Method and system for determining distribution network resilience index considering energy storage

Abstract

The present application discloses a method and system for determining distribution network resilience indicators taking energy storage into consideration, including: obtaining a variety of meteorological data and distribution network operation data; determining the physical characteristics corresponding to various natural disaster events based on the various meteorological data and the distribution network operation data, and based on the changes in the parameters of each component in the distribution network under the physical characteristics, determining the changes in the parameters of each component in the distribution network under the various natural disaster events; and determining distribution network resilience assessment indicators in multiple dimensions based on the changes in the parameters of each component in the distribution network under the various natural disaster events. The method provided by the present application at least solves the technical problem of low accuracy in the distribution network's own resilience assessment under extreme natural disasters.

Description

Technical Field

[0001] The present application relates to the field of power supply and distribution technology, and in particular to a method and system for determining a distribution network resilience index taking energy storage into consideration. Background Art

[0002] With the increasing number of distributed power sources, electric vehicles, DC power distribution, AC / DC hybrid distribution networks, and battery energy storage systems connected to distribution networks on a large scale, coupled with the increasing number of sensitive, critical, and nonlinear loads in distribution networks, traditional AC distribution networks are facing increasingly severe problems such as high line losses, tight power corridors, voltage sags, voltage fluctuations, grid harmonics, and three-phase imbalance. Traditional distribution networks are no longer able to meet society's needs, and distribution networks are gradually evolving from traditional passive, AC, and passive distribution networks to active, AC / DC, and proactive new distribution networks.

[0003] Traditional distribution network reliability assessment methods can no longer cope with the impact of new distribution network disturbances. Summary of the Invention

[0004] The embodiments of the present application provide a method and system for determining a distribution network resilience index taking into account energy storage, so as to at least solve the technical problem of low accuracy in the distribution network's own resilience assessment under extreme natural disasters.

[0005] According to one aspect of an embodiment of the present application, a method for determining a distribution network resilience index taking into account energy storage is provided, comprising: acquiring a variety of meteorological data and distribution network operation data; determining physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, and determining changes in parameters of various components in the distribution network under the various natural disaster events based on changes in parameters of various components in the distribution network under the physical characteristics; determining distribution network resilience assessment indicators in multiple dimensions based on changes in parameters of various components in the distribution network under the various natural disaster events.

[0006] Optionally, based on the changes in parameters of each component in the distribution network under the various natural disaster events, distribution network resilience assessment indicators of multiple dimensions are determined, including: respectively determining the operating status information of the distribution network during the changes in parameters of each component in the distribution network; determining distribution network resilience assessment indicators of multiple dimensions based on the operating status information of the distribution network, and the distribution network resilience assessment indicators of multiple dimensions include at least: the grid structure class, load change class, energy storage class, equipment usage class and power management class of the distribution network, wherein the grid structure class of the distribution network is used to reflect the power output of the distribution network, the load change class is used to reflect the load condition of the distribution network, the energy storage class is used to reflect the energy storage condition of the distribution network, the equipment usage class is used to reflect the equipment usage of the distribution network, and the power management class is used to reflect the usage of distributed power sources in the distribution network.

[0007] Optionally, the method of determining the distribution network resilience assessment indicators of multiple dimensions based on the operating status information of the distribution network includes: dividing the acquisition period of the sub-indicators of the distribution network resilience assessment indicators of multiple dimensions into three stages according to the time of occurrence of the extreme disaster event, including: a first stage before the occurrence of the natural disaster event, a second stage during the occurrence of the extreme disaster event, and a third stage after the occurrence of the natural disaster event; and determining the sub-indicators of the distribution network resilience assessment indicators of multiple dimensions in the three stages according to the operating status information of the distribution network in the three stages.

[0008] Optionally, the sub-indicators of the distribution network resilience assessment indicators of multiple dimensions in the three stages are determined respectively according to the operating status information of the distribution network in the three stages, including: determining the first indicator of the distribution network resilience assessment indicators of multiple dimensions in the first stage, wherein the first indicator includes: the voltage qualification rate and three-phase voltage imbalance in the grid structure class of the distribution network, the important load distribution balance in the load change class, the proportion of energy storage in the distribution network in the energy storage class, the equipment redundancy proportion in the equipment use class and the distributed power supply distribution information and the proportion of distributed power supply in the distribution network in the power management class; determining the second indicator of the distribution network resilience assessment indicators of multiple dimensions in the second stage, wherein the second indicator includes: The number of AC / DC reconstruction switch actions in the grid structure class of the distribution network, the economic loss of load shedding in the load change class, the proportion of controllable energy storage in the entire energy storage in the energy storage class, the proportion of equipment use in the equipment in the entire distribution network, and the proportion of controllable distributed power sources in the power management class in the distributed power sources in the entire distribution network; determine the third indicator of the distribution network resilience evaluation indicators in multiple dimensions in the third stage, wherein the third indicator includes: voltage stability in the grid structure class of the distribution network, the amount of important load recovery in the load change class, the proportion of energy storage use in the energy storage class, the equipment failure rate and the number of component repairs in the equipment use class, and the proportion of distributed power use in the power management class.

[0009] Optionally, after determining the sub-indicators in the distribution network resilience assessment indicators of multiple dimensions in the three stages, the method further includes: assigning weights to the distribution network resilience assessment indicators of the multiple dimensions according to predetermined weights; determining an adjustment plan for the distribution network based on the distribution network resilience assessment indicators of the multiple dimensions after the weights are assigned, and adjusting the distribution network according to the adjustment plan.

[0010] Optionally, after adjusting the distribution network according to the adjustment plan, the method further includes: determining the target type of natural disaster events corresponding to the adjustment plan to form a training data set; inputting the training data set into a predetermined initial model for training to obtain a trained distribution network resilience adjustment model; using the distribution network resilience adjustment model to output multiple adjustment plans corresponding to different types of natural disaster events to adjust the distribution network.

[0011] Optionally, after determining the physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, the method further includes: constructing a physical model corresponding to the various natural disaster events based on the physical characteristics, and using the physical model to determine the changes in the parameters of each component in the distribution network.

[0012] According to another aspect of an embodiment of the present application, a system for determining distribution network resilience assessment indicators is also provided, including: a data acquisition module, a data analysis module, and an indicator construction module, wherein the data acquisition module is used to collect a variety of meteorological data and distribution network operation data; the data analysis module is used to determine the physical characteristics corresponding to various natural disaster events based on the various meteorological data and the distribution network operation data, and based on the changes in the parameters of each component in the distribution network under the physical characteristics, determine the changes in the parameters of each component in the distribution network under the various natural disaster events; the indicator construction module is used to determine distribution network resilience assessment indicators in multiple dimensions based on the changes in the parameters of each component in the distribution network under the various natural disaster events.

[0013] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, in which a program is stored, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned method for determining the distribution network resilience index considering energy storage.

[0014] According to another aspect of an embodiment of the present application, a computer device is further provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program executes the above-mentioned method for determining the resilience index of a distribution network considering energy storage when running.

[0015] In an embodiment of the present application, a method is adopted in which a variety of meteorological data and distribution network operation data are obtained; the physical characteristics corresponding to various natural disaster events are determined based on the various meteorological data and the distribution network operation data, and based on the changes in the parameters of each component in the distribution network under the physical characteristics, the changes in the parameters of each component in the distribution network under the various natural disaster events are determined; according to the changes in the parameters of each component in the distribution network under the various natural disaster events, a method is adopted in which distribution network resilience evaluation indicators of multiple dimensions are determined. By determining distribution network resilience evaluation indicators of multiple dimensions according to the changes in the parameters of each component in the distribution network under the various natural disaster events, the technical effect of accurately determining evaluation indicators according to various natural disaster events is achieved, thereby solving the technical problem in related technologies of solving the low accuracy of the distribution network's own reliability evaluation under extreme natural disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0017] Figure 1This is a hardware structure block diagram of a computer terminal (or mobile device) for a method for determining a distribution network resilience index taking energy storage into consideration according to an embodiment of the present application;

[0018] Figure 2 This is a flow chart of a method for determining a distribution network resilience index considering energy storage according to the present application;

[0019] Figure 3 2 is a schematic diagram of a new distribution network resilience evaluation index based on distributed energy storage according to an embodiment of the present application;

[0020] Figure 4 1 is a schematic diagram of a system structure for determining a distribution network resilience index taking energy storage into consideration according to an embodiment of the present application;

[0021] Figure 5 This is a schematic diagram of the system structure for determining the resilience index of a distribution network taking into account energy storage according to another embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0023] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for determining a distribution network resilience index considering energy storage is shown. Figure 1As shown, the computer terminal 10 (or mobile device 10) may include one or more processors 102 (illustrated as 102a, 102b, ..., 102n in the figure) (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission module 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply, and / or a camera. Those skilled in the art will understand that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0025] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry." This data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. Furthermore, the data processing circuitry may be a single, independent processing module, or may be fully or partially integrated into any of the other components of the computer terminal 10 (or mobile device). As discussed in the embodiments of this application, this data processing circuitry functions as a processor control (e.g., selecting a variable resistor terminal path connected to an interface).

[0026] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the method for determining the resilience index of the distribution network considering energy storage in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, to implement the above-mentioned method for determining the resilience index of the distribution network considering energy storage. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0027] Transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of computer terminal 10. In one embodiment, transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, transmission device 106 may be a radio frequency (RF) module configured to communicate with the Internet wirelessly.

[0028] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0029] In the above operating environment, the embodiment of the present application also provides a method for determining the resilience index of the distribution network considering energy storage, such as Figure 2 As shown, the method includes the following steps:

[0030] Step S202, obtaining various meteorological data and distribution network operation data;

[0031] Step S204: determining the physical characteristics corresponding to various natural disaster events based on various meteorological data and distribution network operation data, and determining the changes in the parameters of various components in the distribution network under various natural disaster events based on the changes in the parameters of various components in the distribution network under the physical characteristics;

[0032] Step S206: Determine distribution network resilience assessment indicators in multiple dimensions based on changes in parameters of various components in the distribution network under various natural disaster events.

[0033] Through the above steps, by determining the distribution network resilience evaluation indicators in multiple dimensions according to the changes in the parameters of each component in the distribution network under various natural disaster events, the technical effect of accurately determining the evaluation indicators according to various natural disaster events is achieved, thereby solving the technical problem of low accuracy in the distribution network's own reliability evaluation under extreme natural disasters in related technologies.

[0034] It should be noted that the new distribution network based on distributed energy storage is an active distribution network equipped with distributed energy storage power sources. The new distribution network based on distributed energy storage includes an active new distribution network that includes an active distribution system and an AC / DC distribution system.

[0035] To overcome the shortcomings of related technologies, this application proposes a method for determining distribution network resilience indicators that consider energy storage. This method comprehensively evaluates the resilience of new distribution networks, constructing dynamic and static multidimensional resilience assessment indicators for new distribution networks before, during, and after disasters, taking into account multiple perspectives, including their grid structure, load variations, energy storage, equipment usage, and distributed power management. This method aims to enhance the resilience of new distribution networks, enabling them to better respond to adverse events such as extreme natural disasters.

[0036] In step S202, various meteorological data and distribution network operation data include but are not limited to: meteorological data, meteorological data of extreme natural disasters and operation data of new distribution networks, distributed energy distribution data, operation flow data, voltage and other data.

[0037] In step S204, physical modeling can be performed on various natural disaster events. The physical characteristics of each natural disaster event can be characterized by the established physical model. In an optional embodiment, the established physical model can be used to analyze the impact of various natural disaster events on the parameters of various components in the distribution network, such as lines, towers, and substations. In some examples of the present application, a model can be established to characterize how the parameters of new distribution network lines and components change with extreme natural disaster events.

[0038] In step S206, the distribution network resilience assessment indicators of multiple dimensions can be determined based on the power distribution capability of the distribution network during the process of parameter changes of each component in the distribution network under the various natural disaster events, and then based on the power distribution capability of the distribution network under natural disasters.

[0039] In some embodiments of the present application, determining the distribution network resilience assessment indicators of multiple dimensions can be achieved in the following ways: separately determining the operating status information of the distribution network during the parameter changes of each component in the distribution network; determining the distribution network resilience assessment indicator categories of multiple dimensions based on the operating status information of the distribution network, and the distribution network resilience assessment indicator categories of the multiple dimensions include at least: the grid structure class, load change class, energy storage class, equipment usage class and power management class of the distribution network, wherein the grid structure class of the distribution network is used to reflect the power output and grid structure changes of the distribution network, the load change class is used to reflect the load condition of the distribution network, the energy storage class is used to reflect the energy storage condition of the distribution network, the equipment usage class is used to reflect the equipment usage of the distribution network, and the power management class is used to reflect the usage of distributed power sources in the distribution network.

[0040] Specifically, the acquisition period of the indicators in the distribution network resilience assessment index category of the multiple dimensions is divided into three stages according to the time of occurrence of the natural disaster event, including: the first stage before the natural disaster event occurs, the second stage during the natural disaster event, and the third stage after the natural disaster event occurs; the indicators in the distribution network resilience assessment index category of the multiple dimensions in the three stages are determined respectively according to the operating status information of the distribution network in the three stages.

[0041] Among them, the first indicator in the distribution network resilience assessment indicator category of multiple dimensions in the first stage includes: the voltage qualification rate and three-phase voltage imbalance in the grid structure category of the distribution network, the important load distribution balance in the load change category, the proportion of energy storage power supply in the distribution network in the energy storage category, the equipment redundancy ratio in the equipment use category, and the distributed power supply distribution information and the proportion of distributed power supplies in all power sources in the distribution network in the power management category;

[0042] The second indicators in the distribution network resilience assessment indicator categories of multiple dimensions in the second stage include: the number of AC / DC reconfiguration switch actions in the grid structure category of the distribution network, the economic loss of load shedding in the load change category, the proportion of controllable energy storage in the entire energy storage in the energy storage category, the proportion of equipment used in the entire distribution network in the equipment use category, and the proportion of controllable distributed power sources in the entire distribution network in the power management category;

[0043] The third indicator of the distribution network resilience assessment indicators in multiple dimensions in the third stage includes: voltage stability in the grid structure category of the distribution network, the important load recovery amount in the load change category, the energy storage utilization ratio in the energy storage category, the equipment failure rate and component repair quantity in the equipment utilization category, and the distributed power supply utilization ratio in the power management category.

[0044] Figure 3 It shows multiple indicators of the distribution network's grid structure, load change, energy storage, equipment usage, and power management, such as Figure 3 As shown in the figure, dynamic and static indicators for a new distribution network based on distributed energy storage are established for the pre-, mid-, and post-disaster phases, taking into account multiple dimensions, including the distribution network's grid structure, load variations, energy storage, and distributed power management. Grid structure indicators primarily include static indicators such as the voltage compliance rate and three-phase voltage imbalance of the new distribution network before a disaster; dynamic indicators for the number of AC / DC reconfiguration switch operations in the new distribution network during a disaster; and static indicators for voltage stability after a disaster. Indicators for changes in the new distribution network's grid topology during a disaster may also be considered.

[0045] The load change dimension primarily includes static indicators for the balanced distribution of critical loads before a disaster occurs; dynamic indicators for the real-time economic losses from load shedding during a disaster; and static indicators for the restoration of critical loads after a disaster. The energy storage dimension primarily includes static indicators for the proportion of energy storage in the new distribution network before a disaster occurs; dynamic indicators for the proportion of energy storage that can be adjusted in real time during a disaster; and static indicators for the proportion of energy storage used after a disaster. It may also include the proportion of critical loads restored by energy storage during a disaster, highlighting the role of energy storage in enhancing the resilience of the new distribution network.

[0046] The equipment utilization dimension primarily includes static indicators such as the equipment redundancy ratio before a disaster occurs; dynamic indicators such as the real-time equipment utilization ratio during a disaster; and dynamic indicators such as equipment failure rate and component repair after a disaster. The distributed power management dimension primarily includes static indicators such as the distribution and utilization ratio of distributed power sources before a disaster occurs; dynamic indicators such as the real-time control ratio of distributed power sources during a disaster; and static indicators such as the utilization ratio of distributed power sources after a disaster occurs.

[0047] After determining the indicators in the distribution network resilience assessment indicator categories of multiple dimensions in the three stages, the method further includes: assigning weights to the distribution network resilience assessment indicators of the multiple dimensions according to predetermined weights; determining the adjustment plan of the distribution network according to the distribution network resilience assessment indicators of the multiple dimensions after assigning weights, and adjusting the distribution network according to the adjustment plan, wherein the weight assignment method can be a subjective weighting method such as the hierarchical analysis method, the interval hierarchy process, the itemized binary comparison method, etc., or an objective weighting method such as the coefficient of variation method, the entropy weight method, etc., or a method combining the subjective weighting method and the objective weighting method. While determining the indicator weight assignment, the weak links existing in the new distribution network are identified through the weak links, and then a comprehensive assessment proposal for the resilience of the new distribution network based on distributed energy storage is formed, so that an adjustment plan is generated through the emergency plan production link.

[0048] After adjusting the distribution network according to the adjustment plan, the method also includes: determining the target type of natural disaster events corresponding to the adjustment plan to form a training data set; inputting the training data set into a predetermined initial model for training to obtain a trained distribution network resilience adjustment model; using the distribution network resilience adjustment model to output multiple adjustment plans corresponding to different types of natural disaster events to adjust the distribution network.

[0049] Resilience optimization learning will be conducted based on historical disaster data and existing scenario data to enhance the resilience of the new distribution network. At the same time, the data from resilience optimization learning will be collected to provide experience for subsequent planning of new distribution network disaster reduction and economic loss mitigation.

[0050] After determining the physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, it is also necessary to construct a physical model corresponding to the various natural disaster events based on the physical characteristics, so as to use the physical model to determine the changes in the parameters of each component in the distribution network.

[0051] The embodiment of the present application also provides a system for determining the resilience index of a distribution network taking energy storage into consideration, such as Figure 4 As shown, it includes: a data acquisition module 40, a data analysis module 42, and an indicator construction module 44, wherein the data acquisition module 40 is used to collect a variety of meteorological data and distribution network operation data; the data analysis module 42 is used to determine the physical characteristics corresponding to various natural disaster events based on the various meteorological data and the distribution network operation data, and based on the changes in the parameters of each component in the distribution network under the physical characteristics, determine the changes in the parameters of each component in the distribution network under the various natural disaster events; the indicator construction module 44 is used to determine multiple dimensions of distribution network resilience assessment indicators based on the changes in the parameters of each component in the distribution network under the various natural disaster events.

[0052] The embodiment of the present application also provides a system for determining the resilience index of a distribution network taking energy storage into consideration, such as Figure 5 As shown, it includes: a data acquisition module, a data analysis module 1, a data analysis module 2, a data analysis module 3, an indicator construction module, a system evaluation scheme module, and a data storage and a new distribution network resilience optimization learning module based on distributed energy storage. The data acquisition module, data analysis module 1, data analysis module 2, data analysis module 3, an indicator construction module, and a system evaluation scheme module are connected in sequence, and the data storage and the new distribution network resilience optimization learning module based on distributed energy storage are separately connected to the data acquisition module, data analysis module 1, data analysis module 2, data analysis module 3, an indicator construction module, and a system evaluation scheme module. Among them, the data storage and the new distribution network resilience optimization learning module based on distributed energy storage are bidirectionally connected to the data acquisition module.

[0053] The data acquisition module mainly collects meteorological information data, new distribution network basic data, distributed power distribution and operation data, as well as data storage and optimization learning data of the new distribution network resilience optimization learning module based on distributed energy storage.

[0054] The data analysis module 1 primarily determines the type of extreme natural disaster occurring based on the data provided by the data acquisition module and creates physical models for the extreme natural disaster event, such as earthquake, hurricane, flood, and sandstorm scenarios. By establishing the extreme natural disaster physical models, the characteristics and features of the extreme natural disaster itself are captured.

[0055] The data analysis module 2 is mainly used to establish an impact model of extreme natural disaster events on the lines and components of the new distribution network, mainly considering the impact of various extreme natural disaster events on the new distribution network lines, towers, insulators and other components. It can use simplified vulnerability curves and Monte Carlo random simulation methods to characterize the parameter changes of the new distribution network lines and components with the occurrence of extreme natural disaster events and the randomness of the impact of extreme natural disaster events on the new distribution network.

[0056] The data analysis module 3 is primarily responsible for establishing a new distribution network operation response model. The model can include a minimum load shedding model, an economic maximization model, and a critical load restoration model during the operation of the new distribution network. This module highlights the new distribution network's ability to respond to extreme natural disasters during operation and analyzes its operational status under extreme natural disasters, thereby helping to better respond to and reduce the losses caused by extreme natural disasters.

[0057] The indicator construction module is primarily concerned with establishing multidimensional resilience assessment indicators and reliability assessment indicators for new distribution networks based on distributed energy storage. In establishing these indicators, the multidimensional resilience assessment of new distribution networks is primarily based on the multidimensional nature of the new distribution network, while simultaneously considering dynamic and static multidimensional resilience assessment indicators for the three stages before, during, and after a disaster. Combined with the evaluation indicators of distributed energy storage, this system is used to construct a comprehensive resilience assessment indicator system for new distribution networks based on distributed energy storage. This system highlights the key role of energy storage in improving the resilience of new distribution networks and provides new ideas for improving their resilience.

[0058] The system assessment solution module primarily includes indicator assignment, weak link identification, system assessment recommendations, and emergency plan generation. The indicator assignment step assigns weights to the multidimensional resilience assessment indicators and reliability assessment indicators for the new distribution network based on distributed energy storage. Indicators are assigned using subjective and objective weight assignment methods. Weak link identification identifies weak links within the new distribution network, forming a system assessment recommendation for the new distribution network based on distributed energy storage. This in turn generates an emergency plan to maximize protection against the impact and losses caused by extreme natural disasters on the new distribution network.

[0059] The data storage and new distribution network resilience optimization learning module based on distributed energy storage mainly stores data from the data acquisition module, data analysis module 1, data analysis module 2, data analysis module 3, indicator construction module and system evaluation scheme module, and at the same time conducts new distribution network resilience optimization learning based on distributed energy storage for existing data and extreme natural disaster scenarios. The data of resilience optimization learning is connected to the data acquisition module, so that the new distribution network can gain experience from extreme natural disaster events that have occurred in history and continuously improve the resilience enhancement capabilities of the new distribution network.

[0060] According to another aspect of an embodiment of the present application, a non-volatile storage medium is further provided, in which a program is stored, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the above-mentioned method for determining the distribution network resilience index considering energy storage.

[0061] According to another aspect of an embodiment of the present application, a computer device is further provided, including: a memory and a processor, the processor being configured to run a program stored in the memory, wherein the program executes the above-mentioned method for determining the resilience index of a distribution network considering energy storage when running.

[0062] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0063] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0064] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0065] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0066] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0067] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the relevant technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, and other media that can store program code.

[0068] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for determining the resilience index of a distribution network considering energy storage, characterized in that: include: Obtain various meteorological data and distribution network operation data; Determining physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, and determining changes in parameters of various components in the distribution network under the various natural disaster events based on changes in parameters of various components in the distribution network under the physical characteristics; Determine distribution network resilience assessment indicators in multiple dimensions based on changes in parameters of various components in the distribution network under various natural disaster events; Determining distribution network resilience assessment indicators in multiple dimensions based on changes in parameters of each component in the distribution network under the various natural disaster events, including: respectively determining the operating information of the distribution network during changes in parameters of each component in the distribution network; determining distribution network resilience assessment indicators in multiple dimensions based on the operating information of the distribution network, wherein the categories of distribution network resilience assessment indicators in the multiple dimensions include at least: grid structure category, load change category, energy storage category, equipment usage category, and power management category of the distribution network, wherein the energy storage category is used to reflect the energy storage situation of the distribution network; The method of determining the distribution network resilience assessment indicators of multiple dimensions based on the operating information of the distribution network includes: dividing the acquisition period of multiple sub-indicators in the distribution network resilience assessment indicators of multiple dimensions into three stages according to the time of occurrence of the extreme disaster event, including: a first stage before the occurrence of the extreme disaster event, a second stage during the occurrence of the extreme disaster event, and a third stage after the occurrence of the extreme disaster event; and determining the indicators in the distribution network resilience assessment indicator categories of multiple dimensions in the three stages according to the operating information of the distribution network in the three stages respectively; Determine the indicators in the distribution network resilience assessment index category of multiple dimensions in the three stages respectively according to the operation status information of the distribution network in the three stages, including: determining the first indicator in the distribution network resilience assessment index category of multiple dimensions in the first stage, wherein the first indicator includes: the proportion of energy storage in the energy storage category in the distribution network; determining the second indicator in the distribution network resilience assessment index category of multiple dimensions in the second stage, wherein the second indicator includes: the proportion of controllable energy storage in the energy storage category in the entire energy storage; determining the third indicator in the distribution network resilience assessment index category of multiple dimensions in the third stage, wherein the third indicator includes: the proportion of energy storage utilization in the energy storage category; After determining the physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, the method also includes: constructing a physical model corresponding to the various natural disaster events based on the physical characteristics, and using the physical model to determine the changes in the parameters of each component in the distribution network.

2. The method according to claim 1, characterized in that The grid structure of the distribution network is used to reflect the power output and grid structure changes of the distribution network, the load change class is used to reflect the load situation of the distribution network, the equipment usage class is used to reflect the equipment usage of the distribution network, and the power management class is used to reflect the usage of distributed power sources in the distribution network.

3. The method according to claim 1, characterized in that The first indicators include: voltage qualification rate and three-phase voltage imbalance in the grid structure category of the distribution network, important load distribution balance in the load change category, equipment redundancy ratio in the equipment use category, and distributed power distribution information and the proportion of distributed power in the distribution network in the power management category; The second indicators include: the number of AC / DC reconfiguration switch actions in the grid structure category of the distribution network, the economic loss of load shedding in the load change category, the proportion of equipment used in the entire distribution network in the equipment use category, and the proportion of distributed power sources that can be controlled in the entire distribution network in the power management category; The third indicator includes: voltage stability in the grid structure category of the distribution network, the important load recovery amount in the load change category, the equipment failure rate and component repair quantity in the equipment usage category, and the proportion of distributed power supply usage in the power management category.

4. The method according to claim 3, characterized in that After determining the indicators in the distribution network resilience assessment indicator category of multiple dimensions in the three stages, the method further includes: Assigning weights to the distribution network resilience assessment indicators of the multiple dimensions according to predetermined weights; An adjustment plan for the distribution network is determined based on the distribution network resilience assessment indicators in the multiple dimensions after being assigned weights, and the distribution network is adjusted according to the adjustment plan.

5. The method according to claim 4, characterized in that After adjusting the distribution network according to the adjustment plan, the method further includes: Determine the target type of natural disaster events corresponding to the adjustment plan to form a training data set; Inputting the training data set into a predetermined initial model for training to obtain a trained distribution network resilience adjustment model; The distribution network resilience adjustment model is used to output multiple adjustment plans corresponding to different types of natural disaster events to adjust the distribution network.

6. A system for determining distribution network resilience indicators considering energy storage, characterized in that: include: Data collection module, data analysis module, indicator construction module, The data acquisition module is used to collect various meteorological data and distribution network operation data; The data analysis module is used to determine the physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, and based on the changes in the parameters of each component in the distribution network under the physical characteristics, determine the changes in the parameters of each component in the distribution network under the various natural disaster events; The indicator construction module is used to determine the distribution network resilience assessment indicators in multiple dimensions according to the changes in the parameters of each component in the distribution network under the various natural disaster events; Determining distribution network resilience assessment indicators in multiple dimensions based on changes in parameters of each component in the distribution network under the various natural disaster events, including: respectively determining the operating information of the distribution network during changes in parameters of each component in the distribution network; determining distribution network resilience assessment indicators in multiple dimensions based on the operating information of the distribution network, wherein the categories of distribution network resilience assessment indicators in the multiple dimensions include at least: grid structure category, load change category, energy storage category, equipment usage category, and power management category of the distribution network, wherein the energy storage category is used to reflect the energy storage situation of the distribution network; The method of determining the distribution network resilience assessment indicators of multiple dimensions based on the operating information of the distribution network includes: dividing the acquisition period of multiple sub-indicators in the distribution network resilience assessment indicators of multiple dimensions into three stages according to the time of occurrence of the extreme disaster event, including: a first stage before the occurrence of the extreme disaster event, a second stage during the occurrence of the extreme disaster event, and a third stage after the occurrence of the extreme disaster event; and determining the indicators in the distribution network resilience assessment indicator categories of multiple dimensions in the three stages according to the operating information of the distribution network in the three stages respectively; Determine the indicators in the distribution network resilience assessment index category of multiple dimensions in the three stages respectively according to the operation status information of the distribution network in the three stages, including: determining the first indicator in the distribution network resilience assessment index category of multiple dimensions in the first stage, wherein the first indicator includes: the proportion of energy storage in the energy storage category in the distribution network; determining the second indicator in the distribution network resilience assessment index category of multiple dimensions in the second stage, wherein the second indicator includes: the proportion of controllable energy storage in the energy storage category in the entire energy storage; determining the third indicator in the distribution network resilience assessment index category of multiple dimensions in the third stage, wherein the third indicator includes: the proportion of energy storage utilization in the energy storage category; After determining the physical characteristics corresponding to various natural disaster events based on the multiple meteorological data and the distribution network operation data, a physical model corresponding to the various natural disaster events is constructed based on the physical characteristics, so as to use the physical model to determine the changes in the parameters of each component in the distribution network.

7. A non-volatile storage medium, characterized in that: The non-volatile storage medium stores a program, wherein when the program is running, the device where the non-volatile storage medium is located is controlled to execute the method for determining the distribution network resilience index considering energy storage as described in any one of claims 1 to 5.

8. A computer device, characterized in that: include: A memory and a processor, the processor being configured to run a program stored in the memory, wherein the program, when running, executes the method for determining a distribution network resilience index taking into account energy storage as described in any one of claims 1 to 5.

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