A power distribution network post-disaster recovery power supply optimization method and system based on a multi-source model

Abstract

The application is suitable for the technical field of power distribution network post-disaster recovery and resilience improvement, and provides a power distribution network post-disaster recovery power supply optimization method and system based on a multi-source model. The method comprises the following steps: collecting power distribution network post-disaster recovery global basic working condition data, constructing a power distribution network power flow model, a dynamic topology reconfiguration model, a traffic interruption model based on a node dynamic reachable state, a distributed energy model, a repair team scheduling model and a line repair constraint, wherein the distributed energy model at least comprises a cascade small hydropower hydrological coupling operation model; constructing a time-step post-disaster recovery joint optimization model, integrating each model and constraint to constrain the decision variables; taking the sum of the weighted load reduction and the economic loss of the damaged small hydropower in the recovery time domain as the minimum as the target for solving, generating a power distribution network post-disaster recovery power supply scheme, and improving the utilization rate of the post-disaster small hydropower resources, the executability of the post-disaster recovery power supply scheme and the important load supply capacity.

Description

Technical Field

[0001] This application belongs to the field of power distribution network post-disaster recovery and resilience enhancement technology, and in particular relates to a power supply optimization method and system for power distribution network post-disaster recovery based on a multi-source model. Background Technology

[0002] Extreme disasters such as floods, typhoons, and earthquakes can damage electrical facilities in power distribution networks, including lines and switching equipment, leading to widespread and prolonged power outages. Compared to transmission systems, branch lines, overhead lines, and end-point power supply facilities in distribution networks are more exposed to the external environment. Post-disaster recovery typically requires operating under conditions of damaged network topology, limited repair resources, and constrained power supply capacity from the upper-level grid. Especially in flood-prone areas, factors such as flooding, road blockages, and changes in the operational status of distributed energy sources interact. Therefore, effectively managing repair teams, network equipment, and distributed energy sources to prioritize the restoration of power to critical loads is a key issue in enhancing the post-disaster resilience of distribution networks.

[0003] Existing power distribution network post-disaster recovery technologies typically rely on power flow calculations, network reconfiguration, and load recovery models to collaboratively optimize faulty line repair, switching operations, distributed power generation output, and mobile energy storage resources. Some solutions also incorporate microgrid islanding operation, rolling optimization, or robust optimization to reduce post-disaster load loss. However, existing solutions often simplify traffic conditions to fixed travel states or preset routes, failing to fully reflect the characteristics of road capacity changes over time during floods. This can lead to repair teams and mobile energy storage equipment being unable to reach their designated locations as planned. Furthermore, for regional distributed energy sources such as small hydropower, existing solutions usually focus on the output capacity of individual power plants and local operational constraints, neglecting the hydrological coupling relationships between upstream and downstream small hydropower plants within the same river system, as well as the coordination needs between islanded power supply support and flood control and discharge safety. This may result in recovery solutions with unsafe resource scheduling or practically unenforceable issues.

[0004] Therefore, how to coordinate and optimize emergency repair teams, mobile energy storage, network reconfiguration, and power supply resources to improve the feasibility of post-disaster power restoration plans and the ability to restore critical loads while meeting the requirements of safe operation of the distribution network and disaster constraints has become an urgent technical problem to be solved. Summary of the Invention

[0005] To address at least one of the aforementioned technical issues, this application proposes a method and system for optimizing post-disaster power restoration in distribution networks based on a multi-source model. This method aims to improve the utilization rate of small hydropower resources, the feasibility of post-disaster power restoration plans, and the recovery capability of critical loads while meeting the requirements of safe operation of the distribution network and disaster constraints.

[0006] Firstly, this application provides an optimization method for power supply restoration after a disaster in a distribution network based on a multi-source model, including: Collect basic operating data of the entire distribution network after disaster recovery, and construct distribution network power flow model, dynamic topology reconfiguration model, traffic interruption model based on node dynamic reachability, distributed energy model, emergency repair team scheduling model, and line repair constraints respectively; wherein, the distributed energy model includes at least a cascade small hydropower hydrological coupling operation model. The post-disaster recovery time domain is discretized into several equal-time long steps to construct a post-disaster recovery joint optimization model with time steps. The decision variables of the joint optimization model include at least the repair tasks and locations of the repair teams, the availability status of the faulty lines, the operational status of the distribution network lines, the location and charging / discharging status of the mobile energy storage system, the output of each distributed energy source, and the load reduction of each node. The power flow model, dynamic topology reconfiguration model, traffic disruption model based on node dynamic reachability, hydrological coupling operation model of cascade small hydropower, emergency repair team scheduling model, and line repair constraints are integrated as constraints on all decision variables in the joint optimization model. Specifically, the traffic disruption model based on node dynamic reachability constrains the emergency repair team and mobile energy storage system to move only between reachable nodes based on the node dynamic reachability at each time step. The hydrological coupling operation model of cascade small hydropower constructs the coupling relationship of water volume transfer between upstream and downstream small hydropower stations in the same water system, simultaneously constraining the power generation output and discharge flow of small hydropower stations. The joint optimization model is solved with the goal of minimizing the sum of the weighted load reduction and the economic losses from small hydropower damage within the recovery time domain. Based on the model solution results, a power supply recovery scheme for the distribution network after a disaster is generated. The power supply recovery scheme includes at least one of the following: a dispatch scheme for emergency repair teams, a dispatch scheme for mobile energy storage systems, a distribution network reconfiguration scheme, a dispatch scheme for distributed energy output, and a node load recovery scheme.

[0007] In one possible implementation, the basic operating condition data for the entire distribution network post-disaster recovery includes distribution network electrical operating parameters, flood disaster traffic constraint parameters, emergency repair resource parameters, and hydrological operating parameters of cascade small hydropower stations. Collect basic operating data of the entire distribution network for post-disaster recovery, and construct distribution network power flow models, dynamic topology reconfiguration models, traffic interruption models based on node dynamic reachability, distributed energy models, emergency repair team scheduling models, and line repair constraints, including: The power flow model of the distribution network is constructed based on the electrical operating parameters of the distribution network. The power flow model of the distribution network includes node power balance, line power flow limit and node voltage safety constraint. The dynamic topology reconfiguration model is constructed based on the electrical operating parameters of the distribution network. The dynamic topology reconfiguration model is used to ensure that each energized area has power support and maintains the radial topology during the restoration process. The traffic disruption model based on the dynamic reachability of nodes is constructed based on the traffic constraint parameters of the flood disaster. It is used to determine the dynamic reachability of each node at each time step and to constrain the optional movement paths of the repair team and the mobile energy storage system. The hydrological coupled operation model of the cascade small hydropower stations is constructed based on the hydrological operation parameters of the cascade small hydropower stations, which is used to constrain the power generation output and discharge safety of each cascade small hydropower station in the same water system. Based on the emergency repair resource parameters, the emergency repair team scheduling model is constructed to determine the location and movement path of the emergency repair team and the mobile energy storage system at each time step. The line repair constraints are constructed based on the emergency repair resource parameters to determine the availability status of the faulty line at each time step and restrict the faulty line to be put into operation only after the repair is completed.

[0008] In one possible implementation, the power flow model of the distribution network adopts a linearized DistFlow power flow model, which includes at least the following constraints: The active power balance constraint at a node is used to limit the active power flow inflow, active power injection from distributed sources, and active power of load and active power reduction at any node of the distribution network to satisfy the power conservation relationship. The reactive power balance constraint at a node is used to limit the reactive power flow inflow, reactive power injection from distributed sources, and the reactive power of load and the reactive power of load reduction at any node of the distribution network to satisfy the power conservation relationship. The voltage drop constraint coupled with the line commissioning status is used to bind the line voltage drop calculation logic to the line commissioning status variables, and the corresponding voltage drop calculation is only performed when the line is commissioned. The active power flow capacity constraint is used to limit the absolute value of active power transmitted on the line to not exceed the upper limit of the line's rated active power transmission capacity. Line reactive power flow capacity constraint is used to limit the absolute value of reactive power transmitted on the line to not exceed the upper limit of the line's rated reactive power transmission capacity. Node voltage range constraint is used to limit the voltage amplitude of all energized nodes in the distribution network to always remain within the preset upper and lower voltage limits.

[0009] In one possible implementation, the dynamic topology reconfiguration model uses a single-commodity flow model to implement radial topology constraints, including at least the following constraints: Radial topology line quantity constraint is used to limit the relationship between the total number of operational lines, the total number of energized nodes, and the total number of virtual power sources, ensuring a ring-free radial structure of the distribution network from the branch scale level. A node virtual commodity flow balance constraint is used to define the flow conservation relationship of virtual commodities at each node, ensuring that each charged area is internally connected; A virtual power injection constraint is used to limit the injection of virtual commodity flow to only nodes selected as virtual power sources, and the injection amount is bound to the virtual power state variable; A line commissioning state and commodity flow coupling constraint is used to limit virtual commodity flow to only be transmitted on lines in the commissioning state, and disconnected lines do not carry virtual commodity flow; A charged node and adjacent line association constraint is used to define the local association relationship between the node charging state and the adjacent line commissioning state, to avoid inconsistent situations where a node is determined to be charged but the surrounding lines are not in the commissioning state; A charged node connectivity constraint is used to limit the charged node to be adjacent to at least one commissioned line, and the node charging state and the surrounding line commissioning state are associated with each other.

[0010] In a possible implementation, the traffic interruption model based on the dynamic reachable state of the node includes at least the following constraints: A flooded node unreachable constraint is used to mark the node flooded by the flood as unreachable at the corresponding time step; A non-flooded node reachable constraint is used to mark the node not flooded by the flood as reachable at the corresponding time step; A node dynamic reachable state determination constraint is used to determine the dynamic reachable state of the corresponding node according to the flood inundation range at each time step, and use it as the basis for scheduling the repair team and the mobile energy storage system.

[0011] In a possible implementation, the gradient small hydropower hydrological coupling operation model is constructed for a plurality of gradient small hydropower stations arranged in series in the same water system, and at least includes the following constraints: An upstream and downstream small hydropower station water quantity transmission coupling constraint: ; A forced shutdown state triggering lower limit constraint: ; A forced shutdown state triggering upper limit constraint: ; A damaged state triggering lower limit constraint: ; A damaged state triggering upper limit constraint: ; An irreversible damaged state constraint: ; The step small hydropower includes a reservoir type small hydropower with regulation and storage capacity and a runoff type small hydropower without regulation and storage capacity, the reservoir type small hydropower further comprises a storage capacity dynamic balance constraint for limiting the storage capacity of the reservoir type small hydropower at a current time step, which is equal to the sum of the storage capacity at a previous time step and the inflow water quantity, minus the total outflow water quantity; wherein, is the total outflow of the small hydropower i at the time step t; is a forced shutdown threshold comparison result; is a damaged threshold comparison result; is a forced shutdown flow threshold; is a damaged flow threshold; is a damaged state variable of the small hydropower i at the time step t; is a damaged state variable of the small hydropower i at the time step t-1; is the inflow of the small hydropower i at the time step t; is an upstream small hydropower station set of the small hydropower i; is the total outflow of the upstream small hydropower j at the time step t-1; is the natural inflow of the small hydropower i at the time step t; is a corresponding river flow loss, and M is a large M constant.

[0012] In a possible implementation, the repair team scheduling model simultaneously constrains the position scheduling of the repair team and the mobile energy storage system, and at least includes the following constraints: An initial position constraint for limiting the starting time of the recovery time domain, the nodes where the repair team and the mobile energy storage system are located are completely consistent with the preset initial deployment position; A position transfer constraint of a scheduling object leaving a node, for limiting the position transfer relationship of the scheduling object from the node at a current time step, and ensuring that the path selection state of leaving the current node is consistent with the position state at the time; A position transfer constraint of a scheduling object arriving at a node, for limiting the position transfer relationship of the scheduling object arriving at a target node at a next time step, and ensuring that the path selection state of arriving at the target node is consistent with the position state at the next time; A position uniqueness constraint in a single time step, for limiting a single repair team or a single mobile energy storage system to be located at only one power distribution network node in a same time step; A path passing feasibility constraint, for limiting the moving path selected by the repair team and the mobile energy storage system to satisfy the road basic passing condition; A passing node reachability constraint, for limiting all nodes passed by the moving path of the repair team and the mobile energy storage system to be in a reachable state at a corresponding time step based on a traffic interruption model based on a dynamic reachable state of the nodes.

[0013] In a possible implementation, the line repair constraint is specifically used to constrain the fault line to be converted to an available state only after the cumulative repair time of the repair team reaches the preset repair time; the non-fault line remains in an available state within the entire recovery time domain; and only the line in the available state is allowed to be put into operation of the power distribution network.

[0014] In a possible implementation, the optimization objective function in the joint optimization model is solved with the sum of the weighted load reduction amount in the recovery time domain and the economic loss of the small hydropower station as the optimization objective, and is as follows: ; wherein, is the load reduction weight of the node i; is the active load reduction amount of the node i at the time step t; is the total number of nodes of the power distribution network; is the time step; is the total number of scheduling time steps; is the economic loss weight of the small hydropower station j; is the damaged state variable of the small hydropower station j at the last time step T; is the total number of small hydropower stations.

[0015] In a second aspect, the present application further provides a computer system, comprising a memory and a processor; the memory stores program code executable by the processor; and the program code is used to execute the method according to the first aspect or any of the implementation manners thereof.

[0016] In a third aspect, the present application provides a computer readable storage medium, which stores a computer program; when the computer program is executed by a processor, the method according to the first aspect or any of the implementation manners thereof is implemented.

[0017] In a fourth aspect, the present application provides a computer program product, which comprises a computer program; when the computer program is executed by a processor, the steps of the method according to the first aspect or any of the implementation manners thereof are implemented.

[0018] The advantages of this application compared to existing technologies are as follows: By collecting comprehensive basic operating data of the power distribution network during post-disaster recovery, a power flow model, a dynamic topology reconfiguration model, a traffic interruption model based on the dynamic reachability of nodes, a hydrological coupling operation model for cascade small hydropower, a dispatching model for emergency repair teams, and line repair constraints are constructed. Furthermore, in a time-step joint optimization model for post-disaster recovery, emergency repair resource scheduling, faulty line repair, network reconfiguration, distributed energy output, and load recovery are solved collaboratively. Specifically, the traffic interruption model based on the dynamic reachability of nodes utilizes the dynamic reachability of nodes at each time step to constrain the movement range of the emergency repair teams and mobile energy storage systems. This reduces the problem of scheduling plans being unable to be executed due to road or node inaccessibility caused by floods, and improves the feasibility of emergency repair and mobile energy storage scheduling plans. The cascade small hydropower hydrological coupling operation model constructs the water volume transfer coupling relationship between upstream and downstream small hydropower stations in the same water system, and simultaneously constrains the power generation output and discharge flow of small hydropower stations. It can utilize small hydropower to support island power supply and restore loads while taking into account hydrological operation and discharge safety requirements. Furthermore, under the condition of meeting the constraints of power flow, topology operation, disaster traffic and resource operation of distribution network, it improves the restoration capacity of important loads and the reliability of power supply restoration plans for distribution network after disasters, while also improving the utilization rate of small hydropower resources after disasters.

[0019] It is understood that the computer system, computer-readable storage medium, and computer program product provided in this application have the same beneficial effects as the above-described multi-source model-based power supply optimization method for post-disaster power restoration in distribution networks, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in this application, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for optimizing power supply recovery after a disaster in a distribution network based on a multi-source model, provided in an embodiment of this application; Figure 2 A schematic diagram of an improved IEEE 33-node power distribution network topology provided in one embodiment of this application; Figure 3 A comparison chart of the load maintenance ratio of the method of this application and a literature benchmark modified for real-world conditions, provided as an embodiment of this application; Figure 4 A comparison diagram of the output of a small hydropower station under strict retracement is provided as an embodiment of this application; Figure 5A 33-node system key disaster and resource parameter sensitivity analysis chart of recovery performance and calculation time is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0022] In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular architectures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application can be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known methods, devices, circuits, and

[0023] It is to be understood that the terminology "includes", "has", "holds", "contains" used in the present specification and in the appended claims indicates the presence of the described features, integers, steps, operations, elements, and / or components but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0024] It is also to be understood that the terminology "and / or" used in the present specification and in the appended claims indicates any combination of the associated listed items as well as all possible combinations of the items.

[0025] As used in the present specification and in the appended claims, the term "if" can be interpreted as meaning "when" or "once" or "in response to a determination" or "in response to detecting" depending on the context. Similarly, the phrase "if it is determined" or "if [a described condition or event] is detected" can be interpreted as meaning "once it is determined" or "in response to the determination" or "once [the described condition or event] is detected" or "in response to detecting [the described condition or event]" depending on the context.

[0026] In addition, in the description of the present specification and the appended claims, the description of the terms "first, second", "S1, S2", "step one, step two", etc. is only for the purpose of description, and cannot be understood as indicating or implying the relative importance of the indicated technical features or implying the number of the indicated technical features or indicating the execution order of the method, etc. Any embodiment within the technical concept of the present application without violating the technical concept of the present application should be included in the protection scope of the present application.

[0027] Reference within the specification of this application to "one embodiment" or "some embodiments" means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the application. The appearances of the phrases "in one embodiment" or "in some embodiments" in various places within specified descriptions in this specification are not necessarily all referring to the same embodiment, however, but can refer to one or more but not all embodiments. The terms "including," "comprising," "carrying," "having," "containing," and variations thereof do not exclude the presence of additional items in addition to those specifically recited. Thus, any statement in this specification that an embodiment includes, comprises, carries, or contains one or more features means that the embodiment includes, comprises, carries, or contains those one or more features but not excluding the presence of additional features.

[0028] The application provides a power distribution network post-disaster recovery power supply optimization method based on a multi-source model, which is suitable for a power distribution network outage recovery scenario caused by disasters such as floods, typhoons, and earthquakes, and is particularly suitable for a recovery power supply scenario in which a fault line is damaged, roads are blocked, repair resources are limited, mobile energy storage is dispatched, and safe operation constraints of cascade small hydropower are simultaneously present.

[0029] For ease of understanding, the technical solutions of the application will be described in detail below with reference to the accompanying drawings.

[0030] Figure 1 A flowchart of a power distribution network post-disaster recovery power supply optimization method based on a multi-source model provided by an embodiment of the application is shown in FIG. 1. As shown in FIG. 1, for ease of illustration, only parts related to the embodiment are shown, and the method provided by the embodiment includes the following steps: Figure 1 S1, collect post-disaster recovery global basic working condition data of a power distribution network, and construct a power distribution network power flow model, a dynamic topology reconstruction model, a traffic interruption model based on a node dynamic reachable state, a distributed energy model, a repair team scheduling model, and a line repair constraint, respectively; wherein the distributed energy model at least includes a hydrology coupled operation model of cascade small hydropower.

[0031] Specifically, this step is used to uniformly organize power grid operation information, flood traffic information, repair resource information, and hydrology information of cascade small hydropower involved in the post-disaster recovery process, and to complete the basic modeling of the joint optimization model accordingly.

[0032] ​In a possible implementation, the post-disaster recovery global basic working condition data of the power distribution network includes power distribution network electrical operation parameters, flood disaster traffic constraint parameters, emergency repair resource parameters, and cascade small hydropower hydrological operation parameters. The power distribution network electrical operation parameters at least include a power distribution network node set, a line set, line impedance, node load, node voltage upper and lower limits, line flow limits, and distributed energy access locations. The flood disaster traffic constraint parameters at least include a flood inundation range, road blockage information, node dynamic accessibility, a candidate path set, and candidate path travel times. The emergency repair resource parameters at least include a repair team initial position, a mobile energy storage initial position, a fault line set, fault line corresponding repair nodes, and repair durations. The cascade small hydropower hydrological operation parameters at least include natural inflow, upstream water, river flow loss, total outflow threshold, power generation water flow upper limit, and storage capacity upper and lower limits.

[0033] Correspondingly, step S1 can optionally include: S11, constructing a power distribution network power flow model based on the power distribution network electrical operation parameters, the power distribution network power flow model including node power balance, line power flow limits, and node voltage safety constraints.

[0034] Optionally, the power distribution network power flow model adopts a linearized DistFlow power flow model, and at least includes the following constraints: a node active power balance constraint for limiting the inflow active power flow, distributed power active injection, load active power, and load reduction active power of any node of the power distribution network to satisfy a power conservation relationship; a node reactive power balance constraint for limiting the inflow reactive power flow, distributed power reactive injection, load reactive power, and load reduction reactive power of any node of the power distribution network to satisfy a power conservation relationship; a line operation state coupled voltage drop constraint for binding line voltage drop calculation logic and line operation state variables, and performing corresponding voltage drop calculation only when the line is in operation; a line active power flow capacity constraint for limiting the absolute value of the active power transmitted on the operating line to be less than the rated active power transmission capacity upper limit of the line; a line reactive power flow capacity constraint for limiting the absolute value of the reactive power transmitted on the operating line to be less than the rated reactive power transmission capacity upper limit of the line; a node voltage range constraint for limiting the voltage amplitude of all live nodes of the power distribution network to be maintained within a preset voltage upper limit and voltage lower limit interval.

[0035] Illustratively, the expression of the node active power balance constraint is: ; The expression of the node reactive power balance constraint is: ; The expression of the upper limit constraint of the voltage drop coupled with the line operation state is: ; The expression of the lower limit constraint of the voltage drop coupled with the line operation state is: ; The expression of the line active power flow capacity constraint is: ; The expression of the line reactive power flow capacity constraint is: ; The expression of the node voltage range constraint is: ; wherein, and respectively represent the child node set and the parent node set of node i; and represent the active power and the reactive power flow on the line ; and represent the active power and the reactive power injection of the distributed energy at node i; and are the maximum active power and the maximum reactive power load of node i; and are the active power and the reactive power load shedding of node i; and are the node voltage amplitudes; and are the resistance and the reactance of the line ; is the reference voltage; is the line state variable, taking 1 to represent that the line is in operation, and taking 0 to represent that the line is not in operation; is a large M constant; and are the line flow limits; is the node electrification state variable, representing whether node i is electrified; and are the upper and lower voltage limits respectively; L is the line set of the distribution network; N bus is the total number of nodes of the distribution network; the above variables are time-varying variables.

[0036] S12, based on the electrical operation parameters of the distribution network, constructs a dynamic topology reconfiguration model, and the dynamic topology reconfiguration model is used to ensure that each electrified area has power support and maintains a radial topology structure in the restoration process.

[0037] Optionally, the dynamic topology reconfiguration model adopts a single commodity flow model to ensure radial topology, at least including the following constraints: a radial topology line quantity constraint for limiting the quantity relationship between the total number of lines in operation and the total number of live nodes and the total number of virtual power sources, to ensure a loopless radial structure of the distribution network from the branch scale level; a node virtual commodity flow balance constraint for limiting the flow conservation relationship of virtual commodities at each node, to ensure the connectivity of nodes in each live area; a virtual power source injection constraint for limiting that only the nodes selected as virtual power sources can inject virtual commodity flow, and the injection amount is bound to the virtual power source state variable; a line operation state and commodity flow coupling constraint for limiting that virtual commodity flow is only transmitted on lines in operation, and a disconnected line does not carry virtual commodity flow; a live node and adjacent line in operation association constraint for limiting the local association relationship between the live state of a node and the operation state of its adjacent line, to avoid the inconsistent situation that a node is determined to be live but the surrounding lines are not in operation; a live node connectivity constraint for limiting that a live node must be adjacent to at least one line in operation, and the live state of the node and the operation state of the surrounding line are associated with each other.

[0038] For example, the expression of the radial topology line quantity constraint is as follows: ; The expression of the node virtual commodity flow balance constraint is as follows: ; The expression of the virtual power source injection constraint is as follows: ; The expression of the line operation state and commodity flow coupling constraint is as follows: ; The expression of the live node and adjacent line in operation association constraint is as follows: ; The expression of the live node connectivity constraint is as follows: ; wherein, is the virtual commodity flow on the line ; is the injection commodity flow from the virtual source at node i; is the virtual source state variable, indicating whether node i is selected as a virtual source; is a large M constant; z ij is the switch state variable of the line between node i and node j; is a node electrification state variable, indicating whether node i is electrified; all the above variables are time-varying variables.

[0039] S13, constructing a traffic interruption model based on node dynamic accessibility state based on the flood disaster traffic constraint parameter, for determining the dynamic accessibility state of each node at each time step, and constraining the selectable moving path of the repair team and the mobile energy storage system.

[0040] Optionally, the traffic interruption model based on the node dynamic accessibility state is specifically determined according to the flood inundation range of each time step to determine the dynamic accessibility state of the corresponding node. The flooded node is marked as inaccessible, and the node not flooded by the flood is marked as accessible, which is used as the scheduling basis for the repair team and the mobile energy storage system. The traffic interruption model based on the node dynamic accessibility state at least includes the following constraints: Inundated node inaccessible constraint, for limiting the nodes flooded by the flood to be marked as inaccessible at the corresponding time step; Non-inundated node accessible constraint, for limiting the nodes not flooded by the flood to be marked as accessible at the corresponding time step; Node dynamic accessibility state determination constraint, for determining the dynamic accessibility state of the corresponding node according to the flood inundation range of each time step, and using it as the scheduling basis for the repair team and the mobile energy storage system.

[0041] Exemplarily, the expression of the inundated node inaccessible constraint is: ; The expression of the non-inundated node accessible constraint is: ; Wherein, is a node i dynamic accessibility state variable at time step t, taking 1 to represent accessible, and taking 0 to represent inaccessible; is a set of nodes flooded or blocked by roads within time step t. The node dynamic accessibility state can be determined by monitoring data, flood prediction model or expert evaluation. Through this step, the flood traffic impact can be directly coupled into the scheduling process of the repair team and the mobile energy storage system.

[0042] S14, constructing a cascade small hydropower hydrological coupling operation model based on the cascade small hydropower hydrological operation parameter, for constraining the power generation output and discharge safety of each cascade small hydropower station in the same river system.

[0043] Optionally, the cascade small hydropower hydrological coupling operation model is constructed for a plurality of cascade small hydropower stations arranged in series in the same river system, and at least includes the following constraints: water quantity transmission coupling constraint of upstream and downstream small hydropower stations, forced shutdown state triggering lower limit constraint, forced shutdown state triggering upper limit constraint, damaged state triggering lower limit constraint, damaged state triggering upper limit constraint, and damaged state irreversible constraint; Further, the cascade small hydropower includes the reservoir type small hydropower with regulation and storage capacity and the runoff type small hydropower without regulation and storage capacity, and the reservoir type small hydropower further includes a storage capacity dynamic balance constraint for limiting the storage capacity of the reservoir type small hydropower at a current time step, which is equal to the sum of the storage capacity at a previous time step and the inflow water quantity minus the total outflow water quantity.

[0044] For example, the cascade small hydropower hydrology coupling operation model at least includes the following constraints: A small hydropower output and power generation water flow conversion constraint: ; A power generation water flow constraint that is not greater than a total outflow flow constraint: ; A power generation water flow constraint in a forced shutdown state: ; A power generation water flow constraint in a damaged state: ; A small hydropower station storage capacity dynamic balance constraint: ; An upstream and downstream small hydropower station water quantity transmission coupling constraint: ; A forced shutdown state triggering lower limit constraint: ; A forced shutdown state triggering upper limit constraint: ; A damaged state triggering lower limit constraint: ; A damaged state triggering upper limit constraint: ; A forced shutdown state, damaged state and shutdown state association constraint: ; ; An irreversible constraint of the damaged state: ; A state variable binary value constraint: ; A storage capacity range constraint: ; A water flow non-negative constraint: ; wherein, is the active output of the small hydropower i at time step t; is the conversion coefficient between the water flow and the active power; is the total water flow of the small hydropower i at time step t; is the forced shutdown threshold comparison result, indicating whether the small hydropower i exceeds the forced shutdown flow threshold at time step t; is the damaged threshold comparison result, indicating whether the small hydropower i exceeds the damaged flow threshold at time step t; is the forced shutdown flow threshold; is the damaged flow threshold; is the damaged state variable of the small hydropower i at time step t; is the damaged state variable of the small hydropower i at time step t-1; is the maximum water flow for power generation; is the water storage amount; is the maximum water storage amount; is the inflow of the small hydropower i at time step t; is the set of upstream small hydropower stations of the small hydropower i; is the total water flow of the upstream small hydropower j at time step t-1; is the natural inflow of the small hydropower i at time step t; is the corresponding river flow loss, is the time step length, and M is a large M constant.

[0045] Through the above constraints, the coupled operation relationship between the small hydropower in island power supply support and flood discharge safety can be simultaneously described.

[0046] In some embodiments, the distributed energy source model can further include a mobile energy storage model, a photovoltaic power generation model, a diesel generator model, and an electric vehicle charging station model in addition to the cascade small hydropower hydrological coupling operation model; The mobile energy storage model is based on the position state, the upper limit of the charge and discharge power, the charge and discharge efficiency, and the upper and lower limits of the state of charge of the mobile energy storage system, to construct the charge and discharge power constraints and the state of charge dynamic balance constraints of the mobile energy storage system; The photovoltaic power generation model is based on the available active power and the power factor of the photovoltaic power source, to construct the active output range constraints and the active-reactive output relationship constraints of the photovoltaic power source; The diesel generator model is based on the upper and lower limits of the active output and the upper and lower limits of the reactive output of the diesel generator, to construct the active output constraints and the reactive output constraints of the diesel generator; The electric vehicle charging station model is based on the upper limit of the charge and discharge power, the charge and discharge efficiency, and the upper limit and lower limit of the energy storage amount of the electric vehicle charging station, to construct the charge and discharge power constraints, the energy storage amount dynamic balance constraints, and the energy storage amount range constraints of the electric vehicle charging station.

[0047] As an example, a mobile energy storage model includes at least the following constraints: Non-negative mobile energy storage discharge power constraint: ; Non-negative mobile energy storage charge power constraint: ; Mobile energy storage charge / discharge power upper limit constraint: ; Mobile energy storage state of charge dynamic balance constraint: ; Mobile energy storage state of charge range constraint: ; wherein, and are the discharge power and charge power, respectively; is the power upper limit; denotes whether the repair crew or mobile energy storage k is located at node i at time step t; is the energy state; and are the charge and discharge efficiencies, respectively; and are the energy lower and upper limits, respectively.

[0048] As an example, a photovoltaic power generation model includes at least the following constraints: Photovoltaic active power upper limit constraint: ; Photovoltaic power factor constraint: ; wherein, and are the photovoltaic active and reactive outputs, respectively, is the available photovoltaic power, is the photovoltaic power factor.

[0049] As an example, a diesel generator model includes at least the following constraints: Diesel generator active power output range constraint: ; Diesel generator reactive power output range constraint: ; wherein, and are the diesel generator active and reactive outputs, respectively, and upper and lower limits for active output of the diesel generator; and upper and lower limits for reactive output of the diesel generator.

[0050] As an example, an electric vehicle charging station model includes at least the following constraints: non-negative constraint on discharging power of the electric vehicle charging station: ; non-negative constraint on charging power of the electric vehicle charging station: ; upper limit constraint on charging and discharging power of the electric vehicle charging station: ; state of charge dynamic balance constraint of the electric vehicle charging station: ; state of charge range constraint of the electric vehicle charging station: ; wherein, and are discharging and charging power respectively, is a power upper limit, is an energy state, and are charging and discharging efficiencies, and are energy state upper and lower limits.

[0051] S15, based on the emergency repair resource parameters, a repair team scheduling model is constructed, which is used to determine the positions and moving paths of the repair teams and the mobile energy storage systems at each time step.

[0052] Optionally, the repair team scheduling model simultaneously constrains the position scheduling of the repair teams and the mobile energy storage systems, and at least includes the following constraints: initial position constraint, used to limit the starting time of the recovery time domain, and the nodes where the repair teams and the mobile energy storage systems are located are completely consistent with the preset initial deployment positions; position transfer constraint of a scheduling object leaving a node, used to limit the position transfer relationship of the scheduling object leaving the node at the current time step, so as to ensure that the path selection state of leaving the current node is consistent with the position state at this time; position transfer constraint of a scheduling object arriving at a node, used to limit the position transfer relationship of the scheduling object arriving at the target node at the next time step, so as to ensure that the path selection state of arriving at the target node is consistent with the position state at the next time; uniqueness constraint of position in a single time step, used to limit that a single repair team or a single mobile energy storage system is located at only one power distribution network node in a same time step; path passability constraint, used to limit the selected moving path of the repair team and mobile energy storage system to satisfy the road infrastructure passability condition; passing node reachability constraint, used to limit all nodes passed by the moving path of the repair team and mobile energy storage system to be in a reachable state at the corresponding time step based on a traffic interruption model based on the dynamic reachability state of the nodes.

[0053] For example, the expression of the initial position constraint is: ; The expression of the position transfer constraint of the dispatch object leaving the node is: ; The expression of the position transfer constraint of the dispatch object arriving at the node is: ; The expression of the position uniqueness constraint in a single time step is: ; The expression of the path passability constraint is: ; The expression of the passing node reachability constraint is: ; wherein, represents whether the repair team or mobile energy storage k is located at node i at time step t; is the initial number of repair teams or mobile energy storages at node i; is a path selection state variable, representing whether the dispatch object k is moved from node i to node j by node i during t to t+1 by path p; is a path passability index determined based on the travel time; is a set of nodes passed by path p at time step t; is a set of repair teams and mobile energy storages; is a set of power distribution network nodes.

[0054] Through the above constraints, it can be ensured that the repair team and the mobile energy storage system move along a path that satisfies the passability condition and passes through nodes that are reachable.

[0055] S16, constructing a line repair constraint based on the emergency repair resource parameters, used to determine the available state of the fault line at each time step, and limit the fault line to be put into operation only after repair is completed.

[0056] Optionally, the line repair constraint is specifically used to constrain the fault line to be converted to an available state only after the cumulative repair time of the repair team reaches the preset repair time; the non-fault line remains in an available state within the entire recovery time domain; and only the line in the available state is allowed to be put into operation of the power distribution network.

[0057] For example, the line repair constraint at least includes the following constraints: The association constraint between the line operation state and the available state: ; The upper limit constraint of the fault line repair completion state: ; The lower limit constraint of the fault line repair completion state: ; The available state constraint of the non-fault line: ; Wherein, is the line available state variable at time step t; is the set of fault lines; is the number of repair teams; is the preset repair time of a single fault line; represents the repair team position state variable, indicating that the repair team k is located at the fault line corresponding to the repair node j.

[0058] Through step S1, the multi-source model required for post-disaster recovery power supply joint optimization can be constructed, so that the power grid, transportation, hydrology, repair and line repair process are unified and mapped into the same optimization framework.

[0059] S2, the post-disaster recovery time domain is discretized into several equal-length time steps, and a post-disaster recovery joint optimization model is constructed; wherein the decision variables of the joint optimization model at least include the repair task and position of the repair team, the available state of the fault line, the line operation state of the power distribution network, the position and charge-discharge state of the mobile energy storage system, the output of each distributed energy and the load reduction of each node.

[0060] Specifically, this step is used to discretize the continuous post-disaster recovery process into a solvable time sequence decision problem, so that the repair, movement, reconstruction and energy supply decisions can be solved in a unified time scale.

[0061] Optionally, the discretization can be performed in 1 hour as a time step, or other equal-length time steps can be set according to the actual recovery time.

[0062] S3, integrate the power distribution network power flow model, dynamic topology reconfiguration model, traffic interruption model based on node dynamic reachable state, hydrological coupling operation model of cascade small hydropower, repair team scheduling model and line repair constraints as constraint conditions of the joint optimization model to constrain all decision variables; wherein the traffic interruption model based on node dynamic reachable state is based on the node dynamic reachable state corresponding to each time step, and the repair team and the mobile energy storage system are only allowed to move between reachable nodes; the hydrological coupling operation model of cascade small hydropower constructs the water quantity transmission coupling relationship between upstream and downstream small hydropowers in the same water system, and synchronously constrains the small hydropower generation output and the discharge flow.

[0063] Specifically, this step is used to uniformly apply electrical operation constraints, network topology constraints, traffic constraints, hydrological safety constraints, repair execution constraints and line repair constraints to the joint optimization model, so as to ensure that the recovery scheme meets the power supply safety and execution feasibility requirements at the same time.

[0064] S4, taking the minimum sum of weighted load reduction amount in the recovery time domain and economic loss of damaged small hydropower as the optimization objective, solving the joint optimization model.

[0065] Specifically, this step is used to prioritize the recovery of important loads while reducing the risk of small hydropower damage due to unsafe operation under the premise of meeting all constraint conditions.

[0066] As an example, the optimization objective function can be represented as: ; wherein the first term is the total amount of weighted load reduction in the recovery time domain, and the second term is the economic loss of damaged small hydropower. is the load reduction weight of node i, used to distinguish important loads from ordinary loads; is the active load reduction amount of node i at time step t; is the total number of distribution network nodes; is the time step; is the total number of scheduling time steps; is the economic loss weight of small hydropower j; is the damaged state variable of small hydropower j at the last time step T; is the total number of small hydropower stations. Through this objective function, the important load recovery benefit and the small hydropower safe operation demand can be considered.

[0067] S5, generating a post-disaster power restoration scheme of the power distribution network according to the model solving result, wherein the post-disaster power restoration scheme includes at least one of a repair team scheduling scheme, a mobile energy storage system scheduling scheme, a power distribution network reconfiguration scheme, a distributed energy output scheduling scheme and a node load recovery scheme.

[0068] Specifically, the step is used for converting the optimization result into an executable post-disaster recovery plan. Among them, the repair team scheduling scheme is used to determine the repair task allocation, travel path and residence position of each time step; the mobile energy storage system scheduling scheme is used to determine the position transfer and charging and discharging arrangement thereof; the distribution network reconstruction scheme is used to determine the input state of each time step line or tie switch; the distributed energy output scheduling scheme is used to determine the output of small hydropower, photovoltaic, diesel generator, electric vehicle charging station and mobile energy storage and the like resources; and the node load recovery scheme is used to determine the recovery time and recovery proportion of each node load.

[0069] Optionally, the post-disaster recovery joint optimization model is a time-indexed mixed integer linear programming model, which is solved by calling a Gurobi solver through MATLAB / YALMIP.

[0070] The technical scheme of the present application will be further described below in conjunction with an example.

[0071] In one specific embodiment, as shown in Figure 2 , an improved IEEE 33-node distribution feeder is used for example testing. Figure 2 MESS1 and MESS2 in the figure are mobile energy storage system 1 and mobile energy storage system 2 respectively, DEG1 to DEG5 are diesel generators 1 to 5 respectively, PV1 to PV5 are photovoltaic power generation units 1 to 5 respectively, EV1 to EV3 are electric vehicle charging stations 1 to 3 respectively, SHP1 to SHP3 are small hydropower stations 1 to 3 respectively, CREW1 and CREW2 are repair teams 1 and 2 respectively, Critical loads represent important loads, Non-critical loads represent ordinary loads, and Tie line represents a tie line.

[0072] The example is used to represent a post-disaster recovery system of a distribution network with Hunan flood disaster scenario as a reference, which contains important loads, ordinary loads, distributed energy, small hydropower, mobile energy storage and traffic interruption and the like elements. In the standard scenario, 4 faulty branches are set, which are branches 3, 7, 28 and 35; the system configuration includes 2 repair teams, 2 run-of-river small hydropower stations, 1 reservoir-type small hydropower station, 2 mobile energy storages, 5 diesel generators, 5 photovoltaics and electric vehicle charging stations. The 3 small hydropower stations are located in the same water system, among which the reservoir-type small hydropower station is located in the upstream, and the 2 run-of-river small hydropower stations are located in the midstream and downstream respectively. The dispatching step is set to 1 hour, and the repair time of each faulty branch is set to 3 hours. In order to simulate the excess inflow and road interruption near the small hydropower related assets caused by the flood, the traffic nodes corresponding to branches 35, 21, 24 and 37 are unreachable in the first 7 hours in the standard scenario, so the movement of the repair team and the mobile energy storage system must meet the candidate path traffic condition and the passing node dynamic accessibility condition at the same time.

[0073] In a comparative example, as shown in Figure 3 the proposed method is compared with the literature benchmark corrected by real flood environment. Figure 3 Time / h represents time / hour, Load Maintenance Ratio / % represents load maintenance ratio / %, Proposed Critical represents the important load maintenance ratio corresponding to the proposed method, SoTA Critical Corrected represents the important load maintenance ratio corresponding to the literature benchmark after correction; Proposed Ordinary represents the ordinary load maintenance ratio corresponding to the proposed method; SoTA Ordinary Corrected represents the ordinary load maintenance ratio corresponding to the literature benchmark after correction; Proposed Total represents the total load maintenance ratio corresponding to the proposed method; SoTA Total Corrected represents the total load maintenance ratio corresponding to the literature benchmark after correction.

[0074] The literature benchmark includes repair scheduling, mobile energy storage scheduling, distributed energy scheduling and network reconstruction functions, but assumes road accessibility in the prediction stage and does not endogenously consider upstream and downstream small hydropower discharge coupling. The scheduling results of the literature benchmark are strictly replayed under real inaccessible road windows and small hydropower safety limits, and the stranded vehicles and unsafe hydropower states are fixed, and then the remaining feasible scheduling is re-optimized. The proposed method directly solves the scheduling scheme that meets all constraint conditions under the same real flood setting.

[0075] The results show that in the standard 33-node scenario, the proposed method achieves a comprehensive important load maintenance rate of 94.87% before the feeder is completely reconnected, and restores all loads after the fault branch is repaired and the feeder is reconnected at 15:00. In contrast, the predicted target value of the literature benchmark is 18534.65, but after applying the inaccessible road and small hydropower feasibility check, its real corrected target value rises to 40486.41, and the average load recovery rate after correction is 39.75%, while the average load recovery rate corresponding to the proposed method is 70.84%.

[0076] As shown in Figure 4 , further comparison of small hydropower output under strict replay conditions is made. Figure 4Power output / kW indicates power output / kilowatt, Time / h indicates time / hour, Prop. indicates the result of the method of the present application, SoTA indicates the literature benchmark result, R-SHP1 indicates reservoir small hydropower 1, RORHP1 indicates run-of-river small hydropower 1, and RORHP2 indicates run-of-river small hydropower 2. The red cross and status bar in the figure indicate that in the literature benchmark corrected by the real environment, the downstream run-of-river small hydropower is damaged and its power output is forced to zero after the damage. When the full-load operation according to the electrical rated capacity will violate the downstream discharge safety constraint, the method of the present application will not allow all small hydropower stations to operate at the apparent electrical maximum, but limit the total discharge of the upstream by the cascade hydrological coupling model. The total discharge of the upstream reservoir small hydropower is limited to 5.5 cubic meters per second (m 3 / s), so as to avoid damage to the downstream small hydropower. In contrast, the literature benchmark, although retaining the local protection constraint of the single-station small hydropower, does not consider the influence of the upstream total outflow on the downstream power station in the prediction stage, and the downstream run-of-river small hydropower is damaged from t=2 and t=3, respectively, and its power output is forced to zero thereafter. The results show that the cascade small hydropower hydrological coupling model can significantly improve the safety of the restoration scheme.

[0077] As shown in Figure 5 , scenario sensitivity analysis under random fault and flood scenarios can also be performed. Figure 5 Restoration (%) indicates restoration rate, Solve time (s) indicates solving time / second, Fault branches or Fault count indicates fault branch number, Repair time or Repair time (h) indicates repair time / hour, Block duration or Block window (h) indicates flood blockage duration / hour, Block range or Blocked midpoints indicates flood blockage range or blocked traffic midpoint number, and Repair crews or Crew count indicates the number of repair crews. The marked points in the figure represent the mean value under random implementation; the restoration error bar represents one standard deviation, and the solving time adopts a logarithmic scale.

[0078] The sensitivity analysis objects include the number of fault branches, repair time, flood blockage duration, flood blockage range, and the number of repair crews. In the embodiment, a total of 310 random perturbation samples are re-optimized, and all samples obtain feasible scheduling results. The representative sensitivity results are shown in Table 1.

[0079] Table 1 Representative sensitivity results of the 33-node system

[0080] Table 1 andFigure 5 It can be seen that when the number of fault branches increases from 2 to 8, the average recovery rate decreases from 78.82% to 56.43%, and the average solving time increases from 18.75s to 843.81s; when the repair time increases from 1h to 7h, the average recovery rate decreases from 80.05% to 43.73%; when the blocking duration increases from 3h to 14h, the average recovery rate decreases from 73.85% to 68.41%; when the blocking range increases from 2 midpoints to 8 midpoints, the average recovery rate decreases from 72.98% to 65.89%; when the number of repair teams increases from 1 to 4, the average recovery rate increases from 53.98% to 82.67%. The above results show that the method of the application is not only suitable for a single fixed disaster scenario, but also has good adaptability when the fault location, disaster duration and resource allocation change.

[0081] Further, under the condition of fixed repair task allocation, the repair time and road accessibility can also be disturbed for checking. In this checking, the high-level task allocation relationship is kept unchanged, and only the time-step-by-time-step repair sequence, team path, mobile energy storage movement, network reconstruction, distributed energy scheduling and load reduction are re-optimized. The representative results in this embodiment are shown in Table 2.

[0082] Table 2 Feasibility of fixed tasks under disturbance of repair time and road accessibility

[0083] As can be seen from Table 2, when the branch repair time is disturbed at three levels of 1h, 4h and 7h, the recovery rates are 74.67%, 66.73% and 52.57% respectively, and the corresponding target values are 17262, 23385 and 34358 respectively, all of which remain 100% feasible; when the road inaccessible window is disturbed at three levels of 3h, 8h and 14h, the recovery rates are 74.71%, 66.73% and 49.95% respectively, and the corresponding target values are 18900, 23385 and 32815 respectively, also remaining 100% feasible. The results show that when the repair time and road state deviate within a certain range, as long as the operation scheduling is re-optimized according to the updated physical conditions, the task allocation structure of the application scheme still has good executability.

[0084] In addition, to verify the scalability, the method of the application can also be applied to improve the IEEE 69-node feeder. The example includes 68 branches, 6 fault branches, 3 repair teams, 3 mobile energy storage, 6 diesel generators, 6 photovoltaic, 1 reservoir small hydropower and 2 runoff small hydropower, and uses the same traffic accessibility model, cascade small hydropower cascade model and mixed integer linear programming solving framework as the 33-node example. The embodiment shows that the solving time of the 33-node example is 38.88s, and the solving time of the 69-node example is 151.73s; the YALMIP solver state of the two examples is 0, and Gurobi reports successful solving; the 69-node example realizes an average load recovery rate of 57.49% within the recovery time domain, and an average important load maintenance rate of 67.12%. The results show that the model and method proposed in the application can be extended to larger scale feeders without changing the overall modeling structure.

[0085] In summary, by integrating the power distribution network power flow model, dynamic topology reconfiguration model, traffic interruption model based on node dynamic accessibility state, cascade small hydropower hydrology coupled operation model, repair team scheduling model and line repair constraint into the post-disaster recovery joint optimization framework, the application can form a recovery power supply scheme that takes into account important load recovery capability, resource scheduling executability and regional hydropower operation safety under the constraint conditions of safe operation of the power distribution network, dynamic accessibility of the road and flood safety of the small hydropower.

[0086] On the other hand, the application also provides a computer storage medium storing executable program codes; the executable program codes are used to execute any of the post-disaster recovery power supply optimization methods of the power distribution network based on the multi-source model.

[0087] On the other hand, the application also provides a computer program product, which includes executable program codes; the executable program codes are used to execute any of the post-disaster recovery power supply optimization methods of the power distribution network based on the multi-source model.

[0088] On the other hand, the application also provides a computer system, which includes a memory and a processor; the memory stores program codes executable by the processor; the program codes are used to execute any of the post-disaster recovery power supply optimization methods of the power distribution network based on the multi-source model.

[0089] For example, the program codes can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the application. One or more modules / units can be a series of computer program instruction segments capable of completing a specific function, which are used to describe the execution process of the program codes in the computer system.

[0090] The computer system can be a desktop computer, a notebook computer, a palm computer, a cloud server and the like computing device, and can optionally but not exclusively include a processor, a memory. Those skilled in the art can understand that it can also include input / output devices, network access devices, buses and the like.

[0091] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor.

[0092] The memory can be an internal storage unit of the terminal device, such as a hard disk or a memory. The memory can also be an external storage device of the terminal device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card and the like provided on the terminal device. Further, the memory can include both the internal storage unit and the external storage device of the computer system. The memory is used to store program codes and other programs and data required by the computer system. The memory can also be used to temporarily store data that has been output or will be output.

[0093] The above computer system is created based on the above power distribution network post-disaster recovery power supply optimization method based on a multi-source model, and its technical effects and advantages will not be repeated here. The technical features of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combinations of the technical features do not exist contradictory, they should be considered as the scope of the present application.

[0094] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the present application should be subject to the appended claims.

Claims

1. A method for optimizing power supply recovery after a disaster in a distribution network based on a multi-source model, characterized in that, include: Collect basic operating data of the entire distribution network after disaster recovery, and construct distribution network power flow model, dynamic topology reconfiguration model, traffic interruption model based on node dynamic reachability, distributed energy model, emergency repair team scheduling model, and line repair constraints respectively; wherein, the distributed energy model includes at least a cascade small hydropower hydrological coupling operation model. The post-disaster recovery time domain is discretized into several equal-time long steps to construct a post-disaster recovery joint optimization model with time steps. The decision variables of the joint optimization model include at least the repair tasks and locations of the repair teams, the availability status of the faulty lines, the operational status of the distribution network lines, the location and charging / discharging status of the mobile energy storage system, the output of each distributed energy source, and the load reduction of each node. The power flow model, dynamic topology reconfiguration model, traffic interruption model based on node dynamic reachability, hydrological coupling operation model of cascade small hydropower, emergency repair team scheduling model, and line repair constraints are integrated as constraints on all decision variables in the joint optimization model. Specifically, the traffic interruption model based on node dynamic reachability constrains the emergency repair team and mobile energy storage system to move only between reachable nodes based on the node dynamic reachability at each time step. The hydrological coupling operation model of cascade small hydropower constructs the coupling relationship of water volume transfer between upstream and downstream small hydropower stations in the same water system, simultaneously constraining the power generation output and discharge flow of small hydropower stations. The joint optimization model is solved with the goal of minimizing the sum of the weighted load reduction and the economic losses from small hydropower damage within the recovery time domain. Based on the model solution results, a power supply recovery scheme for the distribution network after a disaster is generated. The power supply recovery scheme includes at least one of the following: a dispatch scheme for emergency repair teams, a dispatch scheme for mobile energy storage systems, a distribution network reconfiguration scheme, a dispatch scheme for distributed energy output, and a node load recovery scheme.

2. The method according to claim 1, characterized in that, The basic operating data for the entire distribution network post-disaster recovery area includes distribution network electrical operating parameters, flood disaster traffic constraint parameters, emergency repair resource parameters, and cascade small hydropower hydrological operating parameters; Collect basic operating data of the entire distribution network for post-disaster recovery, and construct distribution network power flow models, dynamic topology reconfiguration models, traffic interruption models based on node dynamic reachability, distributed energy models, emergency repair team scheduling models, and line repair constraints, including: The power flow model of the distribution network is constructed based on the electrical operating parameters of the distribution network. The power flow model of the distribution network includes node power balance, line power flow limit and node voltage safety constraint. The dynamic topology reconfiguration model is constructed based on the electrical operating parameters of the distribution network. The dynamic topology reconfiguration model is used to ensure that each energized area has power support and maintains the radial topology during the restoration process. The traffic disruption model based on the dynamic reachability of nodes is constructed based on the traffic constraint parameters of the flood disaster. It is used to determine the dynamic reachability of each node at each time step and to constrain the optional movement paths of the repair team and the mobile energy storage system. The hydrological coupled operation model of the cascade small hydropower stations is constructed based on the hydrological operation parameters of the cascade small hydropower stations, which is used to constrain the power generation output and discharge safety of each cascade small hydropower station in the same water system. Based on the emergency repair resource parameters, the emergency repair team scheduling model is constructed to determine the location and movement path of the emergency repair team and the mobile energy storage system at each time step. The line repair constraints are constructed based on the emergency repair resource parameters to determine the availability status of the faulty line at each time step and restrict the faulty line to be put into operation only after the repair is completed.

3. The method according to claim 2, characterized in that, The power flow model of the distribution network adopts the linear DistFlow power flow model, which includes at least the following constraints: The active power balance constraint at a node is used to limit the active power flow inflow, active power injection from distributed sources, and active power of load and active power reduction at any node of the distribution network to satisfy the power conservation relationship. The reactive power balance constraint at a node is used to limit the reactive power flow inflow, reactive power injection from distributed sources, and the reactive power of load and the reactive power of load reduction at any node of the distribution network to satisfy the power conservation relationship. The voltage drop constraint coupled with the line commissioning status is used to bind the line voltage drop calculation logic to the line commissioning status variables, and the corresponding voltage drop calculation is only performed when the line is commissioned. The active power flow capacity constraint is used to limit the absolute value of active power transmitted on the line to not exceed the upper limit of the line's rated active power transmission capacity. Line reactive power flow capacity constraint is used to limit the absolute value of reactive power transmitted on the line to not exceed the upper limit of the line's rated reactive power transmission capacity. Node voltage range constraint is used to limit the voltage amplitude of all energized nodes in the distribution network to always remain within the preset upper and lower voltage limits.

4. The method according to claim 2, characterized in that, The dynamic topology reconfiguration model uses a single-commodity flow model to implement radial topology constraints, which include at least the following constraints: Radial topology line quantity constraint is used to limit the relationship between the total number of operational lines, the total number of energized nodes, and the total number of virtual power sources, ensuring a ring-free radial structure of the distribution network from the branch scale level. The node virtual goods flow balance constraint is used to limit the flow conservation relationship of virtual goods in each node and ensure the connectivity of nodes within each energized area; Virtual power injection constraints are used to limit the injection of virtual goods streams to nodes that are selected as virtual power sources, and the injection amount is bound to the virtual power source state variables. Line commissioning status and commodity flow coupling constraint are used to limit the transmission of virtual commodity flow only on lines that are in commissioning status, and disconnected lines do not carry virtual commodity flow; The association constraint between energized nodes and adjacent operational lines is used to limit the local association between the energized state of a node and the operational state of its adjacent lines, so as to avoid the situation where a node is judged to be energized but the surrounding lines are not operational. The connectivity constraint of energized nodes is used to limit the requirement that energized nodes must be adjacent to at least one operational line, and the energized state of a node is related to the operational state of the surrounding lines.

5. The method according to claim 2, characterized in that, The traffic disruption model based on the dynamic reachability of nodes includes at least the following constraints: The unreachability constraint for flooded nodes is used to limit the nodes that are flooded to be marked as unreachable at the corresponding time step; Non-flooded node reachability constraints are used to limit the number of nodes that are not flooded to be marked as reachable at the corresponding time step; The node dynamic reachability determination constraint is used to determine the dynamic reachability of the corresponding node based on the flood inundation range at each time step, and to use this as the basis for scheduling the emergency repair team and the mobile energy storage system.

6. The method according to claim 2, characterized in that, The hydrological coupling operation model for cascade small hydropower stations is designed for multiple cascade small hydropower stations arranged in series within the same water system, and includes at least the following constraints: Coupling constraints of water transfer between upstream and downstream small hydropower stations: ； Forced shutdown triggers lower limit constraint: ； Forced shutdown triggers upper limit constraint: ； Damaged state triggers lower bound constraint: ； Damaged state triggers upper limit constraint: ； Irreversible constraint on damaged state: ； The cascade small hydropower includes reservoir-type small hydropower with storage capacity and run-of-river small hydropower without storage capacity. The reservoir-type small hydropower also includes a dynamic balance constraint on water storage, which is used to limit the water storage of the reservoir-type small hydropower at the current time step to be equal to the sum of the water storage and the inflow of water at the previous time step, minus the total outflow. in, Let i be the total outflow of the small hydropower station at time step t; The results are compared to the forced shutdown threshold. The results show the comparison of the damaged threshold; Forced shutdown traffic threshold; The threshold for damaged traffic flow; Let i be the damaged state variable of small hydropower station i at time step t; Let i be the damaged state variable of small hydropower station i at time step t-1; The inflow rate of small hydropower station i at time step t; For small hydropower stations upstream of small hydropower station i; The total outflow of the upstream small hydropower station j at time step t-1; The natural inflow of a small hydropower station i at time step t; M is a large M constant to correspond to the loss of river flow.

7. The method according to claim 2, characterized in that, The emergency repair team scheduling model simultaneously constrains the location scheduling of both the emergency repair team and the mobile energy storage system, including at least the following constraints: Initial location constraints are used to limit the start time of the recovery time domain, ensuring that the nodes of each repair team and mobile energy storage system are completely consistent with the preset initial deployment location; The position transfer constraint for a scheduled object leaving a node is used to limit the position transfer relationship of the scheduled object from its current node at the current time step, ensuring that the path selection state leaving the current node is consistent with the position state at that moment. The position transfer constraint of the scheduling object to the node is used to limit the position transfer relationship of the scheduling object to the target node in the next time step, and ensure that the path selection state to the target node is consistent with the position state in the next time step. The location uniqueness constraint within a single time step is used to limit a single emergency repair team or a single mobile energy storage system to be located at only one distribution network node within the same time step. Route feasibility constraints are used to limit the mobility routes selected by the emergency repair team and the mobile energy storage system to meet the basic road access conditions. The reachability constraint of the nodes is used to ensure that all nodes traversed by the repair team and the mobile energy storage system are in a reachable state at the corresponding time step, based on the traffic disruption model based on the dynamic reachability of nodes.

8. The method according to claim 2, characterized in that, The line repair constraints are specifically designed to restrict faulty lines to a usable state only after the repair team has accumulated a preset repair time; non-faulty lines remain usable throughout the entire recovery time domain; and only lines in a usable state are allowed to be put into operation in the distribution network.

9. The method according to any one of claims 1 to 8, characterized in that, With the goal of minimizing the sum of the weighted load reduction and the economic losses from small hydropower generation within the recovery time domain, the objective function of the joint optimization model is: ； in, Reduce the load on node i by weighting it; This represents the active power load reduction at time step t for node i. This represents the total number of nodes in the distribution network. For time step; This represents the total number of scheduling time steps; The weight of the economic loss of small hydropower station j; Let J represent the damaged state variables of the small hydropower station J at the last time step T. This represents the total number of small hydropower stations.

10. A computer system, characterized in that, It includes a memory and a processor; the memory stores program code that can be executed by the processor; the program code is used to execute the power supply optimization method for post-disaster power restoration based on a multi-source model of the distribution network as described in any one of claims 1 to 9.

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