A pre-disaster site arrangement decision method for mobile emergency resources of an urban regional power distribution network

Abstract

The application provides a kind of urban area power distribution network mobile emergency resource pre-disaster point arrangement decision method.The method comprises: establishing a mobile emergency resource scheduling model;Establish a power balance demand model in different areas;Based on the mobile emergency resource scheduling model and the power balance demand model in different areas, a scenario-based urban area power distribution network mobile emergency resource pre-disaster point arrangement model considering the impact of disaster and the potential recovery demand of power distribution network is established;The scenario-based urban area power distribution network mobile emergency resource pre-disaster point arrangement model is solved to obtain the mobile emergency resource pre-disaster deployment result, the mobile emergency resource scheduling result and the load recovery result under each scenario.The method of the application considers the influence of the coupling relationship between mobile emergency resource scheduling and power distribution network load recovery, and also considers the power balance of multiple different regional power distribution networks within the city, to achieve the optimal pre-disaster point arrangement of mobile emergency resources and improve the efficiency of resource post-disaster scheduling.

Description

Technical Field

[0001] This invention relates to the field of mobile resource pre-disaster deployment technology, and in particular to a method for decision-making on the pre-disaster deployment of mobile emergency resources in urban power distribution networks. Background Technology

[0002] The distribution network is a crucial component of the urban power grid, directly serving electricity users. Ensuring the rapid restoration of critical loads within the urban distribution network after disasters is of paramount importance. For certain predictable disasters, considering the disaster's impact and potential recovery needs, the rational deployment of mobile resources within the city is a vital means to achieve rapid resource allocation in the distribution network after disasters, rapid restoration of critical loads, reduction of power outage losses, and enhancement of urban resilience.

[0003] Regarding the coordinated emergency recovery of power distribution networks and transportation networks, domestic and international scholars have conducted relevant research. Some solutions propose a two-stage framework considering pre-disaster deployment and optimized scheduling of mobile emergency power supplies, forming multiple microgrids centered on the power supply's access location to achieve rapid recovery of power outages after extreme events. Other solutions consider multiple stages before, during, and after a disaster, coordinating local fixed power supplies, mobile power supplies, and remote switches, and considering the pre-configuration and real-time scheduling of mobile power supplies to improve the resilience of the power distribution network. Still other solutions primarily consider the pre-deployment decision-making problem of mobile power supplies and switch operators, and construct a mixed-integer linear programming model based on scenario generation technology to solve this problem.

[0004] Existing research mostly focuses on a single distribution network, and the mobile resource type considered is mainly mobile emergency power supply, with little consideration given to the impact of extreme disasters on transportation networks and the potential scheduling time of mobile resources.

[0005] In reality, cities typically involve multiple power distribution networks, and post-disaster operations require the coordinated deployment of mobile emergency resources within the city. Furthermore, extreme disasters can damage critical power transmission routes within the distribution network, and the deployment of post-disaster repair personnel will significantly impact recovery efforts. Therefore, pre-disaster planning must comprehensively consider mobile emergency power supplies and repair teams. Finally, extreme disasters may also damage transportation networks, further affecting post-disaster resource allocation and recovery. Summary of the Invention

[0006] The embodiments of the present invention provide a method for pre-disaster deployment decision-making of mobile emergency resources in urban power distribution networks, so as to effectively improve the efficiency of post-disaster resource scheduling.

[0007] To achieve the above objectives, the present invention adopts the following technical solution.

[0008] A method for pre-disaster deployment decision-making of mobile emergency resources in urban power distribution networks includes:

[0009] Establish a scheduling model for mobile emergency resources;

[0010] Establish power balance demand models for different regions;

[0011] Based on the scheduling model of the mobile emergency resources and the power balance demand model of different regions, a scenario-based pre-disaster deployment model of mobile emergency resources for urban distribution networks is established, taking into account the impact of disasters and the potential recovery needs of the distribution network.

[0012] The pre-disaster deployment model of mobile emergency resources in urban power distribution networks based on scenarios is solved to obtain the pre-disaster deployment results of mobile emergency resources, the scheduling results of mobile emergency resources in various scenarios, and the load recovery results.

[0013] Preferably, the process further includes, before establishing the scheduling model for mobile emergency resources:

[0014] The research subjects are multiple power distribution networks located in different areas within a city. There is a certain distance between each power distribution network, and each power distribution network has a mobile emergency resource pre-disaster deployment point and a mobile emergency power access point.

[0015] Based on the acquired extreme disaster information, the severity of the disaster and its impact on power distribution lines and transportation network roads are quantified, and several extreme disaster scenarios with damage to power distribution network and transportation network are generated.

[0016] Preferably, the establishment of the mobile emergency resource scheduling model includes:

[0017] The scheduling time of mobile resources is affected by the degree of damage to the transportation network. Whether or not a mobile power source is connected determines whether the access point has the ability to restore the load. Whether or not a repair team arrives and the repair time determine whether the damaged line is repaired. The following scheduling model for mobile emergency resources is established:

[0018]

[0019]

[0020]

[0021]

[0022] In the formula: Let m be the state of the mobile power supply m in scenario s, which is scheduled from the pre-disaster deployment point z to node i in time period t. 1 indicates that it is scheduled to the node, and 0 indicates otherwise. In scenario s, the state of the emergency repair team r being dispatched from the pre-disaster deployment point z to the damaged line l during time period t is represented by 1, indicating dispatch to that line, and 0 indicating otherwise; N m A set of mobile power bank access points; Let be the set of damaged routes in scenario s; t(i,z) are the known quantities in each scenario, representing the travel time required from the pre-disaster deployment point z to node i; t(l,z) are the known quantities in each scenario, representing the travel time required from the pre-disaster deployment point z to route l; N T For time periods; The variable is 0-1, representing the state of line ij in time period t under scenario s, where 1 indicates that the line is connected and 0 indicates otherwise; t R P represents the emergency repair time required to restore the power distribution network lines, and is a known quantity that comprehensively considers the disaster scenario and the extent of damage to the power distribution network lines; min P max These are the lower and upper limits of the power supply's active power output, respectively. M represents the active power output of the mobile power supply m when it is dispatched from the pre-disaster deployment point z to the access point i during time period t in scenario s. G M ESS These are collections consisting of mobile generators and mobile energy storage, respectively.

[0023] Equation (1) indicates that in scenario s, if the mobile power supply m does not reach the access point i during time period t, the access point i will not have the ability to restore load during that time period; Equation (2) indicates that in scenario s, if the emergency repair team r does not reach the damaged line l during time period t, the line l cannot start emergency repair during that time period; Equation (3) indicates that in scenario s, the damaged line l will be repaired after the emergency repair personnel arrive and after the emergency repair time t has elapsed. R The path is then restored; Equation (4) indicates that whether the mobile power source arrives or not determines whether the point has the ability to generate electricity, and limits the power output to its allowable range.

[0024] Preferably, the establishment of power balance demand models for different regions includes:

[0025] The city involves multiple distribution networks. Considering the different load restoration needs within different areas, and to achieve a balanced distribution of mobile resources among the various distribution networks while satisfying the power balance of the distribution networks, the following power balance demand model for different areas is established:

[0026]

[0027]

[0028]

[0029] In the formula: The power flow of line ij in time period t under scenario s; P represents the power requirement of node i in scenario s during time period t. i,load The active power of the load connected to node i; This represents the recovery status of load i in scenario s during time period t, where 1 indicates that the load has recovered and 0 indicates that the load has not. M represents the active power output of node i in scenario s at time period t; M is a positive real number.

[0030] Equation (5) represents the power balance constraint at node i of the distribution network within each region; Equation (6) represents the difference between the output power of the power source connected to node i and the load power; Equation (7) represents that if the line within each region is disconnected, the power flowing through it is limited to 0, otherwise no constraint is imposed.

[0031] Preferably, the scheduling model based on the mobile emergency resources and the power balance demand model for different regions, establishing a scenario-based pre-disaster deployment model for urban area distribution network mobile emergency resources that considers disaster impact and potential distribution network recovery needs, includes:

[0032] Based on the aforementioned mobile emergency resource scheduling model and power balance demand models for different regions, the objective function expression for the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model is as follows:

[0033]

[0034] In the formula: Assume that the set of all possible fault scenarios generated is S, and define the power outage time in each scenario as the period from the power outage to the repair of the transmission network, let its duration be T0, and divide it into several equal time periods, each time period having a length of ΔT. int N T N E These represent time periods and sets of distribution network nodes, respectively; ω i The weight of load i represents the importance of the load;

[0035] The constraints of the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model include:

[0036] (1) Pre-disaster deployment constraints

[0037] Pre-disaster deployment constraints include constraints on the deployment of mobile power supplies and emergency repair teams;

[0038] ③Constraints on pre-disaster deployment of mobile power banks

[0039] Pre-disaster deployment constraints for mobile power banks include the number of mobile power banks to be deployed and their status constraints.

[0040]

[0041]

[0042]

[0043] In the formula: Z is the set of pre-disaster deployment points; α m,z The variable is 0-1, representing the deployment status of the power bank m at deployment point z. 1 indicates that the power bank m is deployed to point z, and 0 indicates otherwise; N z,m The maximum number of portable power banks that can be deployed at a single point; N m Total number of power banks;

[0044] Equation (9) represents the limit on the number of mobile power supplies that can be placed at a single deployment point; Equation (10) represents the limit on the total number of mobile power supplies that can be deployed; Equation (11) represents that any mobile power supply can only be placed at one deployment point.

[0045] ④ Pre-disaster deployment constraints for emergency repair teams

[0046] Pre-disaster deployment constraints for emergency repair teams include the number of teams deployed and their status constraints.

[0047]

[0048]

[0049]

[0050] In the formula: R represents the assembly of the emergency repair team; b r,z The variable is 0-1, representing the deployment status of the repair team r at deployment point z; 1 indicates that the team has been deployed to point z, and 0 indicates otherwise; N z,r N represents the maximum number of repair teams that can be deployed at a single point; r This refers to the total number of repair teams.

[0051] Equation (10) represents the limit on the number of emergency repair teams that can be accommodated at a single deployment point; Equation (13) represents the limit on the total number of emergency repair teams that can be deployed; Equation (14) represents that any emergency repair team can only be deployed at one deployment point.

[0052] (2) Post-disaster dispatch constraints

[0053] Post-disaster dispatch constraints include those for mobile power supplies and emergency repair teams.

[0054] Mobile power scheduling constraints include the number of mobile power sources to be scheduled and state constraints.

[0055]

[0056]

[0057]

[0058]

[0059] Equation (15) indicates that any mobile power supply can only be scheduled to a maximum of one access point; Equation (16) indicates that only one mobile power supply is allowed to be connected to one access point; Equations (17) and (18) indicate that once a mobile power supply is scheduled to any access point, it will not move again.

[0060] The constraints on emergency repair team dispatching include the number of emergency repair teams dispatched and their status constraints.

[0061]

[0062]

[0063]

[0064]

[0065] Equation (19) indicates that any emergency repair team can only be dispatched to a maximum of one damaged line; Equation (20) indicates that a damaged line only needs to be repaired by dispatching one emergency repair team; Equations (21) and (22) indicate that the emergency repair team will not move after being dispatched to any damaged line.

[0066] (3) Power balance constraints of distribution network

[0067] The distribution network islands within each region need to meet the balance between load power and total power generation, as shown in equations (5) to (7);

[0068] (4) Mobile resource scheduling state constraints

[0069] Mobile resource scheduling status includes constraints on the scheduling status of mobile power supplies and emergency repair teams;

[0070] ③Mobile power supply scheduling state constraints

[0071] The scheduling state constraints of mobile power supplies are mainly limited by their access state, as shown in equation (1);

[0072] ④ Constraints on the dispatch status of emergency repair teams

[0073] The main constraint on the dispatch status of the emergency repair team is its access status restriction, as shown in equation (2);

[0074] (5) Line status constraints

[0075] Line status constraints include constraints related to connected and damaged lines. The line is restored to normal operation after the repair team arrives and the repair time has elapsed, as shown in equation (3):

[0076]

[0077]

[0078]

[0079] Equation (23) indicates that the undamaged line is always connected at any time in scenario s; Equations (24) and (25) indicate that the mobile power supply will not move after being dispatched to any access point;

[0080] (6) Load condition constraints

[0081] Load condition constraints include load condition change constraints throughout the entire power outage period;

[0082]

[0083]

[0084] Equation (26) represents the change in the recovery state of the load. Considering that the load change will bring transient fluctuations such as frequency and voltage, the load state can only change once during the entire period. Equation (27) indicates that after the load is restored, power supply should continue during the power outage period.

[0085] (7) Power safety constraints

[0086] Power safety constraints include constraints related to stationary power supplies and mobile power supplies. Constraints related to the output limit of mobile power supplies are shown in Equation (4).

[0087]

[0088]

[0089]

[0090] Where: K G K ESS These are a stationary generator and a stationary energy storage unit, respectively; P min P max These are the lower and upper limits of the power supply's active power output, respectively. E represents the active power output of a fixed power supply k in scenario s during time period t. k Let be the initial energy of generator k; For the initial state of charge (SOC) of energy storage i, the SoC i,min SoC i,max These are the lower and upper limits of the state of charge (SOC) for normal operation of energy storage, respectively; t′ represents any time; λ is the conversion coefficient for converting energy into the SOC of energy storage.

[0091] Equation (28) limits the output of each power source to its allowable range; Equation (29) represents the remaining fuel constraint in the generator; Equation (30) represents the SOC constraint of energy storage.

[0092] Preferably, the solution to the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model to obtain the mobile emergency resource pre-disaster deployment results, mobile emergency resource scheduling results under each scenario, and load recovery results includes:

[0093] Commercial modeling tools and solvers were used to solve the pre-disaster deployment model of mobile emergency resources in the urban area power distribution network, resulting in the pre-disaster deployment results of mobile emergency resources, the scheduling results of mobile emergency resources under various scenarios, and the load recovery results. The scheduling results of mobile emergency resources include the scheduling location and travel time, and the load recovery status includes the weighted load recovery time and the load recovery power.

[0094] As can be seen from the technical solutions provided by the embodiments of the present invention above, the embodiments of the present invention propose a pre-disaster deployment method for mobile emergency resources that considers the impact of disasters and the potential recovery needs of the distribution network. By considering the influence of the coupling relationship between mobile emergency resource scheduling and distribution network load recovery, and taking into account the power balance of multiple different areas of the distribution network within the city, the optimal pre-disaster deployment of mobile emergency resources can be achieved, thereby improving the efficiency of post-disaster resource scheduling.

[0095] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and will become apparent from the description or may be learned by practice of the invention. Attached Figure Description

[0096] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0097] Figure 1 A flowchart illustrating a pre-disaster deployment decision-making method for mobile emergency resources in urban power distribution networks, provided by an embodiment of the present invention.

[0098] Figure 2 A diagram of a city power distribution network-transportation network coupling system is provided for an embodiment of the present invention;

[0099] Figure 3 This invention provides a load recovery result for various scenarios as described in its embodiments.

[0100] Figure 4 This invention provides a post-disaster dispatch result for a power distribution network under scenario 7, as provided in an embodiment of the invention.

[0101] Figure 5 This is a schematic diagram comparing the recovery status of different scenarios under different strategies, provided as an embodiment of the present invention. Detailed Implementation

[0102] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0103] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or couplings. The term “and / or” as used herein includes any and all combinations of one or more of the associated listed items.

[0104] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless defined as herein.

[0105] To facilitate understanding of the embodiments of the present invention, the following will provide further explanation and description with reference to the accompanying drawings and several specific embodiments. These embodiments do not constitute a limitation on the embodiments of the present invention.

[0106] The method described in this invention focuses on mobile emergency resource scheduling modeling methods and power balance demand modeling methods for different regions. Finally, by combining the above methods, a scenario-based pre-disaster deployment model for mobile emergency resources in urban distribution networks is established. This method can maximize the speed of post-disaster resource scheduling and the recovery effect of important loads in the distribution network, achieving optimal on-demand allocation of mobile resources within the urban area before a disaster.

[0107] The processing flow of a pre-disaster deployment decision-making method for mobile emergency resources in urban power distribution networks provided by this invention is as follows: Figure 1 As shown, the processing steps include the following:

[0108] Step S10: Generate an extreme disaster scenario involving damage to the power distribution network and transportation network.

[0109] This invention considers disasters that can be predicted in advance, such as typhoons and rainstorms. It assumes that the impending disaster is known at a certain time (hourly) beforehand, and that certain disaster information is available. The disaster will cause some degree of damage to both the urban power distribution network and the transportation network. The research object of this invention's method is multiple power distribution networks located in different areas within a city, with a certain distance between them. Each power distribution network has a pre-disaster deployment point for mobile emergency resources and a mobile emergency power access point (depending on the actual situation, the mobile emergency power access point is usually located at important loads). Mobile emergency resources are deployed and dispatched through urban road traffic, with each power distribution network and transportation network having a geographical correspondence.

[0110] Based on the acquired extreme disaster information, this invention quantifies the degree of disaster and its impact on power distribution lines and transportation network roads, generates and selects several representative disaster scenarios to characterize the impact of extreme disasters on the system; further, combining each scenario, with the goal of maximizing the expected weighted load recovery effect, and considering various constraints such as post-disaster mobile resource scheduling and load and line recovery, a mixed integer linear programming model for the pre-disaster deployment of mobile emergency resources is established, and finally the pre-disaster deployment scheme is obtained by solving the model.

[0111] Step S20: Establish a scheduling model for mobile emergency resources.

[0112] The scheduling time of mobile resources is affected by the extent of damage to the transportation network. Whether or not a mobile power source is connected determines whether the access point has the capability to restore load. The arrival of repair teams and the repair time determine whether the damaged line is repaired, further affecting subsequent load restoration. The following scheduling model for mobile emergency resources is established:

[0113]

[0114]

[0115]

[0116]

[0117] In the formula: Let m be the state of the mobile power supply m in scenario s, which is scheduled from the pre-disaster deployment point z to node i in time period t. 1 indicates that it is scheduled to the node, and 0 indicates otherwise. In scenario s, the state of the emergency repair team r being dispatched from the pre-disaster deployment point z to the damaged line l during time period t is represented by 1, indicating dispatch to that line, and 0 indicating otherwise; N m A set of mobile power bank access points; Let be the set of damaged routes in scenario s; t(i,z) are the known quantities in each scenario, representing the travel time required from the pre-disaster deployment point z to node i; t(l,z) are the known quantities in each scenario, representing the travel time required from the pre-disaster deployment point z to route l; N T For time periods; The variable is 0-1, representing the state of line ij in time period t under scenario s, where 1 indicates that the line is connected and 0 indicates otherwise; t R P represents the emergency repair time required to restore the power distribution network lines, and is a known quantity that comprehensively considers the disaster scenario and the extent of damage to the power distribution network lines; min P max These are the lower and upper limits of the power supply's active power output, respectively. M represents the active power output of the mobile power supply m when it is dispatched from the pre-disaster deployment point z to the access point i during time period t in scenario s. G M ESS It consists of mobile generators and mobile energy storage.

[0118] Equation (1) indicates that in scenario s, if the mobile power supply m does not reach the access point i during time period t, the access point i will not have the ability to restore load during that time period; Equation (2) indicates that in scenario s, if the emergency repair team r does not reach the damaged line l during time period t, the line l cannot start emergency repair during that time period; Equation (3) indicates that in scenario s, the damaged line l will be repaired after the emergency repair personnel arrive and after the emergency repair time t has elapsed. R The path is then restored; Equation (4) indicates that whether the mobile power source arrives or not determines whether the point has the ability to generate electricity, and limits the power output to its allowable range.

[0119] Step S30: Establish power balance demand models for different regions.

[0120] The city involves multiple distribution networks. Considering the different load restoration needs within different areas, the goal is to achieve a balanced distribution of mobile resources among these networks while maintaining power balance. The following power balance demand models for different areas are established:

[0121]

[0122]

[0123]

[0124] In the formula: The power flow of line ij in time period t under scenario s; P represents the power requirement of node i in scenario s during time period t. i,load The active power of the load connected to node i; This represents the recovery status of load i in scenario s during time period t, where 1 indicates that the load has recovered and 0 indicates that the load has not. Let M be the active power output of node i in scenario s at time period t; M is a sufficiently large positive real number.

[0125] Equation (5) represents the power balance constraint at node i of the distribution network within each region; Equation (6) represents the difference between the output power of the power source connected to node i and the load power; Equation (7) represents that if the line within each region is disconnected, the power flowing through it is limited to 0, otherwise no constraint is imposed.

[0126] Step S40: Establish a scenario-based pre-disaster deployment model for mobile emergency resources in urban power distribution networks.

[0127] 1) Objective function

[0128] Based on the aforementioned mobile emergency resource scheduling model and power balance demand model for different regions, this invention establishes a scenario-based pre-disaster deployment model for mobile emergency resources in urban distribution networks, considering the impact of disasters and the potential recovery needs of the distribution network. This model provides a basis for pre-disaster deployment of mobile resources. The model aims to maximize the expected load recovery effect, considering pre-disaster deployment constraints of mobile emergency resources, as well as scheduling constraints, distribution network power balance constraints, mobile emergency resource scheduling status constraints, line status constraints, load status constraints, and power supply security constraints under various scenarios.

[0129] The objective function expression of the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model of this invention is as follows:

[0130]

[0131] In the formula: Assume that the set of all possible fault scenarios generated is S, and define the power outage time in each scenario as the period from the power outage to the repair of the transmission network, let its duration be T0, and divide it into several equal time periods, each time period having a length of ΔT. int N T N E These represent time periods and sets of distribution network nodes, respectively; ω i The weight of load i represents the importance of the load.

[0132] 2) Constraints

[0133] (1) Pre-disaster deployment constraints

[0134] Pre-disaster deployment constraints include constraints on the deployment of mobile power supplies and emergency repair teams.

[0135] ⑤ Pre-disaster deployment constraints of mobile power banks

[0136] Pre-disaster deployment constraints for mobile power banks include the number of mobile power banks to be deployed and their status constraints.

[0137]

[0138]

[0139]

[0140] In the formula: Z is the set of pre-disaster deployment points; α m,z The variable is 0-1, representing the deployment status of the power bank m at deployment point z. 1 indicates that the power bank m is deployed to point z, and 0 indicates otherwise; N z,m The maximum number of portable power banks that can be deployed at a single point; N m This represents the total number of portable power banks.

[0141] Equation (9) represents the limit on the number of mobile power supplies that can be placed at a single deployment point; Equation (10) represents the limit on the total number of mobile power supplies that can be deployed; Equation (11) represents that any mobile power supply can only be placed at one deployment point.

[0142] ②Pre-disaster deployment constraints for emergency repair teams

[0143] The pre-disaster deployment constraints for emergency repair teams include the number of teams deployed and their status constraints.

[0144]

[0145]

[0146]

[0147] In the formula: R represents the assembly of the emergency repair team; b r,z The variable is 0-1, representing the deployment status of the repair team r at deployment point z; 1 indicates that the team has been deployed to point z, and 0 indicates otherwise; N z,r N represents the maximum number of repair teams that can be deployed at a single point; r This represents the total number of repair teams.

[0148] Equation (10) represents the limit on the number of emergency repair teams that can be accommodated at a single deployment point; Equation (13) represents the limit on the total number of emergency repair teams that can be deployed; Equation (14) represents that any emergency repair team can only be deployed at one deployment point.

[0149] (2) Post-disaster dispatch constraints

[0150] Post-disaster dispatch constraints include those for mobile power supplies and emergency repair teams.

[0151] Mobile power supply scheduling constraints

[0152] Mobile power scheduling constraints include the number of mobile power sources to be scheduled and state constraints.

[0153]

[0154]

[0155]

[0156]

[0157] Equation (15) indicates that any mobile power supply can only be scheduled to a maximum of one access point; Equation (16) indicates that an access point can only allow one mobile power supply to be connected; Equations (17) and (18) indicate that once a mobile power supply is scheduled to any access point, it will not move again.

[0158] Emergency repair team dispatch constraints

[0159] The constraints on emergency repair team dispatch include the number of emergency repair teams and their status constraints.

[0160]

[0161]

[0162]

[0163]

[0164] Equation (19) indicates that any emergency repair team can only be dispatched to a maximum of one damaged line; Equation (20) indicates that a damaged line only needs to be repaired by dispatching one emergency repair team; Equations (21) and (22) indicate that the emergency repair team will not move after being dispatched to any damaged line.

[0165] (3) Power balance constraints of distribution network

[0166] The distribution network islands within each region need to meet the balance between load power and total power generation, as shown in equations (5) to (7).

[0167] (4) Mobile resource scheduling state constraints

[0168] The mobile resource scheduling status includes constraints on the scheduling status of mobile power supplies and emergency repair teams.

[0169] ⑤ Mobile power supply scheduling state constraints

[0170] The scheduling state constraints of mobile power supplies are mainly limited by their access state, as shown in equation (1).

[0171] ⑥ Emergency repair team dispatch status constraints

[0172] The main constraint on the dispatch status of the emergency repair team is its access status restriction, as shown in equation (2).

[0173] (5) Line status constraints

[0174] Line status constraints include constraints related to connected lines and damaged lines. Among them, the line is restored to normal operation after the repair team arrives and the repair time has elapsed, as shown in equation (3).

[0175]

[0176]

[0177]

[0178] Equation (23) indicates that the undamaged line is always connected at any time in scenario s; Equations (24) and (25) indicate that the mobile power supply will not move after being dispatched to any access point.

[0179] (6) Load condition constraints

[0180] Load condition constraints include load condition change constraints throughout the entire power outage period.

[0181]

[0182]

[0183] Equation (26) represents the change in the recovery state of the load. Considering that the change in load will bring transient fluctuations such as frequency and voltage, the load state is limited to only changing once during the entire period. Equation (27) indicates that after the load is restored, power supply should continue during the power outage period.

[0184] (7) Power safety constraints

[0185] Power safety constraints include those related to stationary power sources and mobile power sources. Among them, the constraints related to the output limit of mobile power sources are shown in equation (4).

[0186]

[0187]

[0188]

[0189] Where: K G K ESS These are a stationary generator and a stationary energy storage unit, respectively; P min P max These are the lower and upper limits of the power supply's active power output, respectively. E represents the active power output of a fixed power supply k in scenario s during time period t. k Let be the initial energy of generator k; For the initial state of charge (SOC) of energy storage i, SoC i,min SoC i,max , respectively, represent the lower and upper limits of the state of charge (SOC) for normal operation of energy storage; t′ represents any time; λ is the conversion coefficient for converting energy into the SOC of energy storage.

[0190] Equation (28) limits the output of each power source to its allowable range; Equation (29) represents the remaining fuel constraint in the generator; Equation (30) represents the SOC constraint of energy storage.

[0191] Step S50: Solve the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model to obtain the mobile emergency resource pre-disaster deployment results, mobile emergency resource scheduling results and load recovery results under each scenario.

[0192] Commercial modeling tools and solvers were used to solve the pre-disaster deployment model of mobile emergency resources in the urban distribution network. For example, the Yalmip optimization modeling toolkit and Gurobi solver in Matlab were used to solve the pre-disaster deployment model of mobile emergency resources in the urban distribution network based on the scenario. The results of pre-disaster deployment of mobile emergency resources, scheduling results of mobile emergency resources in each scenario, and load recovery results were obtained. The scheduling results of mobile emergency resources include scheduling location and travel time. The load recovery results include weighted load recovery time and load recovery power.

[0193] The topology diagram of a test system for a power distribution network-transportation network coupled system provided in this embodiment of the invention is as follows: Figure 2 As shown, the research object is multiple distribution networks located in different areas within a city. These distribution networks are some distance apart, and each distribution network has a pre-disaster deployment point for mobile emergency resources and a mobile emergency power access point (depending on the actual situation, the mobile emergency power access point is usually located at important loads). Mobile emergency resources are deployed and dispatched through urban road traffic, and each distribution network and transportation network has a geographical correspondence.

[0194] The mobile emergency resource pre-disaster deployment modeling method proposed in this invention is used in... Figure 2 The system shown was tested, and the load recovery results under different scenarios were obtained as follows: Figure 3 As shown. Then, scenario 7 is specifically selected for analysis of mobile emergency resource allocation. The post-disaster damage and post-disaster dispatch results for scenario 7 are as follows. Figure 4 As shown in Table 1, the scheduling time and location of mobile emergency resources are as follows.

[0195] Finally, the method proposed in this invention is compared with methods that do not consider pre-disaster deployment of mobile emergency resources, methods that consider random pre-disaster deployment of mobile emergency resources, and methods that consider pre-disaster deployment of mobile emergency resources, to illustrate the effectiveness and superiority of the method proposed in this invention. Figure 5 This diagram illustrates the comparison of recovery performance under different strategies in various scenarios.

[0196] Table 1. Post-disaster allocation of mobile emergency resources in Scenario 7

[0197]

[0198]

[0199] In summary, the mobile emergency resource pre-disaster deployment strategy obtained by the method of the present invention can maximize the speed of post-disaster resource scheduling and the recovery effect of important loads in the power distribution network, and realize the optimal on-demand configuration of mobile resources within the city before a disaster.

[0200] The method of this invention takes into account the coupling relationship between mobile emergency resource scheduling and distribution network load recovery, and also considers the power balance of distribution networks in multiple different areas within the city, in order to achieve optimal pre-disaster deployment of mobile emergency resources and improve the efficiency of post-disaster resource scheduling.

[0201] This invention takes into account the impact of disasters and potential recovery needs, enabling the pre-disaster deployment of mobile emergency power supplies and repair teams, thereby accelerating post-disaster resource allocation, reducing power outage losses, and mitigating the impact of damage to power distribution networks and transportation networks on load recovery.

[0202] Those skilled in the art will understand that the accompanying drawings are merely schematic diagrams of one embodiment, and the modules or processes shown in the drawings are not necessarily essential for implementing the present invention.

[0203] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that the present invention can be implemented by means of software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of the present invention, 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 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 various embodiments or some parts of the embodiments of the present invention.

[0204] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for apparatus or system embodiments, since they are basically similar to method embodiments, the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments. The apparatus and system 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 creative effort.

[0205] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for pre-disaster deployment decision-making of mobile emergency resources in urban power distribution networks, characterized in that, include: Establish a scheduling model for mobile emergency resources; Establish power balance demand models for different regions; Based on the scheduling model of the mobile emergency resources and the power balance demand model of different regions, a scenario-based pre-disaster deployment model of mobile emergency resources for urban distribution networks is established, taking into account the impact of disasters and the potential recovery needs of the distribution network. The pre-disaster deployment model of mobile emergency resources in urban power distribution networks based on scenarios is solved to obtain the pre-disaster deployment results of mobile emergency resources, the scheduling results of mobile emergency resources in various scenarios, and the load recovery results. The aforementioned model for establishing a mobile emergency resource scheduling system includes: The scheduling time of mobile resources is affected by the degree of damage to the transportation network. Whether or not a mobile power source is connected determines whether the access point has the ability to restore the load. Whether or not a repair team arrives and the repair time determine whether the damaged line is repaired. The following scheduling model for mobile emergency resources is established: In the formula: Let m be the state of the mobile power supply m in scenario s, which is scheduled from the pre-disaster deployment point z to node i in time period t. 1 indicates that it is scheduled to the node, and 0 indicates otherwise. In scenario s, the state of the emergency repair team r being dispatched from the pre-disaster deployment point z to the damaged line l during time period t is represented by 1, indicating dispatch to that line, and 0 indicating otherwise; N m A set of mobile power bank access points; Let be the set of damaged routes in scenario s; t(i,z) are the known quantities in each scenario, representing the travel time required from the pre-disaster deployment point z to node i; t(l,z) are the known quantities in each scenario, representing the travel time required from the pre-disaster deployment point z to route l; N T For time periods; The variable is 0-1, representing the state of line ij in time period t under scenario s, where 1 indicates that the line is connected and 0 indicates otherwise; t R P represents the emergency repair time required to restore the power distribution network lines, and is a known quantity that comprehensively considers the disaster scenario and the extent of damage to the power distribution network lines; min P max These are the lower and upper limits of the power supply's active power output, respectively. M represents the active power output of the mobile power supply m when it is dispatched from the pre-disaster deployment point z to the access point i during time period t in scenario s. G M ESS These are collections consisting of mobile generators and mobile energy storage, respectively. Equation (1) indicates that in scenario s, if the mobile power supply m does not reach the access point i during time period t, the access point i will not have the ability to restore load during that time period; Equation (2) indicates that in scenario s, if the emergency repair team r does not reach the damaged line l during time period t, the line l cannot start emergency repair during that time period; Equation (3) indicates that in scenario s, the damaged line l will be repaired after the emergency repair personnel arrive and after the emergency repair time t has elapsed. R The path is then restored; Equation (4) indicates that whether the mobile power source arrives or not determines whether the point has the ability to generate electricity, and limits the power output to its allowable range; The establishment of power balance demand models for different regions includes: The city involves multiple distribution networks. Considering the different load restoration needs within different areas, and to achieve a balanced distribution of mobile resources among the various distribution networks while satisfying the power balance of the distribution networks, the following power balance demand model for different areas is established: In the formula: The power flow of line ij in time period t under scenario s; P represents the power requirement of node i in scenario s during time period t. i,load The active power of the load connected to node i; This represents the recovery status of load i in scenario s during time period t, where 1 indicates that the load has recovered and 0 indicates that the load has not. M represents the active power output of node i in scenario s at time period t; M is a positive real number. Equation (5) represents the power balance constraint at node i of the distribution network within each region; Equation (6) represents the difference between the output power of the power source connected to node i and the load power; Equation (7) represents that if the line within each region is disconnected, the power flowing through it is limited to 0, otherwise no constraint is imposed.

2. The method according to claim 1, characterized in that, Before establishing the mobile emergency resource scheduling model, the following steps are also included: The research subjects are multiple power distribution networks located in different areas within a city. There is a certain distance between each power distribution network, and each power distribution network has a mobile emergency resource pre-disaster deployment point and a mobile emergency power access point. Based on the acquired extreme disaster information, the severity of the disaster and its impact on power distribution lines and transportation network roads are quantified, and several extreme disaster scenarios with damage to power distribution network and transportation network are generated.

3. The method according to claim 1, characterized in that, Based on the aforementioned scheduling model of mobile emergency resources and power balance demand models for different regions, a scenario-based pre-disaster deployment model for mobile emergency resources in urban distribution networks is established, considering the impact of disasters and the potential recovery needs of the distribution network. This includes: Based on the aforementioned mobile emergency resource scheduling model and power balance demand models for different regions, the objective function expression for the scenario-based pre-disaster deployment model of mobile emergency resources for urban power distribution networks is as follows: In the formula: Assume that the set of all possible fault scenarios generated is S, and define the power outage time in each scenario as the period from the power outage to the repair of the transmission network, let its duration be T0, and divide it into several equal time periods, each time period having a length of ΔT. int N T N E These represent time periods and sets of distribution network nodes, respectively; ω i The weight of load i represents the importance of the load; The constraints of the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model include: (1) Pre-disaster deployment constraints Pre-disaster deployment constraints include constraints on the deployment of mobile power supplies and emergency repair teams; ①Constraints on pre-disaster deployment of mobile power banks Pre-disaster deployment constraints for mobile power banks include the number of mobile power banks to be deployed and their status constraints. In the formula: Z is the set of pre-disaster deployment points; α m,z The variable is 0-1, representing the deployment status of the power bank m at deployment point z. 1 indicates that the power bank m is deployed to point z, and 0 indicates otherwise; N z,m The maximum number of portable power banks that can be deployed at a single point; N m Total number of power banks; Equation (9) represents the limit on the number of mobile power supplies that can be placed at a single deployment point; Equation (10) represents the limit on the total number of mobile power supplies that can be deployed; Equation (11) represents that any mobile power supply can only be placed at one deployment point. ②Pre-disaster deployment constraints for emergency repair teams Pre-disaster deployment constraints for emergency repair teams include the number of teams deployed and their status constraints. In the formula: R represents the assembly of the emergency repair team; b r,z The variable is 0-1, representing the deployment status of the repair team r at deployment point z; 1 indicates that the team has been deployed to point z, and 0 indicates otherwise; N z,r N represents the maximum number of repair teams that can be deployed at a single point; r This refers to the total number of repair teams. Equation (10) represents the limit on the number of emergency repair teams that can be accommodated at a single deployment point; Equation (13) represents the limit on the total number of emergency repair teams that can be deployed; Equation (14) represents that any emergency repair team can only be deployed at one deployment point. (2) Post-disaster dispatch constraints Post-disaster dispatch constraints include those for mobile power supplies and emergency repair teams. Mobile power scheduling constraints include the number of mobile power sources to be scheduled and state constraints. Equation (15) indicates that any mobile power supply can only be scheduled to a maximum of one access point; Equation (16) indicates that only one mobile power supply is allowed to be connected to one access point; Equations (17) and (18) indicate that once a mobile power supply is scheduled to any access point, it will not move again. The constraints on emergency repair team dispatching include the number of emergency repair teams dispatched and their status constraints. Equation (19) indicates that any emergency repair team can only be dispatched to a maximum of one damaged line; Equation (20) indicates that a damaged line only needs to be repaired by dispatching one emergency repair team; Equations (21) and (22) indicate that the emergency repair team will not move after being dispatched to any damaged line. (3) Power balance constraints of distribution network The distribution network islands within each region need to meet the balance between load power and total power generation, as shown in equations (5) to (7); (4) Mobile resource scheduling state constraints Mobile resource scheduling status includes constraints on the scheduling status of mobile power supplies and emergency repair teams; ①Mobile power supply scheduling state constraints The scheduling state constraints of mobile power supplies are mainly limited by their access state, as shown in equation (1); ②Constraints on the dispatch status of emergency repair teams The main constraint on the dispatch status of the emergency repair team is its access status restriction, as shown in equation (2); (5) Line status constraints Line status constraints include constraints related to connected and damaged lines. The line is restored to normal operation after the repair team arrives and the repair time has elapsed, as shown in equation (3): Equation (23) indicates that the undamaged line is always connected at any time in scenario s; Equations (24) and (25) indicate that the mobile power supply will not move after being dispatched to any access point; (6) Load condition constraints Load condition constraints include load condition change constraints throughout the entire power outage period; Equation (26) represents the change in the recovery state of the load. Considering that the load change will bring transient fluctuations such as frequency and voltage, the load state can only change once during the entire period. Equation (27) indicates that after the load is restored, power supply should continue during the power outage period. (7) Power safety constraints Power safety constraints include constraints related to stationary power supplies and mobile power supplies. Constraints related to the output limit of mobile power supplies are shown in Equation (4). Where: K G K ESS These are a stationary generator and a stationary energy storage unit, respectively; P min P max These are the lower and upper limits of the power supply's active power output, respectively. E represents the active power output of a fixed power supply k in scenario s during time period t. k Let be the initial energy of generator k; For the initial state of charge (SOC) of energy storage i, the SoC i,min SoC i,max These are the lower and upper limits of the state of charge (SOC) for normal operation of energy storage, respectively; t′ represents any time; λ is the conversion coefficient for converting energy into the SOC of energy storage. Equation (28) limits the output of each power source to its allowable range; Equation (29) represents the remaining fuel constraint in the generator; Equation (30) represents the SOC constraint of energy storage.

4. The method according to claim 3, characterized in that, The aforementioned solution to the scenario-based urban area distribution network mobile emergency resource pre-disaster deployment model yields the mobile emergency resource pre-disaster deployment results, mobile emergency resource scheduling results under various scenarios, and load recovery results, including: Commercial modeling tools and solvers were used to solve the pre-disaster deployment model of mobile emergency resources in the urban area power distribution network, resulting in the pre-disaster deployment results of mobile emergency resources, the scheduling results of mobile emergency resources under various scenarios, and the load recovery results. The scheduling results of mobile emergency resources include the scheduling location and travel time, and the load recovery status includes the weighted load recovery time and the load recovery power.

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