Hydrogen hub planning method and device for multi-region power distribution network interconnection

Abstract

The application provides a hydrogen hub planning method and device for multi-region power distribution network interconnection, and applies to the technical field of power distribution network energy storage planning. The method comprises the following steps: constructing a hydrogen hub planning model according to a target function and multiple constraint conditions which are constructed based on power distribution network parameters of a multi-region power distribution network, photovoltaic load data, planning target configuration information and hydrogen hub planning parameters; solving multiple robust planning models which are constructed by multiple uncertain source-load data sets determined according to multiple uncertain budget values and certain source-load data sets and the hydrogen hub planning model, to obtain multiple hydrogen hub planning schemes; obtaining multiple target function values according to multiple offset test scenario sets obtained by taking intersections of a predetermined uncertain source-load data set and multiple offset source-load scenario sets, the hydrogen hub planning scheme and the robust planning model; and determining a hydrogen hub planning scheme corresponding to a target uncertain budget value determined according to the multiple target function values as a target hydrogen hub planning scheme.

Description

Technical Field

[0001] This invention relates to the field of energy storage planning technology for power distribution networks, and more specifically, to a method and apparatus for planning hydrogen hubs for interconnecting multi-regional power distribution networks. Background Technology

[0002] In multi-regional distribution network collaborative optimization, distribution network energy storage systems play an energy balancing role over time. Hydrogen hubs are a type of distribution network energy storage within the distribution network scenario; therefore, hydrogen hubs can serve as energy exchange stations connecting multiple regional flexible distribution networks. Consequently, the hydrogen hub planning problem is a distribution network energy storage planning problem.

[0003] When dealing with the problem of energy storage planning in distribution networks, related technologies often consider short-term planning of electrochemical energy storage, lacking adaptability to seasonal imbalances in the power supply and load of the distribution network; or they consider long-term energy management needs but ignore the collaborative planning of flexible distribution networks in multiple regions; or they fail to consider the uncertainty of renewable energy output and load levels, resulting in poor robustness of the planning scheme; or it is difficult to select appropriate parameters to obtain a robust and cost-effective planning scheme. Summary of the Invention

[0004] In view of this, the present invention provides a method and apparatus for planning hydrogen hubs for interconnection of multi-regional power distribution networks.

[0005] According to one aspect of the present invention, a hydrogen hub planning method for multi-regional distribution network interconnection is provided, comprising: constructing an objective function based on distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters; constructing a hydrogen hub planning model based on the objective function, multi-regional distribution network operation constraints, power capacity constraints of distribution devices corresponding to distribution devices in each regional distribution network, hydrogen hub planning constraints, energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and power constraints between the hydrogen hub and the DC-DC converters in the distribution devices; determining multiple uncertain source load datasets based on multiple uncertain budget values ​​and a deterministic source load dataset obtained based on photovoltaic load data, and using a solver to solve the model constructed based on the multiple uncertain source load datasets and the hydrogen hub planning model. Multiple robust planning models are solved separately to obtain multiple hydrogen hub planning schemes. The planning target configuration information includes the planning parameters in the hydrogen hub planning scheme. The intersection of the predetermined uncertain source load dataset and multiple offset source load scenario sets obtained from the deterministic source load dataset and multiple sets of random coefficients is taken to obtain multiple offset test scenario sets. Based on the multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, multiple objective function values ​​are obtained. The predetermined uncertain source load dataset is obtained from the uncertain budget value equal to the predetermined value. Based on the multiple objective function values ​​corresponding to each of the multiple uncertain budget values, the target uncertain budget value is determined from the multiple uncertain budget values, and the hydrogen hub planning scheme corresponding to the target uncertain budget value is determined as the target hydrogen hub planning scheme.

[0006] According to another aspect of the present invention, a hydrogen hub planning device for multi-regional distribution network interconnection is provided, comprising: a first construction module for constructing an objective function based on the distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters; a second construction module for constructing a hydrogen hub planning model based on the objective function, multi-regional distribution network operation constraints, power capacity constraints of distribution devices corresponding to distribution devices in each regional distribution network, hydrogen hub planning constraints, energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and power constraints between the hydrogen hub and the DC-DC converters in the distribution devices; and a first obtaining module for determining multiple uncertain source load datasets based on multiple uncertain budget values ​​and a deterministic source load dataset obtained based on photovoltaic load data, and using a solver to process the data based on the multiple uncertain source load datasets and the hydrogen hub planning parameters. The first module solves multiple robust planning models constructed from the planning model to obtain multiple hydrogen hub planning schemes. The planning target configuration information includes the planning parameters in the hydrogen hub planning scheme. The second module is used to take the intersection of the predetermined uncertain source load dataset with multiple offset source load scenario sets obtained from the deterministic source load dataset and multiple sets of random coefficients to obtain multiple offset test scenario sets. Based on the multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, multiple objective function values ​​are obtained. The predetermined uncertain source load dataset is obtained from the uncertain budget value equal to the predetermined value. The third module is used to determine the target uncertain budget value from the multiple uncertain budget values ​​based on the multiple objective function values ​​corresponding to each of the multiple uncertain budget values, and determine the hydrogen hub planning scheme corresponding to the target uncertain budget value as the target hydrogen hub planning scheme.

[0007] According to an embodiment of the present invention, the objective function is constrained by introducing multi-regional distribution network operation constraints, power capacity constraints of distribution devices corresponding to distribution devices in each regional distribution network, hydrogen hub planning constraints, energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and power constraints between the hydrogen hub and the DC-DC converter in the distribution device into the hydrogen hub planning model, so as to fully consider the seasonal energy balance of the distribution network and the collaborative planning of multi-regional flexible distribution networks during modeling.

[0008] Simultaneously, based on multiple uncertain budget values ​​and a deterministic source-load dataset obtained from photovoltaic load data, multiple uncertain source-load datasets can be determined. A solver is then used to solve multiple robust planning models constructed from these datasets and the hydrogen hub planning model, yielding multiple hydrogen hub planning schemes. The intersection of the predetermined uncertain source-load dataset with multiple offset source-load scenario sets obtained from the deterministic dataset and multiple sets of random coefficients is used to obtain multiple offset test scenario sets. Based on these offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, multiple objective function values ​​are obtained. Finally, based on the multiple objective function values ​​corresponding to each uncertain budget value, a target uncertain budget value is determined from the multiple uncertain budget values, and the hydrogen hub planning scheme corresponding to the target uncertain budget value is identified as the target hydrogen hub planning scheme. This approach fully considers the uncertainty of source and load issues, performs uncertainty modeling and robust transformation of the planning problem, establishes multiple robust planning models corresponding to multiple uncertain budget values, and robustly verifies the offset scenarios based on multiple offset test scenario sets and multiple objective function values ​​obtained from the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value. Simultaneously, it can select a robust and low-cost target hydrogen hub planning scheme by referring to the multiple objective function values ​​corresponding to each uncertain budget value. Attached Figure Description

[0009] The above and other objects, features and advantages of the present invention will become clearer from the following description of embodiments of the invention with reference to the accompanying drawings.

[0010] Figure 1 An exemplary system architecture according to an embodiment of the present invention can be applied to a hydrogen hub planning method for multi-regional power grid interconnection.

[0011] Figure 2 A flowchart of a hydrogen hub planning method for multi-regional power distribution network interconnection according to an embodiment of the present invention is shown.

[0012] Figure 3A A schematic diagram illustrating the variation of the photovoltaic power output fluctuation coefficient in a residential area over time, according to an embodiment of the present invention, is shown.

[0013] Figure 3B A schematic diagram illustrating the variation of the residential area's electrical load fluctuation coefficient over time, according to an embodiment of the present invention, is shown.

[0014] Figure 3C A schematic diagram illustrating the variation of the photovoltaic power output fluctuation coefficient in an industrial area over time, according to an embodiment of the present invention, is shown.

[0015] Figure 3DA schematic diagram illustrating the variation of the electrical load fluctuation coefficient of an industrial area over time, according to an embodiment of the present invention, is shown.

[0016] Figure 3E A schematic diagram illustrating the variation of the photovoltaic power output fluctuation coefficient in a commercial area over time, according to an embodiment of the present invention.

[0017] Figure 3F A schematic diagram illustrating the variation of the electrical load fluctuation coefficient of a commercial area over time, according to an embodiment of the present invention, is shown.

[0018] Figure 4 A schematic diagram illustrating the variation of daily average hourly hydrogen load data of a hydrogen hub over time according to an embodiment of the present invention is shown.

[0019] Figure 5 A schematic diagram of a multi-regional flexible distribution network and a hydrogen hub for interconnection of the multi-regional flexible distribution network is shown according to an embodiment of the present invention.

[0020] Figure 6 A schematic diagram of the average daily power of a residential area in hydrogen hub injection region 1 according to an embodiment of the present invention is shown.

[0021] Figure 7 A schematic diagram of the daily average power of an industrial area in hydrogen hub injection zone 2 according to an embodiment of the present invention is shown.

[0022] Figure 8 A schematic diagram of the daily average power of the commercial area of ​​the hydrogen hub injection zone 3 according to an embodiment of the present invention is shown.

[0023] Figure 9 A schematic diagram of the sequential hydrogen storage capacity of the high-pressure gaseous hydrogen storage tank of the hydrogen hub according to an embodiment of the present invention is shown.

[0024] Figure 10 A block diagram of a hydrogen hub planning device for interconnecting multi-regional power distribution networks according to an embodiment of the present invention is shown.

[0025] Figure 11 A block diagram of an electronic device suitable for implementing a hydrogen hub planning method for multi-regional power grid interconnection according to an embodiment of the present invention is shown. Detailed Implementation

[0026] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the invention. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the invention for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0027] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0028] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0029] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0030] In the embodiments of this invention, the collection, updating, analysis, processing, use, transmission, provision, disclosure, and storage of data (e.g., including but not limited to user personal information) comply with relevant laws and regulations, are used for legitimate purposes, and do not violate public order and good morals. In particular, necessary measures have been taken to prevent unauthorized access to user personal information data and to maintain the security of user personal information and network security.

[0031] In the embodiments of the present invention, the user's authorization or consent is obtained before acquiring or collecting the user's personal information.

[0032] With the development of renewable energy, renewable energy sources such as wind, hydro, and solar power, due to their green, economical, and environmentally friendly characteristics, are gradually being integrated into the power system through distributed renewable energy generation, thus forming a green, clean, and low-carbon energy system with renewable energy as the mainstay. Meanwhile, natural gas, hydrogen, and other gas-fired energy sources, due to their economical, environmentally friendly, and convenient characteristics, are also being used as high-quality alternatives to fossil fuels.

[0033] Against this backdrop, the integrated transformation of traditional distribution networks into power generation, grid, load, and storage systems has become a key platform for carrying distributed renewable energy, providing crucial support for achieving a green and low-carbon energy transition. Furthermore, with the widespread application of smart distribution devices, the interaction between power sources and loads in the distribution network has become more flexible and proactive. Specifically, the integration of new power electronic devices, such as smart soft switches, into the distribution network has created a more flexible distribution network. As an important power device in flexible distribution networks, smart soft switches can perform precise power control and systematically regulate power flow distribution, achieving error-free power transmission regulation within their own capacity range between nodes. However, the development of flexible distribution networks still faces the following challenges: significant differences in power source and load characteristics across distribution network areas, low utilization rates of renewable energy, and severe wind and solar curtailment. Therefore, research on regional collaborative optimization of distribution networks is necessary.

[0034] Distribution network energy storage systems play a role in energy balancing over time, serving as the physical carrier for addressing wind and solar power curtailment and a core component for achieving regional coordinated optimization and supporting the integration of power generation, grid, load, and storage. Therefore, since hydrogen hubs are a form of distribution network energy storage, they can leverage the high energy density of hydrogen for large-scale energy storage, enabling seasonal energy transfer within the distribution network over time. However, the energy regulation methods for hydrogen hubs in the spatial dimension still need to be explored.

[0035] Hydrogen hubs can serve as energy exchange stations connecting flexible distribution networks across multiple regions. By using electro-hydrogen coupled energy storage, they can balance the differences in source-load characteristics between regions, improve the local consumption rate of renewable energy, and reduce the annual comprehensive cost of flexible distribution networks in multiple regions. The planning problem of hydrogen hubs needs to consider the long-term operational status and uncertainties in source-load data during the planning period, which increases the difficulty of solving the problem.

[0036] When addressing the issue of energy storage planning in distribution networks, related technologies often focus on short-term planning for electrochemical energy storage, lacking adaptability to seasonal imbalances in the power supply and load of the distribution network; or they consider long-term energy management needs but neglect collaborative planning of flexible distribution networks in multiple regions; or they fail to consider the uncertainties in renewable energy output and load levels, resulting in poor robustness of the planning scheme; or they lack robustness verification for off-scenario scenarios to assist users in parameter selection, making it difficult to select suitable parameters to obtain a robust and cost-effective planning scheme.

[0037] Therefore, there is an urgent need for a hydrogen hub planning method that can take into account the seasonal energy balance of the distribution network, the source-load differences of the multi-regional distribution network, and the uncertainty of the source-load, so as to achieve multi-regional collaborative robust planning based on multi-regional distribution networks and hydrogen hubs, and reduce the annual comprehensive cost of multi-regional flexible distribution networks.

[0038] In view of this, the present invention provides a hydrogen hub planning method and apparatus for multi-regional power distribution network interconnection, which can be applied to the field of power distribution network energy storage planning technology.

[0039] Figure 1 An exemplary system architecture, applicable to a hydrogen hub planning method for multi-regional distribution network interconnection according to embodiments of the present invention, is illustrated. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to embodiments of the present invention, in order to help those skilled in the art understand the technical content of the present invention, but do not mean that embodiments of the present invention cannot be used in other devices, systems, environments or scenarios.

[0040] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a first terminal device 101, a second terminal device 102, a third terminal device 103, a network 104, and a server 105. The network 104 is a medium for providing communication links between the first terminal device 101, the second terminal device 102, the third terminal device 103, and the server 105. The network 104 may include various connection types, such as wired and / or wireless communication links, etc.

[0041] Users can use the first terminal device 101, the second terminal device 102, and the third terminal device 103 to interact with the server 105 via the network 104 to receive or send messages, etc. Various communication client applications can be installed on the first terminal device 101, the second terminal device 102, and the third terminal device 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, and / or social media platform software, etc. (for example only).

[0042] The first terminal device 101, the second terminal device 102, and the third terminal device 103 can be various electronic devices with displays and support web browsing, including but not limited to smartphones, tablets, laptops, and desktop computers.

[0043] Server 105 can be a server that provides various services, such as a backend management server that supports websites browsed by users using the first terminal device 101, the second terminal device 102, and the third terminal device 103 (this is just an example). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0044] It should be noted that the hydrogen hub planning method for multi-regional power distribution network interconnection provided in this embodiment of the invention can generally be executed by server 105. Correspondingly, the hydrogen hub planning device for multi-regional power distribution network interconnection provided in this embodiment of the invention can generally be located in server 105. The hydrogen hub planning method for multi-regional power distribution network interconnection provided in this embodiment of the invention can also be executed by a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Correspondingly, the hydrogen hub planning device for multi-regional power distribution network interconnection provided in this embodiment of the invention can also be located in a server or server cluster that is different from server 105 and capable of communicating with the first terminal device 101, the second terminal device 102, the third terminal device 103, and / or server 105. Alternatively, the hydrogen hub planning method for multi-regional power distribution network interconnection provided in this embodiment of the invention can also be executed by the first terminal device 101, the second terminal device 102, or the third terminal device 103, or by other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103. Correspondingly, the hydrogen hub planning device for multi-regional power distribution network interconnection provided in this embodiment of the invention can also be installed in the first terminal device 101, the second terminal device 102, or the third terminal device 103, or in other terminal devices different from the first terminal device 101, the second terminal device 102, or the third terminal device 103.

[0045] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0046] Figure 2 A flowchart of a hydrogen hub planning method for multi-regional power distribution network interconnection according to an embodiment of the present invention is shown.

[0047] like Figure 2 As shown, the hydrogen hub planning method for multi-regional power grid interconnection may include operations S201 to S205.

[0048] In operation S201, an objective function is constructed based on the distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters.

[0049] According to embodiments of the present invention, a multi-regional distribution network can be a multi-regional flexible distribution network. The hydrogen hub may include: a proton exchange membrane hydrogen production electrolyzer, a proton exchange membrane hydrogen fuel cell, and a hydrogen storage system. The hydrogen storage system may include a high-pressure gaseous hydrogen storage tank.

[0050] According to an embodiment of the present invention, the distribution network parameters of a multi-regional distribution network characterize the basic data used to describe the topology, line parameters, and electrical quantity relationships of the multi-regional distribution network.

[0051] According to an embodiment of the present invention, the distribution network parameters, photovoltaic load data, and planning target configuration information can be determined based on the selected multi-region flexible distribution network, and the hydrogen hub planning parameters can be determined according to the actual situation, thereby constructing the objective function.

[0052] In operation S202, a hydrogen hub planning model is constructed based on the objective function, the multi-regional distribution network operation constraints, the power capacity constraints of the distribution devices corresponding to the distribution devices in each regional distribution network, the hydrogen hub planning constraints, the energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and the power constraints between the hydrogen hub and the DC-to-DC converters in the distribution devices.

[0053] According to an embodiment of the present invention, the power distribution device in each regional power distribution network can be a flexible power distribution device.

[0054] In operation S203, based on multiple uncertain budget values ​​and a deterministic source-load dataset obtained from photovoltaic load data, multiple uncertain source-load datasets are determined. Then, a solver is used to solve multiple robust planning models constructed based on these uncertain source-load datasets and the hydrogen hub planning model, resulting in multiple hydrogen hub planning schemes. The planning target configuration information includes the planning parameters in the hydrogen hub planning scheme.

[0055] According to an embodiment of the present invention, multiple uncertain budget values ​​correspond one-to-one with multiple uncertain source load datasets.

[0056] According to embodiments of the present invention, each uncertain source load dataset can replace the deterministic source load dataset in the hydrogen hub planning model, thereby obtaining each robust planning model. Thus, multiple uncertain source load datasets correspond one-to-one with multiple robust planning models. Furthermore, since multiple robust planning models correspond one-to-one with multiple hydrogen hub planning schemes, multiple uncertain source load datasets correspond one-to-one with multiple hydrogen hub planning schemes, and multiple uncertain budget values ​​correspond one-to-one with multiple hydrogen hub planning schemes.

[0057] In operation S204, the intersection of the predetermined uncertain source load dataset with multiple offset source load scenario sets obtained from the deterministic source load dataset and multiple sets of random coefficients is taken to obtain multiple offset test scenario sets. Then, based on the multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, multiple objective function values ​​are obtained. The predetermined uncertain source load dataset is obtained based on the uncertain budget value equal to the predetermined value.

[0058] For example, the predetermined value can be 1.

[0059] For example, when the uncertainty budget value is equal to 1, the uncertainty source load dataset determined based on the uncertainty budget value and the deterministic source load dataset can be used as the predetermined uncertainty source load dataset.

[0060] For example, multiple sets of random coefficients can be used to offset the deterministic source load dataset, resulting in multiple offset source load scene sets corresponding to each set of random coefficients. Therefore, the scene offset degrees of these multiple offset source load scene sets relative to the deterministic source load dataset are different.

[0061] For example, each offset test scenario set and the hydrogen hub planning scheme corresponding to each uncertainty budget value can be input into the robust planning model corresponding to each uncertainty budget value to obtain the objective function value for each offset test scenario set corresponding to each uncertainty budget value. Thus, there is a one-to-one correspondence between multiple objective function values ​​and multiple offset test scenario sets corresponding to each uncertainty budget value.

[0062] In operation S205, based on the multiple objective function values ​​corresponding to the multiple uncertain budget values, the target uncertain budget value is determined from the multiple uncertain budget values, and the hydrogen hub planning scheme corresponding to the target uncertain budget value is determined as the target hydrogen hub planning scheme.

[0063] According to embodiments of the present invention, the multiple objective function values ​​corresponding to each uncertain budget value can reflect the robustness of the hydrogen hub planning scheme corresponding to each uncertain budget value. The multiple objective function values ​​at the same scenario offset corresponding to multiple uncertain budget values ​​can reflect the variation pattern of the objective function values ​​related to the multiple hydrogen hub planning schemes corresponding to multiple uncertain budget values ​​at the same scenario offset.

[0064] For example, when the objective function value represents the annual comprehensive cost of a multi-regional flexible distribution network, multiple objective function values ​​at the same scenario offset corresponding to multiple uncertain budget values ​​can reflect the changing patterns of the annual comprehensive cost of the multi-regional flexible distribution network related to multiple hydrogen hub planning schemes corresponding to multiple uncertain budget values ​​at the same scenario offset. Therefore, multiple objective function values ​​corresponding to each of the multiple uncertain budget values ​​can provide a reference for selecting a robust and cost-effective hydrogen hub planning scheme.

[0065] According to an embodiment of the present invention, the objective function is constrained by introducing multi-regional distribution network operation constraints, power capacity constraints of distribution devices corresponding to distribution devices in each regional distribution network, hydrogen hub planning constraints, energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and power constraints between the hydrogen hub and the DC-DC converter in the distribution device into the hydrogen hub planning model, so as to fully consider the seasonal energy balance of the distribution network and the collaborative planning of multi-regional flexible distribution networks during modeling.

[0066] Simultaneously, based on multiple uncertain budget values ​​and a deterministic source-load dataset obtained from photovoltaic load data, multiple uncertain source-load datasets can be determined. A solver is then used to solve multiple robust planning models constructed from these datasets and the hydrogen hub planning model, yielding multiple hydrogen hub planning schemes. The intersection of the predetermined uncertain source-load dataset with multiple offset source-load scenario sets obtained from the deterministic dataset and multiple sets of random coefficients is used to obtain multiple offset test scenario sets. Based on these offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, multiple objective function values ​​are obtained. Finally, based on the multiple objective function values ​​corresponding to each uncertain budget value, a target uncertain budget value is determined from the multiple uncertain budget values, and the hydrogen hub planning scheme corresponding to the target uncertain budget value is identified as the target hydrogen hub planning scheme. This approach fully considers the uncertainty of source and load issues and performs uncertainty modeling and robust transformation on the planning problem. It establishes multiple robust planning models corresponding to various uncertain budget values. Based on multiple offset test scenario sets and multiple objective function values ​​obtained from the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, robustness verification of the offset scenarios is performed. Simultaneously, by referencing the multiple objective function values ​​corresponding to each uncertain budget value, a robust and low-cost target hydrogen hub planning scheme can be selected.

[0067] According to embodiments of the present invention, the distribution network parameters of a multi-regional distribution network may include: voltage levels, topology, line parameters, access locations, capacity, and loss coefficients of flexible distribution network devices, etc. Photovoltaic load data may include: photovoltaic output datasets for each regional distribution network, electrical load level datasets for each regional distribution network, and hydrogen load level datasets for hydrogen refueling stations at hydrogen hubs.

[0068] According to an embodiment of the present invention, the planned target configuration information may include the planned capacity of the proton exchange membrane hydrogen electrolyzer, the planned capacity of the proton exchange membrane hydrogen fuel cell, the planned capacity of the hydrogen storage tank, and the capacity of the DC-to-DC converter connected to the hydrogen hub.

[0069] According to embodiments of the present invention, for example, Figure 2Operation S201, as shown, constructs an objective function based on the distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters. This function may include the following operations:

[0070] The network loss cost of each regional distribution network at each time is obtained based on the network loss power of each regional distribution network at each time and the power loss generated by the converters of the distribution devices at each distribution network node at each time. The network loss power of each regional distribution network at each time is calculated based on the current between adjacent nodes of each regional distribution network and the resistance of the branch where the adjacent nodes of each regional distribution network are located at each time.

[0071] The curtailment cost of each regional distribution network at each time step is obtained based on the curtailment active power of each photovoltaic access node in each regional distribution network at each time step.

[0072] Based on the network loss cost and curtailment cost of each regional distribution network at each time, the annual equivalent operating cost of each regional distribution network at each time is obtained.

[0073] Based on the hydrogen energy loss generated by the hydrogen devices in the hydrogen hub at each moment and the amount of hydrogen sold by the hydrogen hub at each moment, the annual equivalent operating cost of the hydrogen hub at each moment is obtained.

[0074] Based on the planned capacity of the proton exchange membrane hydrogen electrolyzer, the planned capacity of the proton exchange membrane hydrogen fuel cell, the planned capacity of the hydrogen storage tank, the capacity of the DC-to-DC converter connected to the hydrogen hub, and the capital recovery coefficient, the annual investment cost of the hydrogen hub plan is determined. The capital recovery coefficient is calculated based on the discount rate and the planning period. The hydrogen hub planning parameters include the planning period.

[0075] An objective function is constructed based on the annual equivalent operating cost of the multi-regional distribution network at various times, the annual equivalent operating cost of the hydrogen hub at various times, the annual investment cost of the hydrogen hub planning, and the deterministic source-load dataset obtained based on photovoltaic load data.

[0076] For example, the objective function can be constructed according to the following formulas (1) to (6).

[0077] (1);

[0078] (2);

[0079] (3);

[0080] (4);

[0081] (5);

[0082] (6);

[0083] in, The annual comprehensive cost of a multi-regional flexible distribution network; This represents the total number of time segments used for calculation within each planning period of the planning duration. Set a value for the total number of time segments within the planning period; Number the time; The total number of distribution network areas is divided; For area code; for The area is The annual equivalent operating cost at any given time; For hydrogen hub in The annual equivalent operating cost at any given time; Annual investment cost for hydrogen hub planning; The unit cost of network loss; Cost per unit of abandoned light; This refers to the unit price of hydrogen sold. The unit cost of hydrogen energy consumption; Unit capacity cost of proton exchange membrane hydrogen production electrolyzers; Cost per unit capacity of proton exchange membrane hydrogen fuel cells; Cost per unit capacity of high-pressure gaseous hydrogen storage tank; Cost per unit capacity of a DC / DC (direct current to direct current) converter; The capacity of the DC-to-DC converter connected to the hydrogen hub; for The set of branches in a regional power distribution network; for The set of distribution network nodes that connect flexible distribution devices in a regional distribution network; for The set of photovoltaic access nodes in a regional power distribution network; A collection of hydrogen devices within a hydrogen hub; For a single time segment interval; for Regional distribution network branches The resistance; for Regional distribution network in Branches of time Nodes in Flow to Node The current; for Flexible distribution devices in regional distribution networks at distribution network nodes The converter at the location Power loss generated at any time; for Photovoltaic access nodes in regional distribution networks exist The amount of work done by the discarded light at any given moment; For the hydrogen hub Hydrogen device in The hydrogen energy loss that occurs constantly; For hydrogen hub in Hydrogen sales volume at any given time; Planning capacity for proton exchange membrane hydrogen electrolyzers; Planning capacity for proton exchange membrane hydrogen fuel cells; Plan the capacity of high-pressure gaseous hydrogen storage tanks in the hydrogen hub; This is the capital recovery coefficient; The discount rate; For planning duration; For deterministic source load datasets; Data sets of photovoltaic power output for distribution networks in various regions; Data sets of power load levels for power distribution networks in various regions; Data set of hydrogen load levels for hydrogen refueling stations in hydrogen hubs; This is a set of deterministic source load scenarios for the system.

[0084] According to an embodiment of the present invention, the planning duration also represents the service life of the equipment.

[0085] According to an embodiment of the present invention, the capacity of the DC-to-DC converter connected to the hydrogen hub can be: connected to the hydrogen hub DC bus. The capacity of the DC-to-DC converter on the DC side of the flexible distribution equipment in the regional power distribution network. The hydrogen unit complex in the hydrogen hub may include a proton exchange membrane hydrogen electrolyzer, a proton exchange membrane hydrogen fuel cell, and a hydrogen storage system. The hydrogen storage system may include a high-pressure gaseous hydrogen storage tank.

[0086] According to an embodiment of the present invention, the objective function is constructed with the goal of minimizing the annual comprehensive cost of a multi-regional flexible distribution network. The annual comprehensive cost of the multi-regional flexible distribution network is the value of the objective function obtained by solving the objective function.

[0087] According to embodiments of the present invention, the planning target configuration information may further include: planning parameters in the hydrogen hub planning scheme, the annual comprehensive cost of the multi-regional flexible distribution network, and the energy transmission time series values ​​of the simulated operation of the multi-regional distribution network. The planning parameters in the hydrogen hub planning scheme may include: Regional distribution network in Branches of time Nodes in Flow to Node current, Flexible distribution devices in regional distribution networks at distribution network nodes The converter at the location Power loss generated at all times Photovoltaic access nodes in regional distribution networks exist The amount of light power wasted at any given moment, and the amount of hydrogen in the hub Hydrogen device in The constant hydrogen energy loss and hydrogen hub The data includes the amount of hydrogen sold at any given time, the planned capacity of the proton exchange membrane hydrogen electrolyzer, the planned capacity of the proton exchange membrane hydrogen fuel cell, the planned capacity of the high-pressure gaseous hydrogen storage tank in the hydrogen hub, and the capacity of the DC-to-DC converter connected to the hydrogen hub. The time-series energy transmission values ​​for the multi-regional distribution network simulation operation can include: the average daily power injected by the hydrogen hub into each regional distribution network (i.e., the average daily active power injected by the hydrogen hub into the DC side of the distribution devices in each regional distribution network) and the time-series hydrogen storage capacity of the high-pressure gaseous hydrogen storage tank in the hydrogen hub.

[0088] According to an embodiment of the present invention, the multi-regional distribution network operation constraints characterize the constraints that the power, current and voltage amplitudes of each node in each regional distribution network must satisfy.

[0089] According to an embodiment of the present invention, the operating constraints of the multi-regional distribution network are shown in the following formulas (7) to (15).

[0090] (7);

[0091] (8);

[0092] (9);

[0093] (10);

[0094] (11);

[0095] (12);

[0096] (13);

[0097] (14);

[0098] (15);

[0099] in, for Regional distribution network in Branches of time Nodes in Flow to Node The active power; for Regional distribution network in Branches of time Nodes in Flow to Node The active power; for Regional distribution network in Branches of time Nodes in Flow to Node The active power; for Regional distribution network branches The resistance; for Regional distribution network in Branches of time Nodes in Flow to Node reactive power; for Regional distribution network in Branches of time Nodes in Flow to Node reactive power; for Regional distribution network in Branches of time Nodes in Flow to Node reactive power; for Regional distribution network branches The reactance; for Regional distribution network branches The reactance; for Regional distribution network in Branches of time Nodes in Flow to Node The current; for Regional distribution network in Node of time The sum of the active power injected upwards; for Regional distribution network in Node of time The sum of reactive power injected upwards; , They are respectively Regional distribution network in Node of time , The voltage amplitude; , They are respectively Regional distribution network in Photovoltaic access nodes at any time Photovoltaics always have both active and reactive power outputs; for Regional distribution network in The amount of reactive power lost at any given moment; , They are respectively Flexible distribution equipment in regional power distribution networks Injecting data into distribution network nodes at all times Active and reactive power; , They are respectively Regional distribution network in Node of time The active and reactive power consumed by the load at the location; for Regional distribution network in The active power transmitted from the main network to the distribution network at any given time; , They are respectively The minimum and maximum allowable node voltage amplitudes for the regional distribution network; for The maximum allowable branch current amplitude of the regional distribution network.

[0100] It should be noted that one Regional distribution network The time node can be used as a distribution network node and / or a photovoltaic access node. All nodes in formula (12) are the same node.

[0101] According to an embodiment of the present invention, the power capacity constraints of the distribution devices corresponding to the distribution devices in each regional distribution network are shown in the following formulas (16) to (20).

[0102] (16);

[0103] (17);

[0104] (18);

[0105] (19);

[0106] (20);

[0107] in, for Flexible distribution equipment in regional power distribution networks Injecting data into distribution network nodes at all times The active power; for Flexible distribution devices in regional distribution networks at distribution network nodes The converter at the location Power loss generated at any time; For hydrogen hub in Injecting at all times Active power on the DC side of flexible distribution devices in a regional power distribution network; , They are respectively Flexible distribution devices in regional distribution networks at distribution network nodes , The loss coefficient of the converter at that location; for Flexible distribution equipment in regional power distribution networks Injecting data into distribution network nodes at all times reactive power; , They are respectively Flexible distribution devices in regional distribution networks at distribution network nodes , The capacity of the converter.

[0108] According to embodiments of the present invention, the planning parameters for a hydrogen hub may include: upper and lower limits for the capacity planning of hydrogen storage tanks, upper and lower limits for the capacity planning of proton exchange membrane hydrogen electrolyzers, ramp-up parameters, average energy efficiency, upper and lower limits for the capacity planning of proton exchange membrane hydrogen fuel cells, ramp-up parameters, average energy efficiency, and planning duration.

[0109] According to an embodiment of the present invention, the hydrogen hub planning constraints are as follows: the active power output of the proton exchange membrane hydrogen fuel cell at each moment is within a first power range, wherein the first power range is determined based on the lower limit of the active power output of the proton exchange membrane hydrogen fuel cell and the planned capacity of the proton exchange membrane hydrogen fuel cell; the planned capacity of the proton exchange membrane hydrogen fuel cell is within a second power range, wherein the second power range is determined based on the lower limit and upper limit of the active power output of the proton exchange membrane hydrogen fuel cell; the active power input of the proton exchange membrane hydrogen electrolyzer at each moment is within a third power range, wherein the third power range is determined based on the lower limit of the active power input of the proton exchange membrane hydrogen electrolyzer and the planned capacity of the proton exchange membrane hydrogen electrolyzer; the planned capacity of the proton exchange membrane hydrogen electrolyzer is within a fourth power range, wherein the fourth power range is determined based on the lower limit and upper limit of the active power input of the proton exchange membrane hydrogen electrolyzer; at each moment, the active power output of the proton exchange membrane hydrogen fuel cell is within a third power range, wherein the third power range is determined based on the lower limit of the active power input of the proton exchange membrane hydrogen electrolyzer and the planned capacity of the proton exchange membrane hydrogen electrolyzer; at each moment, the active power output of the proton exchange membrane hydrogen fuel cell is within a second power range, wherein the planned capacity of the proton exchange membrane hydrogen electrolyzer is within a fourth power range, wherein the fourth power range is determined based on the lower limit and upper limit of the active power input of the proton exchange membrane hydrogen electrolyzer; at each moment, the active power output of the proton exchange membrane hydrogen fuel cell is within a third power range, wherein the planned capacity of the proton exchange membrane hydrogen electrolyzer is within a fourth power range, wherein the planned capacity of the proton exchange membrane hydrogen electrolyzer is within a fourth power range, wherein the planned capacity of the proton exchange membrane hydrogen electrolyzer is within a fourth power range; at each moment, the active power output of the proton exchange membrane hydrogen fuel cell is within a third power range, wherein the planned capacity of the proton exchange membrane hydrogen The power ramp-up is within a first active power ramp-up range, which is determined based on the lower and upper limits of the active power ramp-up of the proton exchange membrane hydrogen fuel cell output. At each moment, the active power ramp-up of the proton exchange membrane hydrogen electrolyzer input is within a second active power ramp-up range, which is determined based on the lower and upper limits of the active power ramp-up of the proton exchange membrane hydrogen electrolyzer input. At each moment, the hydrogen mass stored in the hydrogen storage tank corresponding to the hydrogen storage tank in the hydrogen hub is within a first hydrogen mass range, which is determined based on the lower limit of the hydrogen mass stored in the hydrogen storage tank and a predetermined value of the hydrogen mass stored in the hydrogen storage tank. The predetermined value of the hydrogen mass stored in the hydrogen storage tank is obtained based on the hydrogen mass-energy conversion coefficient and the planned capacity of the hydrogen storage tank. The predetermined value of the hydrogen mass stored in the hydrogen storage tank is within a second hydrogen mass range, which is determined based on the lower and upper limits of the hydrogen mass stored in the hydrogen storage tank.

[0110] According to an embodiment of the present invention, the hydrogen hub planning constraints are shown in the following formulas (21) to (31).

[0111] (twenty one);

[0112] (twenty two);

[0113] (twenty three);

[0114] (twenty four);

[0115] (25);

[0116] (26);

[0117] (27);

[0118] (28);

[0119] (29);

[0120] (30);

[0121] (31);

[0122] in, For proton exchange membrane hydrogen fuel cells The active power output at all times; For proton exchange membrane hydrogen production electrolyzers in The active power input at any given time; This represents the lower limit of the active power output of a proton exchange membrane hydrogen fuel cell. This represents the upper limit of the active power output of a proton exchange membrane hydrogen fuel cell. The lower limit of active power input to the proton exchange membrane hydrogen electrolyzer; The upper limit of active power input to the proton exchange membrane hydrogen electrolyzer; In order to be in At any given moment, the ramp-up of the active power output of the proton exchange membrane hydrogen fuel cell; , The upper and lower limits of the ramp-up of active power output for proton exchange membrane hydrogen fuel cells; In order to be in At any given moment, the ramp-up of the input active power of the proton exchange membrane hydrogen electrolyzer; , Upper and lower limits of the ramp-up amount of active power input to the proton exchange membrane hydrogen electrolyzer; In order to be in At any given time, the hydrogen storage tank stores the mass of hydrogen. The lower limit of the mass of hydrogen stored in the hydrogen storage tank; The upper limit for the mass of hydrogen that can be stored in a hydrogen storage tank; The mass-energy conversion factor for hydrogen; For in ( At any given time, the mass of hydrogen stored in the hydrogen storage tank; for The mass of hydrogen produced by the proton exchange membrane hydrogen electrolyzer at any given time; for The mass of hydrogen consumed by a proton exchange membrane hydrogen fuel cell at any given time; It is Faraday's constant; For Faraday efficiency; This refers to the molar mass of hydrogen gas. for Current in the proton exchange membrane hydrogen production electrolyzer at any given time; for The current in a proton exchange membrane hydrogen fuel cell at any given moment; Number of proton exchange membrane hydrogen production electrolyzers; This refers to the number of proton exchange membrane hydrogen fuel cells; for Energy efficiency of a proton exchange membrane hydrogen electrolyzer at any given time; for Energy efficiency of proton exchange membrane hydrogen fuel cells at any given time; Hydrogen has a low calorific value; It has a high calorific value, which is due to hydrogen.

[0123] For example, the upper limit of the hydrogen mass that a hydrogen storage tank can store can be determined. Hydrogen mass energy conversion coefficient Multiplying these values ​​yields the predetermined mass of hydrogen stored in the hydrogen storage tank.

[0124] According to an embodiment of the present invention, the energy balance constraint condition for the hydrogen hub input into the multi-regional distribution network is: the active power injected by the hydrogen hub into the DC side of the distribution device in the multi-regional distribution network at each time is equal to the active power injected by the hydrogen hub at each time. The active power injected by the hydrogen hub at each time is calculated based on the difference between the active power output by the proton exchange membrane hydrogen fuel cell in the hydrogen hub at each time and the active power input by the proton exchange membrane hydrogen electrolyzer in the hydrogen hub at each time.

[0125] According to an embodiment of the present invention, the energy balance constraint condition for the hydrogen hub input into the multi-regional distribution network is shown in formula (32).

[0126] (32).

[0127] According to an embodiment of the present invention, the power constraint condition between the hydrogen hub and the DC-to-DC converter in the power distribution device is: the square of the active power injected by the hydrogen hub into the DC side of the power distribution device in each regional power distribution network at each time is less than or equal to the square of the capacity of the DC-to-DC converter on the DC side of the power distribution device in each regional power distribution network.

[0128] According to an embodiment of the present invention, the power constraint condition between the hydrogen hub and the DC-to-DC converter in the power distribution unit is as shown in formula (33).

[0129] (33)

[0130] According to an embodiment of the present invention, a hydrogen hub planning model can be constructed based on formulas (1) to (33). As can be seen from formulas (1) to (33), the hydrogen hub planning method for multi-regional distribution network interconnection provided by the embodiments of the present invention can combine the objective function with the multi-regional distribution network operation constraints, the power capacity constraints of the distribution devices corresponding to the distribution devices in each regional distribution network, the hydrogen hub planning constraints, the energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and the power constraints between the hydrogen hub and the DC-DC converter in the distribution device, to establish a hydrogen hub planning model that takes into account the cost of the DC-DC converter and performs multi-regional collaborative planning of the multi-regional distribution network and the hydrogen hub.

[0131] According to an embodiment of the present invention, a robust planning model can be constructed based on the uncertain source load dataset and the hydrogen hub planning model according to formulas (1) to (5) and formulas (7) to (38). The compact form of the robust planning model is shown in formula (39).

[0132] (34);

[0133] (35);

[0134] (36);

[0135] (37);

[0136] (38);

[0137] (39);

[0138] in, For uncertain source load datasets; Data sets on the uncertainty of photovoltaic output in distribution networks of various regions; Data sets on the uncertainty of power load levels in power distribution networks in various regions; Data set on the uncertainty of hydrogen load levels at hydrogen refueling stations in hydrogen hubs; For a set of load scenarios representing sources of uncertainty in the system; The fluctuation coefficient of the photovoltaic power output random variable; The fluctuation coefficient of the random variable of electrical load level; The fluctuation coefficient of the random variable for hydrogen load level; Uncertain budget for photovoltaic power generation; Budgeting for uncertainties in electrical load levels; Budgeting for uncertainties in hydrogen load levels; This represents an uncertain budget value.

[0139] According to an embodiment of the present invention, the hydrogen hub planning method for multi-regional distribution network interconnection provided by the present invention can perform uncertainty modeling on the hydrogen hub planning problem according to formulas (1) to (5) and formulas (7) to (38), thereby performing robust transformation of the hydrogen hub planning model and establishing a robust planning model for multi-regional collaborative robust planning of multi-regional distribution networks and hydrogen hubs.

[0140] According to an embodiment of the present invention, in the case of uncertain budget values When taking different values, multiple uncertain budget values ​​will be considered. Substituting each value into formula (39), we obtain multiple uncertain budget values. The corresponding robust programming models.

[0141] According to embodiments of the present invention, for example, Figure 2 The operation S203 shown uses a solver to solve multiple robust planning models constructed based on multiple uncertain source load datasets and hydrogen hub planning models, respectively, to obtain multiple hydrogen hub planning schemes. This can include: linearizing each robust planning model constructed based on each uncertain source load dataset and hydrogen hub planning model using the second-order cone transformation method to obtain a linearized planning model corresponding to each uncertain source load dataset; and solving the linearized planning model corresponding to each uncertain source load dataset using a solver to obtain a hydrogen hub planning scheme corresponding to each uncertain source load dataset.

[0142] For example, you can first set the planning duration and the granularity of the system operation simulation time (i.e., the total number of time segments within the planning period), and , , Numerical values. Then set the uncertainty budget value. The possible values ​​of , where, The budget representing all uncertainty parameters is set to 1.0, which is the most conservative scenario. This represents the scenario with the worst robustness, ignoring the uncertainties of various parameters. Therefore, the solver is invoked to optimize the robust planning model for multi-regional collaborative robust planning of multi-regional distribution networks and hydrogen hubs under different uncertain budget values, generating solution results and achieving autonomous control over the robustness and conservatism of the optimization results. The solution results include: hydrogen hub planning schemes, annual comprehensive costs of multi-regional flexible distribution networks, and time-series values ​​of energy transmission during simulated operation of multi-regional distribution networks.

[0143] According to an embodiment of the present invention, Figure 2The hydrogen hub planning method shown for multi-regional distribution network interconnection can also include: using each set of random coefficients to offset the deterministic source load dataset to obtain the offset source load scenario set corresponding to each set of random coefficients.

[0144] For example, the offset source load scene set can be calculated according to formula (40) and formula (41).

[0145] (40);

[0146] in, for The reference source load data vector at time t; For generated The random offset source load data vector at time step; Generate coefficients for random photovoltaic scenarios; Generate coefficients for random electrical load scenarios; These are the generation coefficients for random hydrogen load scenarios. The random coefficients can include the following three types: random photovoltaic scenario generation coefficients, random electrical load scenario generation coefficients, and random hydrogen load scenario generation coefficients.

[0147] (41);

[0148] in, For offset source load scene set; Number the generated offset source load scene set; This represents the total number of offset source load scene sets generated.

[0149] According to an embodiment of the present invention, the generated offset source load scene set is compared with... Uncertainty Sources of Time in Load Dataset Taking the intersection yields the fluctuation coefficient of the photovoltaic power output random variable that satisfies the system settings. Fluctuation coefficient of random variable in electrical load level Fluctuation coefficient of random variable in hydrogen load level Offset test scenario set , which is represented by formula (42).

[0150] (42);

[0151] in, For offset test scenario set; This represents the number of offset test scenarios obtained.

[0152] According to an embodiment of the present invention, Figure 2The hydrogen hub planning method for multi-regional distribution network interconnection shown may further include: calculating the Wasserstein distances between the deterministic source-load dataset and multiple offset test scenario sets, and determining multiple scenario offsets corresponding to the multiple offset test scenario sets based on the multiple Wasserstein distances; and determining multiple scenario offsets corresponding to the multiple objective function values ​​based on the correspondence between the multiple offset test scenario sets and the multiple scenario offsets and the correspondence between the multiple offset test scenario sets and the multiple objective function values.

[0153] For example, the solution can be obtained using formulas (43) and (44). The scene offset based on Wasserstein distance corresponds to each offset test scene set. The process of calculating the Wasserstein distance can be expressed as formula (43). The process of calculating the scene offset based on Wasserstein distance can be expressed as formula (44).

[0154] (43);

[0155] in, Wasserstein distance metric; , Let the probability distribution of the random variable be used. , Obey respectively and A random variable with a specific distribution; for and The joint probability distribution of ; For the support set of random variables; For the infimum operator; The cost function; It is the operator for double integrals.

[0156] According to an embodiment of the present invention, the deterministic source load dataset and the offset test scenario set can be replaced respectively. and Then, the Wasserstein distance metric between the deterministic source load dataset and the offset test scene is calculated using formula (43). Subsequently, the Wasserstein distance metric is normalized using the following formula (44) to obtain the corresponding scene offset.

[0157] (44);

[0158] in, The scene offset is based on Wasserstein distance; To set the maximum Wasserstein distance for a random scenario under the random variable fluctuation coefficient.

[0159] According to the embodiments of the present invention, it can be seen from formulas (40) to (44) that the hydrogen hub planning method for multi-regional distribution network interconnection provided by the embodiments of the present invention can use the offset scenario generation method based on Wasserstein distance to generate offset test scenarios corresponding to different scenario offset degrees, so that they can be substituted into the hydrogen hub planning scheme for operation simulation and complete the offset scenario robustness verification.

[0160] According to an embodiment of the present invention, after obtaining multiple scene offsets corresponding to multiple offset test scene sets respectively, the generated offset test scene sets can be adjusted based on the calculated scene offsets based on Wasserstein distance. Labeling and categorizing the data allows for the subsequent incorporation of different offset scenarios into the hydrogen hub planning scheme for operational simulation, enabling robustness verification of offset scenarios and assisting users in determining uncertain budget values.

[0161] According to embodiments of the present invention, for example, Figure 2 Operation S204, as shown, obtains multiple objective function values ​​based on multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertainty budget value. It may include the following operations: using each offset test scenario set, replacing the uncertainty source load dataset in the robust planning model corresponding to each uncertainty budget value to obtain the hydrogen hub offset planning model corresponding to each offset test scenario set; and obtaining the objective function value corresponding to each offset test scenario set based on the hydrogen hub planning scheme corresponding to each uncertainty budget value and the hydrogen hub offset planning model corresponding to each offset test scenario set.

[0162] According to embodiments of the present invention, for example, Figure 2 The operation S205 shown, which determines the target uncertainty budget value from multiple uncertainty budget values ​​based on multiple objective function values ​​corresponding to each of the multiple uncertainty budget values, may include the following operation: determining the target uncertainty budget value from multiple uncertainty budget values ​​based on multiple objective function values ​​corresponding to each of the multiple uncertainty budget values ​​and the scene offset corresponding to each objective function value.

[0163] According to an embodiment of the present invention, after obtaining the robust programming model, the second-order cone transformation method can be used to linearize the model, and the corresponding solver can be called to perform the programming solution using the Wasserstein scenario-enhanced budget uncertainty set robust optimization method. In this invention, [the following is related to...] Figure 2The related content corresponding to operations S203 to S205 shown is the specific implementation method of the Wasserstein scenario-enhanced budget uncertainty set robust optimization method, which will not be elaborated here.

[0164] According to embodiments of the present invention, the hydrogen hub planning method for multi-regional distribution network interconnection provided by the present invention is a multi-regional collaborative robust planning method that can establish multi-regional collaborative robust planning for multi-regional distribution networks and hydrogen hubs, consider source-load uncertainties, generate target planning schemes, include robustness verification links, and reduce the annual comprehensive cost of multi-regional flexible distribution networks.

[0165] According to embodiments of the present invention, the hydrogen hub planning method for multi-regional distribution network interconnection provided by the present invention is based on solving the energy storage planning problem of distribution networks. It fully considers the seasonal energy balance of distribution networks, the source-load differences of multi-regional distribution networks, and source-load uncertainties, and establishes a hydrogen hub planning model that takes into account the cost of DC-DC converters and can perform multi-regional collaborative planning of multi-regional distribution networks and hydrogen hubs. Subsequently, uncertainty modeling and robust transformation are performed on the planning problem to establish a robust planning model for multi-regional collaborative robust planning of multi-regional distribution networks and hydrogen hubs. The model is linearized using the second-order cone transformation method, and the Wasserstein scenario-enhanced budget uncertainty set robust optimization method is used to solve the problem using relevant mathematical solvers, obtaining the target hydrogen hub planning scheme. Using the method of the present invention, which enables multi-regional collaborative robust planning of multi-regional flexible distribution networks and hydrogen hubs, can effectively reduce the annual comprehensive cost of multi-regional flexible distribution networks.

[0166] The following will further illustrate the hydrogen hub planning method for multi-regional power distribution network interconnection provided by the embodiments of the present invention with specific examples.

[0167] Figure 3A A schematic diagram illustrating the variation of the photovoltaic power output fluctuation coefficient in a residential area over time, according to an embodiment of the present invention, is shown. Figure 3B A schematic diagram illustrating the variation of the residential area's electrical load fluctuation coefficient over time, according to an embodiment of the present invention, is shown. Figure 3C A schematic diagram illustrating the variation of the photovoltaic power output fluctuation coefficient in an industrial area over time, according to an embodiment of the present invention, is shown. Figure 3D A schematic diagram illustrating the variation of the electrical load fluctuation coefficient of an industrial area over time, according to an embodiment of the present invention, is shown. Figure 3E A schematic diagram illustrating the variation of the photovoltaic power output fluctuation coefficient in a commercial area over time, according to an embodiment of the present invention. Figure 3F A schematic diagram illustrating the variation of the electrical load fluctuation coefficient of a commercial area over time, according to an embodiment of the present invention, is shown. Figure 4 A schematic diagram illustrating the variation of daily average hourly hydrogen load data of a hydrogen hub over time according to an embodiment of the present invention is shown. Figure 5 A schematic diagram of a multi-regional flexible distribution network and a hydrogen hub for interconnection of the multi-regional flexible distribution network is shown according to an embodiment of the present invention.

[0168] During the experiment, the following parameters were first input: voltage level, topology, line parameters, connection location, capacity, and loss coefficient of the flexible distribution network device; then, the following parameters were input: Figures 3A-3F The photovoltaic load data of the multi-region flexible distribution network shown are as follows: Figure 4 The data shows the daily average hourly hydrogen load of the hydrogen hub; input the planning target configuration information and hydrogen hub planning parameters, including: upper and lower limits of high-pressure gaseous hydrogen storage tank capacity planning, upper and lower limits of proton exchange membrane hydrogen electrolyzer capacity planning, ramping parameters, and average energy efficiency, and upper and lower limits of proton exchange membrane hydrogen fuel cell capacity planning, ramping parameters, and average energy efficiency; set the planning duration.

[0169] like Figure 5 As shown, a multi-region flexible distribution network is set up in three regions. The three regions are the residential area of ​​Region 1, the industrial area of ​​Region 2, and the commercial area of ​​Region 3. The distribution network of Region 1 includes 33 nodes, 9 photovoltaic (PV) units, and flexible distribution device 1. Nodes 4, 8, 12, 18, 22, 24, 25, 30, and 32 are each connected to one PV unit. The distribution network of Region 2 includes 33 nodes, 9 PV units, and flexible distribution device 2. Nodes 37, 41, 45, 51, 55, 57, 58, 63, and 65 are each connected to one PV unit. The distribution network of Region 3 includes 33 nodes, 9 PV units, and flexible distribution device 3. Nodes 70, 74, 78, 84, 88, 90, 91, 96, and 98 are each connected to one PV unit. The DC-to-DC converters on the DC side of flexible distribution device 1, flexible distribution device 2, and flexible distribution device 3 are all connected to the DC bus of the hydrogen hub.

[0170] exist Figure 5 In the multi-regional flexible distribution network shown, the voltage level is 12.66kV, the total active load is 11145kW, and the total reactive load is 6900kvar.

[0171] exist Figure 5 In the middle, three two-port flexible power distribution devices are connected to nodes 12 and 22, nodes 45 and 55, and nodes 78 and 88, respectively. Figure 5Each port converter has a capacity of 2 MVA and a loss factor of 0.01. The per-unit voltage per-unit value is set to [0.95, 1.05]. The photovoltaic output base power is set to 500 kW. The planned capacity range for the high-pressure gaseous hydrogen storage tank is [10, 200] MWh, the planned capacity range for the proton exchange membrane hydrogen electrolyzer is [20, 1000] kW, and the planned capacity range for the proton exchange membrane hydrogen fuel cell is [20, 1000] kW. The equipment ramp-up limit is 20% of the maximum equipment power. The average energy efficiency of the proton exchange membrane hydrogen electrolyzer and the proton exchange membrane hydrogen fuel cell are set to 80% and 55%, respectively. The planning period is set to 25 years. The random variable fluctuation coefficient for photovoltaic output is set to 0.20, the random variable fluctuation coefficient for electrical load level is set to 0.15, and the random variable fluctuation coefficient for hydrogen load level is set to 0.25. Detailed parameters are shown in Table 1.

[0172] Table 1 System Composition and Parameters

[0173]

[0174] To verify the effectiveness of the method proposed in this invention, for Figure 5 The multi-regional flexible distribution network and hydrogen hub shown are planned and solved, and the following two schemes are compared for control:

[0175] Option I: Initial scenario, no hydrogen hub planned;

[0176] Option II: Conduct multi-regional collaborative planning of distribution networks and hydrogen hubs in a deterministic source-load scenario (i.e., solve the hydrogen hub planning model).

[0177] Figure 6 A schematic diagram of the average daily power of a residential area in hydrogen hub injection region 1 according to an embodiment of the present invention is shown. Figure 7 A schematic diagram of the daily average power of an industrial area in hydrogen hub injection zone 2 according to an embodiment of the present invention is shown. Figure 8 A schematic diagram of the daily average power of the commercial area of ​​the hydrogen hub injection zone 3 according to an embodiment of the present invention is shown. Figure 9 A schematic diagram of the sequential hydrogen storage capacity of the high-pressure gaseous hydrogen storage tank of the hydrogen hub according to an embodiment of the present invention is shown.

[0178] Table 2 shows the hydrogen hub planning scheme for Scheme II, and Table 3 shows a comparison of the annual comprehensive costs of the multi-regional flexible distribution network between Scheme I and Scheme II. The simulated energy transmission time series values ​​for the multi-regional distribution network under the hydrogen hub planning scheme of Scheme II are as follows: The average daily power of the residential area in hydrogen hub injection area 1 is shown in Table 3. Figure 6 The average daily power of the industrial area in hydrogen hub injection zone 2 is shown below. Figure 7 The average daily power of the commercial area in hydrogen hub injection zone 3 is shown below. Figure 8The time-series hydrogen storage capacity of the high-pressure gaseous hydrogen storage tank at the hydrogen hub is shown in [reference needed]. Figure 9 .

[0179] Table 2 Hydrogen Hub Planning Scheme of Scheme II

[0180]

[0181] Table 3. Comparison of Annual Comprehensive Costs of Different Schemes for Multi-Regional Flexible Distribution Networks

[0182]

[0183] As can be seen from the comparison of the annual comprehensive cost of the hydrogen hub planning scheme of Scheme II shown in Table 2 and the multi-regional flexible distribution network of different schemes shown in Table 3, Scheme II can significantly reduce the annual comprehensive cost of the multi-regional flexible distribution network under deterministic scenarios.

[0184] from Figure 6 The average daily power of the residential area in the hydrogen hub injection zone 1 shown is... Figure 7 The average daily power of the hydrogen hub injection area 2 industrial zone shown is Figure 8 The daily average power of the hydrogen hub injected into the commercial area of ​​Zone 3 shows that the hydrogen hub plays a role in spatial energy transmission during the whole year. The load level of the residential area in Zone 1 is relatively low, while the load level of the industrial area in Zone 2 is relatively high. Overall, energy is transferred from lightly loaded areas to heavily loaded areas.

[0185] from Figure 9 The time-series hydrogen storage capacity of the high-pressure gaseous hydrogen storage tank in the hydrogen hub shows that the hydrogen hub performs energy scheduling in the time dimension, reducing the impact of seasonal imbalance between source and load on multi-regional power distribution networks.

[0186] To verify the effectiveness of the method proposed in this invention, for Figure 5 The multi-regional flexible distribution network and hydrogen hub shown are planned and solved using the Wasserstein scenario-enhanced budget uncertainty set robust optimization method. The robustness of the planning scheme based on the offset scenario based on Wasserstein distance is verified. The robustness verification results are shown in Table 4. The annual comprehensive cost of the initial scenario of the multi-regional distribution network (i.e., the annual comprehensive cost obtained without planning the hydrogen hub) under different offset scenarios based on Wasserstein distance is shown in Table 5.

[0187] Table 4. Robustness verification results of the offset scene based on Wasserstein distance (RMB 10,000)

[0188]

[0189] Table 5. Annual Comprehensive Cost of Initial Scene under Different Wasserstein Distance-Based Scene Offsets

[0190]

[0191] As shown in Table 4, the robustness verification results of the offset scenario based on Wasserstein distance, and Table 5, the initial annual comprehensive cost under different offset scenarios based on Wasserstein distance, demonstrate that the larger the uncertainty budget value, the stronger the robustness of the generated hydrogen hub planning scheme, and the better the performance under larger scenario offsets. Furthermore, the robustness verification results of the offset scenario based on Wasserstein distance can provide users with a reference for determining the value of the uncertainty budget.

[0192] According to embodiments of the present invention, the hydrogen hub planning method for multi-regional distribution network interconnection provided by the present invention is a method for multi-regional collaborative robust planning of multi-regional flexible distribution networks and hydrogen hubs. The method considers the seasonal energy balance of the distribution network, the source-load difference of the multi-regional distribution network, the source-load uncertainty problem, and the cost of DC-DC converters. Compared with the initial scenario without hydrogen hub planning, it can significantly reduce the annual comprehensive cost of multi-regional flexible distribution networks. At the same time, in uncertain scenarios with high scenario deviation, the generated target hydrogen hub planning scheme has strong robustness and adaptability.

[0193] Based on the above-described hydrogen hub planning method for multi-regional power distribution network interconnection, this invention provides a hydrogen hub planning device for multi-regional power distribution network interconnection.

[0194] Figure 10 A block diagram of a hydrogen hub planning device for interconnecting multi-regional power distribution networks according to an embodiment of the present invention is shown.

[0195] like Figure 10 As shown, the hydrogen hub planning device for multi-regional power distribution network interconnection may include a first construction module 1010, a second construction module 1020, a first obtaining module 1030, a second obtaining module 1040, and a determining module 1050.

[0196] The first construction module 1010 is used to construct an objective function based on the distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters.

[0197] The second construction module 1020 is used to construct a hydrogen hub planning model based on the objective function, the multi-regional distribution network operation constraints, the power capacity constraints of the distribution devices corresponding to the distribution devices in each regional distribution network, the hydrogen hub planning constraints, the energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and the power constraints between the hydrogen hub and the DC-to-DC converters in the distribution devices.

[0198] The first module 1030 is used to determine multiple uncertain source load datasets based on multiple uncertain budget values ​​and a deterministic source load dataset obtained from photovoltaic load data. It then uses a solver to solve multiple robust planning models constructed based on these uncertain source load datasets and the hydrogen hub planning model, resulting in multiple hydrogen hub planning schemes. The planning target configuration information includes the planning parameters in the hydrogen hub planning scheme.

[0199] The second module 1040 is used to take the intersection of the predetermined uncertain source load dataset with multiple offset source load scenario sets obtained from the deterministic source load dataset and multiple sets of random coefficients to obtain multiple offset test scenario sets, and to obtain multiple objective function values ​​based on the multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value. The predetermined uncertain source load dataset is obtained based on the uncertain budget value equal to the predetermined value.

[0200] The determination module 1050 is used to determine the target uncertainty budget value from multiple uncertainty budget values ​​based on multiple objective function values ​​corresponding to each of the multiple uncertainty budget values, and to determine the hydrogen hub planning scheme corresponding to the target uncertainty budget value as the target hydrogen hub planning scheme.

[0201] Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention, or at least part of the functions of any one or more of them, can be implemented in a single module. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be implemented by being divided into multiple modules. Any one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be at least partially implemented as hardware circuits, such as field-programmable gate arrays (FPGAs), programmable logic arrays (PLAs), systems-on-a-chip, systems-on-a-substrate, systems-on-package, application-specific integrated circuits (ASICs), or implemented in hardware or firmware by any other reasonable means of integrating or packaging circuits, or implemented in software, hardware, and firmware, or in any suitable combination of any of these three implementation methods. Alternatively, one or more of the modules, submodules, units, and subunits according to embodiments of the present invention can be at least partially implemented as computer program modules, which, when run, can perform corresponding functions.

[0202] It should be noted that the hydrogen hub planning device part for multi-regional power distribution network interconnection in the embodiments of the present invention corresponds to the hydrogen hub planning method part for multi-regional power distribution network interconnection in the embodiments of the present invention. The description of the hydrogen hub planning device part for multi-regional power distribution network interconnection is specifically referred to the hydrogen hub planning method part for multi-regional power distribution network interconnection, and will not be repeated here.

[0203] Figure 11 A block diagram of an electronic device suitable for implementing a hydrogen hub planning method for multi-regional power grid interconnection according to an embodiment of the present invention is shown. Figure 11 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0204] like Figure 11 As shown, an electronic device 1100 according to an embodiment of the present invention includes a processor 1101, which can perform various appropriate actions and processes according to a program stored in ROM 1102 or a program loaded from storage portion 1108 into RAM 1103. The processor 1101 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1101 may also include onboard memory for caching purposes. The processor 1101 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present invention.

[0205] RAM 1103 stores various programs and data required for the operation of electronic device 1100. Processor 1101, ROM 1102, and RAM 1103 are interconnected via bus 1104. Processor 1101 executes various operations of the method flow according to embodiments of the present invention by executing programs in ROM 1102 and / or RAM 1103. It should be noted that the programs may also be stored in one or more memories other than ROM 1102 and RAM 1103. Processor 1101 may also execute various operations of the method flow according to embodiments of the present invention by executing programs stored in said one or more memories.

[0206] According to an embodiment of the present invention, the electronic device 1100 may further include an input / output (I / O) interface 1105, which is also connected to the bus 1104. The electronic device 1100 may also include one or more of the following components connected to the input / output (I / O) interface 1105: an input section 1106 including a keyboard, mouse, etc.; an output section 1107 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1108 including a hard disk, etc.; and a communication section 1109 including a network interface card such as a LAN card, modem, etc. The communication section 1109 performs communication processing via a network such as the Internet. A drive 1110 is also connected to the input / output (I / O) interface 1105 as needed. A removable medium 1111, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1110 as needed so that computer programs read from it can be installed into the storage section 1108 as needed.

[0207] According to embodiments of the present invention, the method flow according to embodiments of the present invention can be implemented as a computer software program. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a computer-readable storage medium, the computer program containing program code for performing the method shown in the flowchart. In such embodiments, the computer program can be downloaded and installed from a network via communication section 1109, and / or installed from removable medium 1111. When the computer program is executed by processor 1101, it performs the functions defined in the system of the embodiments of the present invention. According to embodiments of the present invention, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0208] The present invention also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs, which, when executed, implement the method according to the embodiments of the present invention.

[0209] According to embodiments of the present invention, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples include, but are not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In the present invention, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0210] For example, according to embodiments of the present invention, a computer-readable storage medium may include one or more memories other than the ROM 1102 and / or RAM 1103 described above and / or ROM 1102 and RAM 1103.

[0211] Embodiments of the present invention also include a computer program product comprising a computer program containing program code for performing the methods provided in the embodiments of the present invention. When the computer program product is run on an electronic device, the program code is used to enable the electronic device to implement the methods provided in the embodiments of the present invention.

[0212] When the computer program is executed by the processor 1101, it performs the functions defined in the system / apparatus of this embodiment of the invention. According to embodiments of the invention, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0213] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1109, and / or installed from the removable medium 1111. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0214] According to embodiments of the present invention, program code for executing the computer programs provided in the embodiments of the present invention can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0215] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions. Those skilled in the art will understand that the features described in the various embodiments of the present invention can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in the present invention. In particular, the features described in the various embodiments of the present invention can be combined and / or combined in various ways without departing from the spirit and teachings of the present invention. All such combinations and / or pairings fall within the scope of this invention.

[0216] The embodiments of the present invention have been described above. However, these embodiments are merely illustrative and not intended to limit the scope of the invention. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. The scope of the invention is defined by the appended embodiments and their equivalents. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of the invention, and all such substitutions and modifications should fall within the scope of the invention.

Claims

1. A hydrogen hub planning method for multi-regional power distribution network interconnection, characterized in that, include: Based on the distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters, an objective function is constructed. Based on the objective function, the operation constraints of the multi-regional distribution network, the power capacity constraints of the distribution devices corresponding to the distribution devices in each regional distribution network, the planning constraints of the hydrogen hub, the balance constraints of the energy input from the hydrogen hub to the multi-regional distribution network, and the power constraints between the hydrogen hub and the DC-to-DC converters in the distribution devices, a hydrogen hub planning model is constructed. Based on multiple uncertain budget values ​​and a deterministic source load dataset obtained from photovoltaic load data, multiple uncertain source load datasets are determined. A solver is then used to solve multiple robust planning models constructed based on these uncertain source load datasets and the hydrogen hub planning model, resulting in multiple hydrogen hub planning schemes. These schemes include: linearizing each robust planning model constructed based on each uncertain source load dataset and the hydrogen hub planning model using the second-order cone transformation method, obtaining a linearized planning model corresponding to each uncertain source load dataset; and then solving the linearized planning model corresponding to each uncertain source load dataset using the solver to obtain a hydrogen hub planning scheme corresponding to each uncertain source load dataset. The planning target configuration information includes the planning parameters in the hydrogen hub planning scheme. The intersection of the predetermined uncertain source load dataset with the multiple offset source load scenario sets obtained from the deterministic source load dataset and multiple sets of random coefficients is taken to obtain multiple offset test scenario sets. Based on the multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value, multiple objective function values ​​are obtained. The predetermined uncertain source load dataset is obtained from the uncertain budget value that is equal to the predetermined value. Based on the multiple objective function values ​​corresponding to each of the multiple uncertain budget values, the target uncertain budget value is determined from the multiple uncertain budget values, and the hydrogen hub planning scheme corresponding to the target uncertain budget value is determined as the target hydrogen hub planning scheme.

2. The method according to claim 1, characterized in that, The energy balance constraints for the hydrogen hub input into the multi-regional distribution network are as follows: The active power injected by the hydrogen hub into the DC side of the power distribution device in the multi-regional distribution network at each moment is equal to the active power injected by the hydrogen hub at each moment. The active power injected by the hydrogen hub at each moment is calculated based on the difference between the active power output by the proton exchange membrane hydrogen fuel cell in the hydrogen hub at each moment and the active power input by the proton exchange membrane hydrogen electrolyzer in the hydrogen hub at each moment.

3. The method according to claim 1, characterized in that, The power constraint condition between the hydrogen hub and the DC-to-DC converter in the power distribution unit is: The square of the active power injected by the hydrogen hub into the DC side of the distribution equipment in each regional distribution network at each moment is less than or equal to the square of the capacity of the DC-to-DC converter on the DC side of the distribution equipment in each regional distribution network.

4. The method according to claim 2, characterized in that, The constraints for the hydrogen hub planning are as follows: The active power output of the proton exchange membrane hydrogen fuel cell at each moment is within a first power range, wherein the first power range is determined based on the lower limit of the active power output of the proton exchange membrane hydrogen fuel cell and the planned capacity of the proton exchange membrane hydrogen fuel cell; The planned capacity of the proton exchange membrane hydrogen fuel cell is within the second power range, which is determined based on the lower and upper limits of the active power output of the proton exchange membrane hydrogen fuel cell. The active power input to the proton exchange membrane hydrogen production electrolyzer at each moment is within the third power range, wherein the third power range is determined based on the lower limit of the active power input to the proton exchange membrane hydrogen production electrolyzer and the planned capacity of the proton exchange membrane hydrogen production electrolyzer; The planned capacity of the proton exchange membrane hydrogen production electrolyzer is within the fourth power range, which is determined based on the lower and upper limits of the input active power of the proton exchange membrane hydrogen production electrolyzer. At any given moment, the active power ramp-up of the proton exchange membrane hydrogen fuel cell output is within a first active power ramp-up range, wherein the first active power ramp-up range is determined based on the lower limit and upper limit of the active power ramp-up of the proton exchange membrane hydrogen fuel cell output. At each moment, the ramp rate of the active power input to the proton exchange membrane hydrogen electrolyzer is within the range of the second active power ramp rate, wherein the range of the second active power ramp rate is determined based on the lower limit and the upper limit of the ramp rate of the active power input to the proton exchange membrane hydrogen electrolyzer. At any given time, the hydrogen mass stored in the hydrogen storage tank corresponding to the hydrogen storage tank in the hydrogen hub is within a first hydrogen mass range, wherein the first hydrogen mass range is determined based on the lower limit of the hydrogen mass stored in the hydrogen storage tank and the predetermined value of the hydrogen mass stored in the hydrogen storage tank, and the predetermined value of the hydrogen mass stored in the hydrogen storage tank is obtained based on the hydrogen mass energy conversion coefficient and the planned capacity of the hydrogen storage tank. The hydrogen storage tank stores a predetermined value of hydrogen within a second hydrogen mass range, wherein the second hydrogen mass range is determined based on the lower and upper limits of the hydrogen storage tank.

5. The method according to claim 4, characterized in that, The planned target configuration information includes the planned capacity of proton exchange membrane hydrogen electrolyzers, the planned capacity of proton exchange membrane hydrogen fuel cells, the planned capacity of hydrogen storage tanks, and the capacity of DC-to-DC converters connected to the hydrogen hub.

6. The method according to claim 5, characterized in that, The objective function constructed based on the distribution network parameters of the multi-regional distribution network, photovoltaic load data, planning target configuration information, and hydrogen hub planning parameters includes: The network loss cost of each regional distribution network at each time is obtained based on the network loss power of each regional distribution network at each time and the power loss generated by the converters of the distribution devices at each distribution network node at each time. The network loss power of each regional distribution network at each time is calculated based on the current between adjacent nodes of each regional distribution network and the resistance of the branch where the adjacent nodes of each regional distribution network are located at each time. The curtailment cost of each regional distribution network at each time step is obtained based on the curtailment active power of each photovoltaic access node in each regional distribution network at each time step. Based on the network loss cost and curtailment cost of each regional distribution network at each time, the annual equivalent operating cost of each regional distribution network at each time is obtained. Based on the hydrogen energy loss generated by the hydrogen devices in the hydrogen hub at each moment and the amount of hydrogen sold by the hydrogen hub at each moment, the annual equivalent operating cost of the hydrogen hub at each moment is obtained. Based on the planned capacity of the proton exchange membrane hydrogen electrolyzer, the planned capacity of the proton exchange membrane hydrogen fuel cell, the planned capacity of the hydrogen storage tank, the capacity of the DC-to-DC converter connected to the hydrogen hub, and the capital recovery coefficient, the annual investment cost of the hydrogen hub plan is determined. The capital recovery coefficient is calculated based on the discount rate and the planning period. The hydrogen hub planning parameters include the planning period. An objective function is constructed based on the annual equivalent operating cost of the multi-regional distribution network at various times, the annual equivalent operating cost of the hydrogen hub at various times, the annual investment cost of the hydrogen hub planning, and the deterministic source-load dataset obtained based on photovoltaic load data.

7. The method according to claim 1, characterized in that, The method further includes: By using each set of random coefficients to perform data offset on the deterministic source load dataset, we obtain the offset source load scene set corresponding to each set of random coefficients; The process of obtaining multiple objective function values ​​based on multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertainty budget value includes: Using each offset test scenario set, replace the uncertainty source load dataset in the robust planning model corresponding to each uncertainty budget value to obtain the hydrogen hub offset planning model corresponding to each offset test scenario set; Based on the hydrogen hub planning scheme corresponding to each uncertain budget value and the hydrogen hub offset planning model corresponding to each offset test scenario set, the objective function value corresponding to each offset test scenario set is obtained.

8. The method according to claim 7, characterized in that, The method further includes: Calculate the Wasserstein distances between the deterministic source load dataset and multiple offset test scene sets, and determine the multiple scene offsets corresponding to the multiple offset test scene sets based on the multiple Wasserstein distances; Based on the correspondence between multiple offset test scenario sets and multiple scenario offsets, and the correspondence between multiple offset test scenario sets and multiple objective function values, determine the multiple scenario offsets corresponding to the multiple objective function values ​​respectively; The step of determining the target uncertainty budget value from multiple uncertainty budget values ​​based on multiple objective function values ​​corresponding to each of the multiple uncertainty budget values ​​includes: The target uncertainty budget value is determined from the multiple uncertainty budget values ​​based on the multiple objective function values ​​corresponding to each of the multiple uncertainty budget values ​​and the scene offset corresponding to each objective function value.

9. A hydrogen hub planning device for multi-regional power distribution network interconnection, characterized in that, include: The first construction module is used to construct the objective function based on the power distribution network parameters of the multi-regional power distribution network, photovoltaic load data, planning target configuration information and hydrogen hub planning parameters; The second construction module is used to construct a hydrogen hub planning model based on the objective function, the multi-regional distribution network operation constraints, the power capacity constraints of the distribution devices corresponding to the distribution devices in each regional distribution network, the hydrogen hub planning constraints, the energy balance constraints of the hydrogen hub input to the multi-regional distribution network, and the power constraints between the hydrogen hub and the DC-to-DC converters in the distribution devices. The first module is used to determine multiple uncertain source load datasets based on multiple uncertain budget values ​​and a deterministic source load dataset obtained from photovoltaic load data. It then uses a solver to solve multiple robust planning models constructed based on these uncertain source load datasets and the hydrogen hub planning model, resulting in multiple hydrogen hub planning schemes. These schemes include: linearizing each robust planning model constructed based on each uncertain source load dataset and the hydrogen hub planning model using a second-order cone transformation method, obtaining a linearized planning model corresponding to each uncertain source load dataset; and solving the linearized planning model corresponding to each uncertain source load dataset using a solver, obtaining a hydrogen hub planning scheme corresponding to each uncertain source load dataset. The planning target configuration information includes the planning parameters in the hydrogen hub planning scheme. The second module is used to take the intersection of the predetermined uncertain source load dataset with the multiple offset source load scenario sets obtained from the deterministic source load dataset and multiple sets of random coefficients to obtain multiple offset test scenario sets, and to obtain multiple objective function values ​​based on the multiple offset test scenario sets and the hydrogen hub planning scheme and robust planning model corresponding to each uncertain budget value. The predetermined uncertain source load dataset is obtained based on the uncertain budget value equal to the predetermined value. The determination module is used to determine the target uncertainty budget value from multiple uncertainty budget values ​​based on multiple objective function values ​​corresponding to each of the multiple uncertainty budget values, and to determine the hydrogen hub planning scheme corresponding to the target uncertainty budget value as the target hydrogen hub planning scheme.

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