A method for improving the resilience of the distribution network and transportation network coupling system under extreme weather conditions

Abstract

The present invention discloses a method for improving the resilience and reinforcement of a distribution network and traffic network coupling system under extreme weather conditions, which belongs to the field of safe operation of distribution networks. The main steps include: generating several fault scenarios according to the uncertainty of distribution network faults through scenario analysis; establishing a traffic network model based on complex network theory and traffic flow theory models; analyzing the power supply capacity of the distribution network under extreme scenarios, taking the minimum system penalty cost as the objective function, coordinating power reinforcement to optimize the strategy, and establishing a distribution network and traffic network coupling system resilience improvement and reinforcement model that comprehensively considers demand response, energy storage equipment and network reconstruction; solving the model to obtain the best distribution network and traffic network coupling system resilience improvement and reinforcement strategy. The method of the present invention can effectively improve the resilience of the distribution network and traffic network coupling system, so that the system can reduce the load loss as much as possible under fault conditions, and ensure its safe operation under extreme weather conditions.

Description

Technical Field

[0001] The present invention relates to the field of safe operation of distribution networks, and in particular to a method for enhancing the toughness of a distribution network and transportation network coupling system under extreme weather conditions. Background Art

[0002] The resilience of a power system refers to the ability to reduce the losses during the failure process and restore to normal power supply as soon as possible when the power grid suffers a major disaster. As the terminal network of the power system, the distribution network is directly related to the users, and most of the existing direct line failures in extreme weather occur in the distribution network. Therefore, the distribution network is more susceptible to extreme weather. In order to prevent the damage of the distribution network in extreme weather, pre-disaster reinforcement of the distribution network is one of the effective ways to improve the distribution network's ability to cope with natural disasters. By pre-reinforcing some key infrastructure, the distribution network's ability to resist potential disasters can be improved. Obviously, for modern society, improving the resilience of the power system is of great significance. At present, the resilience improvement strategy in the power system is mainly based on post-disaster recovery measures, while there is less research on pre-disaster reinforcement measures.

[0003] Existing research on power grid resilience mainly focuses on transmission networks, while research on distribution networks is relatively rare. Traditional research on pre-disaster reinforcement of distribution networks often ignores the impact of time scale. At present, most research on pre-disaster reinforcement of distribution networks is based on the assumption that there is sufficient time, and it is assumed that relevant reinforcement measures have been taken before the extreme weather arrives. However, due to the uncertainty of extreme weather, it cannot be guaranteed that the distribution network has been reinforced before the disaster occurs. Considering the traffic volume and time changes of the transportation network, the resilience of the distribution network should be improved within a limited time. In view of this, in order to improve the above deficiencies, a method for improving the resilience of the distribution network-transportation network coupling system under extreme weather is provided to ensure the power supply safety of the distribution network system and improve the survivability of the urban power grid under extreme weather. Summary of the invention

[0004] The purpose of the present invention is to overcome the deficiencies of the prior art and provide a method for improving the toughness of a distribution network and transportation network coupling system under extreme weather conditions.

[0005] The objective of the present invention is achieved through the following technical solutions:

[0006] A method for improving the resilience of a distribution network and transportation network coupling system under extreme weather conditions, comprising the following steps:

[0007] Step 1: Based on the historical data of distribution network faults under extreme weather conditions and the uncertainty of faults, the Monte Carlo method is used to simulate and reduce the fault scenario set;

[0008] Step 2: Establish a traffic network model based on complex network theory and traffic flow theory model, including road topology model, flow-speed model and path-time model, and solve the shortest travel time problem based on Floyd algorithm;

[0009] Step 3: Analyze the power supply capacity of the distribution network under extreme scenarios, take the minimum system penalty cost as the objective function, coordinate power reinforcement to optimize the strategy, and establish a distribution network and transportation network coupling system resilience enhancement reinforcement model that comprehensively considers demand response, energy storage equipment and network reconstruction;

[0010] Step 4: Use the Gurobi solver to solve the distribution network and transportation network coupling system resilience enhancement reinforcement model to obtain the optimal system resilience enhancement reinforcement strategy.

[0011] Furthermore, the simulation in step 1 is generated and reduced into a set of fault scenarios, wherein the scenario generation for distribution network fault uncertainty is as follows:

[0012] Through the scenario analysis method, based on the probability distribution of the existing original fault prediction data, a large number of scenarios that can represent the probability distribution of random variables are generated through the Monte Carlo sampling method, and each scenario is assigned the same probability of occurrence to form a deterministic scenario set.

[0013] Furthermore, the simulation in step 1 is generated and reduced to a set of fault scenarios, wherein the reduction of scenarios for distribution network fault uncertainty includes the following steps:

[0014] Step 1.1: Setup is the initial scene set, For the collection of scenes that are about to be cut, The starting condition is an empty set, and the scene set is calculated The Euclidean distance of each scene pair is as follows:

[0015] ;

[0016] in, For the and The Euclidean distance of the scene, and Describe the and Feature vectors within the scene; and For the initial scene set The index in is 1, 2, ... , indicating that in the initial scene set Position in

[0017] Step 1.2: Generate a distribution network fault scenario set Determine the first scene to be cut, and for each scene For example, there will be a scene Minimize the sum of the Euclidean distances with all other scenes, and cut the first scene that meets the following conditions :

[0018] ;

[0019] in, For the scene With scene The The Euclidean distance of each scene is used to determine whether to reduce the scene. and For the and The characteristic attributes in the scene are the vectors used to calculate the Euclidean distance. , Index of lines that need reinforcement;

[0020] Step 1.3: Scene The first deleted scene will be calculated separately. With the remaining scenes The probability distance ,Right now ; Select the scene Make it compatible with the scene The minimum probability distance ,Right now ;

[0021] Step 1.4: Cutting away the scene The set of scenes with the minimum probability distance ,Right now ; Scene Set Added scenes The set of scenes with the minimum probability distance ,Right now ; Scenes to be cut The probability of occurrence is transferred to the scenario On, that is ; Ensure that the sum of the probabilities of all remaining scenarios is always 1;

[0022] Step 1.5: If the number of remaining scenes is still greater than the required number of feature scenes, return to steps 1.2 to 1.4 until the number of remaining scenes meets the requirement.

[0023] Furthermore, in step 2, a traffic network model including a road topology model, a flow-speed model and a path-time model is established, wherein the road topology model is as follows:

[0024] ;

[0025] ;

[0026] ;

[0027] in, Mathematical model for transportation network; It is a vertex set, i.e., a set consisting of the endpoints of a road segment or multiple intersections of a road segment in a transportation network; It is the set of roads in the transportation network; For the remaining scenes, here means the remaining scenes is the last scene calculated, that is, the number of remaining scenes calculated, Nodes in the transportation network and nodes The connection relationship between them; For traffic nodes and nodes The length of the path between Indicates a traffic node and nodes Non-adjacent, the path length is infinite.

[0028] Furthermore, in step 2, a traffic network model including a road topology model, a flow-speed model and a path-time model is established, wherein the flow-speed model is as follows:

[0029] ;

[0030] ;

[0031] in, is the traffic model parameter, which is used to represent the traffic situation of the road. Index of time before the disaster; for Time of day road road traffic volume; For road section The traffic capacity; , and is the road coefficient under different road grades; For traffic nodes and nodes Real-time driving speed after considering traffic flow; Adjacent traffic nodes and nodes The zero flow speed between the two means that the real-time traffic flow speed is not taken into account.

[0032] Furthermore, in step 2, a traffic network model including a road topology model, a flow-speed model and a path-time model is established, wherein the path-time model is as follows:

[0033] ;

[0034] ;

[0035] ;

[0036] Among them, assuming that Node departure point arrival Nodes, with A direct connecting road; For the The driving time of the direct connecting roads; For the Length of direct roads; For the The driving speed of the directly connected road sections; For traffic nodes and Total driving time between is the shortest path between nodes, , and They are Node departure point arrival The power lines of the nodes, Node departure point arrival The power lines of the nodes and Node departure point arrival Power lines at the node.

[0037] Furthermore, in step 2, the shortest travel time problem is solved based on the Floyd algorithm, wherein the solution to the shortest travel time problem based on the Floyd algorithm includes the following steps:

[0038] Step 2.1: Setup Adjacency Matrix To store the path, the elements in the matrix start to represent the initial direct path distance between two points; set Precursor Matrix , the elements in the matrix record the predecessor points of the shortest path;

[0039] Step 2.2: Loop through the matrices, from the 1st to the Each point is added to the relaxation calculation in turn, and a trial enumeration is performed for each point to see if the path length has been changed;

[0040] Step 2.3: Add the remaining scenes in sequence When doing so, you need to traverse every pair of points in the graph to determine whether the distance between each pair of points changes due to the added point. If so, update Distance between two points in a matrix and the matrix The leading vertex in ;

[0041] Step 2.4: Repeat the above steps until all insertion points are completed.

[0042] Furthermore, in step 3, a distribution network and transportation network coupling system resilience enhancement reinforcement model is established. The objective function of the model is the system penalty cost, which minimizes the system loss under extreme weather conditions. The objective function is as follows:

[0043] ;

[0044] in, Index of time after the disaster; is the index of the uncertain scene; For the power load; is the scheduling time set; A set of scenarios that consider line damage; Connect nodes in the distribution network A collection of electrical equipment; Penalty cost per unit for power loss load; Indicates the weight of the electrical load; The power load Loss of load; For the scene Probability of occurrence.

[0045] Furthermore, in step 3, a distribution network and transportation network coupling system resilience enhancement reinforcement model is established. The model includes reinforcement constraints, distribution network flow constraints, safety constraints, power exchange constraints between distribution network and upper network, energy storage equipment constraints, demand response constraints, load loss constraints, topology constraints, network reconstruction constraints and reinforcement scheduling constraints, which are as follows:

[0046] Reinforcement constraints:

[0047] ;

[0048] ;

[0049] in, For power lines; A collection of power lines; is the reinforcement cost coefficient of the power line; For the total reinforcement budget; is a set of reinforcement strategies; Is a 0-1 variable, which refers to the scene Next time line The connection status, when it is 1, the line is connected, and when it is 0, the line is disconnected; It is a 0-1 variable, which refers to the line The reinforcement status, when it is 1, the line is reinforced, and when it is 0, the line is not reinforced; It is a 0-1 variable, which refers to the line In the scene The fault status is 1 when the line is faulty, and 0 when the line is not faulty;

[0050] Distribution network flow constraints:

[0051] ; ;

[0052] ;

[0053] in, Connect nodes in the distribution network A collection of electrical equipment; , They are respectively the distribution lines in the distribution network The set of sending nodes and receiving nodes; , The time line Active and reactive currents passing upstream; , Refers to time The active power and reactive power purchased by the distribution network from the upper power grid; , The time Active load and reactive load; For time power load loss; For time load Power factor; , The energy storage device at time The charging and discharging active power; , The energy storage devices at time Charging and discharging reactive power; and Line resistance and reactance; , Representative Node , Voltage; is a configurable constant;

[0054] Security constraints:

[0055] ;

[0056] ;

[0057] ;

[0058] in, , Indicates the minimum and maximum values ​​of the voltage at the distribution network nodes; , , and Respectively represent the minimum and maximum values ​​of active power and reactive power in the line;

[0059] Power exchange constraints between distribution network and upper network:

[0060] ;

[0061] ;

[0062] in, , , and Respectively represent the minimum and maximum values ​​of active power and reactive power transmitted by the substation;

[0063] Energy storage equipment constraints:

[0064] ;

[0065] ;

[0066] ;

[0067] ;

[0068] ;

[0069] in, For energy storage equipment at all times Capacity; , and They refer to the self-sustaining discharge rate, charging efficiency, and discharge efficiency of energy storage equipment respectively; , is a 0-1 variable representing the charging and discharging status of the energy storage device; , Indicates the minimum and maximum values ​​of the capacity of the energy storage device; , Indicates the maximum value of charging and discharging active power of energy storage equipment;

[0070] Demand response constraints:

[0071] ;

[0072] ;

[0073] ;

[0074] ;

[0075] in, The electric load participating in demand response; , They are active electric load and reactive electric load after demand response respectively; is the maximum electrical load of the node in the distribution network; is the proportion of electric load participating in demand response; Indicates the maximum allowable load reduction during operation. If it is set to 0, the system only has load transfer and no load reduction during operation. If it is not 0, partial load reduction is allowed.

[0076] Loss of load restraint:

[0077] ;

[0078] Topological constraints:

[0079] ;

[0080] ;

[0081] ;

[0082] in, is the total number of distribution network nodes; Representation Node Is it a root node? 1 node It is the root node, otherwise it is not; , Respectively represent nodes is the active power of the virtual branch at the receiving end and the sending end;

[0083] Network Reconfiguration Constraints:

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] ;

[0089] in, To reconstruct the line set; is a binary variable, indicating whether the state of the tie switch changes during the period. If the switch state changes, then is 1, otherwise is 0; The maximum number of switching times allowed for the line;

[0090] Strengthen scheduling constraints:

[0091] ;

[0092] ;

[0093] ;

[0094] in, A collection of all power lines that need to be reinforced; , Index of lines that need reinforcement; is a binary variable for reinforcement personnel scheduling, 1 indicates that the reinforcement personnel are from the required reinforcement location arrive , otherwise not; Indicates whether the reinforcement personnel have access to the area to be reinforced. ; Indicates the location of the reinforcement starting site. 1 means the reinforcement personnel start from the reinforcement starting point Set out to start reinforcement work;

[0095] ;

[0096] ;

[0097] ;

[0098] ;

[0099] in, Help reinforcement personnel reach the required reinforcement nodes the moment of being; Help reinforcement personnel reach the required reinforcement nodes The time of being there; for The time it takes to reinforce; To reinforce personnel from arrive The travel time is calculated from the straight-line distance between the power load nodes; is a binary variable, if If is 1, the reinforcement personnel are Time to complete Otherwise is 0; is a very small number;

[0100] ;

[0101] ;

[0102] in, It is a 0-1 variable, which refers to the line The reinforcement status is 1 when the line is reinforced, and 0 when the line is not reinforced. Indicates the nodes that need to be reinforced exist Reinforcement completed state.

[0103] The beneficial effects of the present invention are:

[0104] (1) Predict future values ​​of the system based on historical data, and obtain judgment results through predicted value analysis, so that professionals can respond and handle them quickly, and enhance the resilience of the power system in a targeted and early manner;

[0105] (2) A variety of operating constraints such as energy storage equipment, demand response and network reconstruction were constructed at the same time, and a variety of disaster reinforcement strategies were comprehensively considered to establish a relatively systematic and complete pre-disaster reinforcement model for the power system;

[0106] (3) Taking into account the traffic volume and time changes of the transportation network, the distribution network can be reinforced within a limited time before the disaster occurs to improve the resilience of the distribution network, thereby fully and efficiently utilizing the precious pre-disaster reinforcement time. BRIEF DESCRIPTION OF THE DRAWINGS

[0107] Figure 1 A flow chart of a method for improving the toughness of a distribution network-coupling system under extreme weather conditions provided by the present invention;

[0108] Figure 2A schematic diagram of system topology and resource configuration according to an embodiment of the present invention;

[0109] Figure 3 A schematic diagram of a transportation network topology according to an embodiment of the present invention;

[0110] Figure 4 A bar graph of power grid load weight according to an embodiment of the present invention;

[0111] Figure 5 This is a schematic diagram of a fault line in an embodiment of the present invention;

[0112] Figure 6 This is a comparison curve of load loss ratios of Examples 1-5 of the present invention;

[0113] Figure 7 This is a schematic diagram of the area disconnected from the distribution network in Example 1 of the present invention under scenario 3;

[0114] Figure 8 This is a schematic diagram of the reinforcement strategy for Example 2-5 of the present invention;

[0115] Fig. 9 It is a curve diagram of energy storage device capacity in scenario 3 of example 3 of the present invention;

[0116] Fig.10 This is the demand response load curve diagram of Example 4 of the present invention;

[0117] Fig.11 This is a schematic diagram of the network reconstruction process in scenario 3 of Example 5 of the present invention. DETAILED DESCRIPTION

[0118] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0119] See also Figure 1 , which is a flow chart of the method for improving the resilience of the distribution network-transportation network coupling system under extreme weather conditions provided by the present invention, which may specifically include the following steps:

[0120] Step 1: Based on the historical data of distribution network faults under extreme weather conditions and fault uncertainties, the Monte Carlo method is used to simulate and reduce the fault scenario set.

[0121] A scenario generation method based on the probability distribution of distribution network faults is adopted. Distribution network faults are uncertain. Based on the probability distribution of the original fault prediction data, a large number of scenarios that can represent the probability distribution of random variables are generated through the Monte Carlo sampling method, and each scenario is assigned the same probability of occurrence to form a deterministic scenario set.

[0122] In order to achieve the requirement that the probability characteristics of the generated scenario samples can fully reflect the overall probability distribution of the original data, it is necessary to generate enough samples during sampling, and then reduce the samples by scenarios. The essence of scenario reduction is to reduce all sampled scenario sets to a set of several characteristic scenarios of load research conditions, under the condition that the optimization results obtained by substituting the reduced scenario set into the model for solution are as similar as possible to those obtained by using the original scenario set for solution, and the few appropriate scenarios finally obtained have corresponding generation probabilities. Since the number of scenarios that need to be substituted into the solution is greatly reduced, the pressure on the model to solve is alleviated. Scenario reduction for distribution network fault uncertainty includes the following steps:

[0123] Step 1.1: Setup is the initial scene set, For the collection of scenes that are about to be cut, The starting condition is an empty set, and the scene set is calculated The Euclidean distance of each scene pair is as follows:

[0124] ;

[0125] in, For the and The Euclidean distance of the scene, and Describe the and Feature vectors within the scene; and For the initial scene set The index in is 1, 2, ... , indicating that in the initial scene set Position in

[0126] Step 1.2: Generate a distribution network fault scenario set Determine the first scene to be cut, and for each scene For example, there will be a scene Minimize the sum of the Euclidean distances with all other scenes, and cut the first scene that meets the following conditions :

[0127] ;

[0128] in, For the scene With scene The The Euclidean distance of each scene is used to determine whether to reduce the scene. and For the and The characteristic attributes in the scene are the vectors used to calculate the Euclidean distance. , Index of lines that need reinforcement;

[0129] Step 1.3: Scene The first deleted scene will be calculated separately. With the remaining scenes The probability distance ,Right now ; Select the scene Make it compatible with the scene The minimum probability distance ,Right now ;

[0130] Step 1.4: Cutting away the scene The set of scenes with the minimum probability distance ,Right now ; Scene Set Added scenes The set of scenes with the minimum probability distance ,Right now ; Scenes to be cut The probability of occurrence is transferred to the scenario On, that is ; Ensure that the sum of the probabilities of all remaining scenarios is always 1;

[0131] Step 1.5: If the number of remaining scenes is still greater than the required number of feature scenes, return to steps 1.2 to 1.4 until the number of remaining scenes meets the requirement.

[0132] Step 2: Establish a traffic network model based on complex network theory and traffic flow theory model, including road topology model, flow-speed model and path-time model, and solve the shortest travel time problem based on Floyd algorithm.

[0133] The topological structure of the traffic network is described using graph theory. Based on complex network theory, the structure of the traffic network is represented and a road topological model is established. The road topological model is as follows:

[0134] ;

[0135] ;

[0136] ;

[0137] in, Mathematical model for transportation network; It is a vertex set, i.e., a set consisting of the endpoints of a road segment or multiple intersections of a road segment in a transportation network; It is the set of roads in the transportation network; For the remaining scenes, here means the remaining scenes is the last scene calculated, that is, the number of remaining scenes calculated, Nodes in the transportation network and nodes The connection relationship between them; For traffic nodes and nodes The length of the path between Indicates a traffic node and nodes Non-adjacent, the path length is infinite.

[0138] In actual traffic networks, due to the complex traffic conditions, there are many factors that affect vehicle speed. Therefore, the flow-speed model is introduced based on traffic flow theory to describe the dynamic characteristics of traffic flow. Road capacity and traffic volume are taken as the two main factors affecting vehicle speed. The driving speed under any traffic load state from zero flow state to saturated traffic flow state is simulated. The flow-speed model is as follows:

[0139] ;

[0140] ;

[0141] in, is the traffic model parameter, which is used to represent the traffic situation of the road. Index of time before the disaster; for Time of day road road traffic volume; For road section capacity of traffic; , and is the road coefficient under different road grades; For traffic nodes and nodes Real-time driving speed after considering traffic flow; Adjacent traffic nodes and nodes The zero flow speed between the two means that the real-time traffic flow speed is not taken into account.

[0142] In the actual traffic network, there are usually multiple roads from the reinforcement starting node to the destination node. In order to ensure the highest time efficiency and minimize losses, it is assumed that all reinforcement teams choose the shortest path to travel. The path-time model is as follows:

[0143] ;

[0144] ;

[0145] ;

[0146] Among them, assuming that Node departure point arrival Nodes, with A direct connecting road; For the The driving time of the direct connecting roads; For the Length of direct roads; For the The driving speed of the directly connected road sections; For traffic nodes and Total driving time between is the shortest path between nodes, , and They are Node departure point arrival The power lines of the nodes, Node departure point arrival The power lines of the nodes and Node departure point arrival Power lines at the node.

[0147] The Floyd algorithm is used to calculate the shortest path problem of the traffic network. The solution to the shortest travel time problem based on the Floyd algorithm includes the following steps:

[0148] Step 2.1: Setup Adjacency Matrix To store the path, the elements in the matrix start to represent the initial direct path distance between two points; set Precursor Matrix , the elements in the matrix record the predecessor points of the shortest path;

[0149] Step 2.2: Loop through the matrices, from the 1st to the Each point is added to the relaxation calculation in turn, and a trial enumeration is performed for each point to see if the path length has been changed;

[0150] Step 2.3: Add the remaining scenes in sequence When doing so, you need to traverse every pair of points in the graph to determine whether the distance between each pair of points changes due to the added point. If so, update Distance between two points in a matrix and the matrix The leading vertex in ;

[0151] Step 2.4: Repeat the above steps until all insertion points are completed.

[0152] Step 3: Analyze the power supply capacity of the distribution network under extreme scenarios, take the minimum system penalty cost as the objective function, coordinate power reinforcement to optimize the strategy, and establish a distribution network-transportation network coupling system resilience enhancement reinforcement model that comprehensively considers demand response, energy storage equipment and network reconstruction.

[0153] The objective function of the model is the system penalty cost, which minimizes the system loss under extreme weather conditions. The objective function is as follows:

[0154] ;

[0155] in, Index of time after the disaster; is the index of the uncertain scene; For the power load; is the scheduling time set; A set of scenarios that consider line damage; Connect nodes in the distribution network A collection of electrical equipment; Penalty cost per unit for power loss load; Indicates the weight of the electrical load; The power load Loss of load; For the scene Probability of occurrence.

[0156] The model constraints include reinforcement constraints, distribution network flow constraints, safety constraints, power exchange constraints between distribution network and upper network, energy storage equipment constraints, demand response constraints, load loss constraints, topology constraints, network reconstruction constraints and reinforcement scheduling constraints, as follows:

[0157] Reinforcement constraints:

[0158] ;

[0159] ;

[0160] in, For power lines; A collection of power lines; is the reinforcement cost coefficient of the power line; For the total reinforcement budget; is a set of reinforcement strategies; Is a 0-1 variable, which refers to the scene Next time line The connection status, when it is 1, the line is connected, and when it is 0, the line is disconnected; It is a 0-1 variable, which refers to the line The reinforcement status, when it is 1, the line is reinforced, and when it is 0, the line is not reinforced; It is a 0-1 variable, which refers to the line In the scene The fault status is 1 when the line is faulty, and 0 when the line is not faulty.

[0161] Distribution network flow constraints:

[0162] ; ;

[0163] ;

[0164] in, Connect nodes in the distribution network A collection of electrical equipment; , They are distribution lines in the distribution network. The set of sending nodes and receiving nodes; , The time line Active and reactive currents passing upstream; , Refers to time The active power and reactive power purchased by the distribution network from the upper power grid; , The time Active load and reactive load; For time power load loss; For time load Power factor; , The energy storage device at time The charging and discharging active power; , The energy storage devices at time Charging and discharging reactive power; and Line resistance and reactance; , Representative Node , Voltage; Is a configurable constant.

[0165] Security constraints:

[0166] ;

[0167] ;

[0168] ;

[0169] in, , Indicates the minimum and maximum values ​​of the voltage at the distribution network nodes; , , and Represent the minimum and maximum values ​​of active power and reactive power in the line respectively.

[0170] Power exchange constraints between distribution network and upper network:

[0171] ;

[0172] ;

[0173] in, , , and Respectively represent the minimum and maximum values ​​of active power and reactive power transmitted by the substation.

[0174] Energy storage equipment constraints:

[0175] ;

[0176] ;

[0177] ;

[0178] ;

[0179] ;

[0180] in, For energy storage equipment at all times Capacity; , and They refer to the self-sustaining discharge rate, charging efficiency, and discharge efficiency of energy storage equipment respectively; , is a 0-1 variable representing the charging and discharging status of the energy storage device; , Indicates the minimum and maximum values ​​of the capacity of the energy storage device; , Indicates the maximum value of charging and discharging active power of energy storage equipment.

[0181] Demand response constraints:

[0182] ;

[0183] ;

[0184] ;

[0185] ;

[0186] in, The electric load participating in demand response; , They are active electric load and reactive electric load after demand response respectively; is the maximum electrical load of the node in the distribution network; is the proportion of electric load participating in demand response; It indicates the maximum allowable load reduction during operation. If it is set to 0, the system will only have load transfer but no load reduction during operation. If it is not 0, partial load reduction is allowed.

[0187] Loss of load restraint:

[0188] ;

[0189] Topological constraints:

[0190] ;

[0191] ;

[0192] ;

[0193] in, is the total number of distribution network nodes; Representation Node Is it a root node? 1 node It is the root node, otherwise it is not; , Respectively represent nodes is the active power of the virtual branch at the receiving and sending ends.

[0194] Network Reconfiguration Constraints:

[0195] ;

[0196] ;

[0197] ;

[0198] ;

[0199] ;

[0200] in, To reconstruct the line set; is a binary variable, indicating whether the state of the tie switch changes during the period. If the switch state changes, then is 1, otherwise is 0; The maximum number of switching times allowed for the line.

[0201] Strengthen scheduling constraints:

[0202] ;

[0203] ;

[0204] ;

[0205] in, A collection of all power lines that need to be reinforced; , Index of lines that need reinforcement; is a binary variable for reinforcement personnel scheduling, 1 indicates that the reinforcement personnel are from the required reinforcement location arrive , otherwise not; Indicates whether the reinforcement personnel have access to the area to be reinforced. ; Indicates the location of the reinforcement starting site. 1 means the reinforcement personnel start from the reinforcement starting point Set out to start reinforcement work.

[0206] ;

[0207] ;

[0208] ;

[0209] ;

[0210] in, Help reinforcement personnel reach the required reinforcement nodes the moment of being; Help reinforcement personnel reach the required reinforcement nodes The time of being there; for The time it takes to reinforce; To reinforce personnel from arrive The travel time is calculated from the straight-line distance between the power load nodes; is a binary variable, if If is 1, the reinforcement personnel are Time to complete Otherwise is 0; Is a very small number.

[0211] ;

[0212] ;

[0213] in, It is a 0-1 variable, which refers to the line The reinforcement status is 1 when the line is reinforced, and 0 when the line is not reinforced. Indicates the nodes that need to be reinforced exist Reinforcement completed state.

[0214] Step 4: Use the Gurobi solver to solve the distribution network-transportation network coupling system resilience enhancement reinforcement model to obtain the optimal system resilience enhancement reinforcement strategy.

[0215] Examples:

[0216] See also Figure 2-4 In order to verify the effectiveness of the resilience enhancement model for the distribution network-transportation network coupling system, tests were conducted based on the improved IEEE 33-node system and the 32-node transportation network system. The corresponding relationship between the distribution network and the transportation network is shown in Table 1. The topological structure of the improved IEEE 33-node system is shown in Figure 2 As shown in Figure 2, the topological structure of the 32-node transportation network system is as follows: Figure 3The improved IEEE33 node system includes two energy storage devices, which are configured at power node 3 and node 28. The basic parameters of the two devices are shown in Table 2. In order to distinguish the importance of the load of each node in the power grid, a five-bit weight is set for the load. The larger the weight value, the more critical the load. The load weight of each node is as follows: Figure 4 As shown. Set the node voltage value range to [0.95, 1.05] (pu), the active power transmitted by the substation satisfies [0, 3] (MW), the reactive power satisfies [-2, 2] (WM), and the unit power loss load cost The total reinforcement budget is 3400 yuan / (MW·h). The simulation of the example is modeled by YALMIP and solved by calling Gurobi solver, with the gap value of 0.1%.

[0217] Table 1. Correspondence between distribution network and transportation network:

[0218]

[0219] Table 2. Basic parameters of energy storage equipment:

[0220]

[0221] See also Figure 5 ,Due to the uncertainty of grid component damage under extreme weather, a set of 5 fault scenarios is generated based on the scenario analysis method. The probability and fault line of each scenario are shown in Table 3. The specific fault lines are as follows Figure 5 As shown. Five examples are set at the same time, and the examples are in a progressive relationship, which can more accurately evaluate the level of system resilience improvement. The measures considered in different examples are represented by √, and the measures not considered are represented by ×. The specific settings are shown in Table 4. Such example settings can well reflect the role of various measures in improving the resilience of the distribution network-transportation network coupling system. Each time a single variable is controlled, the strategy is compared to obtain results, and the survivability of the distribution network in extreme weather is better improved.

[0222] Table 3. Line damage in different fault scenarios:

[0223]

[0224] Table 4. Example settings:

[0225]

[0226] See also Figure 6 , Considering the above five examples, the final optimization cost in the simulated different line fault scenarios is calculated, as shown in Table 5. Comparison of load recovery ratios of different examples during the reinforcement process Figure 6 shown.

[0227] Table 5. Load loss costs for different examples in different scenarios:

[0228]

[0229] According to Table 5, from Example 1 to Example 5, as measures that can be used to improve resilience are added to the system, the load loss value gradually decreases as the available resilience resources increase, so the system cost is also decreasing. The impact of different examples on reducing system load loss and improving system resilience is as follows:

[0230] In Example 1, when the distribution network faces extreme events, the radial power supply structure will bring a very obvious risk of load loss. Therefore, it can be considered that in the absence of distributed equipment to support the gas-electricity combined distribution network, the end nodes of the distribution network connected to the fault line will all cause load loss.

[0231] See also Figure 7 , as shown in Table 3, when the distribution network faces scenario 3, draw the impact of the power line disconnection in Example 1 on the distribution network structure, such as Figure 7 As shown in the figure, the four lines L3-4, L2-19, L6-7 and L6-26 are disconnected, causing three parts to lose connection with the distribution network, forming an island system without power supply, causing all nodes in it to lose load.

[0232] See also Figure 8 In Example 2, physical reinforcement of some key power lines in the distribution network system is considered, the original structure of the distribution network system is maintained, and the corresponding lines are reinforced in the optimal order that minimizes the system loss cost. The power system reinforcement strategies solved by each solution are as follows: Figure 8 As shown, the light-colored area of ​​the corresponding color block represents the journey time to the corresponding route, and the dark-colored area represents the reinforcement time of the corresponding route.

[0233] After the line reinforcement, the load loss of the distribution network was significantly reduced. And the total optimization cost was reduced by about one-half compared with Example 1. For several disaster scenarios considered in the simulation, this solution can provide greater security for the distribution network. Its most important contribution is to reduce the formation of isolated island networks and enhance the connectivity of the distribution network.

[0234] See also Fig. 9 In Example 3, energy storage equipment is considered. The cost of Example 3 is significantly lower than that of Example 2, thanks to the coordination of various distributed devices and reinforcement strategies. For the island area of ​​energy storage equipment, the distribution network island area disconnected from the main grid can form an active island structure, which can realize emergency energy supply for the temporary island network. Fig. 9It can be seen that the energy storage equipment is generally in the energy storage charging stage during the operation phase before a disaster occurs. The energy storage equipment installed at power nodes 3 and 28 can be pre-charged to reach the maximum capacity, and subsequently provide reserve power for the distribution network affected by natural disasters to improve the resilience of the distribution network.

[0235] In Example 4, the demand response of the electric load is considered, and the demand response coefficient Set to 0.2. The load participating in the demand response part can reduce the load power affected by extreme weather by load transfer, load translation or load reduction, which can effectively reduce the load affected by extreme weather. After considering the demand response strategy, the load loss of the gas distribution network has been significantly improved compared with Example 3.

[0236] See also Fig.10 , taking the original predicted load as a reference, draw the electric load curve before and after the demand response of Example 4, as shown in Fig.10 As shown. Moving the transferable load after the accident to the stage before the extreme weather occurs, or directly cutting off the interruptible load after the accident, can reduce the load loss to a certain extent and increase the load demand in the trough stage. It can be seen that the demand response measures on the load side have good application prospects in improving the resilience of the distribution network.

[0237] See also Fig.11 ,In Example 5, network reconstruction is considered and the ,distribution network topology changes dynamically as the required reinforcement ,lines are reinforced in sequence. Fig.11 The changes in the distribution network topology during network reconstruction can be seen, where the red dotted line is the network reconstruction line.

[0238] The addition of network reconstruction enables the distribution network to actively adjust the load composition of each island, control the closing of the interconnection switch, and make the corresponding node no longer in an island state, effectively assisting in reducing the system's load loss and improving power supply reliability. Distribution network reconstruction is an important means to optimize distribution system technology and improve the safety and economy of the distribution system.

[0239] This embodiment provides a method for improving the resilience and reinforcement of the distribution network and traffic network coupling system under extreme weather conditions, focusing on the field of pre-disaster reinforcement of power systems, which is currently less studied. The future values ​​of the system are predicted based on historical data, and the judgment results are obtained through the analysis of the predicted values, which is convenient for professionals to respond quickly, process quickly, and enhance the resilience of the power system in a targeted manner in advance. At the same time, multiple operating constraints such as energy storage equipment, demand response, and network reconstruction are constructed, and a variety of disaster reinforcement strategies are comprehensively considered to establish a relatively systematic and complete pre-disaster reinforcement model for the power system. The time from the release of extreme weather forecasts to the occurrence of extreme weather is often very short. The traditional pre-disaster reinforcement measures for power systems are to make comprehensive pre-disaster adjustments or reinforcements to the equipment. This approach is time-consuming and lacks pertinence, and often preventive measures are not completed before extreme weather has occurred. Through the proposed distribution network and traffic network coupling system under extreme weather conditions, the distribution network is reinforced within a limited time before the disaster occurs, taking into account the traffic volume and time changes of the transportation network, and the resilience of the distribution network is improved, thereby fully and efficiently utilizing the precious pre-disaster reinforcement time.

[0240] The above is only a preferred embodiment of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the concept described herein through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not deviate from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. A method for improving the toughness of a distribution network and transportation network coupling system under extreme weather conditions, characterized in that: The following steps are involved: Step 1: Based on the historical data of distribution network faults under extreme weather conditions and the uncertainty of faults, the Monte Carlo method is used to simulate and reduce the fault scenario set; Step 2: Establish a traffic network model based on complex network theory and traffic flow theory model, including road topology model, flow-speed model and path-time model, and solve the shortest travel time problem based on Floyd algorithm; Step 3: Analyze the power supply capacity of the distribution network under extreme scenarios, take the minimum system penalty cost as the objective function, coordinate power reinforcement to optimize the strategy, and establish a distribution network and transportation network coupling system resilience enhancement reinforcement model that comprehensively considers demand response, energy storage equipment and network reconstruction; Step 4: Using the Gurobi solver, solve the distribution network and transportation network coupling system resilience enhancement reinforcement model to obtain the optimal system resilience enhancement reinforcement strategy; In step 3, a distribution network and transportation network coupling system resilience enhancement reinforcement model is established. The objective function of the model is the system penalty cost, which minimizes the system loss under extreme weather conditions. The objective function is as follows: ； in, Index of time after the disaster; is the index of the uncertain scene; For the power load; is the scheduling time set; A set of scenarios that consider line damage; Connect nodes in the distribution network A collection of electrical equipment; Penalty cost per unit for power loss load; Indicates the weight of the electrical load; The power load Loss of load; For the scene Probability of occurrence; In step 3, a reinforcement model for improving the resilience of the distribution network and transportation network coupling system is established. The model includes reinforcement constraints, distribution network flow constraints, safety constraints, power exchange constraints between the distribution network and the upper network, energy storage equipment constraints, demand response constraints, load loss constraints, topology constraints, network reconstruction constraints and reinforcement scheduling constraints, as follows: Reinforcement constraints: ； ； in, For power lines; A collection of power lines; is the reinforcement cost coefficient of the power line; For the total reinforcement budget; is a set of reinforcement strategies; Is a 0-1 variable, which refers to the scene Next time line The connection status, when it is 1, the line is connected, and when it is 0, the line is disconnected; It is a 0-1 variable, which refers to the line The reinforcement status, when it is 1, the line is reinforced, and when it is 0, the line is not reinforced; It is a 0-1 variable, which refers to the line In the scene The fault status is 1 when the line is faulty, and 0 when the line is not faulty; Distribution network flow constraints: ； ； ； in, Connect nodes in the distribution network A collection of electrical equipment; , They are distribution lines in the distribution network. The set of sending nodes and receiving nodes; , The time line Active and reactive currents passing upstream; , Refers to time The active power and reactive power purchased by the distribution network from the upper power grid; , The time Active load and reactive load; For time power load loss; For time load Power factor; , The energy storage device at time The charging and discharging active power; , The energy storage devices are Charging and discharging reactive power; and Line resistance and reactance; , Representative Node , Voltage; is a configurable constant; Security constraints: ； ； ； in, , Indicates the minimum and maximum values ​​of the distribution network node voltage; , , and Respectively represent the minimum and maximum values ​​of active power and reactive power in the line; Power exchange constraints between distribution network and upper network: ； ； in, , , and Respectively represent the minimum and maximum values ​​of active power and reactive power transmitted by the substation; Energy storage equipment constraints: ； ； ； ； ； in, For energy storage equipment at all times Capacity; , and They refer to the self-sustaining discharge rate, charging efficiency and discharge efficiency of energy storage equipment respectively; , is a 0-1 variable representing the charging and discharging status of the energy storage device; , Indicates the minimum and maximum values ​​of the capacity of the energy storage device; , Indicates the maximum value of charging and discharging active power of energy storage equipment; Demand response constraints: ； ； ； ； in, The electric load participating in demand response; , They are active electric load and reactive electric load after demand response respectively; is the maximum electrical load of the node in the distribution network; is the proportion of electric load participating in demand response; Indicates the maximum allowable load reduction during operation. If it is set to 0, the system only has load transfer and no load reduction during operation. If it is not 0, partial load reduction is allowed. Loss of load restraint: ； Topological constraints: ； ； ； in, is the total number of distribution network nodes; Representation Node Is it a root node? 1 node It is the root node, otherwise it is not; , Respectively represent nodes is the active power of the virtual branch at the receiving end and the sending end; Network Reconfiguration Constraints: ； ； ； ； ； in, To reconstruct the line set; is a binary variable, indicating whether the state of the tie switch changes during the period. If the switch state changes, then is 1, otherwise is 0; The maximum number of switching times allowed for the line; Strengthen scheduling constraints: ； ； ； in, A collection of all power lines that need to be reinforced; , Index of lines that need reinforcement; is a binary variable for reinforcement personnel scheduling, 1 indicates that the reinforcement personnel are from the required reinforcement location arrive , otherwise not; Indicates whether the reinforcement personnel have access to the area to be reinforced. ; Indicates the location of the reinforcement starting site. 1 means the reinforcement personnel start from the reinforcement starting point Set out to start reinforcement work; ； ； ； ； in, Help reinforcement personnel reach the required reinforcement nodes the moment of being; Help reinforcement personnel reach the required reinforcement nodes The time of being there; for The time it takes to reinforce; To reinforce personnel from arrive The travel time is calculated from the straight-line distance between the power load nodes; is a binary variable, if If is 1, the reinforcement personnel are Time to complete Otherwise is 0; is a very small number; ； ； in, It is a 0-1 variable, which refers to the line The reinforcement status is 1 when the line is reinforced, and 0 when the line is not reinforced. Indicates the nodes that need to be reinforced exist Reinforcement completed state.

2. According to a method for improving the toughness of a distribution network and transportation network coupling system under extreme weather conditions in claim 1, it is characterized in that: In step 1, the simulation is generated and reduced into a set of fault scenarios, wherein the scenario generation for distribution network fault uncertainty is as follows: Through the scenario analysis method, based on the probability distribution of the existing original fault prediction data, a large number of scenarios that can represent the probability distribution of random variables are generated through the Monte Carlo sampling method, and each scenario is assigned the same probability of occurrence to form a deterministic scenario set.

3. According to a method for improving the toughness of a distribution network and transportation network coupling system under extreme weather conditions in claim 1, it is characterized in that: In step 1, the simulation is generated and reduced to a set of fault scenarios, wherein the scenario reduction for distribution network fault uncertainty includes the following steps: Step 1.1: Setup is the initial scene set, For the collection of scenes that are about to be cut, The starting condition is an empty set, and the scene set is calculated The Euclidean distance of each scene pair is as follows: ； in, For the and The Euclidean distance of the scene, and Describe the and Feature vectors within the scene; and For the initial scene set The index in is 1, 2, ... , indicating that in the initial scene set Position in Step 1.2: Generate a distribution network fault scenario set Determine the first scene to be cut, and for each scene For example, there will be a scene Minimize the sum of the Euclidean distances with all other scenes, and cut the first scene that meets the following conditions : ； in, For the scene With scene The The Euclidean distance of each scene is used to determine whether to reduce the scene. and For the and The characteristic attributes in the scene are the vectors used to calculate the Euclidean distance. , Index of lines that need reinforcement; Step 1.3: Scene The first deleted scene will be calculated separately. With the remaining scenes The probability distance ,Right now ; Select the scene Make it compatible with the scene The minimum probability distance ,Right now ; Step 1.4: Cutting out the scene The set of scenes with the minimum probability distance ,Right now ; Scene Set Added scenes The set of scenes with the minimum probability distance ,Right now ; Scenes to be cut The probability of occurrence is transferred to the scenario On, that is ; Ensure that the sum of the probabilities of all remaining scenarios is always 1; Step 1.5: If the number of remaining scenes is still greater than the required number of feature scenes, return to steps 1.2 to 1.4 until the number of remaining scenes meets the requirement.

4. According to a method for improving the toughness of a distribution network and transportation network coupling system under extreme weather conditions in claim 1, it is characterized in that: In step 2, a traffic network model including a road topology model, a flow-speed model and a path-time model is established, wherein the road topology model is as follows: ； ； ； in, Mathematical model for transportation network; It is a vertex set, i.e., a set consisting of the endpoints of a road segment or multiple intersections of a road segment in a transportation network; It is the set of roads in the transportation network; For the remaining scenes, here means the remaining scenes is the last scene calculated, that is, the number of remaining scenes calculated, Nodes in the transportation network and nodes The connection relationship between them; For traffic nodes and nodes The length of the path between Indicates a traffic node and nodes Non-adjacent, the path length is infinite.

5. The method for improving the toughness of the distribution network and transportation network coupling system under extreme weather conditions according to claim 1 is characterized in that: In step 2, a traffic network model including a road topology model, a flow-speed model and a path-time model is established, wherein the flow-speed model is as follows: ； ； in, is the traffic model parameter, which is used to represent the traffic situation of the road. Index of time before the disaster; for Time of day road road traffic volume; For road section capacity of traffic; , and is the road coefficient under different road grades; For traffic nodes and nodes Real-time driving speed after considering traffic flow; Adjacent traffic nodes and nodes The zero flow speed between the two means that the real-time traffic flow speed is not taken into account.

6. The method for improving the toughness of the distribution network and transportation network coupling system under extreme weather conditions according to claim 1 is characterized in that: In step 2, a traffic network model including a road topology model, a flow-speed model and a path-time model is established, wherein the path-time model is as follows: ； ； ； Among them, assuming that Node departure point arrival Nodes, with A direct connecting road; For the The driving time of the direct connecting roads; For the Length of direct roads; For the The driving speed of the directly connected road sections; For traffic nodes and Total driving time between is the shortest path between nodes, , and They are Node departure point arrival The power lines of the nodes, Node departure point arrival The power lines of the nodes and Node departure point arrival The power lines of the nodes.

7. The method for improving the toughness of the distribution network and transportation network coupling system under extreme weather conditions according to claim 1 is characterized in that: In step 2, the shortest travel time problem is solved based on the Floyd algorithm, wherein the solution to the shortest travel time problem based on the Floyd algorithm includes the following steps: Step 2.1: Setup Adjacency Matrix To store the path, the elements in the matrix start to represent the initial direct path distance between two points; set Precursor Matrix , the elements in the matrix record the predecessor points of the shortest path; Step 2.2: Loop through the matrices, from the 1st to the Each point is added to the relaxation calculation in turn, and a trial enumeration is performed for each point to see if the path length has been changed; Step 2.3: Add the remaining scenes in sequence When doing so, you need to traverse every pair of points in the graph to determine whether the distance between each pair of points changes due to the added point. If so, update Distance between two points in a matrix and the matrix The leading vertex in ; Step 2.4: Repeat the above steps until all insertion points are completed.

Images