Method for scheduling mobile emergency resources in consideration of power and traffic coupling network

Abstract

The application discloses a kind of mobile emergency resource scheduling optimization methods considering power and traffic coupling network, which comprises the following steps: obtaining power distribution network fault scene and dispatchable mobile emergency resource information;Obtain power and traffic coupling network dynamic information;Based on load classification, node power supply path, loss of load loss, calculate load node weight, give corresponding weight to power distribution network regional load node;Mobile emergency resource scheduling model is constructed, and power and traffic coupling network dynamic information and mobile emergency resource scheduling scheme are used as model input information;Set model decision variable, objective function, constraint condition, solve mobile emergency resource scheduling model, obtain the scheduling scheme of mobile emergency resource, and judge fault element, output the final scheduling scheme of mobile emergency resource.The application improves the dynamic information analysis and load comprehensive weight calculation method of power and traffic coupling network, and improves the optimization effect of mobile emergency resource scheduling.

Description

Technical Field

[0001] This invention relates to the field of resource scheduling technology, and specifically to a mobile emergency resource scheduling optimization method that takes into account power and transportation coupled networks. Background Technology

[0002] Currently, to enhance the resilience and recovery capabilities of power systems in the face of low-probability, high-impact disasters, extensive research and practical work have been conducted on post-disaster distribution network recovery methods, primarily focusing on flexibility resources and grid recovery models and algorithms. Among these, the construction of power-transport coupled networks is based on the grid topology, considering the resource connectivity requirements for the recovery of each load node, and establishing sequential coupling between transportation network nodes and grid nodes. Regarding emergency resource scheduling optimization, existing research mainly compensates for insufficient power supply through mobile emergency resource path planning. However, there is still room for improvement and research value in areas such as how to formulate optimal scheduling schemes to cooperate with the grid in achieving large-scale power outage recovery under extreme events, fully utilizing dynamic information to assist rapid decision-making, responding to real-time allocation needs, and optimizing mobile emergency resource scheduling in a timely manner based on maintenance progress. Summary of the Invention

[0003] To overcome the defects and shortcomings of existing technologies, this invention provides a method for optimizing the scheduling of mobile emergency resources that takes into account the coupled power and transportation networks. This invention improves the dynamic information analysis and load comprehensive weight calculation method of the coupled power and transportation networks, and proposes a corresponding mobile emergency resource scheduling optimization process, which can improve the applicability and feasibility of participating in emergency resources for power grid fault recovery.

[0004] The second objective of this invention is to provide a mobile emergency resource scheduling optimization system that takes into account the coupling network of power and transportation.

[0005] A third objective of this invention is to provide a computer-readable storage medium.

[0006] A fourth objective of this invention is to provide a computer device.

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

[0008] A method for optimizing the scheduling of mobile emergency resources considering power and transportation coupled networks includes the following steps:

[0009] Acquire information on power distribution network fault scenarios and dispatchable mobile emergency resources;

[0010] Acquire dynamic information about the coupled power and transportation networks;

[0011] Based on load classification, node power supply path, and load loss loss, the load node weight is calculated, and corresponding weights are assigned to load nodes in the distribution network area.

[0012] A mobile emergency resource scheduling model is constructed, taking the dynamic information of the power and transportation coupled network and the mobile emergency resource scheduling scheme as the model input information. The model decision variables, objective function and constraints are set, and the mobile emergency resource scheduling model is solved to obtain the scheduling scheme of mobile emergency resources. Faulty components are judged to determine whether there are faulty components in the system after the current scheduling cycle ends. If so, the power and transportation coupled network coupling information of the next scheduling period is re-analyzed and the mobile emergency resource scheduling model is solved. Otherwise, the model calculation is terminated and the final mobile emergency resource scheduling scheme is output.

[0013] The decision variables include the mobile emergency resource travel path, the mobile emergency resource output, the load node recovery sequence, and the distribution network load recovery level.

[0014] The objective function is to achieve the highest possible load recovery level in the distribution network.

[0015] The constraints include operational constraints and mobile emergency resource constraints. The operational constraints include power flow constraints, voltage amplitude constraints, branch current constraints, line power constraints, and node power balance constraints. The mobile emergency resource constraints include load restoration demand constraints, mobile emergency resource connection status constraints, mobile emergency resource travel path constraints, and mobile emergency resource output characteristic constraints.

[0016] As a preferred technical solution, the power distribution network fault scenarios include regional fault lines, repair team repair sequence, and fault line restoration time;

[0017] The information on dispatchable mobile emergency resources includes the location, type, quantity, output power, and discharge efficiency of mobile emergency resource assembly points.

[0018] As a preferred technical solution, the dynamic information of the power and transportation coupling network includes: changes in the fault status of power distribution lines, road traffic status, and changes in road traffic flow;

[0019] As a preferred technical solution, the load node weight is calculated based on load classification, node power supply path, and load loss loss, specifically including:

[0020] The load nodes of the distribution network are divided into multi-level load nodes, and different load weights are assigned according to different classifications to obtain load classification weights.

[0021] Let Λ be the set of all power supply paths originating from node i. i The number of paths is Z, where the k-th path is L. i,k The resulting list of all paths originating from each load node is as follows:

[0022] Λi ={L i,1 ,…,L i,k ,…,L i,Z}

[0023] The number of all paths originating from each load node is used as the node power supply path weight value;

[0024] The weighting of load loss is expressed as follows:

[0025]

[0026] Where, ξ i,loss Let α be the weight of the load loss at node i. loss Let be the normalized coefficient for the load loss weight, and e(i) be the set of downstream nodes of node i. For the unit loss of load at downstream node e, This represents the load capacity of downstream node e;

[0027] The load node weights are represented as follows:

[0028] ξ i =ξ i,level ·ξ i,path ·ξ i,loss

[0029] Where, ξ i,level ξ represents the load classification weight. path ξ represents the power supply path weight of the node. i This indicates the weight of the load node.

[0030] As a preferred technical solution, the objective function takes the highest possible load recovery level of the distribution network as its objective function, specifically expressed as follows:

[0031]

[0032] Where, ξ i Let P be the load weight of node i. i,t Let be the amount of load recovered by node i at time t. Let be a 0-1 variable indicating whether node i is connected at time t.

[0033] As a preferred technical solution, the power flow constraints are described using the linear DistFlow power flow equation, expressed as:

[0034]

[0035]

[0036]

[0037] Among them, lij,t P represents the open / closed state of line ij at time t, where 1 indicates closed and 0 indicates open. ij Q ij These represent the active and reactive power of line ij, respectively, R ij X ij These are the resistance and reactance of line ij, respectively;

[0038] Voltage amplitude constraint is expressed as follows:

[0039]

[0040] Among them, U i,max U is the upper limit of the node voltage. i,min U is the lower limit of the node voltage. n This is the voltage reference value;

[0041] Branch current constraints are expressed as follows:

[0042]

[0043] Among them, I ij,max This is the upper limit of the branch current;

[0044] Line power constraints are expressed as follows:

[0045]

[0046]

[0047] in, These represent the maximum active power and maximum reactive power of line ij, respectively.

[0048] The node power balance constraint is expressed as:

[0049]

[0050]

[0051] Where d(i) is the set of upstream nodes of node i, and e(i) is the set of downstream nodes of node i.

[0052] As a preferred technical solution, the load restoration requirement constraint is expressed as follows:

[0053]

[0054]

[0055] Where, σ i,t Let be the load recovery demand state variable for node i at time t;

[0056] The mobile emergency resource connection state constraint is represented as follows:

[0057]

[0058] Where, β h,y,i Restrictions on the connection status of mobile emergency resources;

[0059] The travel path constraints for mobile emergency resources are represented as follows:

[0060]

[0061]

[0062] Where τ is the mobile emergency resource scheduling period, t i,j t represents the travel time of mobile emergency resources from node i to node j. conf Time for configuring mobile emergency resources;

[0063] The constraints on the output characteristics of mobile emergency resources are expressed as follows:

[0064]

[0065]

[0066] Among them, P h,i Q h,i These represent the active and reactive power outputs of the h-th type of mobile emergency resource at the i-th node, respectively.

[0067] To achieve the second objective mentioned above, the present invention adopts the following technical solution:

[0068] A mobile emergency resource scheduling optimization system considering a power and transportation coupled network includes: an information acquisition module, a load node weight calculation module, a scheduling model construction module, a scheduling model solving module, a fault component judgment module, and a scheduling scheme output module;

[0069] The information acquisition module is used to acquire information on power distribution network fault scenarios and dispatchable mobile emergency resources, and to acquire dynamic information on the power and transportation coupled network.

[0070] The load node weight calculation module is used to calculate the load node weight based on load classification, node power supply path, and load loss, and assign corresponding weights to load nodes in the distribution network area.

[0071] The scheduling model construction module is used to construct a mobile emergency resource scheduling model;

[0072] The scheduling model solving module is used to solve the mobile emergency resource scheduling model. It takes the dynamic information of the power and transportation coupled network and the mobile emergency resource scheduling scheme as the model input information, sets the model decision variables, objective function and constraints, solves the mobile emergency resource scheduling model, and obtains the mobile emergency resource scheduling scheme.

[0073] The fault component judgment module is used to judge fault components and determine whether there are fault components in the system after the current scheduling cycle ends.

[0074] The scheduling scheme output module is used to output the scheduling scheme of mobile emergency resources. When the fault component judgment module determines that there is a fault component in the system after the current scheduling cycle ends, it re-analyzes the power-transport coupling network coupling information for the next scheduling period and performs the solution calculation of the mobile emergency resource scheduling model. Otherwise, it exits the model calculation and outputs the final scheduling scheme of mobile emergency resources.

[0075] The decision variables include the mobile emergency resource travel path, the mobile emergency resource output, the load node recovery sequence, and the distribution network load recovery level.

[0076] The objective function is to achieve the highest possible load recovery level in the distribution network.

[0077] The constraints include operational constraints and mobile emergency resource constraints. The operational constraints include power flow constraints, voltage amplitude constraints, branch current constraints, line power constraints, and node power balance constraints. The mobile emergency resource constraints include load restoration demand constraints, mobile emergency resource connection status constraints, mobile emergency resource travel path constraints, and mobile emergency resource output characteristic constraints.

[0078] To achieve the third objective mentioned above, the present invention adopts the following technical solution:

[0079] A computer-readable storage medium storing a program that, when executed by a processor, implements the above-described method for optimizing the scheduling of mobile emergency resources considering power and transportation coupled networks.

[0080] To achieve the fourth objective mentioned above, the present invention adopts the following technical solution:

[0081] A computer device includes a processor and a memory for storing processor-executable programs. When the processor executes the program stored in the memory, it implements the mobile emergency resource scheduling optimization method considering the power and transportation coupled network described above.

[0082] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0083] (1) This invention improves the dynamic information analysis and load comprehensive weight calculation method of the power and transportation coupled network, and proposes a corresponding mobile emergency resource scheduling optimization process, which can improve the applicability and feasibility of participating in the emergency resources for power grid fault recovery.

[0084] (2) This invention takes into account the impact of dynamic information of power and transportation coupled network on decisions such as load restoration sequence and emergency resource travel path, and proposes a mobile emergency resource scheduling strategy under multiple time sections. It solves the problem of lack of real-time information for post-disaster recovery of existing power distribution networks and can present the actual post-disaster recovery process of power distribution networks.

[0085] (3) When scheduling mobile emergency resources, this invention takes into account the real-time recovery information of the distribution network. The solution has good optimization effects in terms of mobile emergency resource travel path, load recovery level, mobile emergency resource utilization rate and optimization model solution time. It can effectively support load power supply and maintain the functional integrity of the distribution network system during the post-disaster recovery process. Attached Figure Description

[0086] Figure 1 This is a flowchart illustrating the mobile emergency resource scheduling optimization method of the present invention, which takes into account the coupled power and transportation networks.

[0087] Figure 2 This is a topology diagram of the IEEE 33-node transportation network of this invention;

[0088] Figure 3 This is a schematic diagram of the topology and node hierarchy of the IEEE 33-node power distribution system of this invention;

[0089] Figure 4 (a) is a schematic diagram comparing the travel paths of mobile emergency resources of type 1 of the present invention;

[0090] Figure 4 (b) is a comparative diagram of the travel paths of mobile emergency resources of type 2 of the present invention;

[0091] Figure 5 (a) is a schematic diagram of the load active power recovery level under the scheduling optimization of the present invention;

[0092] Figure 5 (b) is a schematic diagram of the load reactive power recovery level under the scheduling optimization of the present invention. Detailed Implementation

[0093] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0094] Example 1

[0095] like Figure 1 As shown, this embodiment provides a method for optimizing the scheduling of mobile emergency resources considering power and transportation coupled networks, including the following steps:

[0096] S1: Obtain information on power distribution network fault scenarios and dispatchable mobile emergency resources;

[0097] In this embodiment, step S1 is as follows:

[0098] S11: Obtain distribution network fault information under fault conditions, including regional fault lines, repair team repair sequence and fault line restoration time; as time goes by, the line fault status information will change dynamically according to the work progress of the repair team, which will affect the number of mobile emergency resources dispatched and the load restoration sequence. The changes in line fault status are determined according to the repair team repair sequence and fault repair time.

[0099] In this embodiment, the obtained distribution network fault scenarios and faulty lines are shown in Table 1 below.

[0100] Table 1. Distribution Network Fault Scenarios and Line Restoration Sequence

[0101] Faulty circuit 5—6 21—22 6—7 15—16 9—10 17—18 32—33

[0102] S12: Obtain information on schedulable mobile emergency resources, including the location, type, quantity, output power, and discharge efficiency of mobile emergency resource assembly points;

[0103] In this embodiment, the parameter information such as the type and quantity of mobile emergency resources available for dispatching is shown in Table 2 below.

[0104] Table 2 Mobile Emergency Resource Parameters

[0105]

[0106]

[0107] The configuration parameters for mobile emergency resources are shown in Table 3 below:

[0108] Table 3 Pre-configuration parameters for mobile emergency resources

[0109] Assembly point location Type 1 / vehicle Type 2 / vehicle Type 3 / vehicle Type 4 / vehicle Total configuration Node 17 5 5 1 5 16

[0110] S2: Obtain dynamic information on the power and transportation coupled network;

[0111] In this embodiment, step S2 is as follows:

[0112] S21: Distribution network dynamic information mainly considers changes in the fault status of distribution lines, while transportation network dynamic information mainly considers changes in road traffic status and traffic flow. Road traffic status and traffic flow are obtained based on real-time data, including traffic flow and road damage status. The current time segment transportation network data is acquired in 30-minute cycles and used as the basis for the model in the next cycle to update the mobile emergency resource dispatching plan under the remaining faults of the distribution network.

[0113] like Figure 2 , Figure 3 As shown, the power distribution network adopts the IEEE standard 33-node power distribution system, and the topology and load classification are obtained. The transportation network topology (2) includes 33 nodes and 51 roads. Extreme weather and other natural disasters will have a certain impact on the roads of the transportation network. In this embodiment of the invention, roads 18-19 and 27-32 are blocked, and vehicles cannot pass. Among them, roads 18-19 are repaired after 130 minutes, and roads 27-32 are repaired after 250 minutes. The repair sequence within the time scale of this embodiment is shown in Table 4 below:

[0114] Table 4 Distribution Network Fault Scenarios and Line Restoration Sequence

[0115] Faulty circuit 5—6 21—22 6—7 15—16 9—10 17—18 32—33 Recovery time 160min 220min 290min 330min >360min >360min >360min

[0116] S22: The nodes of the transportation network and the load nodes of the distribution network are coupled and corresponded according to their actual geographical distribution to form a power and transportation coupled network. Through the interaction between the two, the fault information of the distribution network and the travel path information of emergency resources and their corresponding relationships are included in the model constraints. The fault information is used as input, and the travel path is used as the model decision variable. The model comprehensively considers multiple factors such as the travel path of mobile emergency resources, the utilization rate of mobile emergency resources, the load recovery level, and the timeliness of load recovery to achieve the optimal allocation of mobile emergency resources and provide effective support for power grid recovery.

[0117] S3: Calculate the comprehensive weight of load nodes in the distribution network;

[0118] In this embodiment, step S3 is as follows:

[0119] S31: Assign corresponding weights to load nodes in the distribution network area based on three aspects: comprehensive load classification, node power supply path, and load loss.

[0120] Among them, the load classification weight ξ i,level • Based on power supply reliability requirements, the load is divided into primary load, secondary load and tertiary load. According to the different classification of each load node in the distribution network, the weight of primary load is set to 100, the weight of secondary load is set to 10, and the weight coefficient of tertiary load is set to 1.

[0121] Among them, the node power supply path weight ξ path To reflect the impact of the number of power supply paths on the power outage range in a distribution network when different load nodes experience faults, a depth-first traversal algorithm is used, defining the set of all power supply paths originating from node i as Λ. i The number of paths is Z, where the k-th path is L. i,k ,but:

[0122] Λ i ={L i,1 ,…,L i,k ,…,L i,Z}

[0123] We obtain all paths originating from each load node. Based on the search results, the total number of paths originating from each load node is the node power supply path weight value ξ. path ;

[0124] Among them, the weight of the load loss ξ loss Based on the power supply path of the nodes, the load loss caused by the loss of power to each load node itself and its downstream nodes is further considered, specifically as follows:

[0125]

[0126] Where, ξ i,loss α is the weight for the load loss at node i, with a value range of [0, 10]; loss is the normalized processing coefficient for the load loss weight; e(i) is the set of downstream nodes of node i; The unit loss due to load shedding at downstream node e; This represents the load capacity of downstream node e.

[0127] S32: Calculate the comprehensive weight of load nodes based on three aspects: load classification, node power supply path, and load loss. This weight is then used as the load node weight ξ in the objective function. i Specifically, it is expressed as:

[0128] ξ i =ξ i,level ·ξ i,path ·ξ i,loss

[0129] In this embodiment, the load classification weight is obtained according to the load classification of the distribution network; the power supply path of the load node is determined by the depth-first traversal algorithm, and the power supply path weight of the node is calculated; according to the load node parameters of the distribution network in Table 5 below, combined with the fault information of the distribution network, the unit load loss of each node and the set of downstream nodes are read, and the node load loss weight is calculated.

[0130] Table 5 Parameters of Distribution Network Load Nodes

[0131]

[0132] S4: A mobile emergency resource scheduling optimization scheme for determining the post-disaster load node recovery sequence based on dynamic information of the power and transportation coupled network across multiple time sections.

[0133] In this embodiment, the mobile emergency resource scheduling optimization method determines the post-disaster load node recovery sequence and mobile emergency resource scheduling scheme based on the dynamic information of the power and transportation coupled network at multiple time sections and the pre-configuration of mobile emergency resources. The network dynamic information and pre-configuration are used as input information to the solution model, while the post-disaster load node recovery sequence and mobile emergency resource scheduling scheme are used as model decision variables. A commercial solver is used to solve the model and determine the model decision variables, effectively maintaining the functional integrity of the distribution network system. The steps include analyzing the power and transportation network coupling information at the start of the recovery process, setting model decision variables, objective functions, and constraints, solving the mobile emergency resource scheduling scheme, and identifying faulty components. Specifically, the method includes:

[0134] S41: Obtain the power and transportation network coupling information at the start of the recovery process, including the fault status of distribution network lines and road traffic status, and determine the changes in the line fault status based on the repair team's repair sequence and fault repair time.

[0135] S42: Set decision variables, including mobile emergency resource travel path, mobile emergency resource output, load node recovery sequence, and distribution network load recovery level, etc.

[0136] Taking the maximization of the distribution network load recovery level as the objective function, it can be expressed as:

[0137]

[0138] Where, ξ i P represents the load weight of node i; i,t Let be the amount of load recovered by node i at time t; Let be a 0-1 variable indicating whether node i is connected at time t.

[0139] The operational constraints include power flow constraints, voltage magnitude constraints, branch current constraints, line power constraints, and node power balance constraints. The operational constraints in the model are set to analyze the coupling information between the power and transportation networks at the start of the recovery process.

[0140] In this embodiment, the power flow constraints are described using the linear DistFlow power flow equation, expressed as:

[0141]

[0142]

[0143]

[0144] Among them, l ij,t P represents the open / closed state of line ij at time t, where 1 indicates closed and 0 indicates open; ij Q ij These represent the active and reactive power of line ij, respectively; R ij X ij These represent the line resistance and reactance, respectively.

[0145] In this embodiment, the voltage amplitude constraint is: during the distribution network restoration process, the voltage of each load node should meet the amplitude requirement range;

[0146]

[0147] In the formula, U i,max As the upper limit of the node voltage, this embodiment sets U i,max =1.1U n U i,min As the lower limit of the node voltage, this embodiment sets U i,min =0.95U n U n This is the voltage reference value, i.e., the rated voltage of the distribution network system.

[0148] In this embodiment, the branch current constraint is: during the distribution network restoration process, the current of each branch should not exceed the rated current of the line, and the current value is affected by the line fault status.

[0149]

[0150] In the formula, I ij,max The upper limit of the branch current is determined by the power reference value and rated voltage of the distribution network system.

[0151] In this embodiment, the line power constraint is: during the distribution network restoration process, the power of each line in the distribution network should not exceed the line transmission capacity limit.

[0152]

[0153]

[0154] In the formula, These represent the maximum active power and maximum reactive power of line ij, respectively.

[0155] In this embodiment, the node power balance constraint is as follows: considering the access of mobile emergency resources, each faulty load node should maintain a balance between the load recovery amount and the output of mobile emergency resources, as well as the power transmission of upstream and downstream nodes.

[0156]

[0157]

[0158] In the formula, d(i) is the set of upstream nodes of node i; e(i) is the set of downstream nodes of node i.

[0159] The constraints on mobile emergency resources include load restoration demand constraints, mobile emergency resource connection status constraints, mobile emergency resource travel path constraints, and mobile emergency resource output characteristic constraints.

[0160] In this embodiment, the load restoration requirement constraint is that the amount of faulty load restored should not exceed its load capacity, and the impact of the upstream line repair process on the load restoration requirement status must be considered.

[0161]

[0162]

[0163] In the formula, σ i,t Let be the load restoration demand state variable for node i at time t. 1 indicates that the upstream line has not been restored and mobile emergency resources are still needed to support power supply, while 0 indicates that the upstream line has been restored.

[0164] In this embodiment, the connection status constraint of the mobile emergency resource is: during the power distribution network restoration process, each mobile emergency resource is only allowed to connect to one load node at any given time.

[0165]

[0166] In the formula, β h,y,i Restrictions on the connection status of mobile emergency resources.

[0167] In this embodiment, the travel path constraint for mobile emergency resources is as follows: the connection state is only established when the interval between two adjacent time points is greater than the travel and configuration time of the mobile emergency resources, and the starting point and the initial path are associated with the configuration scheme assembly point.

[0168]

[0169]

[0170] In the formula, τ represents the mobile emergency resource dispatch cycle; t i,j The travel time of mobile emergency resources from node i to node j; tconf Time for configuring mobile emergency resources.

[0171] In this embodiment, the output characteristic constraint of mobile emergency resources is: during the power distribution network restoration process, the total output of mobile emergency resources of access node i should not exceed its maximum power value.

[0172]

[0173]

[0174] In the formula, P h,i Q h,i These represent the active and reactive power outputs of the h-th type of mobile emergency resource at the i-th node, respectively.

[0175] S43: Solve the scheduling scheme of the mobile emergency resource vehicle at the start of the recovery process;

[0176] S44: Determine whether there are faulty components in the system after the current scheduling cycle ends. If so, re-analyze the power-transport coupling network coupling information for the next scheduling period and perform scheduling optimization model calculation, calling the cplex commercial solver to solve the problem; otherwise, exit the model calculation.

[0177] Based on the mobile emergency resource scheduling model in this embodiment, the following three schemes are set up for comparison to verify the effectiveness of the scheduling model.

[0178] Option A: Develop a mobile emergency resource dispatching method that takes into account dynamic information of the power-transportation coupled network and the comprehensive weight of load nodes.

[0179] Option B: Develop a mobile emergency resource dispatching method without considering the impact of dynamic information.

[0180] Option C: The load node weights in the mobile emergency resource scheduling model only consider load grading.

[0181] All three schemes use the CPLEX solver to optimize the model. The optimization solution time for each scheme is shown in Table 6 below.

[0182] Table 6 Solution Time of Optimization Model

[0183]

[0184] The solution results of the two schemes are compared and analyzed from the perspectives of mobile emergency resource travel paths and load recovery levels, respectively.

[0185] Mobile emergency resource travel route analysis: One vehicle is selected as a typical case for each type of mobile emergency resource, and the scheduling optimization results obtained from the three schemes are visually compared. Figure 4 (a) Figure 4 As shown in (b), the solid line, dashed line, and dotted line represent the scheduling results of mobile emergency resources in Scheme A, Scheme B, and Scheme C, respectively.

[0186] Starting from the fifth scheduling period, the road conditions of the transportation network and the fault status of the power distribution network lines will change dynamically. As repair personnel repair the faults in the power distribution network lines, the scheduling scheme for the travel paths of each mobile emergency resource in Scheme A will be significantly adjusted based on the real-time topology of the power and transportation coupled network. Simultaneously, as repair personnel repair the faults in the power distribution network lines, the number of mobile emergency resources dispatched will gradually decrease according to the current load restoration needs, which is consistent with the actual situation of post-disaster recovery.

[0187] Load recovery level analysis: such as Figure 5 (a) Figure 5 As shown in (b), the overall active and reactive power recovery levels of the load under the three schemes are compared over each time period. It can be seen that Scheme A, taking into account dynamic information changes, can adjust the mobile emergency resource dispatch scheme in a timely manner and further improve the overall load recovery level while ensuring priority support for important loads. The overall reactive power recovery level of Scheme A gradually increases to 100%. Therefore, formulating a dispatch method that considers dynamic information has the advantage of flexibly adjusting resources according to load recovery needs.

[0188] Example 2

[0189] Except for the following technical contents, the technical contents of this embodiment are the same as those of Embodiment 1;

[0190] This embodiment provides a mobile emergency resource scheduling optimization system that takes into account the coupled power and transportation network, including: an information acquisition module, a load node weight calculation module, a scheduling model construction module, a scheduling model solving module, a fault component judgment module, and a scheduling scheme output module;

[0191] In this embodiment, the information acquisition module is used to acquire information on power distribution network fault scenarios and dispatchable mobile emergency resources, and to acquire dynamic information on the power and transportation coupled network.

[0192] In this embodiment, the load node weight calculation module is used to calculate the load node weight based on load classification, node power supply path, and load loss, and assign corresponding weights to load nodes in the distribution network area.

[0193] In this embodiment, the scheduling model construction module is used to construct a mobile emergency resource scheduling model;

[0194] In this embodiment, the scheduling model solving module is used to solve the mobile emergency resource scheduling model. It takes the dynamic information of the power and transportation coupled network and the mobile emergency resource scheduling scheme as the model input information, sets the model decision variables, objective function and constraints, solves the mobile emergency resource scheduling model, and obtains the mobile emergency resource scheduling scheme.

[0195] In this embodiment, the fault component judgment module is used to judge whether there are fault components in the system after the current scheduling period ends;

[0196] In this embodiment, the scheduling scheme output module is used to output the scheduling scheme of mobile emergency resources. When the fault element judgment module determines that there is a fault element in the system after the current scheduling period ends, it re-analyzes the power-transport coupling network coupling information of the next scheduling period and performs the solution calculation of the mobile emergency resource scheduling model. Otherwise, it exits the model calculation and outputs the final scheduling scheme of mobile emergency resources.

[0197] In this embodiment, the decision variables include the travel path of mobile emergency resources, the output of mobile emergency resources, the recovery sequence of load nodes, and the recovery level of the distribution network load.

[0198] In this embodiment, the objective function is to achieve the highest possible load recovery level in the distribution network.

[0199] In this embodiment, the constraints include operational constraints and mobile emergency resource constraints. The operational constraints include power flow constraints, voltage amplitude constraints, branch current constraints, line power constraints, and node power balance constraints. The mobile emergency resource constraints include load restoration demand constraints, mobile emergency resource connection status constraints, mobile emergency resource travel path constraints, and mobile emergency resource output characteristic constraints.

[0200] Example 3

[0201] This embodiment provides a computer-readable storage medium, which may be a ROM, RAM, disk, optical disk, or other storage medium. The storage medium stores one or more programs. When the programs are executed by a processor, they implement the mobile emergency resource scheduling optimization method considering the power and transportation coupled network of Embodiment 1.

[0202] Example 4

[0203] This embodiment provides a computing device, which may be a desktop computer, laptop computer, smartphone, PDA handheld terminal, tablet computer or other terminal device with display function. The computing device includes a processor and a memory. The memory stores one or more programs. When the processor executes the program stored in the memory, it implements the mobile emergency resource scheduling optimization method considering the power and transportation coupled network of Embodiment 1.

[0204] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the scheduling of mobile emergency resources considering power and transportation coupled networks, characterized in that, Includes the following steps: Acquire information on power distribution network fault scenarios and dispatchable mobile emergency resources; Acquire dynamic information about the coupled power and transportation networks; Based on load classification, node power supply path, and load loss loss, load node weights are calculated, and corresponding weights are assigned to load nodes in the distribution network area. Specifically, this includes: The load nodes of the distribution network are divided into multi-level load nodes, and different load weights are assigned according to different classifications to obtain load classification weights. Let the set of all power supply paths originating from node i be . The number of paths is Z, where the k-th path is The resulting list of all paths originating from each load node is as follows: ； The number of all paths originating from each load node is used as the node power supply path weight value; The weighting of load loss is expressed as follows: ； in, Let i be the weight of the load loss at node i. This is the normalized coefficient for the weighting of load loss. Let i be the set of downstream nodes of node i. For the unit loss of load at downstream node e, This represents the load capacity of downstream node e; The load node weights are represented as follows: ； in, Indicates the load classification weights. Indicates the power supply path weight of the node. Indicates the weight of the load node; A mobile emergency resource scheduling model is constructed, taking the dynamic information of the power and transportation coupled network and the mobile emergency resource scheduling scheme as the model input information. The model decision variables, objective function and constraints are set, and the mobile emergency resource scheduling model is solved to obtain the scheduling scheme of mobile emergency resources. Faulty components are judged to determine whether there are faulty components in the system after the current scheduling cycle ends. If so, the power and transportation coupled network coupling information of the next scheduling period is re-analyzed and the mobile emergency resource scheduling model is solved. Otherwise, the model calculation is terminated and the final mobile emergency resource scheduling scheme is output. The decision variables include the mobile emergency resource travel path, the mobile emergency resource output, the load node recovery sequence, and the distribution network load recovery level. The objective function aims to achieve the highest possible load recovery level in the distribution network, and is specifically expressed as follows: ； in, Let i be the load weight of node i. Let be the amount of load recovered by node i at time t. Let be a 0-1 variable indicating whether node i is connected at time t; The constraints include operational constraints and mobile emergency resource constraints. The operational constraints include power flow constraints, voltage amplitude constraints, branch current constraints, line power constraints, and node power balance constraints. The mobile emergency resource constraints include load restoration demand constraints, mobile emergency resource connection status constraints, mobile emergency resource travel path constraints, and mobile emergency resource output characteristic constraints.

2. The mobile emergency resource scheduling optimization method considering the power and transportation coupled network as described in claim 1, characterized in that, The power distribution network fault scenarios include regional fault lines, the repair sequence of the emergency repair team, and the restoration time of the fault lines. The information on dispatchable mobile emergency resources includes the location, type, quantity, output power, and discharge efficiency of mobile emergency resource assembly points.

3. The mobile emergency resource scheduling optimization method considering the power and transportation coupled network according to claim 1, characterized in that, Dynamic information of the power and transportation coupled network includes: changes in the fault status of power distribution lines, road traffic status, and changes in road flow.

4. The mobile emergency resource scheduling optimization method considering the power and transportation coupled network according to claim 1, characterized in that, The power flow constraints are described using the linear DistFlow power flow equation, expressed as: ; ； ； in, This represents the on / off state of line ij at time t, where 1 indicates closed and 0 indicates open. , These represent the active power and reactive power of line ij, respectively. , These are the resistance and reactance of line ij, respectively; Voltage amplitude constraint is expressed as: ； in, This is the upper limit of the node voltage. This represents the lower limit of the node voltage. This is the voltage reference value; Branch current constraints are expressed as follows: ； in, This is the upper limit of the branch current; Line power constraints are expressed as follows: ； ； in, , These represent the maximum active power and maximum reactive power of line ij, respectively. The node power balance constraint is expressed as: ； ； in, Let be the set of upstream nodes of node i. Let i be the set of downstream nodes of node i. , These represent the active and reactive power outputs of the h-th type of mobile emergency resource at the i-th node, respectively. Restrictions on the connection status of mobile emergency resources.

5. The mobile emergency resource scheduling optimization method considering the power and transportation coupled network according to claim 1, characterized in that, The load restoration requirement constraint is expressed as: ； ； in, Let be the load recovery demand state variable for node i at time t; The mobile emergency resource connection state constraint is represented as follows: ； in, Restrictions on the connection status of mobile emergency resources; The travel path constraints for mobile emergency resources are represented as follows: ； in, For the mobile emergency resource dispatch cycle, The travel time for mobile emergency resources from node i to node j. Time for configuring mobile emergency resources; The constraints on the output characteristics of mobile emergency resources are expressed as follows: ； ； in, , These represent the active and reactive power outputs of the h-th type of mobile emergency resource at the i-th node, respectively.

6. A mobile emergency resource scheduling and optimization system considering a power and transportation coupled network, characterized in that, include: The system includes an information acquisition module, a load node weight calculation module, a scheduling model construction module, a scheduling model solving module, a fault component judgment module, and a scheduling scheme output module. The information acquisition module is used to acquire information on power distribution network fault scenarios and dispatchable mobile emergency resources, and to acquire dynamic information on the power and transportation coupled network. The load node weight calculation module is used to calculate the load node weight based on load classification, node power supply path, and load loss, and assign corresponding weights to load nodes in the distribution network area. Specifically, it includes: The load nodes of the distribution network are divided into multi-level load nodes, and different load weights are assigned according to different classifications to obtain load classification weights. Let the set of all power supply paths originating from node i be . The number of paths is Z, where the k-th path is The resulting list of all paths originating from each load node is as follows: ； The number of all paths originating from each load node is used as the node power supply path weight value; The weighting of load loss is expressed as follows: ； in, Let i be the weight of the load loss at node i. This is the normalized coefficient for the weighting of load loss. Let i be the set of downstream nodes of node i. For the unit loss of load at downstream node e, This represents the load capacity of downstream node e; The load node weights are represented as follows: ； in, Indicates the load classification weights. Indicates the power supply path weight of the node. Indicates the load node weight; The scheduling model construction module is used to construct a mobile emergency resource scheduling model; The scheduling model solving module is used to solve the mobile emergency resource scheduling model. It takes the dynamic information of the power and transportation coupled network and the mobile emergency resource scheduling scheme as the model input information, sets the model decision variables, objective function and constraints, solves the mobile emergency resource scheduling model, and obtains the mobile emergency resource scheduling scheme. The fault component judgment module is used to judge fault components and determine whether there are fault components in the system after the current scheduling cycle ends. The scheduling scheme output module is used to output the scheduling scheme of mobile emergency resources. When the fault component judgment module determines that there is a fault component in the system after the current scheduling cycle ends, it re-analyzes the power-transport coupling network coupling information for the next scheduling period and performs the solution calculation of the mobile emergency resource scheduling model. Otherwise, it exits the model calculation and outputs the final scheduling scheme of mobile emergency resources. The decision variables include the mobile emergency resource travel path, the mobile emergency resource output, the load node recovery sequence, and the distribution network load recovery level. The objective function aims to achieve the highest possible load recovery level in the distribution network, and is specifically expressed as follows: ； in, Let i be the load weight of node i. Let be the amount of load recovered by node i at time t. Let be a 0-1 variable indicating whether node i is connected at time t; The constraints include operational constraints and mobile emergency resource constraints. The operational constraints include power flow constraints, voltage amplitude constraints, branch current constraints, line power constraints, and node power balance constraints. The mobile emergency resource constraints include load restoration demand constraints, mobile emergency resource connection status constraints, mobile emergency resource travel path constraints, and mobile emergency resource output characteristic constraints.

7. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the mobile emergency resource scheduling optimization method that takes into account the power and transportation coupled network as described in any one of claims 1-5.

8. A computer device comprising a processor and a memory for storing a processor-executable program, characterized in that, When the processor executes the program stored in the memory, it implements the mobile emergency resource scheduling optimization method that takes into account the power and transportation coupled network as described in any one of claims 1-5.

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