A pre-disaster defense decision method and system for an electricity-gas integrated energy system

Abstract

The application discloses a kind of electric-gas integrated energy system before disaster defense decision method and system, the method includes: establishing disaster model, the failure rate of power transmission line is calculated based on the disaster characteristics of disaster model;Simulate possible failure scenarios and obtain failure scenario set by screening;Establish traffic network model and calculate the passing time of vehicle on each road, in combination with the actual length of road, the road weight value considering time cost and transportation cost is calculated, the accessible node set of mobile emergency resources is screened out by weight value analysis;For failure scenario set, with the minimum load loss cost minimization as target, and based on the constraint condition of accessible node set, establish electric-gas integrated energy system prevention control model, and obtain disaster defense decision scheme by solving.The application constructs multiple resource prevention mechanism, which can effectively reduce the system load reduction caused by disaster attack.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system coordination and scheduling technology, and relates to a disaster prevention decision-making method and system for an integrated electric-gas energy system. Background Technology

[0002] With the accelerating global energy transition and the increasing demands for energy security and sustainability, Integrated Energy Systems (IES), as an efficient, low-carbon, and flexible energy supply model, have gradually become a research hotspot and a major direction for future development in the energy field. Through the coordinated planning, operation, and management of multiple energy forms such as electricity, natural gas, and heat, Integrated Energy Systems achieve tiered energy utilization and multi-energy complementarity, thereby significantly improving energy efficiency, reducing dependence on single energy sources, and providing a powerful solution to address energy crises and environmental issues.

[0003] Currently, numerous scholars have studied the resilience enhancement of Integrated Energy Systems (IES). Resilience can be summarized as the ability of an energy system to withstand and recover from high-impact, low-probability disturbances. However, in actual operation, integrated energy systems face many internal and external risks and challenges, which seriously threaten the safe and stable operation of the system. When IES encounters extreme weather or deliberate attacks, coupling characteristics may lead to cascading faults or even backflow propagation within the IES. Complex coupling relationships exist between various energy subsystems within the system, making fault propagation paths more diverse. A single subsystem failure may trigger a chain reaction, leading to widespread power outages. For example, as a key coupling component of the power and heat systems, the failure of a combined heat and power (CHP) unit can not only affect power supply but also potentially cause district heating outages. From an external environmental perspective, frequent occurrences of uncertainties such as extreme natural disasters, cybersecurity attacks, and energy market fluctuations further exacerbate the complexity and vulnerability of integrated energy system operation. In recent years, incidents of energy infrastructure damage caused by extreme weather have become increasingly common globally. IES failures due to extreme weather are increasing year by year, causing significant economic losses. Enhancing the resilience of energy ecosystems (IES) in the face of extreme disasters has become a core issue in ensuring energy security. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a disaster prevention decision-making method and system for an integrated electric-gas energy system.

[0005] The present invention adopts the following technical solution.

[0006] The first aspect of this invention proposes a disaster prevention decision-making method for an integrated electric-gas energy system, comprising:

[0007] Establish a disaster model and calculate the failure rate of transmission lines based on the disaster characteristics of the disaster model;

[0008] Based on the failure rate of transmission lines, possible failure scenarios are simulated and a set of failure scenarios is obtained by filtering.

[0009] A traffic network model is established and the travel time of vehicles on each road is calculated. Based on the actual length of the road, the road weights taking into account time and transportation costs are calculated. Through weight analysis, a set of accessible nodes for mobile emergency resources is selected.

[0010] For the set of fault scenarios, with the goal of minimizing load loss costs, and based on the set of accessible nodes, a prevention and control model for the integrated electric-gas energy system is established, and the solution is obtained to obtain the pre-disaster defense decision scheme.

[0011] Preferably, the calculation of the transmission line failure rate based on disaster characteristics of the disaster model includes: for typhoon disasters, based on the typhoon disaster model, calculating the wind speed that each segment of the transmission line can withstand according to the location information of the line and the typhoon; calculating the failure rate of each segment of the transmission line according to the wind speed; and calculating the failure rate of the transmission line according to the failure rate of each segment.

[0012] Preferably, the step of simulating possible fault scenarios based on the fault rate of transmission lines and filtering to obtain a set of fault scenarios includes:

[0013] Monte Carlo sampling was used to sample the operating status of the transmission lines in the system to simulate possible fault scenarios. The sampling criteria are as follows:

[0014] (7)

[0015] In the formula, For the transmission line at time t ij The binary variable representing the status is set to 1 for normal and 0 for fault. A number randomly generated between 0 and 1; for Transmission lines ij The failure rate; A collection of transmission lines;

[0016] Based on the failure rate and condition of the transmission lines, a set of failure scenarios is obtained:

[0017] (8)

[0018] In the formula, yes t The upper limit of uncertainty for line faults at any given time, where T is the duration of the disaster.

[0019] Preferably, the process of establishing a traffic network model and calculating the travel time of vehicles on each road, combined with the actual length of the road, calculates road weights that take into account time and transportation costs, and uses weight analysis to filter out a set of accessible nodes for mobile emergency resources, including:

[0020] Establish a transportation network model, including the set of roads and nodes in the transportation network. And an adjacency matrix D describing the connectivity relationships between nodes in the transportation network, where Represents a set of roads. Represents the set of nodes, and the adjacency matrix elements in the adjacency matrix D. Indicates road uv The actual length;

[0021] Based on the traffic network model, the travel time of vehicles on each road is calculated;

[0022] Based on the travel time and the actual length of the road, calculate the road weights that take into account time costs and transportation costs;

[0023] Calculate the sum of path weights from emergency resource nodes to road network nodes and select the minimum weight from emergency resource warehouse nodes to road network nodes;

[0024] By comparing the minimum weight and acceptable weight of emergency resource warehouse nodes to road network nodes, a set of accessible nodes for mobile emergency resources is selected.

[0025] Preferably, based on the traffic network model, the travel time of vehicles on each road is calculated, including:

[0026] The traffic conditions of each road are obtained by the ratio of the traffic volume of each road to the maximum allowable traffic volume of each road.

[0027] Calculate the vehicle speed on each road based on the traffic conditions of each road and the vehicle speed on each road when there is zero traffic flow.

[0028] The travel time of a vehicle on each road is obtained by comparing the actual length of each road with the speed at which a vehicle travels on each road.

[0029] Preferably, the road weights, taking into account time costs and transportation costs, are calculated based on the travel time and the actual length of the road, as follows:

[0030] (13)

[0031] In the formula, Indicates road uv The weights; Indicates that the vehicle is on the roaduv Passage time on the road; This indicates the cost of transportation per unit distance. This represents the cost per unit of time.

[0032] Preferably, the calculation of the sum of path weights from emergency resource nodes to road network nodes and the selection of the minimum weight from emergency resource warehouse nodes to road network nodes are as follows:

[0033] (14)

[0034] (15)

[0035] In the formula, Indicates the first m A route from the emergency resource warehouse node to the road network node. g The sum of path weights; Indicates the first m Roads along the route hi The weights; B represents the distance from the emergency resource warehouse node to the road network node. g The total number of paths; Indicates the first m A route from the emergency resource warehouse node to the road network node. g The set of paths traversed; From emergency resource warehouse node to road network node g The minimum weight.

[0036] Preferably, the step of filtering the set of accessible nodes for mobile emergency resources by comparing the minimum weight and acceptable weight of the emergency resource warehouse node to the road network node includes:

[0037] when At that time, the road network nodes g For an accessible node, when At that time, road network nodes g This is an unaccessible node;

[0038] in, For acceptable weights, The acceptable total transport length; For acceptable delivery time; Indicates cost per unit distance; Indicates cost per unit of time; From emergency resource warehouse node to road network node g The minimum weight.

[0039] Preferably, the objective function of the prevention and control model of the integrated electric-gas energy system is:

[0040] (17)

[0041] In the formula, Cost of load loss; F The objective function value; and The cost reduction per unit load for each node in the power distribution network system and the gas distribution network system, respectively. and The loads of the distribution network system and the gas distribution network system are respectively... t The amount of load reduction at any given time; Let i be the electrical load at node i at time t; for t Time Node m Natural gas load; For the set of distribution network nodes; For the set of gas distribution network nodes; denoted as the time interval; T represents the duration of the disaster.

[0042] Preferably, the constraints of the prevention and control model of the integrated electric-gas energy system include: emergency generator configuration constraints, mobile energy storage configuration constraints, gas storage tank configuration constraints based on the set of accessible nodes, as well as distribution network safety operation constraints, gas turbine operation constraints, emergency generator operation constraints, mobile energy storage operation constraints, compressor power consumption constraints, electrical load reduction constraints, topology reconfiguration constraints, electric-gas coupling equipment model constraints, gas distribution network safety operation constraints, gas source supply constraints, gas storage tank operation constraints, and gas shedding load constraints.

[0043] A second aspect of this invention proposes a disaster prevention decision-making system for an integrated electric-gas energy system, comprising:

[0044] The disaster model building and failure rate calculation module is used to build disaster models and calculate the failure rate of transmission lines based on the disaster characteristics of the disaster models;

[0045] The fault scenario filtering module is used to simulate possible fault scenarios based on the fault rate of transmission lines and filter them to obtain a set of fault scenarios.

[0046] The module for filtering accessible nodes is used to build a traffic network model and calculate the travel time of vehicles on each road. It combines the actual length of the road to calculate the road weights that take into account time and transportation costs, and uses weight analysis to filter out the set of accessible nodes for mobile emergency resources.

[0047] The pre-disaster defense decision module is used to target the set of fault scenarios, aim at minimizing load loss costs, and establish a prevention and control model of the integrated electric-gas energy system based on the set of accessible nodes, and solve for the pre-disaster defense decision scheme.

[0048] A third aspect of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0049] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0050] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0051] This invention systematically quantifies the overall uncertainty level of the dynamic operation system of the distribution network based on the failure rate and status of transmission lines. It can flexibly control the degree of screening of failure scenarios, conforming to the randomness of actual disasters. By taking into account transportation costs and travel time costs, it achieves the screening of the set of nodes that can be accessed for emergency resources, reducing unreasonable nodes that can be accessed. This invention constructs a pre-disaster prevention model for deeply coupled integrated energy systems, taking into account the multi-energy coupling characteristics of electricity and gas, and comprehensively considering the pre-disaster prevention strategies of multiple types of resources, including the configuration of emergency generators, mobile energy storage, gas tanks, etc., and coordinating the dispatch of distributed power sources, distribution network interconnection switches, and other equipment within the system. Finally, it constructs a multi-resource prevention mechanism, which can effectively reduce the reduction of system load caused by disaster attacks. Attached Figure Description

[0052] Figure 1 This is a flowchart of the pre-disaster defense decision-making method of the present invention.

[0053] Figure 2 This is a flowchart illustrating the implementation of the pre-disaster defense decision-making method of the present invention.

[0054] Figure 3 This is a schematic diagram of a 4-node road network in an embodiment of the present invention. Detailed Implementation

[0055] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of this invention. The embodiments described in this application are merely some embodiments of this invention, and not all embodiments. Based on the spirit of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of this invention.

[0056] Embodiment 1 of the present invention provides a disaster prevention decision-making method for an integrated electric-gas energy system, such as... Figure 1 and Figure 2 As shown, the method includes:

[0057] S1: Establish a disaster model and calculate the failure rate of transmission lines based on the disaster characteristics of the disaster model;

[0058] More preferably, the extreme disaster model is established as follows:

[0059] Typhoon disaster model: The Batts typhoon wind field model is a mature model used in the engineering field. This model uses a simulated circle to simulate the typhoon wind circle. By knowing the typhoon intensity, i.e. the radius of the maximum wind speed, and the distance of the measured object from the center of the typhoon, the wind speed of the object in the wind field can be obtained. The specific model is as follows.

[0060] (1)

[0061] In the formula: The wind speed is given, and the wind direction is the counterclockwise tangential direction on the simulated circle. This refers to the distance between the power distribution line and the center of the typhoon. The radius is the distance between the cyclone center and the strongest wind belt, i.e., the radius of maximum wind speed. This represents the wind speed at that location.

[0062] The intensity of a typhoon decreases over time after it makes landfall, so the following model is used to describe the intensity decay.

[0063] (2)

[0064] In the formula: for t Extreme wind speeds during typhoons; , Background wind speed in the region and wind speed at typhoon landfall; R It is the attenuation factor; It is the attenuation constant; C A function related to the average distance traveled by a typhoon; e , b A time-dependent constant, D This represents the average distance a typhoon travels to make landfall.

[0065] The typhoon's movement trajectory after landfall is available; storm trajectory model description:

[0066] (3)

[0067] In the formula, to , to All are constant coefficients of the storm trajectory model; and The center of the typhoon was at t The longitude and latitude of the location at that time; Typhoon Center t The speed of movement at any given moment; , The center of the typhoont Time and t The direction angle of movement at time -1.

[0068] More preferably, the failure rate of the transmission line is calculated based on the disaster characteristics of the disaster model;

[0069] Specifically, the equipment vulnerability model is established and the failure probability is calculated as follows:

[0070] For typhoon disasters, the outage rate of power lines during typhoon weather is mainly determined by the effective wind speed acting vertically on the power lines. The effective wind speed is calculated as follows:

[0071] (4)

[0072] In the formula: for Timetable ij No. The wind speed that the line can withstand, x , y For this section of the transmission line (the first The actual coordinates of the section of the line; and for The actual coordinates of the typhoon's center at any given time; , These are the two maximum wind speed coefficients; and The corresponding attenuation coefficient; the two parameters satisfy... > , > ; For wind direction and the first The included angle of the line segment.

[0073] The relationship between line downtime rate and effective wind speed is as follows:

[0074] (5)

[0075] In the formula: Line at time t Middle section line The failure rate; For the line ij Middle section line The wind speed it can withstand; For the line ij Middle section line Design wind speed; For the first Line segment length.

[0076] Assuming the fault probabilities of each line are independent, the entire line The failure rate is:

[0077] (6)

[0078] In the formula: L Representative Line ij The total number of segmented lines; for Transmission lines ij The failure rate.

[0079] Formulas (4), (5), and (6) above constitute the equipment vulnerability model, which is used to calculate the failure probability.

[0080] S2: Simulate possible fault scenarios based on the fault rate of transmission lines and filter to obtain a set of fault scenarios;

[0081] More preferably, the disaster scene generation scheme is as follows:

[0082] The disaster scenario generation uses Monte Carlo sampling to sample the operating status of the lines in the system to simulate possible failure scenarios. The sampling criteria are as follows:

[0083] (7)

[0084] In the formula, Let be a binary variable representing the state of line ij at time t, where 1 indicates normal and 0 indicates fault. A number randomly generated between 0 and 1. Let be the set of lines. In equation (7), if the randomly generated number is greater than the line fault probability, the line is considered to be operating normally; otherwise, the line is considered to be damaged.

[0085] As a dynamic operating system, the fault states of components in a power distribution network exhibit randomness. By analyzing the fault rate and state of transmission lines, the overall uncertainty level of the system can be quantified, providing a theoretical basis for scenario selection and enabling the reasonable extraction of key operating scenarios. The spatiotemporal uncertain fault set established based on geographical location is as follows:

[0086] (8)

[0087] In the formula, yes t The upper limit of uncertainty of line fault at any time; the set of fault scenarios U that meet the constraints is selected by equation (8).

[0088] Equation (8) represents the selection of those that meet the requirements. The scenarios constitute the fault scenario set U. This formula systematically quantifies the uncertainty of the entire fault set based on the fault rate and state of the transmission line, which is more in line with the randomness of actual disasters. By setting an upper limit of uncertainty, the degree of screening of fault scenarios can be flexibly controlled, neither over-considering all extreme faults nor only considering a single fault.

[0089] It is understandable that the set of fault scenarios U is used for subsequent prevention decisions. Specifically, the fault scenarios generated by Equation (8) are used to calculate which selected nodes the emergency power supply and other defense measures are configured to minimize the load loss in the fault scenarios generated by Equation (8) (i.e., the solution of the model). That is, the subsequent prevention and control model is based on the scenario obtained by Equation (8) and needs to satisfy Equation (8). The scenario generated by Equation (8) is a disaster scenario generated by calculating the key parameters obtained through meteorological forecasting before the disaster.

[0090] S3: Establish a traffic network model and calculate the travel time of vehicles on each road. Combine the actual length of the road to calculate the road weights that take into account time and transportation costs. Analyze the weights to select the set of accessible nodes for mobile emergency resources.

[0091] More preferably, the method for selecting nodes to be connected, taking into account transportation time and transportation costs, is as follows:

[0092] (1) Transportation network model

[0093] The set of roads and nodes in a transportation network It means that among them Represents a set of roads. This represents the set of nodes. The adjacency matrix describing the connections between nodes in a transportation network is denoted by D. Taking a 4-node transportation network as an example, as shown... Figure 3 As shown. The adjacency matrix D is as follows:

[0094] (9)

[0095] The adjacency matrix elements Indicates road uv The actual length, in km.

[0096] (2) Calculation of emergency resource transportation time considering road traffic conditions

[0097] In a real traffic network, the traffic conditions on each road are different. The traffic condition can be described as the ratio of the current traffic flow to the maximum allowable traffic flow on the road, as shown in equation (10):

[0098] (10)

[0099] In the formula, Indicates road uv Traffic conditions; Indicates road uv Traffic flow; Indicates road uv Maximum allowable traffic flow; This represents a set of roads.

[0100] Considering the road traffic conditions, the speed of vehicles traveling on this road is calculated as shown in equation (11):

[0101] (11)

[0102] In the formula, This indicates vehicles affected by traffic conditions on the road. uv The speed of traffic, in km / h; Indicates road uv The speed at which vehicles travel on this road when there is zero traffic flow.

[0103] the way uv The travel time can be calculated by the ratio of the actual road length to the vehicle speed considering road traffic conditions, as shown in the following formula (12):

[0104] (12)

[0105] In the formula, Indicates that the vehicle is on the road uv Travel time on the road, in hours (h).

[0106] (3) Calculation of road weights taking into account transportation time and transportation costs

[0107] In a road network, the traditional road weight is the actual length of the road. However, the calculation of the road weight needs to consider the travel time of vehicles on the road and the physical distance of the road. The travel time of vehicles on the road affects the time cost, and the physical distance affects the transportation cost. The calculation of the road weight adopts linear weighted summation, as shown in the following formula (13):

[0108] (13)

[0109] In the formula, Indicates road uv The weights, in yuan; This represents distance cost, specifically fuel cost, in yuan / km. This represents the time cost, specifically the rental and dispatch cost of emergency resources, expressed in yuan / hour.

[0110] By using linear weighted summation to measure the travel time and distance cost of emergency resources, this method supports the selection of accessible nodes. It enables further screening of the original set of accessible nodes before a disaster occurs or when a disaster is imminent, in order to obtain more accessible nodes in terms of time and distance. This allows defense resources to be deployed to the appropriate locations more quickly and economically.

[0111] (4) Minimum path weight selection method

[0112] The model proposed in this invention assumes that vehicles are dispatched in parallel and that road network nodes correspond to power grid nodes. That is, multiple vehicles are dispatched from a certain emergency resource warehouse in a single operation. The travel time of each road is first calculated using equations (10)-(12). After calculating the travel time, the weight of each road is calculated according to formula (13), and the sum of weights from the emergency resource node to other road network nodes is calculated using formula (14). Since the road network is a ring network structure, there are multiple paths from the emergency resource node to other road network nodes. Therefore, the expression of formula (14) is as follows.

[0113] (14)

[0114] In the formula, Indicates the first m A route from the emergency resource warehouse node to the road network node. g The sum of path weights; Indicates the first m Roads along the route hi The weights; B represents the distance from the emergency resource warehouse node to the road network node. g The total number of paths; Indicates the first m A route from the emergency resource warehouse node to the road network node. g The set of paths traversed.

[0115] The distance to the node can be calculated from equation (14). g The path weights of multiple paths are selected by Equation (15) to filter the path with the smallest weight.

[0116] (15)

[0117] In the formula, From emergency resource warehouse node to road network node g The minimum weight.

[0118] It is understandable that the parameters calculated in equation (13) For roads uv The road weights, i.e. the weights of each road segment, are given in equation (14). There are multiple paths from the emergency resource node to the accessible road network node, and each path contains multiple road segments. Indicates the first mRoad segments under each path hi The weights are calculated in the same way as in equation (13).

[0119] (5) Node screening to be connected

[0120] Equation (15) yields the set of accessible nodes that minimizes both time and transportation costs. To reduce unreasonable nodes during the prevention phase, this invention first sets an acceptable weight, then uses the acceptable weight to filter the set of nodes that meet the weight constraints. Since vehicles are dispatched in parallel, the acceptable total transportation time and acceptable total transportation length can be set to determine the acceptable weight, as shown in Equation (16).

[0121] (16)

[0122] In the formula, Acceptable weights; The acceptable total transport length, i.e., the acceptable travel distance before the transport convoy departs; The acceptable transport time is defined as the transport time that the fleet can accept when the disaster strikes, and the transport time must be less than the time difference between the current moment and the time when the disaster strikes.

[0123] Equation (16) yields the selection criteria weights for filtering the set of accessible nodes. Following this method, the final set of accessible nodes is filtered. Then node g is an accessible node, when Node g is removed from the original set of accessible nodes.

[0124] By filtering the set of accessible nodes, meaningless nodes to be accessed can be eliminated. Due to the reduction in the set of accessible nodes, the model solution speed can be greatly accelerated, making the pre-disaster defense decision solution faster.

[0125] S4: For the set of fault scenarios, with the goal of minimizing load loss costs, and based on the set of accessible nodes, establish a prevention and control model for the integrated electric-gas energy system, and solve for the pre-disaster defense decision scheme.

[0126] More preferably, the prevention and control model for the integrated electric-gas energy system is established as follows:

[0127] (1) Objective function

[0128] To achieve rapid and reliable control of load power supply after system energy outages, this invention constructs an IES pre-disaster proactive prevention and control model with the core objective of minimizing load loss costs. The load loss cost coefficient represents the difference in priority between different loads, and the specific objective function expression is as follows:

[0129] (17)

[0130] In the formula, The load shedding penalty cost; F is the objective function value; and These are the unit load reduction costs for each node in the power distribution network system and the gas distribution network system, respectively. Different reduction cost coefficients correspond to different loads, which are used to distinguish the prevention priorities of different nodes. and The loads of the distribution network system and the gas distribution network system are respectively... t The amount of load reduction at any given time; Let i be the electrical load at node i at time t; for t Time Node m Natural gas load; For the set of distribution network nodes; This is the set of nodes in the gas distribution network.

[0131] (2) Constraints

[0132] This invention considers network losses in power flow modeling of distribution networks, and uses binary decision variables to represent line faults and normal states, as well as the allocation and usage states of various resources.

[0133] 1) Emergency Generator (EG) Configuration Constraints:

[0134] (18)

[0135] (19)

[0136] In the formula, node i The binary decision variable for whether to configure an emergency generator is 1, which indicates configuration and 0 indicates no configuration. express t Time period nodes i The binary decision variable for whether to configure an emergency generator; For the reason The matrix formed; The maximum number of emergency generators that can be configured; For decision variables The column vector formed; This is the set of accessible nodes for emergency generators; The total number of configurable emergency generator nodes; for A matrix of all 1s; used to expand configuration variables. The dimension of.

[0137] The set of accessible nodes for EG in the above formula This is obtained through S3. The logic of S3 is as follows: after knowing the location of the warehouse node, calculate the sum of path weights from that warehouse node to all nodes that can access that resource. Here, there are multiple paths, for example, m paths. By comparing these with the acceptable values ​​in S3, the corresponding resource access points that meet the requirements of good transportation cost and travel time are selected. The emergency resource warehouse node is only a general description; it can be an emergency generator warehouse node, a mobile energy storage warehouse node, etc. For these warehouse nodes, they are simply located in different positions in the road network. The calculation of the set of accessible nodes for the corresponding resources at these different locations is implemented through S3.

[0138] Equations (18)-(19) constrain the configuration status and quantity of emergency generators.

[0139] 2) Configuration constraints for Mobile Energy Storage System (MESS):

[0140] (20)

[0141] (twenty one)

[0142] In the formula, Whether or not at the node i The binary decision variable for configuring mobile energy storage is 1, which indicates configuration, and 0 indicates no configuration. express t Time period nodes i Whether to configure mobile energy storage is a binary decision variable; For the reason The matrix formed; To configure an upper limit on the number of mobile energy storage devices; For decision variables The column vector formed; The set of accessible nodes for mobile energy storage (calculated and filtered out from known mobile energy storage warehouse node locations using step S3). The total number of configurable mobile energy storage nodes; for A matrix of all 1s; used to expand configuration variables. The dimension of.

[0143] Equations (20)-(21) constrain the configuration status and number of mobile energy storage units.

[0144] 3) Gas Storage Tank (GST) Configuration Constraints:

[0145] (twenty two)

[0146] (twenty three)

[0147] In the formula, This is a binary decision variable for whether to configure a gas storage tank at node m, where 1 indicates configuration and 0 indicates no configuration. The binary decision variable represents whether to configure a gas storage tank at node m during time period t; For the reason The matrix formed; The maximum number of gas storage tanks that can be configured; For decision variables The column vector formed; The set of accessible nodes for gas storage tanks (calculated and filtered out using step S3, based on the known locations of gas storage tank warehouse nodes, starting from these locations). The total number of configurable gas storage tank nodes; for A matrix of all 1s; used to expand configuration variables. The dimension of.

[0148] Equations (22)-(23) constrain the configuration status and quantity of gas storage tanks.

[0149] 4) Constraints on safe operation of distribution networks:

[0150] (twenty four)

[0151] (25)

[0152] (26)

[0153] (27)

[0154] (28)

[0155] (29)

[0156] (30)

[0157] (31)

[0158] In the formula, and Represents a node The gas turbine at the location t Active and reactive power output at any given moment; and For emergency generator t At every momenti Injected active and reactive power; and To configure on the node i Mobile energy storage t The charging and discharging power at any given time; Represents a node i The compressor is located at t Power consumption at any given moment; and for t Timetable ij The flow of active and reactive power; Indicates the line ij exist t The running status at any given time, with 1 indicating normal operation and 0 indicating disconnection; Represents a node i exist t The power supply status at any given time, where 1 indicates power supply and 0 indicates power cut-off; and For the line ij Resistance and reactance; Represents a node i exist t Reactive load at any given moment; express t Time Node i The power value; express t Timetable ij The current value on it; M is a very large number; and Indicates the line ij The maximum allowable active and reactive power to flow; and Indicates the line ij The maximum and minimum allowable current values; and Represents a node i The upper and lower limits of voltage.

[0159] Equations (24) and (25) are power balance constraints; Equations (26) and (27) are voltage drop constraints related to the topology; Equations (28) and (29) are line power flow constraints; Equation (30) is line current constraint; and Equation (31) is node voltage constraint.

[0160] 5) Gas turbine operating constraints:

[0161] (32)

[0162] (33)

[0163] In the formula: This is a binary state variable for the operation of the gas turbine, where 1 indicates that it is in operation and 0 indicates that it is out of operation. and These are the maximum active and reactive power outputs of the gas turbine during operation. and These represent the minimum active and reactive power outputs of the gas turbine during operation.

[0164] Equations (32) and (33) are the output constraints of the gas turbine.

[0165] 6) Emergency generator operating constraints:

[0166] (34)

[0167] (35)

[0168] In the formula, and The upper limit of active and reactive power output of the configured emergency generator.

[0169] Equations (34) and (35) are the output constraints of the emergency generator.

[0170] 7) Operational constraints of mobile energy storage:

[0171] (36)

[0172] (37)

[0173] (38)

[0174] (39)

[0175] (40)

[0176] In the formula, To configure on the node i Mobile energy storage t Current battery level; and For the charging and discharging efficiency of mobile energy storage; and The upper and lower limits of the capacity of mobile energy storage; and For the mobile energy storage configured at node i, a binary decision variable is used to indicate the charging and discharging status at time t, where 1 represents charging and discharging. and This indicates the upper limit of the charging and discharging power of mobile energy storage.

[0177] Equation (36) is the constraint for calculating the remaining power of mobile energy storage; Equation (37) is the range constraint for the remaining power of mobile energy storage; Equations (38) and (39) are the constraints for the charging and discharging power of mobile energy storage; Equation (40) is the constraint for the charging and discharging of mobile energy storage, which restricts charging or discharging to a certain period of time.

[0178] 8) Compressor power consumption constraints:

[0179] (41)

[0180] In the formula, This is the upper limit for the compressor's power consumption.

[0181] Equation (41) constrains the power consumption of the compressor.

[0182] 9) Electrical load reduction constraints:

[0183] (42)

[0184] (43)

[0185] Equations (42) and (43) constrain the active and reactive load shedding.

[0186] 10) Topology Reconfiguration Constraints:

[0187] (44)

[0188] (45)

[0189] (46)

[0190] (47)

[0191] In the formula: and A binary variable representing the direction of power flow in a line; if the node i It is a node j The parent node, then Select 1 if the value is 1, otherwise select 0. Similarly.

[0192] Equation (44) constrains the relationship between the line's operating state and its attack state. If the line is not attacked, the line breakage scenario simulated by Equation (8) is obtained. When it is 1, It can be put into operation or removed, and vice versa. Exit operation. Equation (45) constrains the circuit. ij The state after an attack, if the line ij exist t Damage at any time t+1 remains damaged. The topology of the constraint system in equations (46) and (47) is radial and acyclic.

[0193] 11) Electro-pneumatic coupling device model:

[0194] (48)

[0195] (49)

[0196] In the formula, For gas turbine t At the gas network node m Gas consumption at the location; B This refers to the energy conversion coefficient of the gas turbine. This refers to the compressor power consumption coefficient. for t Constantly flowing into the compressor pipes mp Natural gas flow rate; It is a collection of gas distribution network pipelines.

[0197] Equations (48) and (49) constrain the gas consumption of the gas turbine and the power consumption of the compressor.

[0198] 12) Constraints on the safe operation of the gas distribution network:

[0199] (50)

[0200] (51)

[0201] (52)

[0202] (53)

[0203] (54)

[0204] In the formula, For gas source t Injecting gas into the distribution network nodes at all times m Natural gas volume; for t Time natural gas pipeline mp airflow rate; and To configure on the node m The gas storage tank is located at t The amount of air intake and supply at any given time; For natural gas pipelines mp The upper limit of air flow rate; For compressor piping mp The upper limit of air flow rate; For pipelines mpPipeline parameters; and for t Time-based gas network nodes m and nodes p Pressure value; and These are the upper and lower limits of the pressure value for node m in the gas network.

[0205] Equation (50) is the gas flow balance constraint of the gas network node; Equation (51) constrains the gas flow rate of the pipeline without compressor; Equation (52) constrains the gas flow rate of the pipeline with the compression molding machine; Equation (53) is the Weymouth equation for calculating the flow rate of natural gas pipeline; Equation (54) is the node gas pressure constraint.

[0206] 12) Gas supply constraints:

[0207] (55)

[0208] In the formula, and This sets the upper and lower limits for gas supply.

[0209] Equation (55) constrains the gas supply volume.

[0210] 13) Operating constraints of gas storage tanks:

[0211] (56)

[0212] (57)

[0213] (58)

[0214] (59)

[0215] (60)

[0216] In the formula, For the gas storage tank configured at node m t The remaining air volume at any given moment; and For the efficiency of air intake and air supply in the gas storage tank; and These are the upper and lower limits of the residual gas volume in the gas storage tank; and The gas tank configured at node m is used as the gas intake and gas supply identifier at time t, with 1 indicating gas intake and gas supply. and This indicates the maximum air intake and supply capacity of the gas storage tank.

[0217] Equation (56) is the formula for calculating the residual gas volume of the gas storage tank; Equation (57) is the range constraint for the residual gas volume of the gas storage tank; Equations (58) and (59) constrain the gas intake and gas supply of the gas storage tank at a single moment; Equation (60) constrains the gas storage tank to maintain only one of the states of gas intake and gas supply at a single moment.

[0218] 14) Gas shedding load constraint:

[0219] (61)

[0220] Equation (61) constrains the range of gas shedding load.

[0221] More preferably, the pre-disaster defense decision scheme is obtained by solving the model after linearizing the constraints in the model. The linearized constraints are as follows:

[0222] The constraints containing nonlinear terms are linearized. The nonlinear constraints include equations (24)-(27), (30)-(31), (38)-(39), (53), and (58)-(59).

[0223] Equations (24)-(27) and (30)-(31) are linearized as follows:

[0224] make , And add a second-order cone constraint.

[0225] (62)

[0226] (63)

[0227] (64)

[0228] (65)

[0229] (66)

[0230] (67)

[0231] (68)

[0232] Equations (38)-(39) and (58)-(59) are linearized as follows:

[0233] Taking equation (38) as an example, since If it is a nonlinear term, then let ,but Equation (69) must be satisfied.

[0234] (69)

[0235] Therefore, the linearized result of equation (38) is:

[0236] (70)

[0237] Similarly, equations (39) and (58)-(59) are linearized as follows:

[0238] (71)

[0239] (72)

[0240] (73)

[0241] The Weymouth equation (53) is linearized using a second-order cone relaxation. The linearization process is as follows:

[0242] Squaring both sides of equation (53) yields equation (74):

[0243] (74)

[0244] Rearranging the terms, we get:

[0245] (75)

[0246] Relaxing equation (75) yields:

[0247] (76)

[0248] Finally, it is written in standard second-order cone form:

[0249] (77)

[0250] Embodiment 2 of the present invention provides a disaster prevention decision-making system for an integrated electric-gas energy system, comprising:

[0251] The disaster model building and failure rate calculation module is used to build disaster models and calculate the failure rate of transmission lines based on the disaster characteristics of the disaster models;

[0252] The fault scenario filtering module is used to simulate possible fault scenarios based on the fault rate of transmission lines and filter them to obtain a set of fault scenarios.

[0253] The module for filtering accessible nodes is used to build a traffic network model and calculate the travel time of vehicles on each road. It combines the actual length of the road to calculate the road weights that take into account time and transportation costs, and uses weight analysis to filter out the set of accessible nodes for mobile emergency resources.

[0254] The pre-disaster defense decision module is used to target the set of fault scenarios, aim at minimizing load loss costs, and establish a prevention and control model of the integrated electric-gas energy system based on the set of accessible nodes, and solve for the pre-disaster defense decision scheme.

[0255] Embodiment 3 of the present invention provides a terminal, including a processor and a storage medium; the storage medium is used to store instructions; the processor is used to perform operations according to the instructions to execute the steps of the method.

[0256] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method.

[0257] Compared with the prior art, the beneficial effects of the present invention include at least the following:

[0258] This invention systematically quantifies the overall uncertainty level of the dynamic operation system of the distribution network based on the failure rate and status of transmission lines. It can flexibly control the degree of screening of failure scenarios, conforming to the randomness of actual disasters. By taking into account transportation costs and travel time costs, it achieves the screening of the set of nodes that can be accessed for emergency resources, reducing unreasonable nodes that can be accessed. This invention constructs a pre-disaster prevention model for deeply coupled integrated energy systems, taking into account the multi-energy coupling characteristics of electricity and gas, and comprehensively considering the pre-disaster prevention strategies of multiple types of resources, including the configuration of emergency generators, mobile energy storage, gas tanks, etc., and coordinating the dispatch of distributed power sources, distribution network interconnection switches, and other equipment within the system. Finally, it constructs a multi-resource prevention mechanism, which can effectively reduce the reduction of system load caused by disaster attacks.

[0259] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0260] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination of the foregoing. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0261] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0262] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0263] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A pre-disaster defense decision method for an electro-gas integrated energy system, characterized in that, Comprise: establishing a disaster model and calculating the failure rate of the power transmission line based on the disaster characteristics of the disaster model; Simulating possible failure scenarios based on the failure rate of the power transmission line and screening to obtain a failure scenario set; Establish a traffic network model and calculate the travel time of vehicles on each road, combined with the actual length of the road, calculate the road weight considering time cost and transportation cost, and screen the accessible node set of mobile emergency resources through weight analysis, including: establishing a traffic network model, including a set of roads and nodes in the traffic network and an adjacency matrix D describing the connection between the nodes of the traffic network, wherein denotes the set of roads, denotes the set of nodes, an adjacency matrix element in the adjacency matrix D denotes the actual length of the road uv . Based on the traffic network model, calculate the travel time of vehicles on each road, including: according to the ratio of traffic flow on each road to the maximum allowable traffic flow on each road, get the traffic condition of each road; According to the traffic condition of each road and the travel speed of vehicles on each road under zero traffic flow, calculate the travel speed of vehicles on each road; According to the ratio of the actual length of each road to the travel speed of vehicles on each road, get the travel time of vehicles on each road; According to the travel time and the actual length of the road, calculate the road weight considering time cost and transportation cost, as follows: （13） In the formula, a weight of a road; uv a travel time of a vehicle on a road; uv a transportation cost per unit distance; a cost per unit time;​​ Calculate the sum of path weights from the emergency resource node to the road network node and screen the minimum weight from the emergency resource warehouse node to the road network node, as follows: （14） （15） In the formula, represents the path weight value of the m th path from the emergency resource warehouse node to the road network node g ; represents the path weight value of the m th path passing through the road hi ; B is the total number of paths from the emergency resource warehouse node to the road network node g ; represents the path set of the m th path from the emergency resource warehouse node to the road network node g ; is the minimum weight value from the emergency resource warehouse node to the road network node g ; By comparing the minimum weight and acceptable weight between emergency resource warehouse nodes and road network nodes, a set of accessible nodes for mobile emergency resources is selected, including: when At that time, the road network nodes g For an accessible node, when At that time, road network nodes g These are unaccessible nodes; among them, For acceptable weights, The acceptable total transport length; For acceptable delivery time; Indicates cost per unit distance; Indicates cost per unit of time; From emergency resource warehouse node to road network node g The minimum weight; For the failure scenario set, the target is to minimize the load loss cost, and based on the accessible node set, a power-gas integrated energy system preventive control model is established, and a pre-disaster defense decision scheme is obtained.

2. The pre-disaster defense decision method of the power-gas integrated energy system according to claim 1, characterized in that: The failure rate of the power transmission line is calculated based on the disaster characteristics of the disaster model, including: for typhoon disaster, based on the typhoon disaster model, the wind speed of each section of the power transmission line is calculated according to the position information of the line and the typhoon; The failure rate of each section of the power transmission line is calculated according to the wind speed; The failure rate of the power transmission line is calculated according to the failure rate of each section of the power transmission line.

3. The pre-disaster defense decision method of the power-gas integrated energy system according to claim 1, characterized in that: The failure scenario set is screened by simulating possible failure scenarios based on the failure rate of the power transmission line, including: Monte Carlo sampling is used to sample the operating state of the power transmission line in the system to simulate possible failure scenarios, and the sampling is based on the following: （7） wherein is the state of transmission line ij is a binary variable of the state of transmission line is a random number between 0 and 1; is the state of transmission line is the failure rate of transmission line ij at time t; is the set of transmission lines; According to the failure rate and state of the power transmission line, the failure scenario set is screened: （8） In the formula, is t the upper limit of uncertainty of the time line of the failure, and T is the duration of the disaster.

4. The pre-disaster defense decision method of the power-gas integrated energy system according to claim 1, characterized in that: The objective function of the power-gas integrated energy system preventive control model is: （17） In the formula, Cost of load loss; F The objective function value; and The cost reduction per unit load for each node in the power distribution network system and the gas distribution network system, respectively. and The loads of the distribution network system and the gas distribution network system are respectively... t The amount of load reduction at any given time; Let i be the electrical load at node i at time t; for t Time Node m Natural gas load; For the set of distribution network nodes; For the set of gas distribution network nodes; denoted as the time interval; T represents the duration of the disaster.

5. The pre-disaster defense decision method of the power-gas integrated energy system according to claim 1, characterized in that: The constraint conditions of the electricity-gas integrated energy system preventive control model include: emergency generator configuration constraints, mobile energy storage configuration constraints, gas tank configuration constraints based on the set of accessible nodes, power distribution network safe operation constraints, gas turbine operation constraints, emergency generator operation constraints, mobile energy storage operation constraints, compressor power consumption constraints, electrical load reduction constraints, topology reconstruction constraints, electricity-gas coupling device model constraints, gas distribution network safe operation constraints, gas source gas supply constraints, gas tank operation constraints, and gas load shedding constraints.

6. A pre-disaster defense decision system for an electro-mechanical integrated energy system, which operates the method of any one of claims 1-5, characterized in that, The system comprises: a disaster model construction and failure rate calculation module configured to establish a disaster model and calculate a failure rate of a power transmission line based on a disaster feature of the disaster model; a fault scenario screening module configured to simulate possible fault scenarios based on the failure rate of the power transmission line and screen a set of fault scenarios; an accessible node screening module configured to establish a traffic network model, calculate a travel time of a vehicle on each road, calculate a road weight value considering a time cost and a transportation cost in combination with an actual length of the road, and screen a set of accessible nodes of a mobile emergency resource through the weight value analysis; a pre-disaster defense decision module configured to, for the set of fault scenarios, minimize a load loss cost as an objective, set constraint conditions based on the set of accessible nodes, establish an electricity-gas integrated energy system preventive control model, and solve a pre-disaster defense decision scheme. 7.A terminal, comprising a processor and a storage medium; characterized in that: the storage medium is configured to store instructions; the processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1-5.

8. A computer readable storage medium having stored thereon a computer program, characterized in that The program is executed by the processor to implement the steps of the method according to any one of claims 1-5.

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