Power coupling equipment-based power grid recovery system and method for reducing power influence

Abstract

The invention relates to the technical field of power grid recovery, in particular to a power grid recovery system and method for reducing power influence based on electric energy coupling equipment, and the method comprises the following steps: S1, constructing a multi-energy coupling power distribution network: coupling the power distribution network, a gas network, a heat network and a traffic network through the electric energy coupling equipment; s2, obtaining an initial splitting scheme of the power distribution network; s3, obtaining a recovery sequence: establishing a power distribution network recovery optimization model on the basis of the predicted values of the power distribution network state, the load state, the traffic network state, the power distribution network power supply and the power distribution network load after the occurrence of the extreme event; obtaining an EV transfer path, a first-aid repair path, a first-aid repair sequence, a power distribution network line recovery sequence and a load recovery sequence based on the power distribution network recovery optimization model; s4, updating the recovery state; s5, recovering the power grid; the method can be applied to urban multi-energy power system fault scenes caused by extreme disasters, continuity and stability of post-disaster power supply are effectively improved, and post-disaster power failure loss is reduced.

Description

Technical Field

[0001] This invention relates to the field of power grid restoration technology, and more specifically to a power grid restoration system and method for reducing power impact based on power coupling devices. Background Technology

[0002] In recent years, the incidence of extreme weather events such as floods, ice storms, and droughts has increased dramatically, leading to frequent large-scale power outages and causing enormous economic losses and social impacts. All types of extreme weather cause severe power outages for users. Utilizing various resources to provide timely energy support to the distribution network after disasters is of great significance.

[0003] With the rapid development and large-scale integration of distributed generators (DG), existing technologies have played an effective role in load restoration by utilizing the spatiotemporal characteristics of DG. However, the power supply fluctuates significantly after a disaster, resulting in unstable power supply for users.

[0004] Therefore, this invention proposes a power grid restoration system and method based on power coupling equipment to reduce the power impact in urban multi-energy power system failure scenarios caused by extreme disasters, effectively ensuring the continuity and stability of power supply after disasters and reducing power outage losses after disasters. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a power grid restoration system and method based on power coupling devices to reduce the impact of power outages. This system can effectively, efficiently, and stably provide power after disasters, thereby reducing losses from power outages.

[0006] To achieve the above objectives, the technical solution of the present invention is as follows: a power grid restoration method based on power coupling equipment to reduce power impact, comprising the following steps:

[0007] S1: Multi-energy coupled distribution network construction: Couple the distribution network, gas network, heating network and transportation network through power coupling equipment;

[0008] S2: Initial disconnection scheme acquisition for distribution network: Statistically analyze the real-time and predicted states of distribution network, load, and transportation network when extreme events occur, disconnect the network with the maximum load recovery as the target, and calculate whether the power surge requirement is met at this time. If it is met, record the state of distribution network, transportation network, and load at this time. If it is not met, recalculate the disconnection scheme.

[0009] S3: Recovery Sequence Acquisition: Based on the distribution network status, load status, traffic network status, and predicted values ​​of distribution network power sources and loads after the extreme event, a distribution network recovery optimization model is established; based on the distribution network recovery optimization model, the EV transfer path, emergency repair path, emergency repair sequence, distribution network line recovery sequence, and load recovery sequence are obtained;

[0010] S4: Power Grid Restoration: Execute the distribution network restoration plan, and repeatedly obtain the restoration sequence and update the restoration status at preset intervals;

[0011] S5: Recovery Status Update: Updates the distribution network recovery status, load recovery amount, traffic network information, and V2G information within the distribution network recovery decision optimization time window.

[0012] The above approach has the following beneficial effects:

[0013] 1. This solution achieves rapid response and flexible dispatch by isolating the power grid system into several islands and combining this with V2G technology, utilizing the energy storage capacity of electric vehicles (EVs). This significantly accelerates the power grid recovery speed, especially after extreme disasters, enabling faster restoration of power to critical loads.

[0014] 2. The introduction of various constraints (such as distributed generator operating power constraints, grid radial constraints, and power surge constraints) ensures the stability and security of the power grid during the recovery process. These constraints effectively avoid the risk of grid collapse caused by power fluctuations or unreasonable grid structure.

[0015] 3. By comprehensively considering the location of V2G sites, the distribution of EVs, and the topology of the power grid, optimal resource allocation is achieved. During the recovery process, the energy storage capacity of EVs can be maximized, reducing dependence on traditional energy sources and lowering recovery costs.

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the power model structure of a multi-energy coupling system according to an embodiment of the power grid restoration method for reducing power impact based on power coupling devices of the present invention;

[0018] Figure 2 This is a flowchart illustrating the multi-objective genetic algorithm II in an embodiment of the power grid restoration method for reducing power impact based on power coupling devices according to the present invention.

[0019] Figure 3 This is a bar chart of the EV number response rate embodiment of the present invention;

[0020] Figure 4 This is a bar chart illustrating the EV number response potential embodiment of the present invention;

[0021] Figure 5 This is a schematic diagram of the structure of an embodiment of the multi-energy coupling power distribution system of the present invention;

[0022] Figure 6 This is a schematic diagram of the distribution network topology after the decoupling of Scheme 1 in the embodiment of the multi-energy coupled power distribution system of the present invention;

[0023] Figure 7 This is a schematic diagram of the power distribution network topology from 8:00 to 8:30, representing an embodiment of the multi-energy coupled power distribution system of the present invention.

[0024] Figure 8 This is a schematic diagram of the power distribution network topology from 8:31 to 9:00, representing an embodiment of the multi-energy coupled power distribution system of the present invention.

[0025] Figure 9 This is a schematic diagram of the power distribution network topology from 9:00 to 9:31, representing an embodiment of the multi-energy coupled power distribution system of the present invention.

[0026] Figure 10 This is a schematic diagram of the power distribution network topology from 9:31 to 10:00, representing an embodiment of the multi-energy coupled power distribution system of the present invention.

[0027] Figure 11 This is a comparative diagram of volatility indicators in an embodiment of the multi-energy coupled power distribution system of the present invention;

[0028] Figure 12 This is a schematic diagram of the electrical load in an embodiment of the multi-energy coupling power distribution system of the present invention;

[0029] Figure 13 This is a schematic diagram illustrating the charging and discharging process of a V2G site embodiment of the present invention. Detailed Implementation

[0030] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The following detailed description illustrates the specific implementation method:

[0032] Example 1:

[0033] As attached Figure 1-13 As shown in Table 1:

[0034] A grid restoration method for reducing power impact based on power coupling devices includes the following steps:

[0035] S1: Multi-energy Coupled Distribution Network Construction: This invention couples the distribution network, gas network, heating network, and transportation network using electrical coupling devices; This invention considers N... G A multi-energy coupled distribution system is formed by the connection of distributed power sources (photovoltaics, wind power, gas turbines, etc.) to the distribution network, comprising N EVEVs, N V2G One V2G site. This system is coupled with the local urban transportation network. The system framework is as follows: Figure 1 As shown.

[0036] S2: Obtaining the initial disconnection scheme for the distribution network: Statistically analyze the real-time and predicted states of the distribution network, load, and transportation network when extreme events occur, disconnect the network with the maximum load recovery as the target, and calculate whether the power surge requirements are met at this time. If they are met, record the distribution network state, transportation network state, and load state at this time. If they are not met, recalculate the disconnection scheme.

[0037] S3: Recovery sequence acquisition: Based on the distribution network status, load status, traffic network status, and predicted values ​​of distribution network power supply and distribution network load after the extreme event, establish a distribution network recovery optimization model; based on the distribution network recovery optimization model, obtain EV transfer path, emergency repair path, emergency repair sequence, distribution network line recovery sequence, and load recovery sequence; EV refers to electric vehicles.

[0038] S4: Power Grid Restoration: Execute the power distribution network restoration plan, and repeatedly obtain the restoration sequence and update the restoration status at preset intervals.

[0039] S5: Recovery Status Update: Updates the distribution network recovery status, load recovery amount, traffic network information, and V2G information within the distribution network recovery decision optimization time window; V2G is a grid interaction technology.

[0040] S1 includes:

[0041] S1-1: The power grid includes wind power generation, photovoltaic power generation, and electrical load; the gas grid includes gas load; the heating grid includes heat load.

[0042] Power coupling equipment includes CHP (cogeneration plant), gas boilers, electric boilers, gas turbines, and V2G (vehicle-to-gas) stations; among which CHP is a combined heat and power (CHP) unit.

[0043] The electrical coupling equipment between the power grid and the gas grid is CHP, the electrical coupling equipment between the power grid and the heating grid is gas boiler and gas turbine, and the electrical coupling equipment between the power grid and the transportation network is V2G station.

[0044] S2 includes:

[0045] S2-1: The objective function is expressed as follows:

[0046]

[0047] In the formula, z is the set of all isolated islands after unpacking; i is the node within an isolated island; P Li Let be the load power of node i.

[0048] S2-2: Add distributed generator set operating power constraints, grid radial constraints, power surge constraints, internal balance constraints after islanding is established, node voltage constraints, and line power flow and phase constraints to the objective function.

[0049] The operating power constraint of distributed generator sets is defined as follows:

[0050]

[0051] In the formula, P represents the active and reactive power outputs of the i-th unit at time t; gmin P gmax Q gmin Q gmax These represent the minimum and maximum values ​​of the unit's active and reactive power, respectively; u is the input factor. r A value of 1 indicates that the power supply is on, and a value of 0 indicates that the power supply is not on. These are the active and reactive ramp rates of the generator unit, respectively.

[0052] Assuming that all lines in the studied distribution network are equipped with flexible switches, the radial constraint of the distribution network is defined as follows to ensure its radial operation:

[0053]

[0054] In the formula, f ij z is an integer variable used to identify the virtual power flow in a radial pattern. ij z is a 0-1 variable representing the opening and closing of the switch at line (i,j). ij =1 indicates that the line (i,j) is connected, otherwise it is disconnected; V pc L pc These are the sets of all grid-connected nodes and their connected lines in the distribution network.

[0055] The power impulse constraint is defined as:

[0056]

[0057] In the formula, Whether node i belongs to the island where the k-th DG is located; The set of nodes of the island to which the k-th DG belongs; The expression represents the connection between node i and node j within the island to which the k-th DG belongs, and is a 0-1 variable; The table shows the power flowing through line (i,j) at time t1; ξ is the frequency response scaling factor, representing the proportion of the allowable power surge in the total DG capacity; ξ is set to 0.1.

[0058] In the formula, connected nodes i and j belong to the same island; in the formula, a sudden change in the flow power on the connected lines is required to generate a power impact on the island; the formula limits the power impact at the moment the island is established.

[0059] Wind and solar power generation are highly volatile, which can be compensated for by stable power sources such as diesel engines, gas turbines, and V2G sites with continuous power supply. The internal balance constraints after islanding are defined as follows:

[0060]

[0061] In the formula, G and NG represent the sets of stable and unstable power sources, respectively; P Gi Q Gi Q Gjmin P Gimin These represent the actual active power, reactive power output, and minimum output of the stable power supply, respectively; P Gjmax and P Gjmin These represent the maximum and minimum output of the unstable power source, respectively; P Lk Q Lk These represent the active and reactive power of the load within the island, respectively.

[0062] Node voltage constraints are defined as follows:

[0063]

[0064] In the formula, U i U imax U imin These represent the node voltage magnitude and its upper and lower limits; i = 1, 2, ..., ne;

[0065] Considering the linearized AC power flow constraints, assuming a branch transformation ratio of 1, the line power flow and phase constraint expressions are as follows:

[0066]

[0067]

[0068] In the formula, S b B is the reference power. ij G ij Let θ be the mutual susceptance and mutual conductance of nodes i and j; i,t V i,t θ represents the phase and voltage value of bus i, respectively; + θ - These are the upper and lower limits of the phase difference, respectively. These represent the upper and lower limits of the active and reactive power that are allowed to flow through the distribution network line (ij), respectively.

[0069] S2-3: Add EV scheduling constraints, V2G operation constraints, recovery sequence constraints, internal balance constraints after island establishment, node voltage constraints, and line power flow and phase constraints to the objective function.

[0070] EV scheduling constraints include EV power constraints and EV charging / discharging constraints;

[0071] EV battery capacity constraint is defined as:

[0072]

[0073] The above formula indicates that during the scheduling process, it is assumed that when a single EV participates in the scheduling, it must be guaranteed that it has enough power to support other choices for the user. Therefore, the power of a single EV must not be lower than the battery capacity limit.

[0074] EV charge / discharge constraints are defined as follows:

[0075]

[0076] The above formula indicates that the charging and discharging power of EVs must not exceed the limit during the scheduling process.

[0077] V2G operation constraints include constraints on the number of EVs within a V2G station and constraints on the charging and discharging power of a V2G station.

[0078] EV quantity constraint is defined as:

[0079] 0≤N sta,i (t)≤N EV.V2G (i,j)∈V

[0080]

[0081] Where: N sta,i (t) represents the total number of vehicles in the i-th V2G station, N EV.V2G The maximum number of EVs that a V2G site can accommodate; the number of EVs accepted by each V2G site is less than the total capacity of that site; during the post-disaster recovery process, the total number of EVs accepted by all V2G sites is less than the maximum number of EVs that can be dispatched in the urban area.

[0082] The V2G site charging and discharging power constraint is defined as follows:

[0083] 0≤S FV2G ≤N EV.V2G ×S v2g

[0084] S v2g S represents the charging and discharging power of a single charging station within the V2G network. FV2GThe discharge power that the V2G station can provide is the sum of the remaining power of all EVs within the station. The above formula indicates that, within the same time period, the power provided by each V2G station cannot exceed the power discharged simultaneously by all charging piles within the station.

[0085] The restoration order constraint is defined as follows:

[0086]

[0087] In the formula, These are the minimum and maximum start-up times for the generator set, respectively; the busbar connected to the generator must be restored before starting the generator; the generator set can only supply power when the busbar connected to the generator is intact; when restoring the line, either of the nodes at both ends of the line must be restored first, and the other node needs to be restored in the next step.

[0088] S3 includes:

[0089] S3-1: The distribution network recovery optimization model includes the distribution network load weight model, the multi-energy coupled system power model, the traffic network model, the spatiotemporal model of EV scheduling, and the V2G operation model;

[0090] The power distribution network load weighting model is used to determine the weights of power allocation at different times based on the proportions of electrical load, gas load, and heat load.

[0091] The power model for multi-energy coupled systems is used to simulate the coupling of various energy systems;

[0092] Traffic network models are used to simulate the state of traffic networks under the influence of extreme weather.

[0093] The spatiotemporal model of EV scheduling is used to simulate the scheduling and operation of EVs under extreme weather conditions;

[0094] The V2G operation model is used to simulate the operation of V2G sites under extreme weather conditions.

[0095] Furthermore, the distribution network load weighting model includes a distribution network weighting model and a distribution network topology model;

[0096] The power grid weighting model is expressed as follows:

[0097]

[0098] In the formula, ω i S represents the weight of node i itself; i S represents the total power of node i; Load The total power of the system; s 1i s 2i s 3i k 1i k 2i k3i Let k represent the load amounts of Class I, Class II, and Class III loads contained in node i, and the proportions of Class I, Class II, and Class III loads to the total load of that node. 1i +k 2i +k 3i =1;

[0099] The distribution network topology model is represented as follows:

[0100] This invention uses a node-weighted tree model T(V,L,W,WL) to describe the distribution network topology under study. Where V, L, W, and WL represent nodes, edges, and node weights in the structure, respectively; where:

[0101]

[0102] S Gi , These represent the total power flowing into node i and the total load carried by node i, respectively. These represent the number of power sources and loads connected to the nodes at both ends of line i, respectively.

[0103] The power model of a multi-energy coupled system includes a thermal system model, a natural gas system model, and a coupling element model;

[0104] The thermal system model is used to simulate energy exchange in a thermal network via hot water, including branch heat power models, supply and return heat temperature models, and nodal temperature mixing models.

[0105]

[0106] In the formula, and represent the temperature at point x in the pipe and the ambient temperature, respectively; is the temperature at the beginning of the pipe; c p , m, λ b These represent the specific heat capacity of water, the flow rate and mass of water, and the thermal conductivity of the pipe, respectively; φ is the nodal thermal power; T s T o These are the heating temperature and the return water temperature, respectively; T k Let m be the temperature of the k-th node; b m j The hot water mass flow rates of pipes j and b are respectively; T j,L Let Θ be the temperature at the end of the j-th pipe; H For the collection of heating networks;

[0107] A natural gas system model is used to simulate the operation of a natural gas system. Gas transmission is treated as an isothermal flow process, with minimal pressure loss in the gas pipeline network. The natural gas system model is as follows:

[0108]

[0109] In the formula, p F p L λ represents the pressure at the beginning and end of the branch; c represents the speed of sound in the gas; D and λ g These are the inner diameter and friction coefficient of the branch, respectively; p base The reference pressure of the gas pipeline network is L; the pipeline length is q; the mass flow rate is Θ. g This refers to the set of nodes in a gas pipeline network. Let q be the collection of pipes within the gas pipeline network, with point k as both the starting and ending point; j,L q j,F Let q be the mass flow rate at the end and beginning of the j-th pipeline, respectively; k q represents the total mass flow rate of the k-th node; j,F p j,L Let p be the pressure at the beginning and end of the j-th pipe, respectively; k K represents the pressure at the k-th node; CP This is the compressor node coefficient. When the node is a compressor node, the compressor pressure ratio is taken; otherwise, 1 is taken.

[0110] Coupled element models are used to simulate the coupling between thermal system models, natural gas system models, and power distribution networks; the coupled element models include CHP models, gas boiler models, electric boiler models, and gas turbine models;

[0111] The CHP model is as follows:

[0112]

[0113] In the formula, C CHP Thermoelectric ratio; P CHP.e Φ CHP,h The electrical and thermal power generated by the unit;

[0114] The gas-fired boiler model is as follows:

[0115] Φ GB =κ GB H g L GB

[0116] In the formula, Φ GB The thermal power of the gas-fired boiler; κ GB For the efficiency of gas-fired boilers, H g L GB For the calorific value and flow rate of natural gas;

[0117] The electric boiler model is as follows:

[0118] Φ EB =κ EB P EB

[0119] In the formula, Φ EB P is thermal power; EB The electrical power consumed; κ EB For efficiency;

[0120] Gas turbine model

[0121] P GT =κ GT H g L GT

[0122] In the formula, P GT The electrical power generated; κ GT For gas turbine efficiency; L GT The injection flow rate of the natural gas system.

[0123] A traffic network model is used to simulate traffic networks. A traffic network model can be written as:

[0124] J = [V J ,L J W J G P (t)]

[0125] In the formula: J is the state set of the transportation network; V J To simplify the set of road network nodes; L J To simplify the set of connections between nodes, i.e., the set of traffic paths between nodes and their intersections; W J G represents the traffic flow coefficient of the transportation network. P (t) represents the shortest travel path between nodes obtained using Dijkstra's algorithm; it is worth noting that the state of the transportation network is affected by extreme weather and needs to be updated dynamically.

[0126] The spatiotemporal model of EV scheduling includes the EV transfer model and the EV quantity model;

[0127] The EV transfer model is used to simulate the probability of EV departure under different conditions; the EV transfer model includes the EV departure model at the time node, the EV transfer direction matrix, and the EV transfer time model;

[0128] The EV departure model at the time node is as follows:

[0129]

[0130] In the formula: f(t1) is the probability density model of the electric vehicle departing at time t1; μ1 and σ1 are the mean and standard deviation of the electric vehicle's departure time each day, respectively, μ1 = 9.24 and σ1 = 3.16;

[0131] Using a Markov chain-based vehicle transfer matrix to simulate the transfer probability of EVs in different areas, the EV transfer direction matrix within the urban area is as follows:

[0132]

[0133] In the formula, p t,m,n Let p be the probability that EV will transition from node m to node n at time t, 0 ≤ p t,m,n ≤1; the sum of any row in the matrix is ​​0;

[0134] Let the time it takes for the EV to travel to its destination be... The EV transfer time model is as follows:

[0135]

[0136] Where: Q represents the traffic volume of a certain road segment, and Q0 represents the traffic volume of that road segment under normal weather conditions; I p V p These represent visibility and rainfall intensity, respectively; τ0, τ1, and τ2 are coefficients; μ is the friction coefficient, determined by rainfall intensity, road congestion, and slipperiness; L k C represents the average body length of an EV. TP α represents the traffic capacity of this road. TP ξ are calibration parameters; the average vehicle speed is χ. k L i,j Let be the distance from the i-th point to the j-th node;

[0137] The EV quantity model is used to describe the operation of EVs; it includes the EV emergency evacuation model, the EV evacuation scheduling model, and the EV capacity model.

[0138] Based on the EV, the travel time t1 is obtained, and the number of EVs at each node and the node that the EV travels to at that time are also obtained; when When a vehicle arrives at its destination, it is in a parked state, the driving variable r = 0, and the number of vehicles at the destination node is incremented by 1; otherwise, it is in a driving state, the driving variable r = 1, and the number of vehicles at the destination remains unchanged.

[0139] Then the matrix N of the number of vehicles on the road at time t2 can be obtained statistically. road (t), and at the same time, the number of EVs at each node can be obtained, denoted as matrix Nev.0;

[0140] Nev = Nev.0 + N road (t)

[0141] In the formula, represents the total number of EVs; this formula indicates that at the same time, the total number of EVs in the urban area is the sum of the number of parked EVs and the number of EVs in motion.

[0142] The EV emergency evacuation model is as follows:

[0143] N ref =βN road (t)

[0144] In the formula, N ref EV is the number of people who choose to seek refuge; β is the refuge willingness coefficient;

[0145] The EV evacuation scheduling model is as follows:

[0146]

[0147] In the formula: Let P be the distance the EV travels from node i to the refuge station at node j; P is the set of refuge stations; the above formula indicates that the EV travels to the nearest refuge station.

[0148] The EV capacity model is as follows:

[0149] Assuming that the initial capacity of each EV follows a normal distribution, denoted as Sev.0; then the capacity S of the k-th EV at time t. EV,k (t) can be expressed as:

[0150]

[0151] Where: Sev.0 k The capacity of the k-th EV before departure; The EV is transferred from node i to node j, and the shortest distance is... W 100 η represents the electrical energy consumed per 100 kilometers; ΔT represents the statistical time period; η represents the electrical energy consumed per 100 kilometers. f η c These are discharge efficiency and charging efficiency, respectively.

[0152] The V2G operation model includes the EV quantity model within a V2G site and the site schedulable model;

[0153] The model for the number of EVs within a V2G site is as follows:

[0154]

[0155] In the formula, N sta Let Nev.0 be the number of EVs within the V2G station at time t. j The number of EVs at the initial node j; To count the number of EVs reaching this station within a given timeframe; The number of EVs departing from this station within the specified time interval;

[0156] The site's schedulable capacity model is as follows:

[0157] The total capacity of the j-th V2G site at time t is expressed as:

[0158]

[0159] The above formula means that a V2G site can only participate in load recovery if it is not damaged.

[0160] The ratio of the required load to all available electrical energy during the multiple time periods from the occurrence of a user's extreme event to the final load recovery is defined as the response potential.

[0161] The response rate is defined as the ratio of the electrical energy required by the user at different times to the electrical energy already supplied by the power source during those times.

[0162] The response potential model and response rate model are as follows:

[0163]

[0164] In the formula, R Q , Response potential and response rate, respectively; This represents the total load demand within the k-th island; These represent the electrical energy provided by all distributed energy sources within the k-th isolated island and the electrical energy already provided by the power source during that time period; T K This represents the number of time periods from the occurrence of an extreme event to the eventual recovery of the load.

[0165] A thermal system model used to simulate energy exchange through hot water in a thermal network, including branch heat power models, supply and return heat temperature models, and nodal temperature mixing models:

[0166]

[0167] φ=c p m(T s -T o )

[0168]

[0169] In the formula, and represent the temperature at point x in the pipe and the ambient temperature, respectively; is the temperature at the beginning of the pipe; c p , m, λ b These represent the specific heat capacity of water, the flow rate and mass of water, and the thermal conductivity of the pipe, respectively; φ is the nodal thermal power; T s T o These are the heating temperature and the return water temperature, respectively; T k Let m be the temperature of the k-th node; b m j The hot water mass flow rates of pipes j and b are respectively; T j,L Let Θ be the temperature at the end of the j-th pipe; HFor the collection of heating networks;

[0170] A natural gas system model is used to simulate the operation of a natural gas system. Gas transmission is treated as an isothermal flow process, with minimal pressure loss in the gas pipeline network. The natural gas system model is as follows:

[0171]

[0172]

[0173] In the formula, p F p L λ represents the pressure at the beginning and end of the branch; c represents the speed of sound in the gas; D and λ g These are the inner diameter and friction coefficient of the branch, respectively; p base The reference pressure of the gas pipeline network is L; the pipeline length is q; the mass flow rate is Θ. g This refers to the set of nodes in a gas pipeline network. Let q be the collection of pipes within the gas pipeline network, with point k as both the starting and ending point; j,L q j,F Let q be the mass flow rate at the end and beginning of the j-th pipeline, respectively; k p represents the total mass flow rate of the k-th node. j,F p j,L Let p be the pressure at the beginning and end of the j-th pipe, respectively; k K represents the pressure at the k-th node; CP This is the compressor node coefficient. When the node is a compressor node, the compressor pressure ratio is taken; otherwise, 1 is taken.

[0174] Coupled element model, used to simulate the coupling between thermal system model, natural gas system model and distribution network.

[0175] A traffic network model is used to simulate traffic networks. A traffic network model can be written as:

[0176] J = [V J ,L J W J G P (t)]

[0177] In the formula: J is the state set of the transportation network; V J To simplify the set of road network nodes; L J To simplify the set of connections between nodes, i.e., the set of traffic paths between nodes and their intersections; W J G represents the traffic flow coefficient of the transportation network. P (t) represents the shortest travel path between nodes obtained using Dijkstra's algorithm; it is worth noting that the state of the transportation network is affected by extreme weather and needs to be updated dynamically.

[0178] The spatiotemporal model of EV scheduling is used to simulate the operation of scheduled EVs.

[0179] The V2G operation model is used to simulate V2G operation.

[0180] The coupling element model includes CHP, gas boiler, electric boiler, and micro gas turbine; CHP stands for combined heat and power.

[0181] The CHP model is as follows:

[0182]

[0183] In the formula, C CHP Thermoelectric ratio; P CHP.e Φ CHP,h The electrical and thermal power generated by the unit;

[0184] The gas-fired boiler model is as follows:

[0185] Φ GB =κ GB H g L GB

[0186] In the formula, Φ GB The thermal power of the gas-fired boiler; κ GB For the efficiency of gas-fired boilers, H g L GB For the calorific value and flow rate of natural gas;

[0187] The electric boiler model is as follows:

[0188] Φ EB =κ EB P EB

[0189] In the formula, Φ EB P is thermal power; EB The electrical power consumed; κ EB For efficiency;

[0190] Gas turbine model

[0191] P GT =κ GT H g L GT

[0192] In the formula, P GT The electrical power generated; κ GT For gas turbine efficiency; L GT The injection flow rate of the natural gas system.

[0193] The spatiotemporal model of EV scheduling includes the EV transfer model;

[0194] The EV transfer model is used to simulate the probability of EV departure under different conditions; the EV transfer model includes the EV departure model at the time node, the EV transfer direction matrix, and the EV transfer time model;

[0195] The EV departure model at the time node is as follows:

[0196]

[0197] In the formula: f(t1) is the probability density model of the electric vehicle departing at time t1; μ1 and σ1 are the mean and standard deviation of the electric vehicle's departure time each day, respectively, μ1 = 9.24 and σ1 = 3.16;

[0198] Using a Markov chain-based vehicle transfer matrix to simulate the transfer probability of EVs in different areas, the EV transfer direction matrix within the urban area is as follows:

[0199]

[0200] In the formula, p t,m,n Let p be the probability that EV will transition from node m to node n at time t, 0 ≤ p t,m,n ≤1; the sum of any row in the matrix is ​​0;

[0201] Let the time it takes for the EV to travel to its destination be... The EV transfer time model is as follows:

[0202]

[0203] Where: Q represents the traffic volume of a certain road segment, and Q0 represents the traffic volume of that road segment under normal weather conditions; I p V p These represent visibility and rainfall intensity, respectively; τ0, τ1, and τ2 are coefficients; μ is the friction coefficient, determined by rainfall intensity, road congestion, and slipperiness; L k C represents the average body length of an EV. TP α represents the traffic capacity of this road. TP ξ are calibration parameters; the average vehicle speed is χ. k L i,j Let be the distance from point i to node j.

[0204] The spatiotemporal model of EV scheduling also includes the EV quantity model;

[0205] The EV quantity model is used to describe the operation of EVs; it includes the EV emergency evacuation model, the EV evacuation scheduling model, and the EV capacity model.

[0206] Based on the EV, the travel time t1 is obtained, and the number of EVs at each node and the node that the EV travels to at that time are also obtained; when When a vehicle arrives at its destination, it is in a parked state, the driving variable r = 0, and the number of vehicles at the destination node is incremented by 1; otherwise, it is in a driving state, the driving variable r = 1, and the number of vehicles at the destination remains unchanged.

[0207] Then the matrix N of the number of vehicles on the road at time t2 can be obtained statistically. road (t), and at the same time, the number of EVs at each node can be obtained, denoted as matrix Nev.0;

[0208] Nev = Nev.0 + N road (t)

[0209] In the formula, represents the total number of EVs; this formula indicates that at the same time, the total number of EVs in the urban area is the sum of the number of parked EVs and the number of EVs in motion.

[0210] The EV emergency evacuation model is as follows:

[0211] N ref =βN road (t)

[0212] In the formula, N ref EV represents the number of people who choose to seek refuge; β represents the refuge willingness coefficient.

[0213] The EV evacuation scheduling model is as follows:

[0214]

[0215] In the formula: Let P be the distance the EV travels from node i to the refuge station at node j; P is the set of refuge stations; the above formula indicates that the EV travels to the nearest refuge station.

[0216] The EV capacity model is as follows:

[0217] Assuming that the initial capacity of each EV follows a normal distribution, denoted as Sev.0; then the capacity S of the k-th EV at time t. EV,k (t) can be expressed as:

[0218]

[0219] Where: Sev.0 k The capacity of the k-th EV before departure; The EV is transferred from node i to node j, and the shortest distance is... W 100 η represents the electrical energy consumed per 100 kilometers; ΔT represents the statistical time period; η represents the electrical energy consumed per 100 kilometers. f η c These are discharge efficiency and charging efficiency, respectively.

[0220] The V2G operation model includes the EV quantity model within a V2G site and the site schedulable model;

[0221] The model for the number of EVs within a V2G site is as follows:

[0222]

[0223] In the formula, N sta Let Nev.0 be the number of EVs within the V2G station at time t. j The number of EVs at the initial node j; To count the number of EVs reaching this station within a given timeframe; The number of EVs departing from this station within the specified time interval;

[0224] The site's schedulable capacity model is as follows:

[0225] The total capacity of the j-th V2G site at time t is expressed as:

[0226]

[0227] The above formula means that a V2G site can only participate in load recovery if it is not damaged.

[0228] The ratio of the required load to all available electrical energy during the multiple time periods from the occurrence of a user's extreme event to the final load recovery is defined as the response potential.

[0229] The response rate is defined as the ratio of the electrical energy required by the user at different times to the electrical energy already supplied by the power source during those times.

[0230] The response potential model and response rate model are as follows:

[0231]

[0232] In the formula, R Q , Response potential and response rate, respectively; This represents the total load demand within the k-th island; These represent the electrical energy provided by all distributed energy sources within the k-th isolated island and the electrical energy already provided by the power source during that time period; T K This represents the number of time periods from the occurrence of an extreme event to the eventual recovery of the load.

[0233] Fastest recovery speed:

[0234] System load recovery methods include the following three: forming an island for recovery, restoring the load by delivering emergency repair resources and personnel to carry out emergency repairs, and having the EV transferred to the V2G station and then the V2G station supplying power to the j-th node in reverse.

[0235] The expression for the minimum power outage time of the system is:

[0236]

[0237] In the formula, This represents the maximum time for EVs at different locations within the load to reach the V2G station; it also represents the time for an EV to transfer from node i to the V2G at node j.

[0238] The system matching degree represents the difference between the electrical energy provided by the distribution network and the total load. This scheme defines the minimum load tracking coefficient to represent the system matching degree at each statistical stage during the distribution network restoration process.

[0239] The expression for the highest system matching degree is as follows:

[0240]

[0241] In the formula, P G ′(t), P L ′(t) represents the per-unit value of all power sources and all loads in the system at time t; ΔP G ′(t), ΔP L ′(t) represents the per-unit value of the change in all power sources and all loads in the system over time Δt; a(t) represents the load tracking factor. The smaller a(t) is, the better the system power matches the load, and the lower the curtailment of distributed energy and other energy forms.

[0242] Phase 1, Basic Model:

[0243]

[0244] The second-stage scheduling model can be represented as the following multi-objective optimization model:

[0245]

[0246] For the polynomial in stage two, the linear weighted sum method can be used to transform it into a single objective. Due to the different dimensions, normalization is performed first, and the model after normalization is shown in the following equation:

[0247]

[0248] This scheme contains some 0-1 variables in its constraints, which are solved using a multi-objective genetic algorithm II.

[0249] The EV transfer model is used to simulate the probability of EV departure under different conditions; the EV transfer model includes the EV departure model at the time node, the EV transfer direction matrix, and the EV transfer time model;

[0250] The EV departure model at the time node is as follows:

[0251]

[0252] In the formula: f(t1) is the probability density model of the electric vehicle departing at time t1; μ1 and σ1 are the mean and standard deviation of the electric vehicle's departure time each day, respectively, μ1 = 9.24 and σ1 = 3.16.

[0253] Using a Markov chain-based vehicle transfer matrix to simulate the transfer probability of EVs in different areas, the EV transfer direction matrix within the urban area is as follows:

[0254]

[0255] In the formula, p t,m,n Let p be the probability that EV will transition from node m to node n at time t, 0 ≤ p t,m,n ≤1. The sum of any row in the matrix is ​​0.

[0256] Let the time it takes for the EV to travel to its destination be... The EV transfer time model is as follows:

[0257]

[0258] Where: Q represents the traffic volume of a certain road segment, and Q0 represents the traffic volume of that road segment under normal weather conditions. p V p These represent visibility and rainfall intensity, respectively, with τ0, τ1, and τ2 being coefficients. μ is the coefficient of friction, determined by rainfall intensity, road congestion, and slipperiness. L k C represents the average body length of an EV. TP α represents the traffic capacity of this road. TP ξ are calibration parameters. The average vehicle speed is χ. k L i,j Let be the distance from point i to node j.

[0259] The EV quantity model is used to describe the operation of EVs; it includes the EV emergency evacuation model, the EV evacuation scheduling model, and the EV capacity model.

[0260] Based on the EV, the travel time t1 is obtained, and the number of EVs at each node and the node that the EV travels to at that time are also obtained; when When a vehicle arrives at its destination, it is in a parked state, the driving variable r = 0, and the number of vehicles at the destination node is incremented by 1; otherwise, it is in a driving state, the driving variable r = 1, and the number of vehicles at the destination remains unchanged.

[0261] Then the matrix N of the number of vehicles on the road at time t2 can be obtained statistically. road (t), and at the same time, the number of EVs at each node can be obtained, denoted as matrix Nev.0;

[0262] Nev = Nev.0 + N road (t)

[0263] In the formula, represents the total number of EVs. This formula indicates that at any given time, the total number of EVs in the urban area is the sum of the number of parked EVs and the number of EVs in motion;

[0264] The EV emergency evacuation model is as follows:

[0265] N ref =βN road (t)

[0266] In the formula, N ref EV represents the number of people who choose to seek refuge; β represents the refuge willingness coefficient.

[0267] The EV evacuation scheduling model is as follows:

[0268]

[0269] In the formula: Let P be the distance the EV travels from node i to the refuge station at node j; P is the set of refuge stations. The above formula represents the EV traveling to the nearest refuge station.

[0270] The EV capacity model is as follows:

[0271] Assume that the initial capacity of each EV follows a normal distribution, denoted as Sev.0. Then the capacity S of the k-th EV at time t is... EV,k (t) can be expressed as:

[0272]

[0273] Where: Sev.0 k The capacity of the k-th EV before departure; The EV is transferred from node i to node j, and the shortest distance is... W 100 η represents the electrical energy consumed per 100 kilometers; ΔT represents the statistical time period; η represents the electrical energy consumed per 100 kilometers. f η c These are discharge efficiency and charging efficiency, respectively.

[0274] The V2G operation model includes the EV quantity model within a V2G site and the site schedulable model;

[0275] The model for the number of EVs within a V2G site is as follows:

[0276]

[0277] In the formula, N sta Let Nev.0 be the number of EVs within the V2G station at time t. jThe number of EVs at the initial node j; To count the number of EVs reaching this station within a given timeframe; The number of EVs departing from this station within the specified time interval;

[0278] The site's schedulable capacity model is as follows:

[0279] The total capacity of the j-th V2G site at time t is expressed as:

[0280]

[0281] The above formula means that a V2G site can only participate in load recovery if it is not damaged.

[0282] The ratio of the required load to all available electrical energy during the multiple time periods from the occurrence of a user's extreme event to the final load recovery is defined as the response potential.

[0283] The response rate is defined as the ratio of the electrical energy required by the user at different times to the electrical energy already supplied by the power source during those times.

[0284] The response potential model and response rate model are as follows:

[0285]

[0286] In the formula, R Q , Response potential and response rate, respectively; This represents the total load demand within the k-th island; These represent the electrical energy provided by all distributed energy sources within the k-th isolated island and the electrical energy already provided by the power source during that time period; T K This represents the number of time periods from the occurrence of an extreme event to the eventual recovery of the load.

[0287] like Figure 5 As shown, it is assumed that in an extreme event, the distribution network lines (3-59), (11-12), and (23-24) will fail, the main network will be cut off, and part of the transportation network (11-12) will be impassable.

[0288] The specific implementation process is as follows:

[0289] Under normal circumstances, the distribution network is powered by both the main grid and distributed generation. The distribution network, gas, heat, and transportation systems are closely interconnected through coupling elements, forming a multi-energy coupled urban energy network. After being affected by extreme weather, to ensure maximum load recovery, the recovery of the distribution system needs to consider the following:

[0290] The distribution network operates in an islanded manner, with each island using unstable power sources (DG) as its main power source, while also considering supplementing with stable power sources such as diesel engines to mitigate the fluctuations of DG.

[0291] In the power distribution network, some loads are supplied in reverse by coupling elements such as gas turbines. The nodes where this part of the electrical energy is connected to the power distribution are fixed, and they can cooperate to form an initial islanded connection.

[0292] The transportation network contains a large number of EVs. Before extreme weather occurs, EV users travel normally and park at V2G stations as needed. In the initial moments after extreme weather occurs, based on the damage to V2G stations, parked EVs participate in island formation according to dispatching intentions.

[0293] After restoration begins, the EV travels to the V2G site to participate in distribution network restoration, considering at least three scenarios: ① If the compensation power of distributed generation and flexible loads is lower than the total load within the island during the predicted time period, the EV discharges to supplement the load. ② If the compensation power of distributed generation and flexible loads is higher than the total load within the island during the predicted time period, and the dispatchable power after the EV arrives is not zero, then the load with the highest weight among the adjacent nodes of the island is selected to restore its connection to the island. ③ If the compensation power of distributed generation and flexible loads is higher than the total load within the island during the predicted time period, the dispatchable power after the EV arrives is not zero, and adjacent lines have not yet been repaired, the EV charges. All of the above processes consider the power surge during the reconfiguration process.

[0294] After the initial moment, the repair team departs from the support point and heads to the fault location for repairs. The time to reach the fault location depends on factors such as road conditions that day; the time from the start to the end of the repair is related to the skill level of the personnel and can be considered a constant. This invention assumes that the repair completion time is not the same as the time it takes for the system to restore its connection with the main network.

[0295] In summary, this invention considers a multi-energy coupled distribution network restoration strategy based on V2G technology, which ensures that the system obtains an initial distribution network disconnection scheme in the first stage and an EV dispatch scheme and line reconfiguration scheme in the second stage, thereby achieving the goal of rapidly restoring the system load and maximizing the system load restoration amount.

[0296] The solution process is as follows:

[0297] Step 1: Statistically analyze the real-time and predicted states of the distribution network, load, and transportation network when extreme events occur. Perform disconnection with the maximum load recovery as the target. Calculate whether the power surge requirements are met at this time. If they are met, record the states of the distribution network, transportation network, and load at this time. If they are not met, recalculate the disconnection scheme.

[0298] Step 2: Based on the current distribution network, traffic, load status, and predicted values ​​of distribution network power supply and load, and based on the model in this paper, establish a distribution network restoration optimization model to obtain the EV transfer path, emergency repair path and emergency repair sequence, and distribution network line and load restoration sequence.

[0299] Step 3: Update the distribution network restoration status, load restoration amount, traffic network information, and V2G information within the distribution network restoration decision optimization time window.

[0300] Step 4: Implement the power distribution network restoration plan. Repeat steps 2 and 3 after the interval.

[0301] I. Experimental Verification

[0302] To verify the effectiveness of the proposed solution, the following comparative cases were designed:

[0303] (1) The present invention.

[0304] (2) The system includes distributed photovoltaic and wind power generation, but only fault maintenance is considered.

[0305] (3) The system includes distributed photovoltaic and wind power generation, but only considers the replacement of flexible load power, and does not consider power supply by V2G.

[0306] (4) The system includes distributed photovoltaic and wind power generation, and considers EV dynamic scheduling, but does not consider the participation of other coupled devices in power supply.

[0307] Comparison of load recovery

[0308] The post-disaster power supply restoration assessment indicators and target calculation results for the four schemes are shown in Table 1.

[0309] Comparing the power restoration capacity of the above schemes, the total restored load in Scheme 1 exceeds that in Scheme 2 by 419 kW. This is because the output of distributed new energy power generation in Scheme 2 is limited (the optimized result is that distributed new energy power generation is at full capacity), and there are no other power supply devices in the network that can provide power to the system. Furthermore, the paths for EVs to reach the access point and for repair materials to reach the faulty line are constrained by the transportation network, making it impossible to quickly restore the load.

[0310] The first-stage load restoration amounts for Scheme 1 and Scheme 3 are the same, but Scheme 3 lacks a recovery strategy after disconnection, making it unable to continuously restore the load.

[0311] In Scheme 4, fault lines were predicted in advance, and EVs were dispatched to the corresponding V2G sites to participate in the restoration at the moment of the fault, ensuring the power supply to important loads. However, since the energy transfer effect of gas boilers and other components was not considered, the islanded areas after disconnection were relatively dispersed, and there were many loads that had not been restored to power, resulting in a low total load restoration amount. In the second stage, due to the high amount of load to be restored, the number of EVs going to V2G sites and the dispatch time were slightly higher than in the scheme of this invention.

[0312] The above comparison illustrates the effectiveness of increasing load recovery during the system disconnection phase by connecting distributed energy sources and transferring flexible loads, and increasing load recovery during system recovery by using the combined power supply of distributed energy sources and V2G.

[0313] like Figure 11 As shown, Figure 11 A comparison of the volatility trends of the four schemes shows that schemes 1 and 3 have lower volatility. This is because flexible loads can track wind and solar power output, transferring and reducing load during periods of low output, and using V2G sites to charge EVs during peak output, thereby reducing wind and solar curtailment rates. Further comparing schemes 1 and 3, scheme 3 does not consider the role of EVs and does not take any further action to restore load after disconnection; therefore, the volatility of scheme 3 is slightly higher than that of the scheme in this invention.

[0314] Based on Table 1, the proposed solution yields the lowest volatility and the highest recovery rate. This is because the proposed solution fully leverages the energy transfer capabilities of EVs and flexible loads, integrating and utilizing all distributed energy sources within the system. This demonstrates the superiority of the proposed strategy in mitigating the volatility of distributed energy sources and restoring loads.

[0315] The following are the data tables for each scheme:

[0316] Table 1

[0317]

[0318] according to Figure 6 , Figure 7 , Figure 8 , Figure 9 and Figure 10 Comparing the response levels of the four schemes, it can be seen that the response potential of Scheme 1 and Scheme 4 is greater than 1. This is because as electric vehicles participate in dispatching, their battery capacity can be converted into electrical energy, thus providing response potential. However, the upload capacity and number of V2G stations are limited, and not all EVs can participate in power supply at the same time. Coupled elements can convert other energy into electrical energy, so the response potential of the scheme of this invention is slightly higher than that of Scheme 4. Similarly, the response rate of the scheme of this invention is slightly higher than that of Scheme 4. However, due to line power flow constraints, the response rate of all four schemes cannot reach 100%.

[0319] In summary, introducing V2G and coupling elements into the disaster recovery strategy of multi-energy coupled distribution network disconnection and reconfiguration can realize the spatiotemporal transfer of energy, thereby reducing the volatility of distributed energy sources and improving the speed and amount of load recovery.

[0320] In this embodiment, the initial fault time is set at 8:00 AM, when the electrical load demand is relatively low, and then shows an upward trend. Figure 7 , Figure 12It can be seen that during the period from 8:00 to 8:30, the power supplied by distributed energy sources is lower than the load demand, and the system reduces the power of flexible loads to meet the demand. The V2G station at node 64 receives EV discharge to supply power to nodes 63-67. Due to the fault in line (66-11) and insufficient V2G power supply, loads 59-62 and 68-69 are still in a state of power loss, and the distribution network system forms 3 source islands. The V2G station in the orange island receives EV discharge to supply power to node 32.

[0321] Combination Figure 8 , Figure 12 It can be seen that during the period from 8:30 to 9:00, the load demand showed an upward trend, and the total output failed to meet the load demand: the output of wind power (DG1) decreased, while the output of photovoltaic power (DG3) increased; the V2G site at node 64 received EV discharge to supply power to nodes 60-62 and 68-69. Due to the power constraints within the island, the electric boilers absorbed more power to maintain balance; the power difference between the green island and the newly formed purple island was large, and the flexible load regulation function contained within it did not meet the power impact constraints, so the green island and the newly formed purple island failed to merge; the V2G site within the orange island continued to supply power, and nodes 33-35 recovered.

[0322] Combination Figure 9 , Figure 12 It can be seen that during the period from 9:00 to 9:30, the wind power (DG1) generation changed little, while the photovoltaic output (DG3) increased. The 64-node V2G site continued to receive EV participation in the restoration, supplying power to 59 nodes. All nodes in the distribution network were restored to power, but the lines were not fully repaired. At this time, the restoration constraint and power surge constraint were met, so the purple island and the orange island merged to form a new island; the green island continued to operate.

[0323] Combination Figure 10 , Figure 12 It can be seen that during the period from 9:31 to 10:00, the line was repaired. At this time, the adjustable system met the constraints of line power flow and power surge, so it merged into an island, and all loads in the distribution network were restored to power supply.

[0324] Each island in the figure maintains its radial topology and operates normally, verifying the effectiveness of the power model for the multi-energy coupled system.

[0325] according to Figure 13 Analysis shows that the output of each V2G station is low in the initial stage because the EVs have not yet arrived at the corresponding stations. As the recovery time increases, the number of EVs arriving at stations increases, and the total output of V2G stations increases.

[0326] Among them, stations 3# and 9# were located on main traffic routes and the roads were not damaged, so many vehicles went to these stations to participate in the restoration. Station 3# was at full capacity from 9:00 to 9:15, and station 9# was also at full capacity from 9:46 to 10:00.

[0327] Station 39 had the lowest recovery rate because the road was damaged and vehicles traveling to the station had to travel a long distance.

[0328] Station 64# began accepting EVs and participating in recovery between 8:16 and 8:30. Because it is isolated, all EVs at this station participated in scheduling. (Combined with...) Figure 4 It can be seen that as the number of EVs heading to this station increases, the islanded coverage area increases, and the load restoration within the island increases. From 9:00 to 9:15, the purple and orange islands merged into one island through the closing of the tie switch. The output of stations #3 and #9 within the original orange island increased, while the output of station #64 decreased. The new island achieved balance through the adjustment capacity of flexible loads. From 9:31 to 10:00, all islands merged into one. The output of station #9 increased, station #39 decreased, and the new island again achieved balance through the adjustment capacity of flexible loads. At this point, the load within the distribution network was restored.

[0329] Secondly, from Figure 13 As can be seen, except for #3 and #9 which are operating at full capacity, the other stations have not reached their maximum power supply. This means that the V2G stations still have the capacity to participate in load restoration. In fact, the dispatchable capacity of the V2G stations when operating at full capacity in this invention is 840kW, which can provide nearly 1 / 3 of the power to the power distribution system.

[0330] Figure 3 , Figure 4 The figures represent the changes in response rate and system response potential at different times when different numbers of EVs participate in scheduling. Figure 3 , Figure 4 It can be seen that when the number of EVs is 200, the response potential is relatively low throughout the entire period, with the response rate approaching 1 during the [09:01, 09:30] period, close to full capacity. When the number of EVs exceeds 300, the response potential is higher, with the response rate approaching 1 during the [08:31, 09:00] period. This indicates that, according to the present invention, the recovery effect of V2G on the distribution network is limited by the total number of charging piles. Further increasing the number of EVs can improve the response potential, but its effect on further improving the load recovery of the distribution network throughout the entire period is relatively limited. In the future, as the number of charging infrastructure and line capacity further increase, it can support more EVs to participate in distribution network recovery.

[0331] II. Experimental Conclusions

[0332] To further verify the role of distributed energy sources such as electric vehicles (EVs) in supporting the restoration of multi-energy coupled distribution networks, and to achieve dynamic scheduling of distribution network restoration schemes and coupled components such as V2G, including multi-energy coordinated distribution network disconnection schemes and multi-energy coupled distribution network restoration schemes jointly optimized with EV scheduling, practical examples are used to verify the feasibility and effectiveness of the proposed method, and the following conclusions are drawn:

[0333] After considering the multi-energy synergy effect of the distribution network, by fully tapping the energy supply potential of the multi-energy coupling system, and considering the impact of islanding, the method proposed in this invention can effectively improve the energy supply recovery of the distribution network and reduce wind and solar fluctuations compared with traditional recovery methods, which is conducive to the safe and stable operation of the system.

[0334] As the penetration rate of coupling devices, represented by V2G, increases, they can be used as backups to increase the system's response potential; however, the response rate is greatly affected by existing facilities.

[0335] Multi-energy coupled distribution network restoration optimization based on the EV spatiotemporal transfer model enables each island to be combined into a minimum number of multi-source islands through V2G and emergency repairs, thereby improving the total load restoration.

[0336] This invention achieves rapid response and flexible dispatch by deconstructing the power grid system into several islands and combining this with V2G technology, utilizing the energy storage capacity of electric vehicles (EVs). This significantly accelerates the recovery speed of the power grid, especially after extreme disasters, enabling faster restoration of power to critical loads.

[0337] This invention introduces various constraints (such as distributed generator operating power constraints, grid radial constraints, and power surge constraints) to ensure the stability and safety of the power grid during the recovery process. These constraints effectively avoid the risk of power grid collapse caused by power fluctuations or unreasonable grid structure.

[0338] This invention achieves optimized resource allocation by comprehensively considering the location of V2G sites, the distribution of electric vehicles (EVs), and the topology of the power grid. During the recovery process, it maximizes the utilization of the energy storage capacity of EVs, reduces dependence on traditional energy sources, and lowers recovery costs.

[0339] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A grid restoration method based on power coupling devices to reduce power impact, characterized in that, Includes the following steps: S1: Multi-energy coupled distribution network construction: Couple the distribution network, gas network, heating network and transportation network through power coupling equipment; S2: Initial disconnection scheme acquisition for distribution network: Statistically analyze the real-time and predicted states of distribution network, load, and transportation network when extreme events occur, disconnect the network with the maximum load recovery as the target, and calculate whether the power surge requirement is met at this time. If it is met, record the state of distribution network, transportation network, and load at this time. If it is not met, recalculate the disconnection scheme. S3: Recovery sequence acquisition: Based on the distribution network status, load status, traffic network status, and predicted values ​​of distribution network power sources and loads after the extreme event, establish a distribution network recovery optimization model; Based on the distribution network restoration optimization model, the EV transfer path, emergency repair path, emergency repair sequence, distribution network line restoration sequence, and load restoration sequence are obtained; S4: Power Grid Restoration: Execute the distribution network restoration plan, and repeatedly obtain the restoration sequence and update the restoration status at preset intervals; S5: Recovery Status Update: Updates the distribution network recovery status, load recovery amount, traffic network information, and V2G information within the distribution network recovery decision optimization time window.

2. The grid restoration method for reducing power impact based on power coupling equipment according to claim 1, characterized in that, S1 includes: S1-1: V2G stands for grid-to-grid interaction technology; EV stands for electric vehicle. The power grid includes wind power generation, photovoltaic power generation, and electrical load; the gas grid includes gas load; the heating grid includes heat load. Power coupling equipment includes CHP (cogeneration plant), gas boilers, electric boilers, gas turbines, and V2G (vehicle-to-gas) stations; among which CHP is a combined heat and power (CHP) unit. The electrical coupling equipment between the power grid and the gas grid is CHP, the electrical coupling equipment between the power grid and the heating grid is gas boiler and gas turbine, and the electrical coupling equipment between the power grid and the transportation network is V2G station.

3. The grid restoration method for reducing power impact based on power coupling equipment according to claim 2, characterized in that, S2 include: S2-1: The objective function is expressed as follows: In the formula, z is the set of all isolated islands after unpacking; i is the node within an isolated island; P Li Let be the load power of node i; S2-2: Add distributed generator set operating power constraints, grid radial constraints, power impulse constraints, internal balance constraints after islanding is established, node voltage constraints, and line power flow and phase constraints to the objective function. S2-3: Add EV scheduling constraints, V2G operation constraints, recovery sequence constraints, internal balance constraints after island establishment, node voltage constraints, and line power flow and phase constraints to the objective function.

4. The grid restoration method for reducing power impact based on power coupling devices according to claim 3, characterized in that... S3 includes S3-1: The distribution network recovery optimization model includes the distribution network load weight model, the multi-energy coupled system power model, the traffic network model, the spatiotemporal model of EV scheduling, and the V2G operation model; The power distribution network load weighting model is used to determine the weights of power allocation at different times based on the proportions of electrical load, gas load, and heat load. The power model for multi-energy coupled systems is used to simulate the coupling of various energy systems; Traffic network models are used to simulate the state of traffic networks under the influence of extreme weather. The spatiotemporal model of EV scheduling is used to simulate the scheduling and operation of EVs under extreme weather conditions; The V2G operation model is used to simulate the operation of V2G sites under extreme weather conditions.

5. The grid restoration method for reducing power impact based on power coupling devices according to claim 4, characterized in that, The distribution network load weighting model includes the distribution network weighting model and the distribution network topology model; The power grid weighting model is expressed as follows: In the formula, ω i S represents the weight of node i itself; i S represents the total power of node i; Load The total power of the system; s 1i s 2i s 3i k 1i k 2i k 3i Let k represent the load amounts of Class I, Class II, and Class III loads contained in node i, and the proportions of Class I, Class II, and Class III loads to the total load of that node. 1i +k 2i +k 3i =1; The distribution network topology model is represented as follows: This invention uses a node-weighted tree model T(V,L,W,WL) to describe the distribution network topology under study. Where V, L, W, and WL represent nodes, edges, and node weights in the structure, respectively; where: S Gi , These represent the total power flowing into node i and the total load carried by node i, respectively. These represent the number of power sources and loads connected to the nodes at both ends of line i, respectively.

6. The grid restoration method for reducing power impact based on power coupling devices according to claim 5, characterized in that, The power model of a multi-energy coupled system includes a thermal system model, a natural gas system model, and a coupling element model; The thermal system model is used to simulate energy exchange in a thermal network via hot water, including branch heat power models, supply and return heat temperature models, and nodal temperature mixing models. φ=c p m(T s -T o ) In the formula, T x T a These represent the temperature at point x in the pipe and the ambient temperature, respectively; T F c is the temperature at the beginning of the pipe. p , m, λ b These represent the specific heat capacity of water, the flow rate and mass of water, and the thermal conductivity of the pipe, respectively; φ is the nodal thermal power; T s T o These are the heating temperature and the return water temperature, respectively; T k Let m be the temperature of the k-th node; b m j The hot water mass flow rates of pipes j and b are respectively; T j,L Let Θ be the temperature at the end of the j-th pipe; H For the collection of heating networks; A natural gas system model is used to simulate the operation of a natural gas system. Gas transmission is treated as an isothermal flow process, with minimal pressure loss in the gas pipeline network. The natural gas system model is as follows: In the formula, p F p L λ represents the pressure at the beginning and end of the branch; c represents the speed of sound in the gas; D and λ g These are the inner diameter and friction coefficient of the branch, respectively; p base The reference pressure of the gas pipeline network is L; the pipeline length is q; the mass flow rate is Θ. g This refers to the set of nodes in a gas pipeline network. Let q be the collection of pipes within the gas pipeline network, with point k as both the starting and ending point; j,L q j,F Let q be the mass flow rate at the end and beginning of the j-th pipeline, respectively; k p represents the total mass flow rate of the k-th node. j,F p j,L Let p be the pressure at the beginning and end of the j-th pipe, respectively; k K represents the pressure at the k-th node; CP This is the compressor node coefficient. When the node is a compressor node, the compressor pressure ratio is taken; otherwise, 1 is taken. Coupled element models are used to simulate the coupling between thermal system models, natural gas system models, and power distribution networks; the coupled element models include CHP models, gas boiler models, electric boiler models, and gas turbine models; The CHP model is as follows: In the formula, C CHP Thermoelectric ratio; P CHP.e Φ CHP,h The electrical and thermal power generated by the unit; The gas-fired boiler model is as follows: F GB =k GB H g L GB In the formula, Φ GB The thermal power of the gas-fired boiler; κ GB For the efficiency of gas-fired boilers, H g L GB For the calorific value and flow rate of natural gas; The electric boiler model is as follows: F EB =k EB P EB In the formula, Φ EB P is the thermal power; EB The electrical power consumed; κ EB For efficiency; Gas turbine model P GT =k GT H g L GT In the formula, P GT The electrical power generated; κ GT For gas turbine efficiency; L GT The injection flow rate of the natural gas system.

7. The power grid restoration system for reducing power impact based on power coupling devices according to claim 6, characterized in that, A traffic network model is used to simulate traffic networks. A traffic network model can be written as: J=[V J ,L J ,W J ,G P (t)] In the formula: J is the state set of the transportation network; V J To simplify the set of road network nodes; L J To simplify the set of connections between nodes, i.e., the set of traffic paths between nodes and their intersections; W J G represents the traffic flow coefficient of the transportation network. P (t) represents the shortest travel path between nodes obtained using Dijkstra's algorithm; it is worth noting that the state of the transportation network is affected by extreme weather and needs to be updated dynamically.

8. The power grid restoration system for reducing power impact based on power coupling devices according to claim 7, characterized in that, The spatiotemporal model of EV scheduling includes the EV transfer model and the EV quantity model; The EV transfer model is used to simulate the probability of EV departure under different conditions; the EV transfer model includes the EV departure model at the time node, the EV transfer direction matrix, and the EV transfer time model; The EV departure model at the time node is as follows: In the formula: f(t1) is the probability density model of the electric vehicle departing at time t1; μ1 and σ1 are the mean and standard deviation of the electric vehicle's departure time each day, respectively, μ1 = 9.24 and σ1 = 3.16; Using a Markov chain-based vehicle transfer matrix to simulate the transfer probability of EVs in different areas, the EV transfer direction matrix within the urban area is as follows: In the formula, p t,m,n Let p be the probability that EV will transition from node m to node n at time t, 0 ≤ p t,m,n ≤1; the sum of any row in the matrix is ​​0; Let the time it takes for the EV to travel to its destination be... The EV transfer time model is as follows: Where: Q represents the traffic volume of a certain road segment, and Q0 represents the traffic volume of that road segment under normal weather conditions; I p V p These represent visibility and rainfall intensity, respectively; τ0, τ1, and τ2 are coefficients; μ is the friction coefficient, determined by rainfall intensity, road congestion, and slipperiness; L k C represents the average body length of an EV. TP α represents the traffic capacity of this road. TP ξ are calibration parameters; the average vehicle speed is χ. k L i,j Let be the distance from the i-th point to the j-th node; The EV quantity model is used to describe the operation of EVs; it includes the EV emergency evacuation model, the EV evacuation scheduling model, and the EV capacity model. Based on the EV's travel time t1, we can obtain the number of EVs at each node at that time and the node that the EV traveled to at that time; when When a vehicle arrives at its destination, it is in a parked state, the driving variable r = 0, and the number of vehicles at the destination node is incremented by 1; otherwise, it is in a driving state, the driving variable r = 1, and the number of vehicles at the destination remains unchanged. Then the matrix N of the number of vehicles on the road at time t2 can be obtained statistically. road (t), and at the same time, the number of EVs at each node can be obtained, denoted as matrix Nev.0; Nev=Nev.0+N road (t) In the formula, represents the total number of EVs; this formula indicates that at the same time, the total number of EVs in the urban area is the sum of the number of parked EVs and the number of EVs in motion. The EV emergency evacuation model is as follows: N ref =βN road (t) In the formula, N ref EV is the number of people who choose to seek refuge; β is the refuge willingness coefficient; The EV evacuation scheduling model is as follows: In the formula: Let P be the distance the EV travels from node i to the refuge station at node j; P is the set of refuge stations; the above formula indicates that the EV travels to the nearest refuge station. The EV capacity model is as follows: Assuming that the initial capacity of each EV follows a normal distribution, denoted as Sev.0; then the capacity S of the k-th EV at time t. EV,k (t) can be expressed as: Where: Sev.0 k The capacity of the k-th EV before departure; The EV is transferred from node i to node j, and the shortest distance is... W 100 η represents the electrical energy consumed per 100 kilometers; ΔT represents the statistical time period; η represents the electrical energy consumed per 100 kilometers. f η c These are discharge efficiency and charging efficiency, respectively.

9. The power grid restoration system for reducing power impact based on power coupling devices according to claim 8, characterized in that, The V2G operation model includes the EV quantity model within a V2G site and the site schedulable model; The model for the number of EVs within a V2G site is as follows: In the formula, N sta Let Nev.0 be the number of EVs within the V2G station at time t. j The number of EVs at the initial node j; To count the number of EVs reaching this station within a given timeframe; The number of EVs departing from this station within the specified time interval; The site's schedulable capacity model is as follows: The total capacity of the j-th V2G site at time t is expressed as: The above formula means that a V2G site can only participate in load recovery if it is not damaged.

10. The power grid restoration system for reducing power impact based on power coupling devices according to claim 9, characterized in that, The ratio of the required load to all available electrical energy during the multiple time periods from the occurrence of a user's extreme event to the final load recovery is defined as the response potential. The response rate is defined as the ratio of the electrical energy required by the user at different times to the electrical energy already supplied by the power source during those times. The response potential model and response rate model are as follows: In the formula, R Q , Response potential and response rate, respectively; This represents the total load demand within the k-th island; These represent the electrical energy provided by all distributed energy sources within the k-th isolated island and the electrical energy already provided by the power source during that time period; T K This represents the number of time periods from the occurrence of an extreme event to the eventual recovery of the load.

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