Urban idle resource and emergency resource coordinated power supply sequence recovery method in extreme scene

Abstract

The invention provides an urban idle resource and emergency resource coordinated power supply sequence recovery method in an extreme scene, and the method comprises the steps: constructing a first scheduling model of available electric vehicles based on a first number of electric vehicles arriving at a parking lot during a fault period; constructing a second scheduling model of the mobile energy storage vehicle based on the road impedance model and the time-varying congestion coefficient under the extreme scene delay; constructing an emergency repair team model based on the fault interruption line; based on the first scheduling model, the second scheduling model and the emergency repair team model, constructing a multi-source cooperative power supply active support model with the minimum sum of load loss cost and resource scheduling cost as an objective function; and solving the multi-source cooperative power supply active support model, and determining a power supply recovery sequence. A multi-source cooperative power supply active support model including the sum of resource scheduling costs is constructed to participate in power distribution network support power supply, so that rapid load support power supply and rapid fault line recovery are realized.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method for sequentially restoring power supply by coordinating idle urban resources with emergency resources in extreme scenarios. Background Art

[0002] Extreme scenarios such as cyberattacks and natural disasters can cause massive power outages in power systems, severely impacting the normal electricity supply of users on the distribution network. With rapid urbanization, the development of power and transportation infrastructure is expected to enhance the proactive support capabilities of urban distribution networks in responding to extreme scenarios, which is also a key trend in the stable operation of new power systems.

[0003] With the rapid penetration of new energy vehicles in cities in recent years, a large amount of electricity resources has become available to support distribution network power supply in emergency situations. However, most current research focuses on dispatching electric vehicles after a disaster strikes, which creates a window of opportunity for vehicle mobility and prevents rapid support when distribution network power is insufficient. Idle EVs (electric vehicles) parked in parking lots can be plug-and-play, or switched between charging and discharging, to quickly support distribution network power supply. Compared to EVs waiting to be driven, these vehicles offer greater timeliness in emergency situations. At the same time, the convergence of transportation and electrification has also brought new challenges to distribution network emergency response. The involvement of mobile resources in distribution network emergency response often requires consideration of actual road traffic conditions. Existing research, however, primarily focuses on single-site road impedance models, failing to consider the social impacts of actual extreme scenarios, which can lead to vehicle travel delays and, in turn, extended power supply availability.

[0004] Furthermore, traditional proactive support methods focus on dispatching local generation resources and mobile resources to support the distribution network. These methods fail to consider the impact of real-time road delays on decision-making, and in particular fail to integrate line repairs with power dispatch. Consequently, methods for sequential restoration of distribution network power supply using emergency resources are neither comprehensive nor practical and require further refinement. Summary of the Invention

[0005] In order to overcome the above technical deficiencies, this application provides a method for sequential power restoration in which idle urban resources and emergency resources are coordinated in extreme scenarios. To achieve the above objectives, this application is implemented according to the following technical solutions:

[0006] This application provides a method for sequential power restoration in extreme scenarios by coordinating idle urban resources with emergency resources, including:

[0007] Based on the first number of electric vehicles arriving at the parking lot during the fault period, a first dispatch model for available electric vehicles is constructed;

[0008] Based on the road impedance model and time-varying congestion coefficient under extreme delay scenarios, a second scheduling model for mobile energy storage vehicles is constructed;

[0009] Build an emergency repair team model based on the fault interrupted line;

[0010] Based on the first scheduling model, the second scheduling model and the emergency repair team model, a multi-source collaborative power supply active support model is constructed with the objective function of minimizing the sum of load loss cost and resource scheduling costs;

[0011] The multi-source collaborative power supply active support model is solved to determine the power supply restoration sequence.

[0012] Optionally, the step of constructing a first scheduling model for available electric vehicles based on a first number of electric vehicles arriving at the parking lot during the fault period includes:

[0013] determining an initial charge of each electric vehicle based on a first number of electric vehicles arriving at the parking lot during the fault period;

[0014] Determine the arrival time and departure time of each electric vehicle based on the vehicle travel characteristics of each electric vehicle;

[0015] determining a second number of electric vehicles that meet a preset screening condition based on the initial power level, the arrival time, and the departure time;

[0016] constructing a first scheduling model for available electric vehicles based on the second number of electric vehicles;

[0017] Optionally, the constraints of the first dispatching model for electric vehicles include the following:

[0018] Power constraints for vehicles to grid charging stations, mutual exclusion constraints for electric vehicle charging and discharging, power constraints for electric vehicle charging and discharging, and power constraints for electric vehicles.

[0019] Optionally, based on the road impedance model and time-varying congestion coefficient under extreme delay scenarios, a second dispatch model for mobile energy storage vehicles is constructed, including:

[0020] Based on the road impedance model and time-varying congestion coefficient under extreme delay scenarios, a time-varying road section impedance model and a time-varying intersection model are obtained;

[0021] Obtaining a total path impedance model based on the time-varying road segment impedance model and the time-varying intersection impedance model;

[0022] Based on the total path impedance model, a second scheduling model for mobile energy storage vehicles is constructed.

[0023] Optionally, the constraints of the second scheduling model include the following:

[0024] Mobile energy storage vehicle power constraints, mobile energy storage vehicle discharge constraints, and mobile energy storage vehicle power constraints.

[0025] Optionally, the constraints of the emergency repair team model include the following:

[0026] The initial status of line maintenance, the maximum number of single repairs by the emergency repair team, the line update status, and the time and space constraints of the repair team.

[0027] Optionally, the multi-source collaborative power supply active support model is specifically:

[0028] F=F1+F2

[0029]

[0030] Where F is the objective function, F1 is the load loss cost, F2 is the resource scheduling cost, ω is the important load coefficient, γ is the common load loss coefficient, The power loss of important loads is is the normal load loss power, is the active power of the photovoltaic at the i-th location, is the active discharge power of the mobile energy storage vehicle at location i, is the discharge active power of the electric vehicle at the i-th location, ξ m,t is the repair status of the mth fault line repaired by the repair team at time t, ρ1, ρ2, ρ3, and ρ4 are respectively the photovoltaic government subsidy coefficient, the mobile energy storage vehicle emergency subsidy coefficient, the output distribution network subsidy coefficient of the parking lot EV, and the subsidy coefficient of the emergency repair team; For electricity price.

[0031] Optionally, the constraints of the multi-source collaborative power supply active support model include the following:

[0032] The output of electric vehicles, active power of photovoltaics, radial constraints of distribution networks, power flow constraints of distribution networks, and Ohm's law constraints can be utilized.

[0033] Optionally, the second-order cone principle is used to relax the Ohm's law constraint to obtain a relaxed Ohm's law constraint.

[0034] This application has the following beneficial effects:

[0035] The method proposed in this application constructs a first scheduling model for electric vehicles taking into account travel randomness to promote the participation of idle urban resources in the field of emergency power supply; then introduces the time-varying impact of roads to improve the impedance of the travel path. On this basis, a second scheduling model for mobile vehicle energy storage is constructed. Then, for the fault-interrupted lines, an emergency repair team model is constructed to improve the optimized scheduling of mobile emergency resources in extreme scenarios; finally, by coordinating various resources to participate in the distribution network to support power supply, rapid load support power supply and rapid recovery of faulty lines are achieved.

[0036] In addition to the above-described purposes, features and advantages, the present application has other purposes, features and advantages. The present application will be further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0038] Figure 1 This is a flow chart of a method for sequentially restoring power supply by coordinating idle urban resources with emergency resources in an extreme scenario provided by an embodiment of the present application;

[0039] Figure 2 This is a schematic diagram of an example of improving the multi-resource power supply support of a 33-node distribution network provided by the experimental simulation provided in the embodiment of the present application;

[0040] Figure 3 It is a city road network topology map provided by the experimental simulation provided in the embodiment of the present application;

[0041] Figure 4 This is a photovoltaic output prediction diagram provided by the experimental simulation provided in the embodiment of the present application;

[0042] Figure 5 This is a time-of-use electricity price chart provided by the experimental simulation provided in the embodiment of the present application;

[0043] Figure 6 It is a mobile resource scheduling diagram provided by the experimental simulation provided by the embodiment of the present application;

[0044] Figure 7 It is an available active power output diagram provided by the experimental simulation provided in the embodiment of the present application;

[0045] Figure 8 This is the active power output diagram of the mobile energy storage vehicle provided by the experimental simulation provided in the embodiment of the present application. DETAILED DESCRIPTION

[0046] The embodiments of the present application are described in detail below with reference to the accompanying drawings, but the present application can be implemented in many different ways as defined and covered by the claims.

[0047] In order to solve the problems raised in the background technology, such as Figure 1 As shown, this application proposes a power supply sequence restoration method for coordinating idle resources and emergency resources in an extreme scenario, including:

[0048] Step S101: constructing a first scheduling model for available electric vehicles based on a first number of electric vehicles arriving at the parking lot during a fault period;

[0049] Since the number of electric vehicles arriving at the parking lot during the fault period is generally electric vehicles (EVs) that are idle, in order to be closer to the actual situation, the first number N of electric vehicles is generated randomly.

[0050] After randomly generating a first number of electric vehicles, the normal distribution function of the following formula (1) is then used to determine the initial power of each of the N electric vehicles.

[0051]

[0052] Where h(x) is the initial charge of the x-th EV; k and λ are the parameters of the normal distribution probability density function.

[0053] It's important to note that confirming the initial charge level of each EV is crucial to ensure that, when power is restored, the EVs providing power have sufficient charge to maintain their safety performance or meet the minimum mileage requirements. Data modeling and forecasting for idle EVs require consideration of their random variations. The normal distribution is applicable to symmetrical continuous data distributions and statistical inference. Therefore, a normal distribution was used to simulate EV travel behavior.

[0054] Then, considering the travel characteristics of each vehicle, the arrival time of each vehicle is modeled to determine the arrival time of each electric vehicle as follows:

[0055]

[0056] Where t1 is the arrival time of the EV, μ1 ​​and σ1 are the parameters of the distribution probability function.

[0057] Then, considering the travel characteristics of each vehicle, the departure time of each vehicle is modeled to determine the departure time of each electric vehicle as follows:

[0058]

[0059] Where t2 is the time when EV leaves the market, μ2 and σ2 are the parameters of the distribution probability function.

[0060] After determining the initial charge, arrival time, and departure time of each electric vehicle, the EV can be screened based on its travel time and initial charge to determine electric vehicles with a second charge that meet the preset screening conditions. The screening process is as follows:

[0061]

[0062] Where, is the discharge active power of the electric vehicle, is the upper limit of the active power of electric vehicle discharge, t le , t ar , t st and t ed They represent the departure time and arrival time of the EV, as well as the start and end time of the distribution network fault; They represent the actual power of the EV at idle and the minimum power for traveling, respectively.

[0063] It should be noted that the above preset screening conditions are the content corresponding to formula (4). The dispatchable conditions of idle EVs have dual attributes of time and power. Based on the travel chain theory, the EV users’ travel, departure time, and initial power are screened layer by layer. Only EVs that are present and meet the supporting power conditions can participate in the output through time and capacity judgment. Otherwise, is 0.

[0064] After determining the second number of electric vehicles, the second number of electric vehicles can be combined to construct a first scheduling model for electric vehicles. After constructing the first scheduling model, corresponding constraints need to be set for the first scheduling model, including the following:

[0065] Vehicle-to-Grid (V2G) charging station power constraints:

[0066]

[0067] Where, is the maximum interaction power of the sth V2G charging station; Z is the number of connected EVs.

[0068] EV charging and discharging mutual exclusion constraints:

[0069] α i,t +β i,t ≤1 (6)

[0070] In the formula, the binary variable α i,t and β i,t They are the charging and discharging indicators of EV respectively.

[0071] EV charging and discharging power constraints:

[0072]

[0073] Where, They are the EV's charge and discharge active power and the corresponding upper limit value; Ω p It is a collection of EVs that have been screened to support the distribution network;

[0074] EV power constraints:

[0075]

[0076] Where η ch ,η dis are the charging and discharging efficiency of EV, are the energy storage capacity and the upper and lower limits of the energy storage capacity of the i-th EV at time t.

[0077] Step S102: constructing a second dispatch model for mobile energy storage vehicles based on the road impedance model and time-varying congestion coefficient under extreme scenario delays;

[0078] Road impedance models for extreme scenarios reflect the relationship between traffic flow and factors such as road capacity, travel time, and cost in a road network. These models generally include a link impedance model and an intersection model. A link impedance model is a mathematical model used to describe the relationship between link traffic impedance and related influencing factors, while an intersection model is a mathematical or conceptual model used to describe and analyze intersection traffic flow characteristics, capacity, and traffic control effectiveness. This application does not specify the specific form of the link impedance model or intersection model.

[0079] The time-varying congestion coefficient is an indicator used to measure the change of road traffic congestion over time. Its expression is generally shown in Table 1:

[0080] Table 1 Time-varying congestion coefficient

[0081] Degree of delay Fast Pass Pass normally Delayed passage Difficult to pass U(t) [0,0.6] (0.6,0.8] (0.8,1.0] (1.0,2.0]

[0082] Then, according to the road section impedance model and the time-varying congestion coefficient, the corresponding time-varying road section impedance model is obtained. The process is as follows:

[0083]

[0084] Where, l ij (t) is the time-varying road section impedance, t0 is the initial normal travel time; v is the model parameter; taking the time-varying delay coefficient of 1 as the boundary, the time-varying road section impedance with delayed passage and difficult passage is described in sections.

[0085] According to the time-varying congestion coefficient and intersection model, the corresponding time-varying intersection impedance model is obtained. The specific process is as follows:

[0086]

[0087] Where, k i (t) is the time-varying intersection impedance, A is the intersection signal cycle, g is the green light time, t ais the estimated time period, h is the traffic capacity, and φ is the green cycle ratio. U(t) is the actual traffic ratio, or road congestion ratio. The first term in the formula represents the uniform delay, derived from field measurements and computer fitting. This model provides excellent results for isolated signalized intersections with low delays. The second term, derived using coordinate transformation, performs better for slightly higher delays. The third term further subdivides high delays, using a piecewise function to obtain a time-varying intersection integrated impedance model.

[0088] Finally, according to the time-varying road section impedance model and the time-varying intersection impedance model, the total path impedance model R can be obtained. ij (t):

[0089]

[0090] It should be noted that:

[0091] The scheduling of mobile resources in distribution networks under extreme scenarios is inseparable from the influence of road impedance. Traditional impedance models cannot reflect the travel delays in extreme scenarios in real time. Therefore, a time-varying congestion coefficient needs to be introduced.

[0092] After obtaining the total path impedance model, since the decision of the mobile energy storage vehicle's travel path will be affected by the total path impedance model, based on this, the second scheduling model of the mobile energy storage vehicle can be constructed according to the initial position of the mobile power vehicle and the destination to be reached, as well as the road impedance between the initial position and the destination.

[0093] Since the decision of the mobile energy storage vehicle's travel path is affected by the total path impedance model, its specific constraints are:

[0094]

[0095] Where, is the dispatching status of the kth mobile energy storage vehicle at the initial time t0; ψ k is the initial parking position of the kth mobile energy storage vehicle; Ω is the road set.

[0096] The second scheduling model also includes the following constraints:

[0097] Power constraints of mobile energy storage vehicles:

[0098]

[0099] Where, is the active discharge power of the mobile energy storage vehicle, is the discharge mark, is the upper limit of active discharge;

[0100] Discharge constraints of mobile energy storage vehicles:

[0101]

[0102] Mobile energy storage vehicle power constraints:

[0103]

[0104] Where, is the power of the i-th mobile energy storage vehicle at time t, are the upper and lower limits of the electric quantity respectively; μ dis are the discharge efficiencies of mobile energy storage vehicles respectively.

[0105] Step S103: Building an emergency repair team model based on the fault interrupted line;

[0106] A fault-disrupted line refers to a situation where a power or communication line, for example, experiences a malfunction due to various reasons, resulting in the interruption of transmission functionality. Therefore, to quickly restore line functionality, an emergency repair team is required to repair the fault. In this case, an emergency repair team model can be constructed based on the fault location, the location of the emergency repair team, and the number of emergency repair teams.

[0107] When building the emergency repair team model, the corresponding constraints include the following:

[0108] Line maintenance initial state setting constraints:

[0109]

[0110] Where N r is the fault line set; is the line status at the initial moment.

[0111] Limit the maximum number of repairs that the emergency repair team can perform at a time:

[0112]

[0113] ξ m,t is the repair status of the mth fault line repaired by the repair team at time t. When repair is in progress, its value is 1, otherwise it is 0. D is the upper limit of the number of repair lines.

[0114] Line status update constraints:

[0115] u ij,t =ξ m,t m∈N r ,ij∈N l (18)

[0116] u ijIt is a {0, 1} variable that indicates whether line ij is connected or disconnected. When the value is 1, it means that line ij is connected. After the repair team repairs the faulty line, the status will change to 1.

[0117] Time and space constraints of the repair team:

[0118]

[0119] Where, δ k,i,t is the scheduling status of the k repair team at node i at the initial time t.

[0120] Step S104: Based on the first scheduling model, the second scheduling model, and the emergency repair team model, a multi-source coordinated power supply active support model is constructed with minimizing the sum of load loss cost and resource scheduling costs as an objective function;

[0121] After constructing the first scheduling model, the second scheduling model, and the emergency repair team model, a multi-source collaborative power supply support model with the minimum sum of the load loss cost and the resource scheduling costs as the objective function can be constructed based on the first scheduling model, the second scheduling model, and the emergency repair team model. The construction process is as follows:

[0122] The objective function F is:

[0123] F=F1+F2 (20)

[0124] Where F1 is the load loss cost and F2 is the sum of the resource scheduling costs.

[0125] F1 is specifically:

[0126]

[0127] Where T is the fault time, ω is the important load coefficient, γ is the common load loss coefficient, The power loss of important loads is This is the power loss of normal load.

[0128] F2 is specifically:

[0129]

[0130] Where, is the active power of the photovoltaic power station at the i-th location (which can also be understood as the output of distributed power supply), is the active discharge power of the mobile energy storage vehicle at location i, is the discharge active power of the electric vehicle at the i-th location, ξ m,tis the repair status of the mth fault line repaired by the repair team at time t, ρ1, ρ2, ρ3, and ρ4 are respectively the photovoltaic government subsidy coefficient, the mobile energy storage vehicle emergency subsidy coefficient, the output distribution network subsidy coefficient of the parking lot EV, and the subsidy coefficient of the emergency repair team; For electricity price.

[0131] After constructing the objective function, it is necessary to determine the constraints for the objective function, specifically:

[0132] Electric vehicles can be used to generate power

[0133]

[0134] Where N p Meet at the parking lot.

[0135] Photovoltaic active power:

[0136]

[0137] Where, P pv，min 、P pv，max are the upper and lower limits of photovoltaic active power, π pv For existing photovoltaic access points.

[0138] Radial constraints of distribution network:

[0139]

[0140] The above formula (25) ensures that there are N nodes (where N f The topological structure of the distribution network with active nodes is composed of tree subgraphs.

[0141] Distribution network flow constraints:

[0142]

[0143] Where δ(i) is the set of end nodes with distribution network node i as the parent node, and π(i) is the set of head-end nodes with distribution network node i as the child node; and are the active and reactive powers of the injection point at node i respectively; is the active power output of EV at node i, is the output of the mobile energy storage vehicle at node i, and are the active power loss and actual active power demand of the load respectively; and are the reactive power loss and reactive power actual demand of the load respectively; P ij (t) and Q ij(t) The downstream active and reactive power flows of line ij at time t, P ki (t) and Q ki (t) is the active and reactive power flow at the upstream of line ki.

[0144] Constraints of Ohm's law:

[0145]

[0146] Where r ij , x ij is the line impedance.

[0147] The above model contains a large number of 0-1 integer variables, quadratic constraints, and a quadratic objective function. It is a mixed-integer nonlinear programming problem that cannot be solved using conventional methods. Therefore, effective methods are needed to reduce the degree or relax the problem. The specific steps are as follows:

[0148] Constructing new variables and Substitute the square term of voltage and the square term of current in the Ohm's law constraint of formula (28) respectively:

[0149]

[0150] According to the second-order cone relaxation principle, the following relaxation is performed to obtain the relaxed Ohm's law constraint:

[0151]

[0152] It's important to note that maintaining radial topology is crucial during the operation and control of distribution networks. To maintain this radial topology, two conditions must be met: 1) no loops (tree topology), i.e., open-loop operation, should be formed; and 2) every busbar in the system must be connected to a substation.

[0153] Step S105: solving the multi-source collaborative power supply active support model to determine a power supply restoration sequence.

[0154] The multi-source coordinated power supply active support model obtained above is solved, and the power supply restoration order can be determined based on the solution results.

[0155] Experimental simulation

[0156] In order to verify the effectiveness and superiority of the method proposed in this application, this application takes the distribution network-traffic network coupling as an example, selects a 33-node distribution network and a city road network for case analysis, and the distribution network structure is as follows: Figure 2 As shown in the figure, the urban road network topology is as follows Figure 3 As shown in Figure 2. Five photovoltaic groups are deployed in the system, and their predicted power generation curves are as follows: Figure 4As shown. Considering the load value, the interruption cost coefficient of important loads is set at 15 yuan / kWh, and that of ordinary loads is 5 yuan / kWh. To ensure that the voltage of the distribution system operates within the safety threshold, the voltage fluctuation range is set to be maintained between 0.9 and 1.10 per unit (pu). In addition, during the analysis process of this study, the time-of-use electricity price implemented in a certain area was referenced to conduct economic scheduling and optimization analysis, such as Figure 5 As shown. The power supply restoration period of the distribution network is selected as 9:00-15:00, a total of 7 hours. The parameters of the arrival and departure probability functions of this application are: μ1=6.92; σ1=1.24; μ2=17.47; σ2=1.80; the total path impedance related parameters are: v=1.85; A=175 seconds, t a =0.5 hours, h=830veh / h.

[0157] Table 1 Example parameter settings

[0158]

[0159]

[0160] In order to verify the effectiveness of the active power supply support method proposed in this application that combines idle urban resources with emergency resources in extreme scenarios, four sets of comparison schemes are set up:

[0161] Option 1: Use fixed resources, namely local photovoltaic power generation, for emergency support, and dispatch a repair team to repair the faulty line;

[0162] Option 2: Use local photovoltaic power generation and mobile energy storage vehicles for coordinated recovery, and dispatch a repair team to repair the faulty line;

[0163] Option 3: Collaborative recovery through local photovoltaics and EVs in public parking lots, and dispatching emergency repair teams to repair the faulty lines;

[0164] Option 4: Considering the power supply priority of emergency resources, the distribution network is coordinated to restore power through local photovoltaics, mobile energy storage vehicles, and EVs in public parking lots. At the same time, a repair team is dispatched to repair the faulty line.

[0165] (1) Economic cost analysis

[0166] A cost analysis was conducted on the power supply sequence restoration method proposed in this application that coordinates idle resources and emergency resources in cities. The load loss costs, photovoltaic scheduling costs, mobile energy storage vehicle costs, idle EV subsidy costs, and comprehensive total costs under different schemes are shown in Table 2.

[0167] Table 2 Costs of different plans (yuan)

[0168]

[0169] As shown in the table, compared to Option 1, Option 2 and Option 3, the load reduction loss costs were reduced by 29% and 44.79%, respectively. This proposed solution can further reduce load loss costs by 74.92%, and the overall cost is reduced by 65.47%. Furthermore, compared to Option 3, although this proposed solution incurs an additional 991 yuan in EV subsidy costs, it reduces the distribution network's load loss costs by 18,822 yuan, ultimately reducing the total cost by 41.59%.

[0170] (2) Analysis of mobile emergency resource scheduling

[0171] This application uses mobile emergency resources to participate in the distribution network power supply to restore the order. The mobile emergency resource scheduling process is as follows Figure 7 As shown in Table 3, the repair process and MESS transfer process are shown in Table 3. Figure 6 It can be seen that the mobile energy storage vehicle starts from the initial parking point and is dispatched with the goal of minimizing load loss and output cost. EPSV1 starts to transfer at 10:00 and accesses node 13 for emergency power supply through one dispatch. EPSV2 starts from nodes 4 and 24 at 10:00 and 12:00 respectively. After the transfer, it accesses nodes 24 and 19 for power supply support. In addition, at different times, the repair team will restore part of the load by repairing the line, such as repairing line 32-33 so that the 33 load nodes of the island can be powered. Finally, all lines were repaired at 15:00, so that the load islands that appeared in extreme scenarios were connected to the root node, and the load was finally supported within a complete power supply range. It can be seen that after multi-party dispatch and sequential repairs, the distribution network has achieved complete power restoration.

[0172] Table 3 Line repair process and mobile energy storage vehicle transfer process

[0173]

[0174] (3) Analysis of emergency resource output

[0175] According to the proposed method, the resource output is analyzed in detail. Figure 7 、 8 As shown in the figure. During the power restoration phase (10:00-12:00), idle EVs in the parking lot discharged power to the distribution grid in V2G mode, serving as the primary source of power support. The mobile energy storage vehicle provided lower output, and the photovoltaic system remained fully powered. Notably, at 2:00 PM, the EVs in Parking Lot 2 experienced a brief period of charging (negative power). This was because the combined output of the mobile energy storage vehicle and EVs exceeded the load demand, and the system automatically adjusted the excess power to recharge the EV batteries. By dynamically coordinating various emergency resources, the system achieved a balance between supply and demand, ensuring reliable power supply for user loads.

[0176] (3) Load recovery results analysis

[0177] This application uses multi-resource collaborative power supply support to enable the distribution network to quickly maintain a high recovery rate for critical loads after a fault occurs. The power supply capacity of ordinary loads continued to improve as the emergency repair progressed, maintaining a recovery level of over 90% throughout the entire process and achieving full recovery by 3:00 PM. The efficient work of the emergency repair team led to the comprehensive repair of the faulty line, the complete reconstruction of the distribution network topology, and the full access to the upper power grid, ultimately achieving reliable power supply for all loads.

[0178] In summary, the method proposed in this application constructs a first scheduling model for electric vehicles taking into account travel randomness to promote the participation of idle urban resources in the field of emergency power supply; then, the time-varying influence of roads is introduced to improve the impedance of the travel path. On this basis, a second scheduling model for mobile vehicle energy storage is constructed. Then, for the fault-interrupted lines, an emergency repair team model is constructed to improve the optimized scheduling of mobile emergency resources in extreme scenarios; finally, by coordinating various resources to participate in the distribution network to support power supply, rapid load support power supply and rapid recovery of fault lines are achieved.

[0179] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Various modifications and variations are possible for those skilled in the art. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A method for sequential power restoration in an extreme scenario by coordinating idle urban resources with emergency resources, characterized in that: include: Based on the first number of electric vehicles arriving at the parking lot during the fault period, a first dispatch model for available electric vehicles is constructed; Based on the road impedance model and time-varying congestion coefficient under extreme delay scenarios, a second scheduling model for mobile energy storage vehicles is constructed; Build an emergency repair team model based on the fault interrupted line; Based on the first scheduling model, the second scheduling model and the emergency repair team model, a multi-source collaborative power supply active support model is constructed with the objective function of minimizing the sum of load loss cost and resource scheduling costs; The multi-source collaborative power supply active support model is solved to determine the power supply restoration sequence.

2. The method according to claim 1, characterized in that The method of constructing a first scheduling model for available electric vehicles based on a first number of electric vehicles arriving at the parking lot during the fault period comprises: determining an initial charge of each electric vehicle based on a first number of electric vehicles arriving at the parking lot during the fault period; Determine the arrival time and departure time of each electric vehicle based on the vehicle travel characteristics of each electric vehicle; determining a second number of electric vehicles that meet a preset screening condition based on the initial power level, the arrival time, and the departure time; Based on the second number of electric vehicles, a first scheduling model for available electric vehicles is constructed.

3. The method according to claim 2, characterized in that The constraints of the first dispatch model for electric vehicles include the following: Power constraints for vehicles to grid charging stations, mutual exclusion constraints for electric vehicle charging and discharging, power constraints for electric vehicle charging and discharging, and power constraints for electric vehicles.

4. The method according to claim 1, wherein Based on the road impedance model and time-varying congestion coefficient under extreme delay scenarios, a second dispatch model for mobile energy storage vehicles is constructed, including: Based on the road impedance model and time-varying congestion coefficient under extreme delay scenarios, a time-varying road section impedance model and a time-varying intersection model are obtained; Obtaining a total path impedance model based on the time-varying road segment impedance model and the time-varying intersection impedance model; Based on the total path impedance model, a second scheduling model for mobile energy storage vehicles is constructed.

5. The method according to claim 4, characterized in that The constraints of the second scheduling model include the following: Mobile energy storage vehicle power constraints, mobile energy storage vehicle discharge constraints, and mobile energy storage vehicle power constraints.

6. The method according to claim 1, characterized in that The constraints of the emergency repair team model include the following: The initial status of line maintenance, the maximum number of single repairs by the emergency repair team, the line update status, and the time and space constraints of the repair team.

7. The method according to claim 1, characterized in that The multi-source collaborative power supply active support model is specifically as follows: F=F1+F2 Where F is the objective function, F1 is the load loss cost, F2 is the resource scheduling cost, ω is the important load coefficient, γ is the common load loss coefficient, The power loss of important loads is is the normal load loss power, is the active power of the photovoltaic at the i-th location, is the active discharge power of the mobile energy storage vehicle at location i, is the discharge active power of the electric vehicle at the i-th location, ξ m,t is the repair status of the mth fault line repaired by the repair team at time t, ρ1, ρ2, ρ3, and ρ4 are respectively the photovoltaic government subsidy coefficient, the mobile energy storage vehicle emergency subsidy coefficient, the output distribution network subsidy coefficient of the parking lot EV, and the subsidy coefficient of the emergency repair team; For electricity price.

8. The method according to claim 7, characterized in that The constraints of the multi-source collaborative power supply active support model include the following: The output of electric vehicles, active power of photovoltaics, radial constraints of distribution networks, power flow constraints of distribution networks, and Ohm's law constraints can be utilized.

9. The method according to claim 7, characterized in that The second-order cone principle is used to relax the Ohm's law constraint to obtain a relaxed Ohm's law constraint.

Images