Configuration method and device for improving power distribution network-traffic network toughness and computer equipment

Abstract

The invention relates to a configuration method and device for improving power distribution network-traffic network toughness and computer equipment. Obtaining traffic network flow data, circuit load data and charging load data of the power distribution network-traffic network, and travel duration, power grid generation power and power utilization power of the power distribution network-traffic network, and inputting the traffic network flow data into a first group of constraint generation models to obtain a first group of constraints, inputting the circuit load data into a second group of constraint generation models to obtain a second group of constraints, inputting the charging load data into a third group of constraint generation models to obtain a third group of constraints, inputting the traffic network travel duration, the power grid generation power and the power utilization power into a coordinated optimization model, and obtaining a first constraint, a second constraint and a third constraint which meet the first constraint, the second constraint and the third constraint; the target travel duration, the target power generation power and the power utilization power of the power grid are obtained when the target function reaches the minimum value, and finally data in the power distribution network-traffic network are adjusted to be target data. By adopting the method, the resource utilization rate of the power distribution network-traffic network can be improved.

Description

Technical Field

[0001] The present application relates to the field of distribution network technology, and in particular to a configuration method, device, computer equipment, computer-readable storage medium and computer program product for improving the resilience of a distribution network-transportation network. Background Art

[0002] In recent years, the popularity of electric vehicles has introduced a significant coupling relationship between the distribution network and the transportation network, which has improved the interaction between the two systems on the one hand, but also increased their complexity and vulnerability due to cascading failures on the other. When extreme events occur, the infrastructure of the distribution network and the transportation network will be damaged, and the coupling relationship will affect each other, further deteriorating the situation or slowing down its recovery.

[0003] Post-disaster emergency response resource allocation is an important way to improve system resilience. Many existing studies focus on post-disaster strategies for a single distribution network or transportation network. However, the current post-disaster emergency response resource allocation methods have the problem of low resource utilization. Summary of the invention

[0004] Based on this, it is necessary to provide a configuration method, device, computer equipment, computer-readable storage medium and computer program product that can improve resource utilization and enhance the resilience of the distribution network-transportation network in response to the above-mentioned technical problems.

[0005] In a first aspect, the present application provides a configuration method for improving the resilience of a distribution network-transportation network, including:

[0006] Obtaining traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, power generation power of the power grid and power consumption power of the power grid of the distribution network-transportation network;

[0007] Inputting traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints, and link reverse constraints through the traffic network constraint generation model, inputting circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints, and line switching constraints through the circuit constraint generation model, and inputting charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model;

[0008] Input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into the pre-built coordinated optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the network load constraint, the link travel time constraint, the link reverse constraint, the linear power flow constraint, the line operation constraint, the line switching constraint, the charging operation constraint and the charging pile management constraint are satisfied and the target cost function in the coordinated optimization model reaches the minimum value;

[0009] The target travel time of the transportation network is adjusted to the travel time of the transportation network of the distribution network-transportation network, the target power generation power of the power grid is adjusted to the power generation power of the distribution network-transportation network, and the target power consumption power of the power grid is adjusted to the power consumption power of the distribution network-transportation network.

[0010] In one embodiment, the coordinated optimization model is constructed by the following steps:

[0011] Linearizing the set constraints in the constraint module in the coordinated optimization model to be trained to obtain linearized constraints;

[0012] Linearize the nonlinear target cost function in the coordinated optimization model to be trained to obtain the target cost function;

[0013] According to the linearized constraints and the target cost function, as well as the coordinated optimization model to be trained, a coordinated optimization model is constructed.

[0014] In one embodiment, linearization is performed on the set constraints in the constraint module in the coordinated optimization model to be trained to obtain linearized constraints, including:

[0015] By using Gray coding method and adaptive piecewise linear weighting method, the number of variables included in setting constraint conditions is reduced to obtain constraint conditions;

[0016] Linearize the constraints to generate linearized constraints.

[0017] In one embodiment, the traffic flow data includes the flow of electric vehicles and gasoline vehicles on the link, the flow into the link, the flow out of the link, the travel path set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand; the free travel time of the link, the link traffic flow, and the link capacity;

[0018] Traffic flow data is input into the pre-built traffic network constraint generation model, and the network load constraint, link travel time constraint and link reverse constraint are obtained through the traffic network constraint generation model, including:

[0019] The flow of electric vehicles and gasoline vehicles on the link, the flow of vehicles flowing into the link, the flow of vehicles flowing out of the link, the travel link set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand are input into the network load constraint generation module of the traffic network constraint generation model, and the network load constraint is generated through the network load constraint generation module;

[0020] Inputting the link free travel time, link traffic flow and link capacity into the link travel time constraint generation module of the traffic network constraint generation model to generate the link travel time constraint;

[0021] The link capacity is output to the link reverse constraint generation module of the transportation network constraint generation model to generate the link reverse constraint.

[0022] In an exemplary embodiment, the circuit load data includes node sets, generator power, load demand, and power shortage information;

[0023] The circuit load data is input into the pre-built circuit constraint generation model, and the linear power flow constraints, line operation constraints and line switching constraints are obtained through the circuit constraint generation model, including:

[0024] Inputting node set, generator power, load demand and power shortage information into a linear power flow constraint generation module of a circuit constraint generation model, and obtaining a linear power flow constraint through the linear power flow constraint generation module;

[0025] Inputting the node set, the generator power and the power shortage information into the line operation constraint generation module of the circuit constraint generation model, and obtaining the line operation constraint through the line operation constraint generation module;

[0026] The generator power and load demand are input into the line switching constraint generation module of the circuit constraint generation model, and the line switching constraint is obtained through the line switching constraint generation module.

[0027] In one embodiment, the charging load data includes: average charging waiting time, charging capacity, charging power, and charging pile resources in use;

[0028] Input charging load data into the pre-built charging constraint generation model, and obtain charging operation constraints and charging pile management constraints through the charging constraint generation model, including:

[0029] The average charging waiting time, charging capacity, charging power and charging pile resources are input into the charging operation constraint generation module of the charging constraint generation model, and the charging operation constraint is obtained through the charging operation constraint generation module;

[0030] The charging pile resources are input into the charging pile management constraint generation module of the charging constraint generation model, and the charging pile management constraints are obtained through the charging pile management constraint generation module.

[0031] In a second aspect, the present application also provides a configuration device for improving the resilience of a distribution network-transportation network, including:

[0032] A data acquisition module, used to acquire traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, and to acquire the travel time of the distribution network-transportation network, the power generation power of the power grid and the power consumption power of the power grid;

[0033] A constraint generation module, for inputting traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints and link reverse constraints through the traffic network constraint generation model, inputting circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints and line switching constraints through the circuit constraint generation model, and inputting charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model;

[0034] The target configuration information acquisition module is used to input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into the pre-built coordinated optimization model, and obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the target cost function in the coordinated optimization model reaches the minimum value, and the link travel time constraint, link reverse constraint, linear power flow constraint, line operation constraint, line switching constraint, charging operation constraint and charging pile management constraint are satisfied;

[0035] A configuration module is used to adjust the target travel time of the transportation network to the transportation network travel time of the distribution network-transportation network, adjust the target power generation power of the power grid to the power generation power of the distribution network-transportation network, and adjust the target power consumption of the power grid to the power consumption power of the distribution network-transportation network.

[0036] In a third aspect, the present application further provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0037] Obtaining traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, power generation power of the power grid and power consumption power of the power grid of the distribution network-transportation network;

[0038] Inputting traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints, and link reverse constraints through the traffic network constraint generation model, inputting circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints, and line switching constraints through the circuit constraint generation model, and inputting charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model;

[0039] Input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into the pre-built coordinated optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the network load constraint, the link travel time constraint, the link reverse constraint, the linear power flow constraint, the line operation constraint, the line switching constraint, the charging operation constraint and the charging pile management constraint are satisfied and the target cost function in the coordinated optimization model reaches the minimum value;

[0040] The target travel time of the transportation network is adjusted to the travel time of the transportation network of the distribution network-transportation network, the target power generation power of the power grid is adjusted to the power generation power of the distribution network-transportation network, and the target power consumption power of the power grid is adjusted to the power consumption power of the distribution network-transportation network.

[0041] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the following steps are implemented:

[0042] Obtaining traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, power generation power of the power grid and power consumption power of the power grid of the distribution network-transportation network;

[0043] Inputting traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints, and link reverse constraints through the traffic network constraint generation model, inputting circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints, and line switching constraints through the circuit constraint generation model, and inputting charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model;

[0044] Input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into the pre-built coordinated optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the network load constraint, the link travel time constraint, the link reverse constraint, the linear power flow constraint, the line operation constraint, the line switching constraint, the charging operation constraint and the charging pile management constraint are satisfied and the target cost function in the coordinated optimization model reaches the minimum value;

[0045] The target travel time of the transportation network is adjusted to the travel time of the transportation network of the distribution network-transportation network, the target power generation power of the power grid is adjusted to the power generation power of the distribution network-transportation network, and the target power consumption power of the power grid is adjusted to the power consumption power of the distribution network-transportation network.

[0046] In a fifth aspect, the present application further provides a computer program product, including a computer program, which implements the following steps when executed by a processor:

[0047] Obtaining traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, power generation power of the power grid and power consumption power of the power grid of the distribution network-transportation network;

[0048] Inputting traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints, and link reverse constraints through the traffic network constraint generation model, inputting circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints, and line switching constraints through the circuit constraint generation model, and inputting charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model;

[0049] Input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into the pre-built coordinated optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the network load constraint, the link travel time constraint, the link reverse constraint, the linear power flow constraint, the line operation constraint, the line switching constraint, the charging operation constraint and the charging pile management constraint are satisfied and the target cost function in the coordinated optimization model reaches the minimum value;

[0050] The target travel time of the transportation network is adjusted to the travel time of the transportation network of the distribution network-transportation network, the target power generation power of the power grid is adjusted to the power generation power of the distribution network-transportation network, and the target power consumption power of the power grid is adjusted to the power consumption power of the distribution network-transportation network.

[0051] The above-mentioned configuration method, device, computer equipment, computer-readable storage medium and computer program product for improving the resilience of the distribution network-transportation network obtains the transportation network flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, power grid power generation power and power grid power consumption of the distribution network-transportation network, inputs the transportation network flow data into a pre-constructed transportation network constraint generation model to obtain network load constraints, link travel time constraints and link reverse constraints, inputs the circuit load data into a pre-constructed circuit constraint generation model to obtain linear power flow constraints, line operation constraints and line switching constraints, and The charging load data is input into the pre-built charging constraint generation model to obtain the charging operation constraints and charging pile management constraints. The travel time of the transportation network, the power generation power of the power grid and the power consumption of the power grid are input into the pre-built coordination optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption of the power grid when the target cost function in the coordination optimization model reaches the minimum value, and finally the corresponding data in the distribution network-transportation network are adjusted to the target data obtained above. By simultaneously studying the relevant data of the distribution network and the transportation network, and obtaining the corresponding constraints in the corresponding time period, and under the constraints of the constraints, the target travel time in the transportation network, the target power generation power and the target power consumption in the distribution network are obtained when the target cost function in the coordination optimization model reaches the minimum value, and corresponding adjustments are made, considering the coupling effect between the distribution network and the transportation network, so as to obtain more accurate and more suitable target data for the current coupling state, and the resource utilization of the entire system is greatly improved after the data adjustment. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the drawings required for use in the embodiments of the present application or related technical descriptions will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0053] Figure 1 An application environment diagram of a configuration method for improving the resilience of a distribution network-transportation network in one embodiment;

[0054] Figure 2 A schematic diagram of a flow chart of a configuration method for improving the resilience of a distribution network-transportation network in one embodiment;

[0055] Figure 3 A flow chart of a configuration method for improving the resilience of a distribution network-transportation network in another embodiment;

[0056] Figure 4 A topological diagram of a 33-node distribution network in one embodiment;

[0057] Figure 5 A topological diagram of a transportation network and a distribution network under an extreme event in another embodiment;

[0058] Figure 6a Active power shortage under a static model and a dynamic model in one embodiment;

[0059] Figure 6b Active power shortage under a static model and a dynamic model in another embodiment;

[0060] Figure 7a is a charging power diagram at different time intervals in one embodiment;

[0061] Figure 7b is a charging power diagram at different time intervals in another embodiment;

[0062] Figure 8a is a line comparison diagram of the performance of a transportation network and a distribution network under different ωT and ωj in one embodiment;

[0063] Figure 8b is a line comparison diagram of the performance of the transportation network and the distribution network under different ωT and ωj in another embodiment;

[0064] Fig. 9 is a diagram comparing system performance under different resource levels in one embodiment;

[0065] Fig.10 A structural block diagram of a configuration device for improving the resilience of a distribution network-transportation network in one embodiment;

[0066] Fig.11 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0067] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0068] The configuration method for improving the resilience of the distribution network-transportation network provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the distribution network-transportation network communicates with the server 102 through the network. The data storage system can store the data that the server 102 needs to process. The data storage system can be integrated on the server 102, or it can be placed on the cloud or other network servers. The server 102 first obtains the traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, grid power generation power and grid power consumption of the distribution network-transportation network, and then inputs the traffic flow data into the pre-built transportation network constraint generation model to obtain the network load constraint, link travel time constraint and link reverse constraint, inputs the circuit load data into the pre-built circuit constraint generation model to obtain the linear power flow constraint, line operation constraint and line switching constraint, and inputs the charging load data into the pre-built charging constraint generation model to obtain the charging operation constraint and charging pile management constraint After that, the travel time of the traffic network, the power generation power of the power grid and the power consumption of the power grid are input into the pre-built coordination optimization model to obtain the target travel time of the traffic network, the target power generation power of the power grid and the target power consumption of the power grid when the target cost function in the coordination optimization model reaches the minimum value, and finally the travel time of the traffic network, the power generation power of the power grid and the power consumption of the power grid are adjusted to the target travel time of the traffic network, the target power generation power of the power grid and the target power consumption of the power grid. Among them, the distribution network-traffic network can be understood as a cluster of buildings in a certain area, including the road information and the power grid structure information in the cluster, which are coupled with each other to work together to maintain the operation of the system. Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0069] In an exemplary embodiment, Figure 2 As shown in the figure, a configuration method for improving the resilience of the distribution network-transportation network is provided. Figure 1 The server 102 in the example is used as an example to illustrate the method, which includes the following steps S201 to S204. Among them:

[0070] Step S201 obtains the traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, grid power generation power and grid power consumption power of the distribution network-transportation network.

[0071] Among them, traffic flow data can be understood as data used to describe road condition information, including vehicle flow, pedestrian flow, corresponding road path information, the time consumed by each link, and the maximum number of pedestrian and vehicle flows that each link can accommodate; circuit load data can be understood as the power consumption or power generation at each node on the distribution network, as well as the difference between power consumption and power generation; charging load data can be understood as the time cost, electricity cost and charging pile resource cost consumed by charging electric vehicles that use electricity to operate; and the travel time of the transportation network can be understood as the total time consumed to complete all links.

[0072] Optionally, the server 102 obtains the traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured through the communication network, and simultaneously obtains the transportation network travel time, grid power generation power and grid power consumption of the distribution network-transportation network, thereby laying a data foundation for the subsequent acquisition of three sets of constraint conditions corresponding to this distribution network-transportation network and the target data information to be configured, and improving the accuracy of the target data information by ensuring the timeliness of the constraint conditions.

[0073] Step S202, input the traffic flow data into a pre-built traffic network constraint generation model, obtain the network load constraint, link travel time constraint and link reverse constraint through the traffic network constraint generation model, input the circuit load data into a pre-built circuit constraint generation model, obtain the linear power flow constraint, line operation constraint and line switching constraint through the circuit constraint generation model, and input the charging load data into a pre-built charging constraint generation model, obtain the charging operation constraint and charging pile management constraint through the charging constraint generation model.

[0074] Among them, the network load constraint can be understood as the maximum link flow that the transportation network can accommodate, the link travel time constraint can be understood as the maximum time limit for completing all links in the transportation network, and the link reverse constraint can be understood as limiting how many links can achieve a to b and b to a; the linear power flow constraint can be understood as constraining the power flow in the distribution network, the line operation constraint can be understood as constraining the line operation conditions and operation restrictions, and the line switching constraint can be understood as restricting the mutual switching between lines; the charging operation constraint can be understood as constraining the operating conditions and operation restrictions of the electric vehicle charging, and the charging pile management constraint can be understood as limiting the number of charging piles deployed;

[0075] The traffic network constraint generation model, the circuit constraint generation model and the charging constraint generation model can be understood as pre-built mathematical models.

[0076] For example, server 102 inputs traffic quantity data into a pre-built traffic network constraint generation model, and brings the corresponding constraint construction formula therein to obtain the network load constraints, link travel time constraints and link reverse constraints corresponding to the distribution network-traffic network in the current state, and inputs circuit load data into a pre-built circuit constraint generation model, and brings the corresponding constraint construction formula therein to obtain the corresponding linear power flow constraints, line operation constraints and line switching constraints, and inputs charging load data into a pre-built charging constraint generation model, and brings the corresponding constraint construction formula therein to obtain the corresponding charging operation constraints and charging management constraints, so as to provide constraint conditions that are more suitable for the current application scenario for the subsequent acquisition of more accurate target data, thereby improving the adaptability of the output target data to the current environment.

[0077] Step S203, input the travel time of the transportation network, the power generation power of the power grid and the power consumption of the power grid into a pre-constructed coordinated optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption of the power grid that satisfy the network load constraints, link travel time constraints, link reverse constraints, linear power flow constraints, line operation constraints, line switching constraints, charging operation constraints and charging pile management constraints, and when the target cost function in the coordinated optimization model reaches the minimum value.

[0078] Step S204, adjust the target travel time of the transportation network to the transportation network travel time of the distribution network-transportation network, adjust the target power generation power of the power grid to the power generation power of the distribution network-transportation network, and adjust the target power consumption of the power grid to the power consumption of the distribution network-transportation network.

[0079] Optionally, the server 102 inputs the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into a pre-built coordination optimization model, obtains the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the target cost function in the coordination optimization model reaches the minimum value, and adjusts the link travel time of each link in the distribution network-transportation network so that the total travel time is ultimately equal to the target travel time of the transportation network, adjusts the power generation power of each power generation node in the distribution network-transportation network so that the total power generation is equal to the target power generation power of the power grid, adjusts the power consumption power of each power consumption node in the distribution network-transportation network so that the total power consumption is equal to the target power consumption power of the power grid, and the target power generation power of the power grid is equal to the target power consumption power of the power grid, and the system reaches a balance. Pay attention to the coupling effect between the distribution network and the transportation network, obtain target data that is more in line with the current application scenario, and thereby improve the resource utilization rate of the system and the cyclic operation effect of the system.

[0080] In the above configuration method for improving the resilience of the distribution network-transportation network, the transportation network flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, power grid power generation power and power grid power consumption of the distribution network-transportation network are obtained, and the transportation network flow data is input into a pre-constructed transportation network constraint generation model to obtain network load constraints, link travel time constraints and link reverse constraints, and the circuit load data is input into a pre-constructed circuit constraint generation model to obtain linear power flow constraints, line operation constraints and line switching constraints, and the charging load data is input into a pre-constructed charging constraint generation model to obtain charging operation constraints and charging pile management constraints, and the transportation network travel time, power grid power generation power and power grid power consumption are input into a pre-constructed coordination optimization model to obtain the transportation network target travel time, power grid target power generation power and power grid target power consumption that meet the network load constraints, link travel time constraints, link reverse constraints, linear power flow constraints, line operation constraints, line switching constraints, charging operation constraints and charging pile management constraints, and the target cost function in the coordination optimization model reaches the minimum value, and finally the corresponding data in the distribution network-transportation network are adjusted to the target data obtained above. By simultaneously studying the relevant data of the distribution network and the transportation network, and obtaining the corresponding constraints within the corresponding time period, and under the constraints of the constraints, the target travel time in the transportation network, the target power generation power and the target power consumption in the distribution network are obtained when the target cost function in the coordinated optimization model reaches the minimum value, and corresponding adjustments are made, considering the coupling effect between the distribution network and the transportation network, so as to obtain more accurate and more suitable target data for the current coupling state. After data adjustment, the resource utilization of the entire system is greatly improved.

[0081] In one embodiment, the coordinated optimization model is constructed by the following steps:

[0082] The set constraints in the constraint module in the coordinated optimization model to be trained are linearized to obtain linearized constraints; the nonlinear target cost function in the coordinated optimization model to be trained is linearized to obtain the target cost function; the coordinated optimization model is constructed based on the linearized constraints and the target cost function, as well as the coordinated optimization model to be trained.

[0083] Optionally, the server 102 linearizes the set constraints in the constraint module in the coordinated optimization model to be trained to obtain linearized constraints, then linearizes the nonlinear target cost function in the coordinated optimization model to be trained to obtain the target cost function, and finally constructs the coordinated optimization model according to the linearized constraints and the target cost function, as well as the coordinated optimization model to be trained. By linearizing the constraints and the target cost function, the progress of obtaining the target cost function is accelerated, thereby improving the speed of obtaining the target travel time of the transportation network, the target power generation power of the power grid, and the target power consumption of the power grid.

[0084] In one of the embodiments, the set constraints in the constraint module in the coordinated optimization model to be trained are linearized to obtain linearized constraints, including: reducing the number of variables included in the set constraints through a Gray coding method and an adaptive piecewise linear weighting method to obtain constraints; and linearizing the constraints to generate linearized constraints.

[0085] Among them, the Gray coding method can be understood as a special binary coding method, whose main feature is that there is only one bit difference between two adjacent code values, thereby reducing the number of variables.

[0086] For example, let Φ:{1,2,...,M}→{0,1} is an injective function, and the vectors Φ(m) and Φ(m+1) differ only in one component. By using binary variables, the SOS2 condition is satisfied by the following constraints:

[0087]

[0088] This method reduces the number of variables from M to Improved computing efficiency.

[0089] B) Adaptive piecewise linear weighting (AAPWL) method:

[0090] AAPWL is used to meet the requirements of approximation accuracy by adding the minimum number of breakpoints. For a continuous and monotonic function, the maximum approximation error occurs at the point where the tangent slope of the original function is equal to the tangent slope of the linear approximation function. Therefore, the maximum approximation error in each interval is calculated based on the positions of the two endpoints. In order to obtain the minimum number of breakpoints, we set the maximum error according to the approximation accuracy and then add breakpoints adaptively, as shown in Algorithm 1. ε is the maximum approximation error. and are the lower and upper bounds of ua, respectively. ustand,k is the selected reference value used to calculate the relative error. P is the set of breakpoints. In equations (58) and (60), g(u) is the linear approximation function and u* is the maximum error point. By combining AAPWL with Gray code-based encoding, both the SOS2 variables and the auxiliary binary variables are reduced.

[0091]

[0092] C) Linearization of quadratic mixed integer problem:

[0093] After linearizing the piecewise function, the remaining nonlinear terms are binary variables λa, λa', With continuous variables The product of ua(t). For example, due to By introducing auxiliary variables Linearize and add the following constraints:

[0094] γ≤λ a

[0095]

[0096] By using Gray coding and adaptive piecewise weighting methods, the number of variables in the constraint conditions is greatly reduced, thereby improving the credibility of the final output data.

[0097] In an exemplary embodiment, the traffic flow data includes the flow of electric vehicles and gasoline vehicles on the link, the flow into the link, the flow out of the link, the travel path set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand; the free travel time of the link, the traffic flow of the link, and the capacity of the link;

[0098] Traffic flow data is input into the pre-built traffic network constraint generation model, and the network load constraint, link travel time constraint and link reverse constraint are obtained through the traffic network constraint generation model, including:

[0099] The flow of electric vehicles and gasoline vehicles on the link, the flow flowing into the link, the flow flowing out of the link, the travel link set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand are input into the network load constraint generation module of the transportation network constraint generation model, and the network load constraint is generated through the network load constraint generation module; the link free travel time, link traffic flow and link capacity are input into the link travel time constraint generation module of the transportation network constraint generation model to generate the link travel time constraint; the link capacity is output to the link reverse constraint generation module of the transportation network constraint generation model to generate the link reverse constraint.

[0100] Optionally, the server 102 inputs the flow of electric vehicles and gasoline vehicles on the link, the flow into the link, the flow out of the link, the travel link set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand into the dynamic traffic network model with the traffic link reversed. The traffic network can be represented by a graph: G'T = [T'N, T'A], where T'N = [T'N, A, T'N, V], T'A = [T'A, G, T'A, C, T'A, A]. Where T'N, A and T'A, G are real intersections and roads in the traffic network, and T'N, V, T'A, C, T'A, A are virtual nodes and virtual links. If the electric car is charging, it will pass through the link T'A, C, otherwise it will pass through the link T'A, A. The travel demand of the car is represented by the OD pair representing the starting point and the midpoint as (r, s), where r, s∈T'N, A. Therefore, taking into account the travel of fuel-consuming vehicles and electric vehicles, the dynamic traffic network model is finally constructed as follows:

[0101]

[0102]

[0103] Among them, xEV / xGV is the flow of electric vehicles and gasoline vehicles on the link, uEV / uGV is the flow into the link, vEV / vGV is the flow out of the link, KEV(a) / KGV(a) is the set of travel paths of electric vehicles and gasoline vehicles whose paths pass through node a, and equations (1-5) describe the relationship between time-varying flow, inflow, and outflow in the transportation network. I(n), O(n) are the sets of links with head node n or tail node n respectively, fEV / fGV is the departure volume from the starting point at a certain moment, EEV / EGV is the total number of arrivals at a certain place within a specific time period, and equations (6-11) are the flow conservation constraints of the transportation network. qEV / qGV represents travel demand, and the meaning of equations (12-13) is that the travel demand is for all travel paths with the same starting point and end point. Equations (14-15) describe the flow propagation based on the travel time of the transportation network links, including arriving at the end point or the path passing through link a. Represents the subpath from the end of the link to the end of the path. Equations (16-17) are the boundary constraints of each variable.

[0104] Among them, the link travel time is determined by the link performance function. This method adopts the public road bureau model, and its function description is as follows:

[0105]

[0106] Link reversal can improve the performance degradation of the traffic network when there is a fault by changing the lane and allowing the driving direction. By introducing an auxiliary binary 0-1 variable λ a To characterize the connection reversal situation, the capacity of the traffic network link is as follows:

[0107]

[0108] Where ca represents the link capacity, a' represents the reverse direction of the link, and λa=1 represents the implementation of link reversal, so the link reversal constraint is as follows:

[0109] λ a +λ a′ ≤≤ (20)

[0110]

[0111] The link reversal constraint means that the reverse directions of links cannot be reversed at the same time, and the number of reverse links has an upper limit NTN.

[0112] Through the above method, the current constraints that the transportation network should meet can be updated in a timely manner, ensuring the timeliness of the constraints, thereby improving the credibility of the final output data.

[0113] In one embodiment, the circuit load data includes node sets, generator power, load demand, and power shortage information;

[0114] The circuit load data is input into the pre-built circuit constraint generation model, and the linear power flow constraints, line operation constraints and line switching constraints are obtained through the circuit constraint generation model, including:

[0115] The node set, generator power, load demand and power shortage information are input into the linear power flow constraint generation module of the circuit constraint generation model, and the linear power flow constraint is obtained through the linear power flow constraint generation module; the node set, generator power and power shortage information are input into the line operation constraint generation module of the circuit constraint generation model, and the line operation constraint is obtained through the line operation constraint generation module; the generator power and load demand are input into the line switching constraint generation module of the circuit constraint generation model, and the line switching constraint is obtained through the line switching constraint generation module.

[0116] For example, the server 102 inputs the node set, generator power, load demand and power shortage information into a multi-period distribution network model considering line switching of the power distribution network. The power flow equation of the model adopts a linear distribution network model. Since this problem mainly considers the limitation of voltage amplitude and power shortage, the linear distribution network model has a high accuracy when calculating the voltage amplitude. Therefore, the multi-period distribution network model considering line switching of the power distribution network is finally constructed as follows:

[0117]

[0118] A(j) / B(j) represents the set of lines whose starting or ending node is j, EB / EL is the set of distribution network nodes, pG / qG is the generator power, pL / qL is the load demand, pS / qS is the power shortage, and constraints (22) and (23) describe the power balance of distribution network nodes. (24-25) A large number M is used to describe that if the line is disconnected, the voltage between nodes no longer meets the power flow constraint. The dynamic load of the transportation-distribution network includes the line load and the charging load of electric vehicles:

[0119]

[0120] The constraints are as follows:

[0121]

[0122] The above constraints are line transmission power constraints, generator power constraints (including ramp power constraints) and voltage amplitude constraints. The power shortage and line switching modeling is as follows:

[0123]

[0124] ∑μ ij ≤N DN (35)

[0125] The above method can timely update the constraints that the distribution network should currently meet, ensuring the timeliness of the constraints and thus improving the credibility of the final output data.

[0126] In one embodiment, the charging load data includes: average charging waiting time, charging capacity, charging power and charging pile resources in use;

[0127] Input charging load data into the pre-built charging constraint generation model, and obtain charging operation constraints and charging pile management constraints through the charging constraint generation model, including:

[0128] The average charging waiting time, charging capacity, charging power and charging pile resources are input into the charging operation constraint generation module of the charging constraint generation model, and the charging operation constraints are obtained through the charging operation constraint generation module; the charging pile resources are input into the charging pile management constraint generation module of the charging constraint generation model, and the charging pile management constraints are obtained through the charging pile management constraint generation module.

[0129] Optionally, the server 102 inputs the average charging waiting time, charging capacity, charging power and charging pile resources into a fast charging station model including charging pile management. and waiting time The modeling is as follows:

[0130]

[0131] in is the average waiting time for charging at link a, obtained from historical data. Ee is the charging capacity, and Pe is the charging power.

[0132] The charging load of the fast charging pile is as follows:

[0133]

[0134] in is the charging pile resource put into use, and da(t) is the charging flow of the link. Constraint (38) describes that the charging flow is restricted when the flow of electric vehicles in link a is greater than the charging pile resource put into use. The charging pile management is modeled as follows:

[0135]

[0136] in is an auxiliary binary variable used to characterize the charging pile control decision. This decision affects the charging time and charging path, thus affecting the operation of the transportation-distribution network.

[0137] Through the above method, the current constraints that should be met by electric vehicle charging in the distribution network-transportation network can be updated in a timely manner, taking into account the coupling effect between the distribution network and the transportation network, while ensuring the timeliness of the constraints, thereby improving the credibility of the final output data.

[0138] In an exemplary embodiment, Figure 3 As shown, a specific implementation method for configuring a method for improving the resilience of a distribution network-transportation network is provided:

[0139] 1) First, construct a dynamic traffic network model that considers the reverse direction of traffic links. The traffic network can be represented by a graph: G'T = [T'N, T'A], where T'N = [T'N, A, T'N, V], T'A = [T'A, G, T'A, C, T'A, A]. T'N, A and T'A, G are real intersections and roads in the traffic network, and T'N, V, T'A, C, T'A, A are virtual nodes and virtual links. If the electric car is charging, it will pass through the link T'A, C, otherwise it will pass through the link T'A, A. The travel demand of the car is represented by the OD pair representing the starting point and the midpoint as (r, s), where r, s∈T'N, A. Therefore, considering the travel of fuel-consuming cars and electric cars, the dynamic traffic network model is finally constructed as follows:

[0140]

[0141]

[0142] Among them, xEV / xGV is the flow of electric vehicles and gasoline vehicles on the link, uEV / uGV is the flow into the link, vEV / vGV is the flow out of the link, KEV(a) / KGV(a) is the set of travel paths of electric vehicles and gasoline vehicles whose paths pass through node a, and equations (1-5) describe the relationship between time-varying flow, inflow, and outflow in the transportation network. I(n), O(n) are the sets of links with head node n or tail node n respectively, fEV / fGV is the departure volume from the starting point at a certain moment, EEV / EGV is the total number of arrivals at a certain place within a specific time period, and equations (6-11) are the flow conservation constraints of the transportation network. qEV / qGV represents travel demand, and the meaning of equations (12-13) is that the travel demand is for all travel paths with the same starting point and end point. Equations (14-15) describe the flow propagation based on the travel time of the transportation network links, including arriving at the end point or the path passing through link a. Represents the subpath from the end of the link to the end of the path. Equations (16-17) are the boundary constraints of each variable.

[0143] Among them, the link travel time is determined by the link performance function. This method adopts the public road bureau model, and its function description is as follows:

[0144]

[0145] Link reversal can improve the performance degradation of the traffic network when a fault occurs by changing the lane's allowed driving direction. By introducing an auxiliary binary 0-1 variable λa to characterize the connection reversal situation, the capacity of the traffic network link is as follows:

[0146]

[0147] Where ca represents the link capacity, a' represents the reverse direction of the link, and λa=1 represents the implementation of link reversal, so the link reversal constraint is as follows:

[0148] λ a +λ a′ ≤1 (20)

[0149]

[0150] The link reversal constraint means that the reverse directions of links cannot be reversed at the same time, and the number of reverse links has an upper limit NTN.

[0151] 2) Construct a multi-period distribution network model that considers line switching of the power distribution network. The power flow equation of this model adopts a linear distribution network model. Since this problem mainly considers the limitation of voltage amplitude and power shortage, the linear distribution network model has high accuracy in calculating the voltage amplitude.

[0152] Therefore, the multi-period distribution network model considering line switching of the power distribution network is finally constructed as follows:

[0153]

[0154] A(j) / B(j) represents the set of lines whose starting or ending node is j, EB / EL is the set of distribution network nodes, pG / qG is the generator power, pL / qL is the load demand, pS / qS is the power shortage, and constraints (22) and (23) describe the power balance of distribution network nodes. (24-25) A large number M is used to describe that if the line is disconnected, the voltage between nodes no longer meets the power flow constraint. The dynamic load of the transportation-distribution network includes the line load and the charging load of electric vehicles:

[0155]

[0156] The constraints are as follows:

[0157]

[0158] The above constraints are line transmission power constraints, generator power constraints (including ramp power constraints) and voltage amplitude constraints. The power shortage and line switching modeling is as follows:

[0159]

[0160] ∑μ ij ≤N DN (35)

[0161] 3) Construct a fast charging station model including charging pile management. Charging time using a fast charging station and waiting time The modeling is as follows:

[0162]

[0163] in is the average waiting time for charging at link a, obtained from historical data. Ee is the charging capacity, and Pe is the charging power.

[0164] The charging load of the fast charging pile is as follows:

[0165]

[0166] in is the charging pile resource put into use, and da(t) is the charging flow of the link. Constraint (38) describes that the charging flow is restricted when the flow of electric vehicles in link a is greater than the charging pile resource put into use. The charging pile management is modeled as follows:

[0167]

[0168] in is an auxiliary binary variable used to characterize the charging pile control decision. This decision affects the charging time and charging path, thus affecting the operation of the transportation-distribution network.

[0169] 4) Modeling of network coordination optimization problem. The goal of this problem is to minimize the performance loss of the transportation network and the distribution network, so the objective function is as follows:

[0170]

[0171] Where F is the cost function, the first term is the penalty cost caused by the shortage of active and reactive power, and the second term is the total travel cost. is the unit penalty coefficient for active and reactive power shortage, and ωT is the monetary value of travel time. The optimization principle is that all traveling vehicles jointly optimize the route selection to achieve the minimum total cost. For the above objective function (40), combined with the previous transportation network and distribution network model, the constraints of the optimization problem are:

[0172] A) Transportation Network:

[0173] Network load constraints (1)-(17).

[0174] Link travel time constraints (18).

[0175] Link reverse constraints (19)-(21).

[0176] B) Distribution Network:

[0177] Linear power flow constraints (22)-(26).

[0178] Run constraints (27)-(34).

[0179] Line switching constraints (35).

[0180] C) Distribution network:

[0181] Run constraints (36)-(38).

[0182] Charging pile management constraints (39).

[0183] Therefore, the coordination optimization problem is constructed as a MINLP problem.

[0184] ②The MINLP problem in the previous article is transformed into a MILP problem by using the linearization method.

[0185] 1) Linearization of travel time in the transportation network.

[0186] In the objective function (50), τ a (t)u a (t) represents the total travel time in link a. By substituting into equations (28)-(29), (46), (49), it is finally expressed as:

[0187]

[0188] Where Ma is a large constant. Because when the link is reversed, the travel time tends to be large. The nonlinear equation is as follows:

[0189]

[0190] Equations f1, f2, f3, and f4 are all high-order nonlinear functions, so the piecewise linearization method is used:

[0191]

[0192] in is the breakpoint between each cell, is a special ordered set of type 2 (SOS2), where all variables are non-negative and at most two adjacent variables are non-zero. Then its variables are derived as follows:

[0193]

[0194]

[0195] in is an auxiliary binary variable.

[0196] A) Gray code based coding scheme:

[0197] Let Φ:{1,2,...,M}→{0,1} is an injective function, and the vectors Φ(m) and Φ(m+1) differ only in one component. By using binary variables, the SOS2 condition is satisfied by the following constraints:

[0198]

[0199] Compared with (51-55), this method reduces the number of variables from M to Improved computing efficiency.

[0200] B) Adaptive piecewise linear weighting (AAPWL) method:

[0201] AAPWL is used to meet the requirements of approximation accuracy by adding the minimum number of breakpoints. For a continuous and monotonic function, the maximum approximation error occurs at the point where the tangent slope of the original function is equal to the tangent slope of the linear approximation function. Therefore, the maximum approximation error in each interval is calculated based on the positions of the two endpoints. In order to obtain the minimum number of breakpoints, we set the maximum error according to the approximation accuracy and then add breakpoints adaptively, as shown in Algorithm 1. ε is the maximum approximation error. and are the lower and upper bounds of ua, respectively. ustand,k is the selected reference value used to calculate the relative error. P is the set of breakpoints. In equations (58) and (60), g(u) is the linear approximation function and u* is the maximum error point. By combining AAPWL with Gray code-based encoding, both the SOS2 variables and the auxiliary binary variables are reduced.

[0202]

[0203] C) Linearization of quadratic mixed integer problem:

[0204] After linearizing f1, f2, f3, f4 in (41)-(42), the remaining nonlinear terms are binary variables λa, λa', With continuous variables The product of ua(t). For example, due to By introducing auxiliary variables Linearize and add the following constraints:

[0205] γ≤λ a (62)

[0206]

[0207] 2) Linearization of the minimum value.

[0208] The minimum value between the two in equation (48) is nonlinear. By introducing a large number M, it is transformed into the following constraint:

[0209] d a (t)≤xa(t) (65)

[0210]

[0211] d a (t)≥x a (t)-M a (1-μ a (t)) (67)

[0212]

[0213] where μ a (t) is a binary variable and Ma is a large constant.

[0214] 3) The overall solution process of the emergency response problem is as follows Figure 3 shown.

[0215] The nonlinear flow propagation constraints (24)-(25) are constructed by linking the variables to the travel time It is treated as a constant integer and managed by iterative updates. The final optimization model is as follows:

[0216]

[0217] .t.(1)-(17),(20)-(35),(37),(41)-(49),(56),(62)-(68) (68)

[0218] Among them, (1)-(17), (20)-(35), (37) are the constraints of transportation network, distribution network, fast charging piles and emergency response resources, and (41)-(49), (56), (62)-(68) are derived from the linearization of nonlinear equations. The variables in the final model are xa(t), ua(t), va(t), da(t), τa(t), φa(t), λa, λa', μij, Uj(t), Pij(t), Qij(t), μa(t),γ, where the decision variables of emergency response resources are μij,λa,λa', and auxiliary variables μa(t) is a binary variable. Therefore, the final model is a MILP model.

[0219] As a further embodiment of the present application, see Figure 4 and Figure 5 , this embodiment involves an IEEE 33-node power distribution system and a 12-node ring traffic network. The link parameters and traffic network requirements are given in Tables 1 and 2. The traffic network baseline flow is 30 vehicles / hour. The charging capacity of the fast charging station is 10kWH and the power is 50kW. (The data appearing in the embodiment are all examples, and it is not limited to this case to implement this method only.

[0220] Table 1 Link parameters:

[0221]

[0222] Table 2: Travel demand of transportation network:

[0223]

[0224] A distributed generator is installed on bus 15, with a maximum active and reactive power output of 1MW and 1MVar, respectively. The base voltage is 12.66kV, and the base power is 10MVA. The lower and upper limits of the node voltage are 0.84pu and 1.05pu, respectively. The monetary value of travel time ωT is 0.25$ / min, and the unit compensation cost of active and reactive power shortages is 0.17$ / kW and 0.17$ / kVar, respectively. The damage scenario under extreme events is designed as follows: links 1-2 and 2-1 in the transportation network (TN), lines 14-15, 30-31 in the power distribution network (DN), and the generator on bus 15 are all out of service. The collapsed poles block links 3-4 and 4-8, and their remaining capacity is set to 77.67%. The values ​​of NTN and NDN are both 2, because the number of damaged transportation network and power distribution network components is 2. △cch a is set to 5p.u., which is 25% of the total number of charging piles. The simulation lasts for one hour, with the traffic network divided into 12 time intervals and the power distribution network divided into 6 time intervals. The following is an experimental evaluation of several aspects of the method:

[0225] 1) Computational efficiency and accuracy analysis:

[0226] Table 3 shows the comparison between different solution methods. The maximum approximation error of uniform piecewise linear (PWL) and adaptive piecewise linear (AAPWL) is set to 0.01, and the range of ua is [0,1.4ca]. Compared with uniform PWL, AAPWL reduces the number of breakpoints ua and improves the computational efficiency by 48.14%. By combining AAPWL with Gray code-based encoding, the number of auxiliary binary variables under SOS2 conditions is reduced, which correspondingly increases the computational speed by 35.09%. Table 4 shows the computational time and target value of each iteration when solving the problem by AAPWL combined with Gray code encoding. The solution process converges within three iterations, and the average computational time per iteration is 67.15 seconds. This verifies the computational efficiency of the proposed method.

[0227] To evaluate the accuracy of the linearized model, the original mixed integer nonlinear programming (MINLP) problem was converted into a nonlinear programming (NLP) problem, using the solution of the linearized model as the emergency response resource decision. The IPOPT solver was used to solve the NLP problem. Table 3 shows that the optimization results obtained by NLP and AAPWL combined with Gray code, i.e., TDN cost, are very close, with a relevant difference of 0.38%. Therefore, the proposed method helps operators make accurate decisions quickly, which is crucial for emergency response.

[0228] Table 3 Comparison of calculation results of different methods:

[0229]

[0230] Table 4 Computation time and results of each iteration of AAPWL based on Gray code encoding method:

[0231]

[0232] 2) Necessity of dynamic model:

[0233] In order to verify the effectiveness of the dynamic TDN model in the emergency response phase, the results are analyzed and compared with those of the static TDN model. In the static TDN model, it is assumed that the travel of electric vehicles (EVs) and generators (GVs) is completed instantly, so the traffic and power flows are constant. It should be noted that the charging pile management is a dynamic response resource and therefore cannot be used in the static model. Therefore, in order to ensure the fairness of the comparison, both the dynamic and static models are used. Set to 0.

[0234] Figure 6a and Figure 6b The active power shortage in the two models is shown. The dynamic TDN model captures the traffic propagation and state changes at different time intervals, so the power shortage varies with the spatiotemporal changes of the charging load, resulting in different power shortages at different time intervals, such as Figure 6a In contrast, the static model ignores the situation that electric vehicles are charged at different time periods, and during the simulation, the charging load of the charging pile (FCS) and the power shortage of the distribution network (DN) of each bus remain unchanged. Figure 6b As shown in Figure 2, the total active power shortage of the static model is greater than that of the dynamic model, which indicates that the static model overestimates the power shortage. Therefore, based on the dynamic model, a more accurate operating status can be obtained, thereby reducing the distribution network cost in the emergency response stage.

[0235] 3) Effectiveness of coordination and optimization:

[0236] Four cases were designed to verify the effectiveness of the collaborative optimization of emergency response resources:

[0237] Case 1: Traffic link reverse: NTN = 2, NDN = 0,

[0238] Case 2: Power line switching: NTN = 0, NDN = 2,

[0239] Case 3: Traffic link reversal and power line switching: NTN = 2, NDN = 2,

[0240] Case 4: Traffic link reversal, power line switching and charging pile management: NTN = 2, NDN = 2,

[0241] Table 5 shows the performance evaluation of TN, DN, and TDN, which are evaluated by total travel cost, compensation cost of power shortage, and the sum of the former two. Compared with the post-disaster interruption state, the total travel cost in Case 1 is smaller, thanks to the reversal of traffic links, but the power shortage is larger due to the lack of measures to restore power supply. In Case 2, the power shortage is reduced by switching power lines. The restoration of power supply in FCS3 and 4 and more charging options alleviate traffic congestion, which correspondingly improves the TN performance. In Case 3, the performance of both TN and DN is improved due to the coordination of the two emergency response resources. Compared with Case 2, the reversal of traffic links in Case 3 reduces travel costs and improves the operational performance of DN, which shows the effectiveness of the coordination of TN and DN. In Case 4, the total cost is further reduced by managing charging piles. According to Figure 7a ,The peak FCS load of bus No. 4 and No. 23 in Case 3 is high, causing a significant supply burden and a large power shortage.,By controlling the number of working charging piles of FCS 7 and 8 in Case 4, the FCS peak load is reduced and the queuing time is longer, e.g. Figure 7b As shown in Figure 3, the power shortage compensation cost in Case 4 is smaller, while the travel cost is larger, compared with that in Case 3. In summary, the TDN performance of Case 4 is the best among the four cases, which verifies the value of the coordinated optimization of different emergency resources.

[0242] Table 5 Performance comparison of TN, DN and TDN:

[0243]

[0244] 4) Sensitivity analysis:

[0245] To illustrate the coordination of TN and DN, Figure 8a and Figure 8b Shown in The total DN power shortage and average TN travel time for each vehicle vary with different ωT and ωj (assuming ωPj=ωQj=ωj) in the case of . When the monetary value of travel time ωT remains unchanged, as the compensation cost coefficient ωj increases, the power shortage decreases and the average travel time increases accordingly. This is because the TDN operator prioritizes providing power to DN loads, thereby limiting the charging service of FCS. Therefore, electric vehicles (EVs) may need to take a detour for charging. The same phenomenon occurs when ωj remains unchanged and ωT is variable. Therefore, the TDN operator makes a balance between TN and DN with performance degradation, which is consistent with the designed objective function (40). In addition, when ωj increases, Figure 8a The DN power shortage in the DN becomes stable, and when ωj decreases, Figure 8bThe smaller reduction in average travel time in the case of , means that the limit of emergency response has been reached and TDN performance can only be further improved in the long-term recovery phase.

[0246] The allocation of TDN emergency response resources, especially TN traffic links that can be reversed and DN power lines that can be switched, deserves further attention in the development of emergency response. Fig. 9 The impact of NTN and NDN on the optimization results is shown, where the TDN loss decreases with the increase in resource levels. When NTN and NDN are set to 3, the performance of DN and TN is almost restored to the original state, and the total cost of TDN is reduced by 31.93% compared with the case without increasing the response resources. After that, the marginal benefit of increasing the response resources will be small. Table 6 gives the reconstruction strategies for TN traffic links and DN power lines. Solutions with limited resources are usually subsets of solutions with abundant resources. For example, TN link 4-1 and DN lines 9-15, 18-33 are always selected as emergency response resources in different scenarios. Therefore, under limited resources, these elements are given high priority to be retained.

[0247] Table 6 Optimization options at different resource levels:

[0248]

[0249] In summary, the algorithm improves the computational efficiency when solving emergency response problems while ensuring the accuracy of the corresponding results.

[0250] Among the above algorithms, the one that is original to this application, has never been disclosed, and its working method is different from any existing literature record is: This application proposes a coordinated optimization method for utilizing emergency response resources in a transportation-power network (TDN), which coordinates TN transportation link reversal, DN power line switching, and charging pile management to enhance the resilience of TDN under extreme events. Based on the dynamic TN model and the multi-period DN model, the coordinated TDN optimization problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which is converted into a more computationally efficient mixed integer linear programming (MILP) problem by applying linearization technology.

[0251] Compared with the prior art, this application has the following advantages:

[0252] 1. The adaptive piecewise linearization method is used in combination with Gray code encoding to reduce the number of variables in the constraint conditions, thereby improving the efficiency of obtaining the target travel time of the transportation network, the target power generation efficiency of the power grid, and the target power consumption efficiency of the power grid.

[0253] 2. Comprehensively consider the coupling between the distribution network and the transportation network, and obtain various constraints in the current application environment in real time, to ensure the high reliability of the output data, thereby improving the resource utilization and operation effect of the current distribution network-transportation network.

[0254] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0255] Based on the same inventive concept, the embodiment of the present application also provides a configuration device for improving the resilience of the distribution network-transportation network for implementing the configuration method for improving the resilience of the distribution network-transportation network involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more embodiments of the configuration device for improving the resilience of the distribution network-transportation network provided below can refer to the limitations of the configuration method for improving the resilience of the distribution network-transportation network above, and will not be repeated here.

[0256] In an exemplary embodiment, Fig.10 As shown, a configuration device for improving the resilience of a distribution network-transportation network is provided, comprising: a data acquisition module 1001, a constraint generation module 1002, a target configuration information acquisition module 1003 and a configuration module 1004, wherein:

[0257] The data acquisition module 1001 is used to acquire the traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, and to acquire the transportation network travel time, power generation power of the power grid and power consumption power of the distribution network-transportation network;

[0258] The constraint generation module 1002 is used to input the traffic flow data into a pre-built traffic network constraint generation model, obtain the network load constraint, link travel time constraint and link reverse constraint through the traffic network constraint generation model, input the circuit load data into a pre-built circuit constraint generation model, obtain the linear power flow constraint, line operation constraint and line switching constraint through the circuit constraint generation model, and input the charging load data into a pre-built charging constraint generation model, obtain the charging operation constraint and charging pile management constraint through the charging constraint generation model;

[0259] The target configuration information acquisition module 1003 is used to input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into the pre-built coordinated optimization model, and obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the target cost function in the coordinated optimization model reaches the minimum value while satisfying the network load constraint, the link travel time constraint, the link reverse constraint, the linear power flow constraint, the line operation constraint, the line switching constraint, the charging operation constraint and the charging pile management constraint;

[0260] Configuration module 1004 is used to adjust the target travel time of the transportation network to the transportation network travel time of the distribution network-transportation network, adjust the target power generation power of the power grid to the power generation power of the distribution network-transportation network, and adjust the target power consumption of the power grid to the power consumption of the distribution network-transportation network.

[0261] In one embodiment, the configuration device for improving the resilience of the distribution network-transportation network also includes a model construction module, which is used to linearize the set constraints in the constraint module in the coordinated optimization model to be trained to obtain linearized constraints; linearize the nonlinear target cost function in the coordinated optimization model to be trained to obtain the target cost function; and construct a coordinated optimization model based on the linearized constraints and the target cost function, as well as the coordinated optimization model to be trained.

[0262] In one of the embodiments, the model building module is also used to reduce the number of variables included in the set constraints through a Gray coding method and an adaptive piecewise linear weighting method to obtain constraints; linearize the constraints to generate linearized constraints.

[0263] In an exemplary embodiment, traffic flow data includes the flow of electric vehicles and oil vehicles on the link, the flow flowing into the link, the flow flowing out of the link, the travel path set of electric vehicles and oil vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and travel demand; the free travel time of the link, the link traffic flow, and the link capacity. The constraint generation module 1002 includes a first constraint generation submodule, which is used to input the flow of electric vehicles and oil vehicles on the link, the flow flowing into the link, the flow flowing out of the link, the travel link set of electric vehicles and oil vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and travel demand into the network load constraint generation module of the transportation network constraint generation model, and generate network load constraints through the network load constraint generation module; input the link free travel time, link traffic flow and link capacity into the link travel time constraint generation module of the transportation network constraint generation model to generate link travel time constraints; output the link capacity to the link reverse constraint generation module of the transportation network constraint generation model to generate link reverse constraints.

[0264] In one embodiment, the circuit load data includes a node set, generator power, load demand and power shortage information. The constraint generation module 1002 also includes a second constraint generation submodule, which is used to input the node set, generator power, load demand and power shortage information into the linear power flow constraint generation module of the circuit constraint generation model, and obtain the linear power flow constraint through the linear power flow constraint generation module; input the node set, generator power and power shortage information into the line operation constraint generation module of the circuit constraint generation model, and obtain the line operation constraint through the line operation constraint generation module; input the generator power and load demand into the line switching constraint generation module of the circuit constraint generation model, and obtain the line switching constraint through the line switching constraint generation module.

[0265] In one embodiment, the charging load data includes: average charging waiting time, charging capacity, charging power and charging pile resources in use. The constraint generation module 1002 also includes a second constraint generation submodule, which is used to input the average charging waiting time, charging capacity, charging power and charging pile resources into the charging operation constraint generation module of the charging constraint generation model, and obtain the charging operation constraints through the charging operation constraint generation module; input the charging pile resources into the charging pile management constraint generation module of the charging constraint generation model, and obtain the charging pile management constraints through the charging pile management constraint generation module.

[0266] Each module in the above-mentioned configuration device for improving the resilience of the distribution network-transportation network can be implemented in whole or in part by software, hardware, and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0267] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.11 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store traffic flow data, circuit load data, charging load data, traffic network travel time, power generation power of the power grid and power consumption power of the power grid. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a configuration method for improving the resilience of the distribution network-transportation network is implemented.

[0268] Those skilled in the art will understand that Fig.11 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0269] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the configuration method for improving the resilience of the distribution network-transportation network of the above embodiment is implemented.

[0270] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the configuration method for improving the resilience of the distribution network-transportation network of the above embodiment is implemented.

[0271] In one embodiment, a computer program product is provided, including a computer program, which, when executed by a processor, implements the configuration method for improving the resilience of the distribution network-transportation network of the above embodiment.

[0272] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0273] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., but are not limited to this.

[0274] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0275] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A configuration method for improving the resilience of a distribution network-transportation network, characterized in that: The method comprises: Obtaining traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, as well as the transportation network travel time, grid power generation power and grid power consumption power of the distribution network-transportation network; Inputting the traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints and link reverse constraints through the traffic network constraint generation model, inputting the circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints and line switching constraints through the circuit constraint generation model, and inputting the charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model; Input the travel time of the transportation network, the power generation power of the power grid and the power consumption of the power grid into a pre-constructed coordinated optimization model to obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption of the power grid when the target cost function in the coordinated optimization model reaches the minimum value and the network load constraint, link travel time constraint, link reverse constraint, linear power flow constraint, line operation constraint, line switching constraint, charging operation constraint and charging pile management constraint are satisfied; The target travel time of the transportation network is adjusted to the travel time of each transportation network of the distribution network-transportation network, the target power generation power of the power grid is adjusted to the power generation power of the power grid of the distribution network-transportation network, and the target power consumption of the power grid is adjusted to the power consumption power of the power grid of the distribution network-transportation network.

2. The method according to claim 1, characterized in that The coordinated optimization model is constructed by the following steps: Linearizing the set constraints in the constraint module in the coordinated optimization model to be trained to obtain linearized constraints; Linearizing the nonlinear target cost function in the coordinated optimization model to be trained to obtain the target cost function; The coordinated optimization model is constructed based on the linearized constraints, the target cost function, and the coordinated optimization model to be trained.

3. The method according to claim 2, characterized in that The linearization process of setting constraints in the constraint condition module in the coordinated optimization model to be trained to obtain linearized constraints includes: By using Gray coding method and adaptive piecewise linear weighting method, the number of variables included in the set constraint condition is reduced to obtain the constraint condition; The constraint condition is linearized to generate the linearized constraint condition.

4. The method according to claim 1, characterized in that The traffic flow data includes the flow of electric vehicles and gasoline vehicles on the link, the flow into the link, the flow out of the link, the travel path set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand; the free travel time of the link, the link traffic flow, and the link capacity; The step of inputting the traffic flow data into a pre-built traffic network constraint generation model, and obtaining a network load constraint, a link travel time constraint, and a link reverse constraint through the traffic network constraint generation model, comprises: Inputting the flow of electric vehicles and gasoline vehicles on the link, the flow of inflow link, the flow of outflow link, the travel link set of electric vehicles and gasoline vehicles, the link set, the departure volume at any time, the arrival volume at any time period, and the travel demand into the network load constraint generation module of the traffic network constraint generation model, and generating the network load constraint through the network load constraint generation module; Inputting the link free travel time, link traffic flow and link capacity into the link travel time constraint generation module of the traffic network constraint generation model to generate the link travel time constraint; The link capacity is output to the link reverse constraint generation module of the traffic network constraint generation model to generate the link reverse constraint.

5. The method according to claim 1, characterized in that The circuit load data includes node sets, generator power, load demand, and power shortage information; The circuit load data is input into a pre-built circuit constraint generation model, and a linear power flow constraint, a line operation constraint and a line switching constraint are obtained through the circuit constraint generation model, including: Inputting node set, generator power, load demand and power shortage information into a linear power flow constraint generation module of the circuit constraint generation model, and obtaining the linear power flow constraint through the linear power flow constraint generation module; Inputting the node set, generator power and power shortage information into a line operation constraint generation module of the circuit constraint generation model, and obtaining the line operation constraint through the line operation constraint generation module; The generator power and the load demand are input into the line switching constraint generation module of the circuit constraint generation model, and the line switching constraint is obtained through the line switching constraint generation module.

6. The method according to claim 1, characterized in that The charging load data includes: average charging waiting time, charging capacity, charging power and charging pile resources in use; The step of inputting the charging load data into a pre-built charging constraint generation model, and obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model, includes: Inputting the charging average waiting time, charging capacity, charging power and charging pile resources into the charging operation constraint generation module of the charging constraint generation model, and obtaining the charging operation constraint through the charging operation constraint generation module; The charging pile resources are input into the charging pile management constraint generation module of the charging constraint generation model, and the charging pile management constraint is obtained through the charging pile management constraint generation module.

7. A configuration device for improving the resilience of a distribution network-transportation network, characterized in that: The device comprises: A data acquisition module, used to acquire traffic flow data, circuit load data and charging load data of the distribution network-transportation network to be configured, and to acquire the transportation network travel time, power generation power of the power grid and power consumption power of the power grid of the distribution network-transportation network; a constraint generation module, for inputting the traffic flow data into a pre-built traffic network constraint generation model, obtaining network load constraints, link travel time constraints and link reverse constraints through the traffic network constraint generation model, inputting the circuit load data into a pre-built circuit constraint generation model, obtaining linear power flow constraints, line operation constraints and line switching constraints through the circuit constraint generation model, and inputting the charging load data into a pre-built charging constraint generation model, obtaining charging operation constraints and charging pile management constraints through the charging constraint generation model; A target configuration information acquisition module is used to input the travel time of the transportation network, the power generation power of the power grid and the power consumption power of the power grid into a pre-built coordinated optimization model, and obtain the target travel time of the transportation network, the target power generation power of the power grid and the target power consumption power of the power grid when the target cost function in the coordinated optimization model reaches the minimum value, and the link travel time constraint, link reverse constraint, linear power flow constraint, line operation constraint, line switching constraint, charging operation constraint and charging pile management constraint are satisfied; A configuration module is used to adjust the target travel time of the transportation network to the travel time of each transportation network of the distribution network-transportation network, adjust the target power generation power of the power grid to the power generation power of the distribution network-transportation network, and adjust the target power consumption of the power grid to the power consumption power of the distribution network-transportation network.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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