Power distribution system resilience evaluation method and system based on unified modeling of resilient power supply resources

Abstract

The application provides a power distribution system resilience evaluation method and system based on a unified modeling of a resilient power supply resource, comprising: obtaining node survival expectation, node load loss expectation and resilience resources in a power distribution network; calculating resilience resource support capability based on the node survival expectation and the node load loss expectation through a unified model of the resilience resources, wherein the unified model of the resilience resources is constructed based on the resilience resources and preset related constraints thereof; calculating a power distribution system resilience index based on the resilience resource support capability, and completing power distribution system resilience evaluation. The application solves the problems of high complexity and great difficulty of existing power distribution system resilience evaluation.

Description

Technical Field

[0001] This invention relates to the field of power grid control technology, and in particular to a method and system for assessing the resilience of distribution systems based on unified modeling of resilient power supply resources. Background Technology

[0002] In the process of low-carbon transformation and development of power distribution systems, distributed power sources and energy storage technologies have become important means to improve the resilience of power distribution systems. Emerging energy forms such as vehicle-to-grid (V2G) electric vehicles may also become potential resilience resources. However, with the increasing variety of resilience resources, the complexity of resilience resource modeling poses a challenge to further expanding resilience-related research models. How to mathematically model various types of resilience resources while considering universality is a difficult problem in the field of resilience research.

[0003] Existing research has modeled resilient resources of various forms from different perspectives, such as resource support capacity and dispatchability, to enhance the resilience of power distribution systems. However, these studies often focus on specific resilient resources, lacking analysis and summarization of common characteristics among different resilient resources. Due to this lack of common modeling, the roles of different resilient resources in enhancing system resilience are difficult to quantify and compare. Furthermore, the complexity of modeling various resilient resources increases the difficulty of system resilience assessment and reduces the computational efficiency of system optimization. To better assess the value of low-carbon resources in resilience enhancement and accelerate resilience assessment efficiency, it is necessary to unify the modeling of different forms of resilient resources. Summary of the Invention

[0004] This invention provides a method and system for assessing the resilience of power distribution systems based on unified modeling of resilient power supply resources, in order to solve the problems of high complexity and difficulty in existing power distribution system resilience assessments.

[0005] This invention provides a method for assessing the resilience of a power distribution system based on unified modeling of resilient power supply resources, comprising: Obtain the expected node survival rate, expected node load loss, and resilience resources in the distribution network; Based on the expected node survival and expected node load loss, the resilience resource support capacity is calculated using a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilience resources and their preset related constraints. Based on the resilience support capacity of the aforementioned resources, the resilience index of the power distribution system is calculated, and the resilience assessment of the power distribution system is completed.

[0006] According to the present invention, a method for assessing the resilience of a power distribution system based on a unified model of resilient power supply resources is provided. The unified model of resilient resources is constructed based on the resilient resources and their preset related constraints, and includes: Obtain the upper and lower limits of energy and power of the resilient resource; A unified model of resilient resources is constructed based on the upper and lower limits of energy and power according to a preset time-series constraint relationship.

[0007] According to the present invention, a method for assessing the resilience of a power distribution system based on a unified model of resilient power supply resources is provided. The energy and power upper and lower limits of the resilient resources are used to construct a unified model of the resilient resources based on preset time-series constraints, including: Obtain the location of the mobile resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilience resources, and determine the temporal constraint relationship; Based on the energy and power upper and lower limits of emergency power supply vehicles and electrified transportation resources, and according to the time-series constraints, a unified model of resilient resources is constructed by unifying the energy and power range resources.

[0008] According to the present invention, a method for assessing the resilience of a power distribution system based on unified modeling of resilient power supply resources includes obtaining the location of movable resources such as emergency power supply vehicles and the energy boundaries of electrified transportation resources, and determining temporal constraints. The location of the mobile resources of the emergency power supply vehicle in the resilience resources is determined by deploying the mobile resources to the node with the greatest expected load loss, calculating the support capacity, correcting and updating the expected load loss, and continuing until the mobile resources are deployed. The energy boundary of the electrified transportation resources is determined by inputting the behavioral pattern statistics of the electrified transportation resources on a daily commuting route as a unit, calculating the energy boundary of the electrified transportation resources for each behavioral pattern, calculating the proportion of electric vehicles for each behavioral pattern, aggregating the energy boundary of the electrified transportation resource clusters on the commuting route, and incorporating the energy boundary of the electrified transportation resources into the energy boundary of the charging station according to the location of the electrified transportation resources. The energy boundary of the mobile resource location of the emergency power supply vehicle and the energy boundary of the electrified transportation resource in the resilient resources are combined into energy constraints to determine the temporal constraint relationship.

[0009] According to the present invention, a method for assessing the resilience of a distribution system based on a unified model of resilient power resources is provided. The step of calculating the resilience support capacity using a unified resilient resource model based on the expected node survival and expected node load loss includes: Based on the node survival expectation and node load loss expectation, the support range of resilience resources is set for the unified model of resilience resources; The upper limit of the resilience resource's support capacity is updated based on the set support range, and the resilience resource's support capacity for the node is calculated. Update the expected load loss of the node, the remaining support capacity of the resilient resources, and the remaining energy based on the support capacity of the resilient resources for the node. Based on the updated expected node load loss, remaining resilience resource support capacity, and remaining energy, if the resilience resource power is not greater than zero and the remaining resilience resource energy is not greater than the lower limit of resource energy, then the resilience resource is completely consumed, and the resilience resource support capacity is obtained.

[0010] According to the present invention, a method for assessing the resilience of a distribution system based on a unified model of resilient power supply resources is provided. This method calculates the resilience index of the distribution system based on the supporting capacity of the resilient resources, thereby completing the distribution system resilience assessment. The method includes: Based on the resilience resources' support capabilities, resilience resources are deployed sequentially at various nodes of the power grid. The distribution system resilience index is calculated based on the real-time power support status feedback from each node, and the distribution system resilience assessment is completed based on the distribution system resilience index.

[0011] This invention also provides a power distribution system resilience assessment system based on unified modeling of resilient power supply resources, the system comprising: The resource acquisition module is used to acquire the expected node survival, expected node load loss, and resilience resources in the distribution network. The support capacity calculation module is used to calculate the resilience resource support capacity based on the node survival expectation and the node load loss expectation through a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilience resources and their preset related constraints; A resilience assessment module is used to calculate the resilience index of the power distribution system based on the resilience resource support capacity, and to complete the power distribution system resilience assessment. The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the power distribution system resilience assessment method based on unified modeling of resilient power supply resources as described above.

[0012] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the power distribution system resilience assessment method based on unified modeling of resilient power supply resources as described above.

[0013] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the power distribution system resilience assessment method based on unified modeling of resilient power supply resources as described above.

[0014] This invention provides a method and system for assessing the resilience of distribution systems based on a unified model of resilient power resources. By analyzing the common characteristics of resilient power resources, the method models resilient power resources as a generalized energy storage resource and provides its key parameter attributes. It proposes a method to transform commonly used resilient power resources into a unified model. Based on the expected node survival and expected node load loss, the method calculates the support capacity of resilient resources using a unified model, and calculates the distribution system resilience index based on the support capacity of resilient resources, thus completing the distribution system resilience assessment and reducing the complexity of distribution system resilience assessment. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0016] Figure 1 This is a flowchart illustrating the power distribution system resilience assessment method based on unified modeling of resilient power supply resources provided by the present invention.

[0017] Figure 2 This is a schematic diagram of the range of resilient power supply resources provided by the present invention.

[0018] Figure 3 This is a schematic diagram of the module connection of the power distribution system resilience assessment system based on unified modeling of resilient power supply resources provided by the present invention.

[0019] Figure 4 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0020] Reference numerals: 110: Resource acquisition module; 120: Support capability calculation module; 130: Resilience assessment module; 410: Processor; 420: Communication interface; 430: Memory; 440: Communication bus. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0022] Existing resilience resource assessment systems only consider the impact of system topology and power nodes on system resilience levels, neglecting other resilience resources in the distribution system. By comprehensively considering other resilience resources, a revised method for calculating expected node load levels can be derived. (1)

[0023] in, Represents a node i exist t The expected resilience of the resources that support the power of the time period.

[0024] Combining equation (1), it can be found that improving the system's resilience level can be achieved by improving... It can also be achieved by improving Implementation. For the former, this mainly includes resilience enhancement measures / resilient resources acting at the system topology level, including but not limited to: adding power transfer lines, adding maintenance resources, line reinforcement, and adding power nodes. For the latter, this invention defines resilient power supply resources as those that can provide emergency support to loads but cannot serve as system power nodes.

[0025] The following is combined Figure 1 This invention describes a method for assessing the resilience of a power distribution system based on unified modeling of resilient power supply resources, comprising: Step 100: Obtain the expected node survival, expected node load loss, and resilience resources in the distribution network; In this invention, to standardize the modeling of resilient power supply resources, it is first necessary to analyze the similarities and differences in the attributes and application modes of commonly used resilient resources, providing a foundation for establishing a unified model. Common forms of resilient resources in power distribution systems are introduced below. Common resilient resources mainly include: distributed power sources (gas turbines / diesel generators / renewable energy), energy storage, emergency power supply vehicles (power generation / energy storage), and electrified transportation resources (electric buses / electric taxis / private electric vehicles), etc. These resources differ significantly in terms of total energy, output power, mobility, and dispatchability; specific differences are shown in Table 1.

[0026] Table 1

[0027] Main grid power sources are generally not considered resilient resources, but as the primary power source for the distribution system, their role is undeniable. They are listed in the table for comparison with resilient resources. The "Is energy finite?" criterion in the table header refers to whether the corresponding resource will deplete its energy during resilience recovery. Therefore, renewable energy sources are marked "×" in this item. For some gas turbines using gas from the gas grid, this item can be marked "×" if the gas grid is reliable. The "Is power finite?" criterion in the table header refers to whether the corresponding resource can support the entire distribution system. In the distribution system, main grid power sources are generally considered to have unlimited power. However, in certain situations (such as a complete power shortage in the upstream power system), main grid power can be considered finite. As shown in Table 1, most resilient power resources have limited energy and power during resilience recovery. Different resilient resources differ significantly in mobility and can be divided into mobile and immobile resources. Among mobile resources, the location dispatchability varies among different resources.

[0028] Step 200: Calculate the resilience resource support capacity based on the node survival expectation and the node load loss expectation using the unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilience resources and their preset related constraints; Specifically, the unified model for resilience resources is constructed based on the resilience resources and their pre-defined related constraints, including: Obtain the upper and lower limits of energy and power of the resilient resource; A unified model of resilient resources is constructed based on the upper and lower limits of energy and power according to a preset time-series constraint relationship.

[0029] From an energy perspective, most resilient resources have limited energy reserves during resilience recovery. Therefore, a unified model for resilient resources should have upper and lower limits on the total energy of these resources. For renewable energy resources, their output depends on the natural environment rather than their own stored energy; for gas turbines supplied by gas grids, their power output depends on the gas grid; and for main grid power, energy depends on the main grid. The total energy of these types of resources can be considered as follows: (or no total energy), meaning that energy will not be exhausted (or there is no concept of total energy).

[0030] Secondly, from the power perspective of resilient resources, their output power is typically insufficient to support the entire distribution system. Therefore, a unified model for resilient resources should have upper and lower limits for resource output power. For resources whose output power cannot be adjusted (such as some renewable energy sources), their upper and lower limits can be considered equal. The main grid power supply can be defined by its upper limit of output power. Modeling is performed in this way.

[0031] Finally, regarding the mobility of resilient resources, apart from emergency power supply vehicles directly dispatched by the power grid company, the dispatching capabilities of other electrified transportation vehicles are weak or impossible. For these types of mobile resilient resources, resilience assessments do not require focusing on which specific electric vehicle or electric bus provides the resource; rather, they focus on the dispatching boundaries of mobile resilient resources contained in each node of the power grid at different times. These boundaries can be modeled from the aforementioned energy and power perspectives. In this invention, resilient resources can be considered as a special type of "energy storage" resource, establishing a unified model for resilient power supply resources in the distribution system.

[0032] In the analysis of commonly used resilient power resources, the criterion of "limited power" actually measures the range of support that the resources can provide. In resilience assessment based on node survival, the support capability of power resources for the system can be reflected in two ways: increasing the expected node survival and reducing the expected system load loss. The former corresponds to the power nodes within the system, while the latter corresponds to resilient power resources.

[0033] In calculating the expected survival of nodes, power nodes need to be set in the probabilistic graph. These power nodes can be considered to support the entire system, and their support capability is reflected in reducing the probability of power outages across all network nodes, thereby improving the system's resilience. These power sources are not modeled as resilient power supply resources. For main grid power sources, their active and reactive power support capabilities are strong and can support the entire distribution system; they are set as power nodes in this study. For some distributed power sources with strong active and reactive power support capabilities, they have the capability to serve as core power sources for islanded grids during distribution system topology reconfiguration; these resources can also be set as power nodes in the probabilistic graph. Although these power sources cannot support the entire system when supplying power alone, in a resilience scenario, their ability to support islanded grids can be approximated as supporting the entire system: when the disaster is minor, the probability of each load node connecting to the main grid power source is high, and the expected loss is small. At this time, these power sources serve as a supplement to the main grid power source and do not need to support the entire system; when the disaster is severe, the probability of line outages is high, and the reliability probability of the power supply path from this power node to other load nodes decays rapidly with distance. Therefore, the actual support range does not need to reach the range of the entire system and cannot reach it.

[0034] When calculating nodal load losses, the impact of resilient power resources on load losses must be considered. Resilient power resources with limited support range should not be designated as power nodes in the probability diagram. Their support capacity can be reflected in the expected nodal load loss when calculating load losses. Regarding the modification, that is, in equation (1) The relationship between the expected load loss and the expected load of a node is as follows:

[0035] in, Resilient resources k The set, Resilient resources k For nodes Its supporting capabilities. Resilient resources k For nodes The expected support capacity is calculated as follows:

[0036] in, Indicates that it is in a resilient resource k The set of nodes supporting the range, express t Time slot resources k Node j To node i The reliability probability of the power supply path is shown in the diagram below. Figure 2 As shown.

[0037] In this invention, it is considered that Let k be the node containing resource k and its neighboring nodes. Resilient resources categorized into this type typically have weak support capabilities, primarily meeting the load demands of their respective nodes. If their support capacity exceeds the demands of their nodes, their support range is also limited. They generally lack the capacity to support distant nodes; therefore, they are considered... The simplified assumptions about the node where resource k is located and its adjacent nodes are more reasonable. It can also be adjusted according to actual needs.

[0038] In the unified model of resilient power supply resources, the variables involved in each resource and their meanings are detailed in Table 2.

[0039] Table 2

[0040] Unlike conventional energy storage modeling, in order to be compatible with different types of resilient resources, resource k During the period t The upper / lower limits of energy and power are defined in a time-varying form.

[0041] To use the unified model of resilient resources in resilient recovery scenarios, the variables related to resources need to satisfy the following constraints:

[0042] Among them, equations (5) and (6) represent resources. k The upper and lower limits of power and energy are constrained. Equation (7) represents the resource... k The temporal constraints of energy, This represents the duration of each time period. Equation (8) represents the resources. k The method for calculating the energy ratio.

[0043] In Equation (7), considering the scarcity of energy resources in the resilience recovery scenario, the charging behavior for resource k is simplified. If the effects of energy storage charging and discharging need to be considered, the charging power can be modeled and Equation (7) modified accordingly. In addition, considering resources with time-varying energy upper and lower limits (such as aggregated electric vehicles), Equation (7) does not use And use express in a way t -1 energy during the time period.

[0044] In this invention, a unified model of resilient resources is constructed based on the upper and lower limits of energy and power according to a preset time-series constraint relationship, including: Obtain the location of the mobile resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilience resources, and determine the temporal constraint relationship; Based on the energy and power upper and lower limits of emergency power supply vehicles and electrified transportation resources, and according to the time-series constraints, a unified model of resilient resources is constructed by unifying the energy and power range resources.

[0045] Specifically, different types of resilience resources are expressed differently in the unified resilience resource model.

[0046] Distributed power sources can be divided into two categories: those with unlimited energy but limited power, and those with limited energy but limited power.

[0047] For distributed power sources with unlimited energy but limited power (such as gas turbines supplied by gas grids and renewable energy sources), their energy ceiling can be set. for The remaining attributes are set according to the actual attributes. For example, the upper and lower limits of renewable energy power should generally meet the following formula:

[0048] For distributed power sources with finite energy and finite power (such as diesel generators), their upper and lower limits of energy and power can be considered constants, and the time-series relationship of their remaining energy can be simplified to the following form:

[0049] Energy storage modules are resilient resources with limited energy and power during resilience recovery. Therefore, they have the same expression as distributed power sources that rely on stored fuel, i.e., the upper and lower limits of energy and power are both constants. Without considering charging the energy storage during resilience recovery, the time-series relationship of its remaining energy can also be simplified to the form of equation (10).

[0050] During the resilience recovery process, the emergency power supply vehicle is a resilient resource with limited energy and power. Therefore, its upper and lower limits of energy and power are constants. Without considering the replenishment of energy to the emergency power supply vehicle during resilience recovery, the time sequence relationship of its remaining energy can also be simplified to the form of equation (10).

[0051] The main difference between emergency power supply vehicles and energy storage lies in their mobility. If their location can be determined, their role in resilience recovery is similar to that of distributed power sources / energy storage. During resilience recovery, the deployment location of emergency power supply vehicles can be determined using mathematical programming methods. In this paper, to simplify the resilience recovery calculation process, a method for determining the location of emergency power supply vehicle resources is designed.

[0052] In scenario-sampling-based resilience assessment methods, mobile resilient resources are typically scheduled using mathematical programming to determine their specific locations at different times. However, in node probability-based resilience assessment processes, calculating the support capacity of resilient power supply resources becomes difficult if the specific locations of mobile resources are unknown.

[0053] Therefore, this invention provides a simple method for determining the location of movable resources: Calculate the system's support capacity for immovable resources and complete the tasks for each node in the system. Corrections and updates; Deploy mobile resources to The largest node; Perform the calculation of the resilience resource support capacity and complete the task. Correction and update

[0054] If there are still movable resources available, return to deploying the movable resources to... Find the largest node, or end the process.

[0055] Electrified transportation differs significantly from other resilient resources. While electrified transportation represents a resilient resource with potentially enormous energy storage capacity, its spatiotemporal distribution exhibits considerable randomness, and the number and willingness of electrified vehicles to participate in resilience recovery also vary considerably. A common approach for this dispersed resource is to aggregate it, providing services through aggregators. Charging stations can be considered aggregators / aggregation units, viewed as a special type of static energy storage resource, assuming they can control the energy storage resources of electrified transportation within their controllable range. In the resilience recovery scenario, the resilient resource of electrified transportation is modeled.

[0056] As a special energy storage resource that integrates electrified transportation, the energy storage capacity and output power range of a charging station are affected by the number of electric vehicles that can participate in V2G within its range. Its upper and lower limits of energy and upper limit of power can be expressed in the following form:

[0057] Equations (11)-(13) represent the upper limit of output power and the upper and lower limits of energy of electrified transportation resources, where Indicates charging station k The number of charging stations, This indicates the maximum charging power of the charging station; express t Always at the charging station k The number of electric vehicles, This indicates the upper and lower limits of the energy of a single electric vehicle. This represents the proportion of vehicles participating in resilience recovery or supporting V2G functionality. If there are electric vehicles with different performance or different types of electrified transportation, equations (11)-(13) can also be expressed as a summation of categories. For the sake of conciseness and to simplify subsequent analysis, it can be assumed that the performance of typical / average electric vehicles has been obtained based on statistical results in the current equations (11)-(13).

[0058] For aggregated electrified transportation resources, their energy timing relationship should satisfy:

[0059] In equation (14), express t Always leave the charging station k Electric vehicle energy, express t Arrive at the charging station in time k The energy of electric vehicles expresses the energy temporal relationship of aggregated electrified transportation resources. Equations (15) and (16) represent... t When leaving / arriving at a charging station k The method for calculating the energy of electric vehicles, in which Represents the power consumption of an electric vehicle while driving. This represents the driving time of the electric vehicle. To simplify the calculation, it is assumed here that the time spent leaving / arriving at the charging station is used. k The average energy of electric vehicles and t -1 charging station k The average energy of electric vehicles is consistent. During the resilience recovery process, the charging of electric vehicles and the power consumption of electric vehicle driving are ignored, and by substituting equations (15) and (16) into equation (14), equations (7) and (8) can be obtained.

[0060] However, calculating the spatiotemporal distribution and movement information of a large amount of electrified transportation resources during resilience recovery would lead to complex computational analysis and would not directly contribute to resilience recovery. Therefore, before resilience recovery, the time-varying energy and power boundaries of electrified transportation resources can be calculated based on the statistical laws governing their spatiotemporal distribution and movement information. These boundaries can then be used as known information and input into the resilience recovery calculation process as constraints. This invention will introduce a method for calculating the time-varying energy and power boundaries of electrified transportation resources, the specific process of which is as follows: Input the behavioral patterns of electric vehicles based on daily commuting routes; Calculate the energy boundary of the electric vehicle for each behavior mode; Calculate the proportion of electric vehicles for each behavior pattern and aggregate them to obtain the energy boundary of the electric vehicle cluster on the commuting route; Based on the location of the electric vehicle, its energy boundary is included in the energy boundary of the charging station.

[0061] By obtaining energy boundaries for electric vehicle resources in different forms, and based on the obtained time-varying energy boundaries, the resilience resources of electrified transportation can be utilized to participate in the resilience recovery process of the power distribution system.

[0062] Step 300: Calculate the distribution system resilience index based on the resilience resource support capability to complete the distribution system resilience assessment.

[0063] In this invention, the distribution system resilience assessment method based on node survival expectation is ineffective because there is no specific system disaster scenario. Therefore, traditional scenario-based assessment methods, which rely on mathematical programming to restructure the system topology and schedule resilient resources, are no longer feasible. At this point, the availability of any lines is uncertain, making it impossible to construct restructured isolated systems. Consequently, the support range of each resilient power supply resource is uncertain, and the location of mobile resilient resources (emergency power supply vehicles) after scheduling is also unclear.

[0064] Refer to Table 3 to determine the calculation process for the support capacity of a single resilience resource.

[0065] Table 3

[0066] In the process outlined in Table 3, step 2 ensures that the settings are correct. t Time slot resources k Output power limit At that time, the constraints of equations (5) to (8) are satisfied. Steps 3-4 and steps 9-10 correspond to the update process of equation (2). The entire calculation process realizes the calculation of equation (4).

[0067] Based on the aforementioned resilience resource support capacity, the resilience index of the power distribution system is calculated, and the resilience assessment of the power distribution system is completed, including: Based on the resilience resources' support capabilities, resilience resources are deployed sequentially at various nodes of the power grid. The distribution system resilience index is calculated based on the real-time power support status feedback from each node, and the distribution system resilience assessment is completed based on the distribution system resilience index.

[0068] This invention provides a method for assessing the resilience of distribution systems based on a unified model of resilient power resources. By analyzing the common characteristics of resilient power resources, the method models resilient power resources as a generalized energy storage resource and provides its key parameter attributes. It proposes a method to transform commonly used resilient power resources into a unified model. Based on the expected node survival and expected node load loss, the method calculates the support capacity of resilient resources using a unified model. Based on the support capacity of resilient resources, it calculates the resilience index of the distribution system, completing the distribution system resilience assessment and reducing the complexity of the assessment.

[0069] refer to Figure 3 The present invention also discloses a power distribution system resilience assessment system based on unified modeling of resilient power supply resources, the system comprising: Resource acquisition module 110 is used to acquire the expected survival of nodes, the expected load loss of nodes, and resilience resources in the distribution network. The support capacity calculation module 120 is used to calculate the resilience resource support capacity based on the node survival expectation and the node load loss expectation through a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilience resource and its preset related constraints. The resilience assessment module 130 is used to calculate the resilience index of the power distribution system based on the resilience resource support capacity, and to complete the resilience assessment of the power distribution system.

[0070] The unified model for resilience resources is constructed based on the resilience resources and their pre-defined constraints, and includes: Obtain the upper and lower limits of energy and power of the resilient resource; A unified model of resilient resources is constructed based on the upper and lower limits of energy and power according to a preset time-series constraint relationship.

[0071] Based on the upper and lower bounds of energy and power for resilient resources, a unified model for resilient resources is constructed according to preset time-series constraints, including: Obtain the location of the mobile resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilience resources, and determine the temporal constraint relationship; Based on the energy and power upper and lower limits of emergency power supply vehicles and electrified transportation resources, and according to the time-series constraints, a unified model of resilient resources is constructed by unifying the energy and power range resources.

[0072] Obtain the mobile resource locations of emergency power supply vehicles and the energy boundaries of electrified transportation resources within the resilience resources, and determine the temporal constraints, including: The location of the mobile resources of the emergency power supply vehicle in the resilience resources is determined by deploying the mobile resources to the node with the greatest expected load loss, calculating the support capacity, correcting and updating the expected load loss, and continuing until the mobile resources are deployed. The energy boundary of the electrified transportation resources is determined by inputting the behavioral pattern statistics of the electrified transportation resources on a daily commuting route as a unit, calculating the energy boundary of the electrified transportation resources for each behavioral pattern, calculating the proportion of electric vehicles for each behavioral pattern, aggregating the energy boundary of the electrified transportation resource clusters on the commuting route, and incorporating the energy boundary of the electrified transportation resources into the energy boundary of the charging station according to the location of the electrified transportation resources. The energy boundary of the mobile resource location of the emergency power supply vehicle and the energy boundary of the electrified transportation resource in the resilient resources are combined into energy constraints to determine the temporal constraint relationship.

[0073] Based on the expected node survival and expected node load loss, the resilience resource support capacity is calculated using a unified resilience resource model, including: Based on the node survival expectation and node load loss expectation, the support range of resilience resources is set for the unified model of resilience resources; The upper limit of the resilience resource's support capacity is updated based on the set support range, and the resilience resource's support capacity for the node is calculated. Update the expected load loss of the node, the remaining support capacity of the resilient resources, and the remaining energy based on the support capacity of the resilient resources for the node. Based on the updated expected node load loss, remaining resilience resource support capacity, and remaining energy, if the resilience resource power is not greater than zero and the remaining resilience resource energy is not greater than the lower limit of resource energy, then the resilience resource is completely consumed, and the resilience resource support capacity is obtained.

[0074] Based on the aforementioned resilience resource support capacity, the resilience index of the power distribution system is calculated, and the resilience assessment of the power distribution system is completed, including: Based on the resilience resources' support capabilities, resilience resources are deployed sequentially at various nodes of the power grid. The distribution system resilience index is calculated based on the real-time power support status feedback from each node, and the distribution system resilience assessment is completed based on the distribution system resilience index.

[0075] This invention provides a distribution system resilience assessment system based on unified modeling of resilient power resources. By analyzing the common characteristics of resilient power resources, the system models resilient power resources as a generalized energy storage resource and provides its key parameter attributes. A method is proposed to transform commonly used resilient power resources into a unified model. Based on the expected node survival and expected node load loss, the system calculates the support capacity of resilient resources using the unified model. Based on the support capacity of resilient resources, the system calculates the distribution system resilience index, completing the distribution system resilience assessment and reducing the complexity of distribution system resilience assessment.

[0076] Figure 4 An example is a schematic diagram of the physical structure of an electronic device, such as... Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430, and a communication bus 440, wherein the processor 410, the communication interface 420, and the memory 430 communicate with each other through the communication bus 440. The processor 410 can call logical instructions in the memory 430 to execute a distribution system resilience assessment method based on a unified model of resilient power supply resources. This method includes: obtaining the expected node survival, expected node load loss, and resilient resources in the distribution network; calculating the resilience resource support capacity based on the expected node survival and expected node load loss using a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilient resources and their preset related constraints; and calculating the distribution system resilience index based on the resilience resource support capacity to complete the distribution system resilience assessment.

[0077] Furthermore, the logical instructions in the aforementioned memory 430 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0078] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the distribution system resilience assessment method based on unified modeling of resilient power supply resources provided by the above methods. The method includes: obtaining the expected node survival, expected node load loss, and resilient resources in the distribution network; calculating the resilience resource support capacity based on the expected node survival and expected node load loss through a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilient resources and their preset related constraints; calculating the distribution system resilience index based on the resilience resource support capacity, and completing the distribution system resilience assessment.

[0079] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements a method for assessing the resilience of a distribution system based on a unified model of resilient power supply resources, as provided by the methods described above. The method includes: obtaining the expected survival of nodes, the expected load loss of nodes, and resilient resources in the distribution network; calculating the resilience support capacity of the resilient resources based on the expected survival of nodes and the expected load loss of nodes using a unified model of resilient resources, wherein the unified model of resilient resources is constructed based on the resilient resources and their preset related constraints; calculating the resilience index of the distribution system based on the resilience support capacity of the resilient resources, thereby completing the resilience assessment of the distribution system.

[0080] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0081] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0082] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the resilience of a power distribution system based on unified modeling of resilient power supply resources, characterized in that, include: Obtain the expected node survival rate, expected node load loss, and resilience resources in the distribution network; Based on the expected node survival and expected node load loss, the resilience resource support capacity is calculated using a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilience resources and their preset related constraints. Based on the resilience support capacity of the aforementioned resources, the resilience index of the power distribution system is calculated, and the resilience assessment of the power distribution system is completed. The unified model of resilient resources is constructed based on the resilient resources and their preset related constraints, including: obtaining the upper and lower limits of energy and power of the resilient resources; obtaining the location of the movable resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilient resources, and determining the temporal constraint relationship; and unifying the energy range and power range resources according to the temporal constraint relationship based on the upper and lower limits of energy and power of the emergency power supply vehicle and the electrified transportation resources to construct the unified model of resilient resources. The process of obtaining the location of the mobile resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilience resources, and determining the temporal constraint relationship, includes: The location of the mobile resources of the emergency power supply vehicle in the resilience resources is determined by deploying the mobile resources to the node with the greatest expected load loss, calculating the support capacity, correcting and updating the expected load loss, and continuing until the mobile resources are deployed, in order to simplify the resilience recovery calculation process. The energy boundary of the electrified transportation resources is determined by inputting the behavioral pattern statistics of the electrified transportation resources on a daily commuting route as a unit, calculating the energy boundary of the electrified transportation resources for each behavioral pattern, calculating the proportion of electric vehicles for each behavioral pattern, aggregating the energy boundary of the electrified transportation resource clusters on the commuting route, and incorporating the energy boundary of the electrified transportation resources into the energy boundary of the charging station according to the location of the electrified transportation resources. The energy boundary of the mobile resource location of the emergency power supply vehicle and the energy boundary of the electrified transportation resource in the resilient resources are combined into energy constraints to determine the temporal constraint relationship.

2. The method for assessing the resilience of a distribution system based on unified modeling of resilient power supply resources according to claim 1, characterized in that, The calculation of resilience resource support capacity based on the expected node survival and expected node load loss using a unified resilience resource model includes: Based on the node survival expectation and node load loss expectation, the support range of resilience resources is set for the unified model of resilience resources; The upper limit of the resilience resource's support capacity is updated based on the set support range, and the resilience resource's support capacity for the node is calculated. Update the expected load loss of the node, the remaining support capacity of the resilient resources, and the remaining energy based on the support capacity of the resilient resources for the node. Based on the updated expected node load loss, remaining resilience resource support capacity, and remaining energy, if the resilience resource power is not greater than zero and the remaining resilience resource energy is not greater than the lower limit of resource energy, then the resilience resource is completely consumed, and the resilience resource support capacity is obtained.

3. The method for assessing the resilience of a distribution system based on unified modeling of resilient power supply resources according to claim 1, characterized in that, Based on the aforementioned resilience resource support capacity, the resilience index of the power distribution system is calculated, and the resilience assessment of the power distribution system is completed, including: Based on the resilience resources' support capabilities, resilience resources are deployed sequentially at various nodes of the power grid. The distribution system resilience index is calculated based on the real-time power support status feedback from each node, and the distribution system resilience assessment is completed based on the distribution system resilience index.

4. A power distribution system resilience assessment system based on unified modeling of resilient power supply resources, characterized in that, The system includes: The resource acquisition module is used to acquire the expected node survival, expected node load loss, and resilience resources in the distribution network. The support capacity calculation module is used to calculate the resilience resource support capacity based on the node survival expectation and the node load loss expectation through a unified resilience resource model, wherein the unified resilience resource model is constructed based on the resilience resources and their preset related constraints; The resilience assessment module is used to calculate the resilience index of the power distribution system based on the resilience resource support capacity, and to complete the resilience assessment of the power distribution system. The unified model of resilient resources is constructed based on the resilient resources and their preset related constraints, including: obtaining the upper and lower limits of energy and power of the resilient resources; obtaining the location of the movable resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilient resources, and determining the temporal constraint relationship; and unifying the energy range and power range resources according to the temporal constraint relationship based on the upper and lower limits of energy and power of the emergency power supply vehicle and the electrified transportation resources to construct the unified model of resilient resources. The process of obtaining the location of the mobile resources of the emergency power supply vehicle and the energy boundary of the electrified transportation resources in the resilience resources, and determining the temporal constraint relationship, includes: The location of the mobile resources of the emergency power supply vehicle in the resilience resources is determined by deploying the mobile resources to the node with the greatest expected load loss, calculating the support capacity, correcting and updating the expected load loss, and continuing until the mobile resources are deployed, in order to simplify the resilience recovery calculation process. The energy boundary of the electrified transportation resources is determined by inputting the behavioral pattern statistics of the electrified transportation resources on a daily commuting route as a unit, calculating the energy boundary of the electrified transportation resources for each behavioral pattern, calculating the proportion of electric vehicles for each behavioral pattern, aggregating the energy boundary of the electrified transportation resource clusters on the commuting route, and incorporating the energy boundary of the electrified transportation resources into the energy boundary of the charging station according to the location of the electrified transportation resources. The energy boundary of the mobile resource location of the emergency power supply vehicle and the energy boundary of the electrified transportation resource in the resilient resources are combined into energy constraints to determine the temporal constraint relationship.

5. An electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the power distribution system resilience assessment method based on unified modeling of resilient power supply resources as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the power distribution system resilience assessment method based on unified modeling of resilient power supply resources as described in any one of claims 1 to 3.

7. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the power distribution system resilience assessment method based on unified modeling of resilient power supply resources as described in any one of claims 1 to 3.

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