge autonomy capabilities is as follows Figure 3 As shown, it includes the thick solid line hydrogen energy flow, the thin solid line electric energy flow, and the dotted line communication line.

[0237] Step S1: Obtain the power information and hydrogen energy data of each energy device in the distributed electricity-hydrogen coupling system; the distributed electricity-hydrogen coupling system is designed in a distributed manner based on edge computing.

[0238] like Figure 6As shown in the figure, the typical topology of the distributed electric-hydrogen coupling system not only relies on the power distribution system of the power grid, but also includes distributed photovoltaic and wind energy as power sources. The energy storage system consists of two parts: battery energy storage and hydrogen energy storage. Battery energy storage realizes the storage and release of electric energy by controlling the charge and discharge of the battery, while hydrogen energy storage produces and stores hydrogen through electrolyzers and uses fuel cells to generate electricity. In addition, the hydrogen energy storage system can also provide hydrogen for hydrogen heavy trucks.

[0239] Electrical loads are divided into three categories:

[0240] Class I load (or Class 1 load): non-interruptible terminal load;

[0241] The second type of load (or type 2 load): loads that can be powered off for a short period of time (e.g., less than 2 hours);

[0242] Category III load (or Category 3 load): load that can stop supplying power for a relatively long time (such as more than 2 hours).

[0243] This classification helps optimize the operation of power supply and management systems.

[0244] Step S2: predicting the power generation of various distributed power generation equipment and the power consumption of various loads based on the power information and the hydrogen energy data.

[0245] Based on the collected power information and hydrogen energy data, this embodiment can make the following predictions:

[0246] (1) Power generation forecast:

[0247] Utilize historical power generation data and current weather conditions (such as sunshine intensity, wind speed, etc.) to predict the future power generation of various distributed power generation equipment (such as photovoltaic and wind power).

[0248] Apply machine learning or time series analysis models to improve forecast accuracy.

[0249] (2) Electricity consumption forecast:

[0250] Analyze historical electricity consumption data and current electricity load, and predict the future electricity demand of various loads.

[0251] The power load is divided into three categories (such as non-interruptible load, short-term stoppable load and long-term stoppable load), and each type of load is predicted separately to facilitate the formulation of a reasonable power dispatching strategy.

[0252] These predictions can not only provide a basis for the selection of the system's operating mode, but also optimize the scheduling and allocation of energy, and improve the overall efficiency and reliability of the system.

[0253] Step S3: selecting a target operation mode of the distributed electricity-hydrogen coupling system according to the power generation of the distributed power generation equipment and the power consumption of various loads.

[0254] As a specific implementation, the target operation model in step S3 is specifically one of a strong grid-connected mode, a weak grid-connected mode, an island mode, or a virtual power plant mode.

[0255] The switching between different operation modes is a combination of manual and automatic methods. The specific switching conditions are as follows: Figure 5 The switching includes the following situations:

[0256] (1) The original state is the virtual power plant mode: when there is no grid dispatch instruction, it automatically switches to the strong grid connection mode.

[0257] (2) The original state is the strong grid-connected mode: when there is a grid dispatch instruction, it automatically switches to the virtual power plant mode; after setting (or limiting) the connection power with the grid, it manually switches to the weak grid-connected mode; when monitoring the disconnection from the grid, it automatically switches to the island mode; when the grid connection switch is turned on, it manually switches to the island mode.

[0258] (3) The original state is the weak grid-connected mode: when the grid connection power needs to be increased, it is manually switched to the strong grid-connected mode; when the monitoring detects that it is disconnected from the grid, it automatically switches to the island mode; when the grid connection switch is turned on, it is manually switched to the island mode.

[0259] (4) The original state is the island mode: when the electrical interconnection switch is closed and the interconnection power is not limited, it is manually switched to the strong grid-connected mode; when the electrical interconnection switch is closed and the interconnection power is set (or limited), it is manually switched to the weak grid-connected mode.

[0260] like Figure 4 The figure shows the switching topology between different operation modes, including the following cases:

[0261] (1) The switching between the strong grid-connected mode and the weak grid-connected mode is performed according to the connection power with the grid. When the connection power with the grid is limited, the strong grid-connected mode is switched to the weak grid-connected mode. When the connection power with the grid is not limited, the weak grid-connected mode is switched to the strong grid-connected mode.

[0262] (2) When receiving grid dispatch, switch from strong grid-connected mode to virtual power plant mode; when disconnected from grid dispatch, switch from virtual power plant mode to strong grid-connected mode.

[0263] (3) When the monitoring detects that the power grid is disconnected, the mode is switched from the strong grid-connected mode or the weak grid-connected mode to the island mode; when the grid connection switch is turned on, the mode is switched from the island mode to the strong grid-connected mode or the weak grid-connected mode.

[0264] Step S4: Obtaining the optimal operation strategy of the target operation mode according to the optimization objective function and constraint conditions of the target operation mode.

[0265] In step S4, according to the optimization objective function and constraint conditions of the target operation mode, the optimal operation strategy of the target operation mode is obtained, which specifically includes the following steps:

[0266] According to the optimization objective function and constraint conditions of the strong grid-connected mode, the optimal operation strategy of the strong grid-connected mode is obtained; or,

[0267] According to the optimization objective function and constraint conditions of the weak grid-connected mode, the optimal operation strategy of the weak grid-connected mode is obtained; or,

[0268] According to the optimization objective function and constraint conditions of the island mode, the optimal operation strategy of the island mode is obtained; or,

[0269] According to the optimization objective function and constraints of the virtual power plant model, the optimal operation strategy of the virtual power plant model is obtained.

[0270] As a specific implementation method, the optimal operation strategy of the strong grid-connected mode is obtained according to the optimization objective function and constraint conditions of the strong grid-connected mode, which specifically includes the following steps:

[0271] Step A1: setting the minimum operating cost of the strong grid-connected mode as the optimization target; and setting the equality constraint condition of the strong grid-connected mode and the inequality constraint condition of the strong grid-connected mode;

[0272] The calculation formula of the minimum operating cost of the strong grid-connected mode is as follows:

[0273]

[0274] In the formula, f 强并网 represents the minimum operating cost of the strong grid-connected mode, F 强并网 represents the operating cost of the strong grid-connected mode;

[0275] E gd，t and E gu，t are the off-grid power and on-grid power at time t, respectively, gd，t and P gu，t are the off-grid electricity price and on-grid electricity price at time t, respectively, t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price;

[0276] The equation constraints of the strong grid connection mode include a first total electricity balance equation constraint, a first battery energy storage balance equation constraint, and a first hydrogen storage tank hydrogen storage balance equation constraint;

[0277] The equation constrained by the first total power balance equation is as follows:

[0278] E gd，t +E w，t +E p，t =E gu，t +E b，t +E l1，t +E l2，t +E l3，t +E h，t

[0279] Among them, E gd，t and E gu，t are the off-grid power and on-grid power at time t, respectively, w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and E l3，t They are the power consumption of the first type of load, the second type of load and the third type of load at time t respectively;

[0280] The equation for the first battery energy storage balance equation constraint is as follows:

[0281] E b0，t+1 =E b0，t +E b，t

[0282] In the formula, E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0283] The equation constrained by the hydrogen storage balance equation of the first hydrogen storage tank is as follows:

[0284] H s，t+1 =H s，t +E h，t *α-H truck，t

[0285] In the formula, H s，t , H s，t+1 are the hydrogen storage capacity in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the amount of hydrogen added to the hydrogen heavy truck at time t;

[0286] The inequality constraint condition of the strong grid-connected mode is the upper and lower limits of the operation of each energy device in the strong grid-connected mode;

[0287] Step A2: according to the minimum operation cost of the strong grid-connected mode, the equality constraint condition of the strong grid-connected mode, and the inequality constraint condition of the strong grid-connected mode, solving and obtaining the optimal operation plan of the strong grid-connected mode;

[0288] Step A3: According to the optimal operation plan of the strong grid-connected mode, an optimal operation strategy under the strong grid-connected mode is obtained.

[0289] As a specific implementation method, the optimal operation strategy of the weak grid-connected mode is obtained according to the optimization objective function and constraint conditions of the weak grid-connected mode, which specifically includes the following steps:

[0290] Step B1: setting the minimum operating cost of the weak grid-connected mode as the optimization target; and setting the equality constraint condition of the weak grid-connected mode and the inequality constraint condition of the weak grid-connected mode;

[0291] The calculation formula of the minimum operating cost of the weak grid-connected mode is as follows:

[0292]

[0293] In the formula, f 弱并网 represents the minimum operating cost of the weak grid-connected mode, F 弱并网 represents the operating cost of the weak grid-connected mode;

[0294] E gd，t is the off-grid power at time t, P gd，t is the off-grid electricity price at time t, H t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E l3s，t is the third type of load reduction, P l，t Compensation price for the third category of load reduction;

[0295] The equation constraints of the weak grid-connected mode include a second total electricity balance equation constraint, a second battery energy storage balance equation constraint, and a second hydrogen storage tank hydrogen storage balance equation constraint;

[0296] The equation of the second total power balance equation constraint is as follows:

[0297] E gd，t +E w，t +E p，t =E b，t +E l1，t +E l2，t +E l3，t -E l3s，t +E h，t ;

[0298] Among them, E gd，t is the off-grid power at time t, E w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and E l3，t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. l3s，t The load reduction for the third category;

[0299] The equation for the second battery energy storage balance equation constraint is as follows:

[0300] E b0，t+1 =E b0，t +E b，t

[0301] In the formula, E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0302] The equation constrained by the hydrogen storage balance equation of the second hydrogen storage tank is as follows:

[0303] H s，t+1 =H s，t +E h，t *α-H truck，t

[0304] In the formula, H s，t , H s，t+1 are the hydrogen storage capacity in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the amount of hydrogen added to the hydrogen heavy truck at time t;

[0305] The inequality constraint condition of the weak grid-connected mode is the upper and lower limits of the operation of each energy device in the weak grid-connected mode;

[0306] Step B2: solving the optimal operation plan of the weak grid-connected mode according to the minimum operation cost of the weak grid-connected mode, the equality constraint condition of the weak grid-connected mode, and the inequality constraint condition of the weak grid-connected mode;

[0307] Step B3: according to the optimal operation scheme of the weak grid-connected mode, an optimal operation strategy in the weak grid-connected mode is obtained.

[0308] As a specific implementation method, the optimal operation strategy of the island mode is obtained according to the optimization objective function and constraint conditions of the island mode, which specifically includes the following steps:

[0309] Step C1: setting the minimum operating cost of the island mode as the optimization target; and setting the equality constraint condition of the island mode and the inequality constraint condition of the island mode;

[0310] The calculation formula for the minimum operating cost of the island mode is as follows:

[0311]

[0312] In the formula, f 孤岛 represents the minimum operating cost of the weak grid-connected mode, F 孤岛 represents the operating cost of the weak grid-connected mode;

[0313] H t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E l2s，t and E l3s，t are the second type of load reduction and the third type of load reduction, P l，t is the reduction compensation price for the second type of load; the reduction compensation price for the third type of load is equal to the reduction compensation price for the second type of load;

[0314] The equation constraints of the island mode include a third total electricity balance equation constraint, a third battery energy storage balance equation constraint, and a third hydrogen storage tank hydrogen storage balance equation constraint;

[0315] The equation of the third total power balance equation constraint is as follows:

[0316] E fc，t +E w，t +E p，t =E b，t +E l1，t +E l2，t -E l2s，t +E l3，t -E l3s，t +E h，t

[0317] In the formula, E fc，t is the power generation of the fuel cell at time t, E w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and El3，t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. l2s，t and E l3s，t They are the second category load reduction and the third category load reduction respectively;

[0318] The equation of the third battery energy storage balance equation constraint is as follows:

[0319] E b0，t+1 =E b0，t +E b，t

[0320] In the formula, E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0321] The equation constrained by the hydrogen storage balance equation of the third hydrogen storage tank is as follows:

[0322] H s，t+1 =H s，t +E h，t *α-H truck，t -E fc，t *β

[0323] In the formula, H s，t , H s，t+1 are the hydrogen storage capacity in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the hydrogenation amount of the hydrogen heavy truck at time t, E fc，t is the power generation of the fuel cell at time t, β is the power generation efficiency of the fuel cell;

[0324] The inequality constraints of the island mode are the upper and lower limits of the operation of each energy device in the island mode;

[0325] Step C2: solving the optimal operation plan of the island mode according to the minimum operation cost of the island mode, the equality constraint condition of the island mode, and the inequality constraint condition of the island mode;

[0326] Step C3: according to the optimal operation scheme of the island mode, obtain the optimal operation strategy in the island mode.

[0327] As a specific implementation method, the optimal operation strategy of the virtual power plant mode is obtained according to the optimization objective function and constraint conditions of the virtual power plant mode, which specifically includes the following steps:

[0328] Step D1: setting the minimum operating cost of the virtual power plant mode as the optimization target; and setting the equality constraint condition of the virtual power plant mode and the inequality constraint condition of the virtual power plant mode;

[0329] The calculation formula of the minimum operating cost of the virtual power plant mode is as follows:

[0330]

[0331] In formula (13), f 虚拟电厂 represents the minimum operating cost of the weak grid-connected mode, F 虚拟电厂 represents the operating cost of the weak grid-connected mode;

[0332] E gd，t and E gu，t are the off-grid power and on-grid power at time t, respectively, gd，t and P gu，t are the off-grid electricity price and on-grid electricity price at time t, respectively, t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E s，t is the total system load reduction, E l2s，t and E l3s，t are the second type of load reduction and the third type of load reduction, P l，t Compensation price for load reduction;

[0333] The equation constraints of the virtual power plant mode include a fourth total electricity balance equation constraint, a fourth battery energy storage balance equation constraint, and a fourth hydrogen storage tank hydrogen storage balance equation constraint;

[0334] The fourth total power balance equation constraint is as follows:

[0335] E fc，t +E w，t +E p，t =E b，t +E l1，t +E l2，t +E l3，t -E s，t +E h，t

[0336] In the formula, E fc，t is the power generation of the fuel cell at time t, E w，t is the wind power generation at time t, E p，t is the photovoltaic power generation at time t, E h，t is the power consumption of the electrolytic cell at time t, E b，t is the battery energy charge and discharge at time t, E l1，t 、E l2，t and E l3，tThey are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t.

[0337] E s，tt is the total system load reduction;

[0338] The equation of the fourth battery energy storage balance equation constraint is as follows:

[0339] E b0，t+1 =E b0，t +E b，t

[0340] In the formula, E b0，t , E bo，t+1 are the remaining power of the battery at time t and time t+1, E b，t is the battery energy storage charge and discharge at time t;

[0341] The equation constrained by the hydrogen storage balance equation of the fourth hydrogen storage tank is as follows:

[0342] H s，t+1 =H s，t +E h，t *α-H truck，t -E fc，t *β

[0343] In the formula, H s，t , H s，t+1 are the amount of hydrogen stored in the hydrogen storage tank at time t and time t+1, E h，t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck，t is the hydrogenation amount of the hydrogen heavy truck at time t, E fc，t is the power generation of the fuel cell at time t, β is the power generation efficiency of the fuel cell;

[0344] The inequality constraints of the virtual power plant mode are the upper and lower limits of the operation of each energy device in the virtual power plant mode;

[0345] Step D2: solving the optimal operation plan of the virtual power plant mode according to the minimum operation cost of the virtual power plant mode, the equality constraint condition of the virtual power plant mode, and the inequality constraint condition of the virtual power plant mode;

[0346] Step D3: According to the optimal operation plan of the virtual power plant mode, the optimal operation strategy under the virtual power plant mode is obtained.

[0347] Step S5: Send the optimal operation strategy to each energy device in the distributed electricity-hydrogen coupling system, so that the energy device operates according to the optimal operation strategy, thereby completing the coordinated control of the distributed electricity-hydrogen coupling system based on edge computing.

[0348] This embodiment adopts a comprehensive coordinated control system, which can significantly improve the control speed of the distributed electric-hydrogen coupling system, thereby improving the overall operating efficiency. At the same time, this method also enhances the reliability and scalability of the distributed electric-hydrogen coupling system, enabling it to better adapt to the needs of different scales and application scenarios, thereby providing a more efficient, safe, economical and reliable operating environment for the distributed electric-hydrogen coupling system.

[0349] Embodiment 2:

[0350] like Figure 8 As shown, this embodiment provides a coordinated control device for a distributed electric-hydrogen coupling system based on edge computing, and the device includes:

[0351] An acquisition unit 10 is used to acquire power information and hydrogen energy data of each energy device in the distributed electricity-hydrogen coupling system;

[0352] The prediction unit 20 is connected to the acquisition unit 10 and is used to predict the power generation of various distributed power generation equipment and the power consumption of various loads according to the power information and the hydrogen energy data;

[0353] The selection unit 30 is connected to the prediction unit 20 and is used to select a target operation mode of the distributed electric-hydrogen coupling system according to the power generation of the distributed power generation equipment and the power consumption of various loads;

[0354] The processing unit 40 is connected to the selection unit 30 and is used to obtain the optimal operation strategy of the target operation mode according to the optimization objective function and constraint conditions of the target operation mode;

[0355] The sending unit 50 is connected to the processing unit 40, and is used to send the optimal operation strategy to each energy device in the distributed electric-hydrogen coupling system, so that the energy device operates according to the optimal operation strategy, thereby completing the coordinated control of the distributed electric-hydrogen coupling system based on edge computing.

[0356] As a specific implementation, the device further includes a design unit;

[0357] The design unit is connected to the acquisition unit and is used to design a distributed electric-hydrogen coupling system based on edge computing;

[0358] The design unit comprises:

[0359] The first design module is used to design the first distributed electricity-hydrogen coupling system consisting of the cloud and edge;

[0360] A second design module, connected to the first design module, for designing a second distributed electric-hydrogen coupling system consisting of an edge terminal and an edge terminal;

[0361] A combination module is respectively connected to the first design module and the second design module, and is used to combine the first distributed electric-hydrogen coupling system and the second distributed electric-hydrogen coupling system to obtain a distributed electric-hydrogen coupling system with cloud control and edge autonomy capabilities.

[0362] As a specific implementation, the processing unit 40 includes:

[0363] A first processing module, connected to the selection unit, for obtaining an optimal operation strategy for the strong grid-connected mode according to the optimization objective function and constraint conditions of the strong grid-connected mode in the target operation model;

[0364] A second processing module, connected to the selection unit, is used to obtain an optimal operation strategy for the weak grid-connected mode according to the optimization objective function and constraint conditions of the weak grid-connected mode in the target operation model;

[0365] A third processing module, connected to the selection unit, is used to obtain an optimal operation strategy of the island mode according to the optimization objective function and constraint conditions of the island mode in the target operation model;

[0366] The fourth processing module is connected to the selection unit and is used to obtain the optimal operation strategy of the virtual power plant mode according to the optimization objective function and constraint conditions of the virtual power plant mode in the target operation model.

[0367] The device in this embodiment can execute the method in embodiment 1.

[0368] Embodiment 3:

[0369] The present embodiment provides a host computer of an integrated energy station, on which a computer program is stored, and is characterized in that when the computer program is executed by a processor, the coordinated control method of a distributed electric-hydrogen coupling system based on edge computing described in Example 1 is implemented.

[0370] It is to be understood that the above embodiments are merely exemplary embodiments used to illustrate the principles of the present invention, but the present invention is not limited thereto. For those of ordinary skill in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A coordinated control method for a distributed electric-hydrogen coupling system based on edge computing, characterized in that: The method comprises the following steps: Step S1: Obtaining power information and hydrogen energy data of each energy device in the distributed electricity-hydrogen coupling system; Step S2: predicting the power generation of various distributed power generation equipment and the power consumption of various loads according to the power information and the hydrogen energy data; Step S3: selecting a target operation mode of the distributed electricity-hydrogen coupling system according to the power generation of the distributed power generation equipment and the power consumption of various loads; Step S4: obtaining the optimal operation strategy of the target operation mode according to the optimization objective function and constraint conditions of the target operation mode; Step S5: Send the optimal operation strategy to each energy device in the distributed electricity-hydrogen coupling system, so that the energy device operates according to the optimal operation strategy, thereby completing the coordinated control of the distributed electricity-hydrogen coupling system based on edge computing.

2. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to claim 1 is characterized in that: Before step S1, the method further includes step S0; Step S0: Designing a distributed electricity-hydrogen coupling system based on edge computing; the distributed electricity-hydrogen coupling system is designed in a distributed manner based on edge computing; The step S0 specifically includes the following steps: Step S01: Design a first distributed electricity-hydrogen coupling system consisting of a cloud and an edge; Step S02: designing a second distributed electricity-hydrogen coupling system consisting of an edge terminal and an edge terminal; Step S03: Combine the first distributed electricity-hydrogen coupling system and the second distributed electricity-hydrogen coupling system to obtain a distributed electricity-hydrogen coupling system with cloud control and edge autonomy capabilities.

3. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to claim 2 is characterized in that: The step S01 specifically includes the following steps: Step S011: designing a first architecture consisting of a cloud control center, edge computing nodes, and energy devices; Step S012: determining a first sensor corresponding to the first architecture; Step S013: designing a first data collection process so that the first sensor collects data of the energy device in real time according to the first data collection process, and transmits the data to the edge computing node and the cloud control center for analysis and processing; Step S014: developing a first scheduling algorithm, and combining the central control of the cloud control center with the local control of the edge computing node according to the first scheduling algorithm, thereby forming a design of a first distributed electric-hydrogen coupling system consisting of the cloud and the edge; The step S02 specifically includes the following steps: Step S021: designing a second architecture consisting of edge computing nodes and energy devices; the edge computing nodes communicate with each other through point-to-point communication; Step S022: determining a second sensor corresponding to the second architecture; Step S023: designing a second data collection process so that the second sensor collects data of the energy device in real time according to the second data collection process, and transmits the data to the edge computing node for analysis and processing; Step S024: Develop a second scheduling algorithm, and combine the local control of the edge computing nodes according to the second scheduling algorithm to form a design of a second distributed electric-hydrogen coupling system consisting of edge terminals and edge nodes.

4. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to any one of claims 1 to 3, characterized in that: The target operation model in step S3 is specifically one of a strong grid-connected mode, a weak grid-connected mode, an island mode, or a virtual power plant mode; In step S4, according to the optimization objective function and constraint conditions of the target operation mode, the optimal operation strategy of the target operation mode is obtained, which specifically includes the following steps: According to the optimization objective function and constraint conditions of the strong grid-connected mode, the optimal operation strategy of the strong grid-connected mode is obtained; or, According to the optimization objective function and constraint conditions of the weak grid-connected mode, the optimal operation strategy of the weak grid-connected mode is obtained; or, According to the optimization objective function and constraint conditions of the island mode, the optimal operation strategy of the island mode is obtained; or, According to the optimization objective function and constraints of the virtual power plant model, the optimal operation strategy of the virtual power plant model is obtained.

5. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to claim 4 is characterized in that: The optimal operation strategy of the strong grid-connected mode is obtained according to the optimization objective function and constraint conditions of the strong grid-connected mode, which specifically includes the following steps: Step A1: setting the minimum operating cost of the strong grid-connected mode as the optimization target; and setting the equality constraint condition of the strong grid-connected mode and the inequality constraint condition of the strong grid-connected mode; The calculation formula of the minimum operation cost of the strong grid-connected mode is as follows: In formula (1), f 强并网 represents the minimum operating cost of the strong grid-connected mode, F 强并网 represents the operating cost of the strong grid-connected mode; E gd,t and E gu,t are the off-grid power and on-grid power at time t, respectively, gd,t and P gu,t are the off-grid electricity price and on-grid electricity price at time t, respectively, t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price; The equation constraints of the strong grid connection mode include a first total electricity balance equation constraint, a first battery energy storage balance equation constraint, and a first hydrogen storage tank hydrogen storage balance equation constraint; The equation constrained by the first total power balance equation is as follows: AND gd,t +E w,t +E p,t =And gu,t +E b,t +E l1,t +E l2,t +E l3,t +E h,t (2) In formula (2), E gd,t and E gu,t are the off-grid power and on-grid power at time t, respectively, w,t is the wind power generation at time t, E p,t is the photovoltaic power generation at time t, E h,t is the power consumption of the electrolytic cell at time t, E b,t is the battery energy charge and discharge at time t, E l1,t 、E l2,t and E l3,t They are the power consumption of the first type of load, the second type of load and the third type of load at time t respectively; The equation constrained by the first battery energy storage balance equation is as follows: AND b0,t+1 =And b0,t +E b,t (3) In formula (3), E b0,t , E bo,t+1 are the remaining power of the battery at time t and time t+1, E b,t is the battery energy storage charge and discharge at time t; The equation constrained by the hydrogen storage balance equation of the first hydrogen storage tank is as follows: H s,t+1 =H s,t +E h,t *α-H truck,t (4) In formula (4), H s,t , H s,t+1 are the amount of hydrogen stored in the hydrogen storage tank at time t and time t+1, E h,t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck,t is the amount of hydrogen added to the hydrogen heavy truck at time t; The inequality constraint condition of the strong grid-connected mode is the upper and lower limits of the operation of each energy device in the strong grid-connected mode; Step A2: according to the minimum operation cost of the strong grid-connected mode, the equality constraint condition of the strong grid-connected mode, and the inequality constraint condition of the strong grid-connected mode, solving and obtaining the optimal operation plan of the strong grid-connected mode; Step A3: According to the optimal operation plan of the strong grid-connected mode, an optimal operation strategy under the strong grid-connected mode is obtained.

6. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to claim 4 is characterized in that: The optimal operation strategy of the weak grid-connected mode is obtained according to the optimization objective function and constraint conditions of the weak grid-connected mode, which specifically includes the following steps: Step B1: setting the minimum operating cost of the weak grid-connected mode as the optimization target; and setting the equality constraint condition of the weak grid-connected mode and the inequality constraint condition of the weak grid-connected mode; The calculation formula of the minimum operating cost of the weak grid-connected mode is as follows: In formula (5), f 弱并网 represents the minimum operating cost of the weak grid-connected mode, F 弱并网 represents the operating cost of the weak grid-connected mode; E gd,t is the off-grid power at time t, P gd,t is the off-grid electricity price at time t, H t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E l3s,t is the third type of load reduction, P l,t The compensation price for the third category of load reduction; The equation constraints of the weak grid-connected mode include a second total electricity balance equation constraint, a second battery energy storage balance equation constraint, and a second hydrogen storage tank hydrogen storage balance equation constraint; The equation constraining the second total power balance equation is as follows: AND gd,t +E w,t +E p,t =And b,t +E l1,t +E l2,t +E l3,t -AND l3s,t +E h,t (6); In formula (6), E gd,t is the off-grid power at time t, E w,t is the wind power generation at time t, E p,t is the photovoltaic power generation at time t, E h,t is the power consumption of the electrolytic cell at time t, E b,t is the battery energy charge and discharge at time t, E l1,t 、E l2,t and E l3,t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. l3s,t The load reduction for the third category; The equation for the second battery energy storage balance equation constraint is as follows: AND b0,t+1 =And b0,t +E b,t (7) In formula (7), E b0,t , E bo,t+1 are the remaining power of the battery at time t and time t+1, E b,t is the battery energy storage charge and discharge at time t; The equation constrained by the hydrogen storage balance equation of the second hydrogen storage tank is as follows (8): H s,t+1 =H s,t +E h,t *α-H truck,t (8) In formula (8), H s,t , H s,t+1 are the amount of hydrogen stored in the hydrogen storage tank at time t and time t+1, E h,t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck,t is the amount of hydrogen added to the hydrogen heavy truck at time t; The inequality constraint condition of the weak grid-connected mode is the upper and lower limits of the operation of each energy device in the weak grid-connected mode; Step B2: solving the optimal operation plan of the weak grid-connected mode according to the minimum operation cost of the weak grid-connected mode, the equality constraint condition of the weak grid-connected mode, and the inequality constraint condition of the weak grid-connected mode; Step B3: according to the optimal operation scheme of the weak grid-connected mode, an optimal operation strategy in the weak grid-connected mode is obtained.

7. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to claim 4 is characterized in that: The optimal operation strategy of the island mode is obtained according to the optimization objective function and constraint conditions of the island mode, which specifically includes the following steps: Step C1: setting the minimum operating cost of the island mode as the optimization target; and setting the equality constraint condition of the island mode and the inequality constraint condition of the island mode; The calculation formula of the minimum operating cost of the island mode is as follows: In formula (9), f 孤岛 represents the minimum operating cost of the weak grid-connected mode, F 孤岛 represents the operating cost of the weak grid-connected mode; H t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E l2s,t and E l3s,t are the second type of load reduction and the third type of load reduction, P l,t is the reduction compensation price for the second type of load; the reduction compensation price for the third type of load is equal to the reduction compensation price for the second type of load; The equation constraints of the island mode include a third total electricity balance equation constraint, a third battery energy storage balance equation constraint, and a third hydrogen storage tank hydrogen storage balance equation constraint; The third total power balance equation constraint is expressed as equation (10): AND fc,t +E w,t +E p,t =And b,t +E l1,t +E l2,t -AND l2s,t +E l3,t -AND l3s,t +E h,t (10) In formula (10), E fc,t is the power generation of the fuel cell at time t, E w,t is the wind power generation at time t, E p,t is the photovoltaic power generation at time t, E h,t is the power consumption of the electrolytic cell at time t, E b,t is the battery energy charge and discharge at time t, E l1,t 、E l2,t and E l3,t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. l2s,t and E l3s,t They are the second category load reduction and the third category load reduction respectively; The equation constrained by the third battery energy storage balance equation is as follows: AND b0,t+1 =And b0,t +E b,t (11) In formula (11), E b0,t , E bo,t+1 are the remaining power of the battery at time t and time t+1, E b,t is the battery energy storage charge and discharge at time t; The equation constrained by the hydrogen storage balance equation of the third hydrogen storage tank is as follows (12): H s,t+1 =H s,t +E h,t *α-H trucl,t -IN fc,t *β (12) In formula (12), H s,t , H s,t+1 are the amount of hydrogen stored in the hydrogen storage tank at time t and time t+1, E h,t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck,t is the hydrogenation amount of the hydrogen heavy truck at time t, E fc,t is the power generation of the fuel cell at time t, β is the power generation efficiency of the fuel cell; The inequality constraints of the island mode are the upper and lower limits of the operation of each energy device in the island mode; Step C2: solving the optimal operation plan of the island mode according to the minimum operation cost of the island mode, the equality constraint condition of the island mode, and the inequality constraint condition of the island mode; Step C3: according to the optimal operation scheme of the island mode, obtain the optimal operation strategy in the island mode.

8. The coordinated control method of a distributed electric-hydrogen coupling system based on edge computing according to claim 4 is characterized in that: The optimal operation strategy of the virtual power plant mode is obtained according to the optimization objective function and constraint conditions of the virtual power plant mode, which specifically includes the following steps: Step D1: setting the minimum operating cost of the virtual power plant mode as the optimization target; and setting the equality constraint condition of the virtual power plant mode and the inequality constraint condition of the virtual power plant mode; The calculation formula of the minimum operating cost of the virtual power plant mode is as follows: In formula (13), f 虚拟电厂 represents the minimum operating cost of the weak grid-connected mode, F 虚拟电厂 represents the operating cost of the weak grid-connected mode; E gd,t and E gu,t are the off-grid power and on-grid power at time t, respectively, gd,t and P gu,t are the off-grid electricity price and on-grid electricity price at time t, respectively, t is the hydrogenation amount of the hydrogen heavy truck at time t, P h is the hydrogenation price, E s,t is the total system load reduction, E l2s,t and E l3s,t are the second type of load reduction and the third type of load reduction, P l,t Compensation price for load reduction; The equation constraints of the virtual power plant mode include a fourth total electricity balance equation constraint, a fourth battery energy storage balance equation constraint, and a fourth hydrogen storage tank hydrogen storage balance equation constraint; The fourth total power balance equation constraint is expressed as equation (14): AND fc,t +E w,t +E p,t =And b,t +E l1,t +E l2,t +E l3,t -AND s,t +E h,t (14) In formula (14), E fc,t is the power generation of the fuel cell at time t, E w,t is the wind power generation at time t, E p,t is the photovoltaic power generation at time t, E h,t is the power consumption of the electrolytic cell at time t, E b,t is the battery energy charge and discharge at time t, E l1,t 、E l2,t and E l3,t are the first type of load power consumption, the second type of load power consumption and the third type of load power consumption at time t, respectively. s,tt is the total system load reduction; The equation of the fourth battery energy storage balance equation constraint is as follows: AND b0,t+1 =And b0,t +E b,t (15) In formula (15), E b0,t , E bo,t+1 are the remaining power of the battery at time t and time t+1, E b,t is the battery energy storage charge and discharge at time t; The equation constrained by the hydrogen storage balance equation of the fourth hydrogen storage tank is as follows (16): H s,t+1 =H s,t +E h,t *α-H truck,t -IN fc,t *β (16) In formula (16), H s,t , H s,t+1 are the amount of hydrogen stored in the hydrogen storage tank at time t and time t+1, E h,t is the power consumption of the electrolyzer at time t, α is the electrolytic cell electricity-to-hydrogen conversion coefficient, H truck,t is the hydrogenation amount of the hydrogen heavy truck at time t, E fc,t is the power generation of the fuel cell at time t, β is the power generation efficiency of the fuel cell; The inequality constraints of the virtual power plant mode are the upper and lower limits of the operation of each energy device in the virtual power plant mode; Step D2: solving the optimal operation plan of the virtual power plant mode according to the minimum operation cost of the virtual power plant mode, the equality constraint condition of the virtual power plant mode, and the inequality constraint condition of the virtual power plant mode; Step D3: According to the optimal operation plan of the virtual power plant mode, the optimal operation strategy under the virtual power plant mode is obtained.

9. A coordinated control device for a distributed electric-hydrogen coupling system based on edge computing, characterized in that: The device comprises: An acquisition unit, used to acquire power information and hydrogen energy data of each energy device in the distributed electric-hydrogen coupling system; A prediction unit, connected to the acquisition unit, for predicting the power generation of various distributed power generation equipment and the power consumption of various loads according to the power information and the hydrogen energy data; A selection unit, connected to the prediction unit, for selecting a target operation mode of the distributed electric-hydrogen coupling system according to the power generation of the distributed power generation equipment and the power consumption of various loads; a processing unit connected to the selection unit, and configured to obtain an optimal operation strategy for the target operation mode according to the optimization objective function and constraint conditions of the target operation mode; A sending unit is connected to the processing unit and is used to send the optimal operation strategy to each energy device in the distributed electric-hydrogen coupling system, so that the energy device operates according to the optimal operation strategy, thereby completing the coordinated control of the distributed electric-hydrogen coupling system based on edge computing.

10. The coordinated control device for a distributed electric-hydrogen coupling system based on edge computing according to claim 9 is characterized in that: The device also includes a design unit; The design unit is connected to the acquisition unit and is used to design a distributed electric-hydrogen coupling system based on edge computing; The distributed electricity-hydrogen coupling system is designed based on edge computing; The design unit comprises: The first design module is used to design the first distributed electricity-hydrogen coupling system consisting of the cloud and edge; A second design module, connected to the first design module, for designing a second distributed electric-hydrogen coupling system consisting of an edge terminal and an edge terminal; A combination module is respectively connected to the first design module and the second design module, and is used to combine the first distributed electric-hydrogen coupling system and the second distributed electric-hydrogen coupling system to obtain a distributed electric-hydrogen coupling system with cloud control and edge autonomy capabilities.

11. The coordinated control device for a distributed electric-hydrogen coupling system based on edge computing according to claim 9 or 10, characterized in that: The processing unit comprises: A first processing module, connected to the selection unit, for obtaining an optimal operation strategy for the strong grid-connected mode according to the optimization objective function and constraint conditions of the strong grid-connected mode in the target operation model; A second processing module, connected to the selection unit, is used to obtain an optimal operation strategy for the weak grid-connected mode according to the optimization objective function and constraint conditions of the weak grid-connected mode in the target operation model; A third processing module, connected to the selection unit, is used to obtain an optimal operation strategy of the island mode according to the optimization objective function and constraint conditions of the island mode in the target operation model; The fourth processing module is connected to the selection unit and is used to obtain the optimal operation strategy of the virtual power plant mode according to the optimization objective function and constraint conditions of the virtual power plant mode in the target operation model.

12. A host computer of an integrated energy station, on which a computer program is stored, characterized in that: When the computer program is executed by a processor, the coordinated control method of a distributed electric-hydrogen coupling system based on edge computing as described in any one of claims 1 to 8 is implemented.

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