Network-road collaborative recovery method and system considering toughness improvement in extreme weather

Abstract

The invention relates to a network-circuit collaborative recovery method and system considering toughness improvement in extreme weather, belongs to the technical field of disaster prevention scheduling of a power system, and solves the problem that high-efficiency power support is difficult to realize because power grid fault recovery neglects voltage stability, power loss and space-time dynamic characteristics of mobile energy storage in the prior art. Comprising the steps of obtaining each partition after a highway power supply system fails; based on each obtained partition, obtaining a recovery capability grade score of each partition, further obtaining each partition after grade division, and then constructing a partition mutual aid support model and a mobile energy storage space-time scheduling model; and constructing a collaborative recovery optimization objective function taking maximization of the mutual aid support capacity as a target based on the recovery capability grade score of each partition, constructing constraint conditions based on each partition after grade division, a partition mutual aid support model and a mobile energy storage space-time scheduling model, and performing solving to obtain an optimal collaborative support scheme. And system power supply recovery is carried out.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system disaster prevention dispatching, and in particular to a network-line collaborative recovery method and system considering resilience improvement under extreme weather. BACKGROUND

[0002] After the highway power supply system is affected by extreme disasters and causes partitioning of faults, a reasonable fault recovery strategy can improve system reliability and reduce load loss. The collaborative optimization of power resources has an important influence on load loss during the recovery process. The gradual application of mobile energy storage as a distributed energy storage unit can provide flexible power support for the load. Therefore, under extreme conditions, how to coordinate various dispatchable resources in the highway power supply system to improve the efficiency of post-disaster recovery and reduce load loss is an important problem that needs attention.

[0003] Current strategy research mainly focuses on fault recovery with distributed power supply access, without considering voltage stability and power loss to optimize the grid structure, and ignoring the time and space dynamic characteristics of mobile energy storage, making it difficult to achieve efficient power support in the event of sudden failures. SUMMARY

[0004] In view of the above analysis, the embodiments of the present application aim to provide a network-line collaborative recovery method and system considering resilience improvement under extreme weather, to solve the problem that existing power grid fault recovery ignores voltage stability and power loss and the time and space dynamic characteristics of mobile energy storage, making it difficult to achieve efficient power support.

[0005] In one aspect, the embodiments of the present application provide a network-line collaborative recovery method considering resilience improvement under extreme weather, comprising the following steps:

[0006] Obtaining each partition after the fault of the highway power supply system;

[0007] Based on the obtained each partition, obtaining the recovery ability grade score of each partition, and then obtaining each partition after grade division, and then constructing a partition mutual aid support model and a mobile energy storage space-time scheduling model;

[0008] Based on the recovery ability grade score of each partition, a collaborative recovery optimization objective function is constructed with the goal of maximizing mutual aid support capacity. Based on the each partition after grade division, the partition mutual aid support model and the mobile energy storage space-time scheduling model, the constraint conditions of the collaborative recovery optimization objective function are constructed, and the solution is obtained, to obtain the optimal collaborative support scheme for system power supply recovery; wherein the collaborative support scheme includes each supported partition, each selected tie line, and the path of the mobile energy storage.

[0009] Further, the recovery ability grade score of each partition is obtained by the following method:

[0010] The recovery capability index of each partition is obtained, and a recovery capability index matrix is constructed; the types of the recovery capability index include positive and negative;

[0011] Based on the recovery capability index matrix, the positive ideal solution of each positive recovery capability index and the negative ideal solution of each negative recovery capability index are calculated, and then the positive ideal Euclidean distance and the negative ideal Euclidean distance of each partition are obtained;

[0012] Based on the positive ideal Euclidean distance and the negative ideal Euclidean distance of each partition, the recovery capability grade score of each partition is obtained.

[0013] Further, the positive recovery capability index includes energy comprehensive utilization rate and mobile energy storage utilization rate; the negative recovery capability index includes load power shortage rate, highway important equipment power outage rate and partition distance from the main network.

[0014] Further, the partition mutual aid support model includes a partition capacity gap calculation model, which is represented as:

[0015] ΔP i (t)=P i,total (t)-P i,av (t)

[0016] Wherein,

[0017]

[0018] P i,av (t)=P i,s (t)+P i,f (t)

[0019] In the formula, ΔP i (t) represents the capacity gap of the partition i at time t, P i,total (t) represents the total power demand of the partition i at time t, P i,load (t) represents the load power of the partition i that needs to be powered at time t, P i,c (t) represents the charging demand power of the energy storage system in the partition i at time t, P loss,i,e (t) represents the power loss of the partition i through the tie line e at time t, E i represents the set of all tie lines of the partition i, P i,av (t) represents the available power of the partition i at time t, P i,s (t) represents the real-time power generation of the distributed power in the partition i at time t, P i,f (t) represents the discharge power of the energy storage system in the partition i at time t.

[0020] Further, the partition mutual aid support model further comprises an actual support power calculation model between partitions, denoted as:

[0021]

[0022] wherein,

[0023]

[0024] in the formula, denotes the actual support power provided by partition j to partition i at time t, α i←j denotes the support contribution degree of partition j to partition i at time t, P i s denotes the actual support power obtained by partition i at time t, P i←j denotes the theoretical support power provided by partition j to partition i at time t, N i denotes the set of partitions providing support to partition i, denotes the support power provided by partition j to partition i at time t according to priority, P denotes the maximum support power that can be provided by partition j to partition i at time t, λ i denotes the mutual aid demand priority coefficient of partition i, P j,av denotes the available power of partition j at time t.

[0025] Further, the mobile energy storage space-time scheduling model is denoted as:

[0026]

[0027] in the formula, denotes the actual space-time distance between mobile energy storage i MESS and partition j at time t, D i←j denotes the actual time for mobile energy storage i i←j to travel from partition j to partition i at time t, v i←j,0 denotes the actual position distance between mobile energy storage i and partition j at time t, D MESS denotes the actual travel speed of mobile energy storage i

[0028] Further, the collaborative recovery optimization objective function F is denoted as:

[0029]

[0030] in the formula, α denotes a load recovery weight coefficient; ρ i denotes the recovery ability level score of partition i; L irepresents the recovered load amount of the partition i; N represents the set of all partitions; β represents the support capacity weight coefficient; x k represents the operating state of the switch of the tie line k, wherein the value of 1 represents connection and 0 represents disconnection; C k represents the support capacity of the tie line k; N k represents the set of all tie lines.

[0031] Further, the constraint conditions include a mobile energy storage constraint, a distributed power supply constraint, a network reconstruction constraint and a partition mutual aid constraint; wherein the mobile energy storage constraint is constructed based on a mobile energy storage space-time scheduling model, and the partition mutual aid constraint is constructed based on each partition after the grade division and a partition mutual aid support model.

[0032] Further, according to the recovery ability grade score of each supported partition in the collaborative support scheme, the corresponding tie line is gradually incorporated into the main network in the order from high to low, and the mobile energy storage is moved based on the path of the mobile energy storage, so as to realize the power supply recovery of the system.

[0033] On the other hand, the embodiment of the present application provides a network-line collaborative recovery system considering resilience improvement under extreme weather, comprising:

[0034] A partition acquisition module is configured to acquire each partition after the fault of the expressway power supply system;

[0035] A model construction module is configured to obtain the recovery ability grade score of each partition based on the acquired each partition, and then obtain each partition after the grade division, and further construct a partition mutual aid support model and a mobile energy storage space-time scheduling model.

[0036] A power supply recovery module is configured to construct a collaborative recovery optimization objective function with the maximum mutual aid support capacity as the target based on the recovery ability grade score of each partition, construct the constraint condition of the collaborative recovery optimization objective function based on each partition after the grade division, the partition mutual aid support model and the mobile energy storage space-time scheduling model, and solve the collaborative recovery optimization objective function to obtain the optimal collaborative support scheme for system power supply recovery; wherein the collaborative support scheme includes each supported partition, each selected tie line and the path of the mobile energy storage.

[0037] Compared with the prior art, the present application can at least realize one of the following beneficial effects:

[0038] The present application provides a network-line collaborative recovery method and system considering resilience improvement under extreme weather,

[0039] 1. By obtaining each partition after the fault of the expressway power supply system, the recovery ability grade score of each partition is obtained, and then each partition after grade division is obtained, and then a partition mutual aid support model is constructed, a partition mutual aid support model considering multiple factors such as energy utilization, load guarantee, main network distance is constructed, accurate allocation of power supply resources in the fault recovery stage is realized, and the practicability and adaptability of the strategy are enhanced;

[0040] 2. The partition mutual aid constraint is constructed through the partition mutual aid mechanism and the power supply of the higher grade partition to the lower grade partition, the priority recovery of the key load is guaranteed, and the overall recovery ability and voltage stability of the system are improved;

[0041] 3. Through the mobile energy storage space-time scheduling model, the distributed power supply constraint and the network reconstruction constraint are constructed, the comprehensive scheduling path, the operation state and the load gap are established, the energy storage vehicle driving and support strategy are optimized, the power supply flexibility and scheduling efficiency in the emergency scene are improved, and the load coverage ability and response speed in the partition recovery process are further guaranteed;

[0042] 4. Through the constructed cooperative recovery optimization objective function and constraint condition, efficient power mutual aid is realized, the fault recovery ability of the power supply system is improved, and the power supply reliability of the power supply system after the disaster is enhanced, and the problem that the existing technology ignores the voltage stability and power loss and the time-space dynamic characteristics of mobile energy storage in the power grid fault recovery, and it is difficult to realize efficient power support is solved.

[0043] In the present application, the above-mentioned technical solutions can be combined with each other to realize more preferred combination schemes. Other features and advantages of the present application will be described in the subsequent specification, and some advantages will become apparent from the specification or by implementing the present application. The purpose and other advantages of the present application can be achieved and obtained from the contents specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0044] The accompanying drawings are included to provide a better understanding of the embodiments, and are not considered as limiting the application, and throughout the drawings, the same reference signs represent the same components.

[0045] Figure 1 The flowchart of the network-road cooperative recovery method considering resilience improvement under extreme weather provided for embodiment 1 of the present application;

[0046] Figure 2 The space-time scheduling model of the expressway mobile energy storage provided for embodiment 1 of the present application;

[0047] Figure 3A partition division topology schematic diagram of a power supply system provided for the embodiment 3 of the present application is shown in the figure;

[0048] Figure 4 A daily load prediction schematic diagram of the power supply system provided for the embodiment 3 of the present application is shown in the figure;

[0049] Figure 5 A partition mutual aid schematic diagram of the strategy 1 provided for the embodiment 3 of the present application is shown in the figure;

[0050] Figure 6 A partition mutual aid schematic diagram of the strategy 2 provided for the embodiment 3 of the present application is shown in the figure;

[0051] Figure 7 A partition mutual aid capacity comparison schematic diagram under two strategies provided for the embodiment 3 of the present application is shown in the figure;

[0052] Figure 8 A partition 4 load recovery amount comparison schematic diagram under two strategies provided for the embodiment 3 of the present application is shown in the figure;

[0053] Figure 9 A load recovery amount comparison schematic diagram under two strategies provided for the embodiment 3 of the present application is shown in the figure;

[0054] Figure 10 A voltage change schematic diagram under two strategies provided for the embodiment 3 of the present application is shown in the figure. DETAILED DESCRIPTION

[0055] The preferred embodiments of the present application will be described in detail below with reference to the accompanying drawings, wherein the accompanying drawings form a part of the present application and are used to explain the principles of the embodiments of the present application, but are not used to limit the scope of the present application.

[0056] Embodiment 1

[0057] One specific embodiment of the present application discloses a network-road collaborative recovery method considering resilience improvement under extreme weather, as shown in the figure, comprising the following steps: Figure 1

[0058] S1, obtaining each partition after the fault of the expressway power supply system.

[0059] Specifically, the partition refers to an island formed after the fault of the expressway power supply system.

[0060] S2, based on the obtained each partition, obtaining the recovery ability grade score of each partition, and then obtaining each partition after grade division, and then constructing a partition mutual aid support model and a mobile energy storage space-time scheduling model.

[0061] In the implementation, the recovery ability grade score of each partition is obtained by the following way:

[0062] ​S21, selecting each recovery capability index of each partition to obtain each recovery capability index data of each partition, and then constructing a recovery capability index matrix; wherein the types of the recovery capability index include positive and negative;

[0063] Specifically, the positive recovery capability index includes energy comprehensive utilization rate and mobile energy storage utilization rate; the negative recovery capability index includes load power shortage rate, highway important equipment power outage rate and partition distance from the main network.

[0064] In specific implementation, each recovery capability index data is obtained by the following way:

[0065] The energy comprehensive utilization rate of each partition is represented as:

[0066]

[0067] In the formula, η E represents the energy comprehensive utilization rate, P in (t) represents the actual power absorbed by the distributed power supply in the partition at time t, P out (t) represents the available power of the partition at time t, and Δt represents the time step length, and T represents the fault recovery time.

[0068] It can be understood that the ratio of the actual power absorbed by the distributed power supply in the partition to the available power of various energies in the partition is used as the energy comprehensive utilization rate, which is used to measure the utilization efficiency of the distributed energy in the partition.

[0069] The mobile energy storage utilization rate of each partition is represented as:

[0070]

[0071] In the formula, η MS,i represents the mobile energy storage utilization rate of the partition i, P MS,i (t) represents the power provided by the mobile energy storage device for the partition i at time t, P load,i (t) represents the total load demand power of the partition i at time t.

[0072] It can be understood that the mobile energy storage utilization rate is mainly related to the actual power supply of the energy storage vehicle, which is used to measure the contribution of the mobile energy storage vehicle to meet the load demand of the partition during the fault recovery process.

[0073] The load power shortage rate of each partition is represented as:

[0074]

[0075] In the formula, η LPSP,i represents the load power shortage rate of the partition i, P loss,i (t) represents the power shortage of the partition i at time t.

[0076] It can be understood that the load power failure rate is mainly used to measure the proportion of power failure of the load in the subarea, and the lower the load power failure rate is, the stronger the power supply capacity in the subarea is.

[0077] The highway important equipment power failure rate of each subarea is represented as:

[0078]

[0079] In the formula, η out represents the highway important equipment power failure rate of the subarea; I out,i (t) represents the power failure state of the gth key equipment in the subarea at time t, wherein 1 represents power failure, and 0 represents normal operation; T total represents the total time of the evaluation period, and G represents the total number of key equipments in the subarea on the highway.

[0080] It can be understood that in the highway power supply system, the power supply situation of the infrastructure along the highway should be considered, and therefore the highway important equipment power failure rate is introduced to evaluate the influence of the subarea power supply on the traffic system.

[0081] The subarea distance from the main network of each subarea is represented as:

[0082]

[0083] In the formula, D iso represents the subarea distance from the main network; represents the straight-line distance from the ith a node in the subarea to the nearest main network node, wherein the main network is a large power grid, and the node represents the large power grid; represents the distance from the ith a node in the subarea to the jth a intermediate node; represents the path weight coefficient of the jth a intermediate node, M represents the number of nodes of the shortest path between the subarea and the main network node, the nodes on the path are intermediate nodes; β represents a path attenuation coefficient, 0<β≤1, which is used to adjust the distance weight.

[0084] It can be understood that the subarea distance from the main network index is crucial for the subarea mutual aid process in fault recovery, and a farther subarea may need additional resource support.

[0085] In specific implementation, the recovery capability index matrix U is represented as:

[0086]

[0087] In the formula, u iqrepresents the normalized value of partition i on the q-th recovery capability metric, m represents the number of partitions, and n represents the number of recovery capability metrics.

[0088] Specifically,

[0089] For positive recovery capacity indicators, maximum value normalization is used:

[0090]

[0091] In the formula, Indicates that partition i is at the qth position. + Data on a positive recovery capability indicator, Indicates that partition i is at the qth position. + Normalized values ​​for each recovery capability indicator.

[0092] For negative recovery capacity indicators, inverse normalization is used:

[0093]

[0094] In the formula, Indicates that partition i is at the qth position. - Data on a negative recovery capacity indicator, Indicates that partition i is at the qth position. - Normalized values ​​for each recovery capability indicator.

[0095] S22. Based on the recovery capability index matrix, calculate the positive ideal solution of each positive recovery capability index and the negative ideal solution of each negative recovery capability index, and then obtain the positive ideal Euclidean distance and the negative ideal Euclidean distance of each partition.

[0096] In practice,

[0097] The positive ideal solutions for each positive recovery capability index are expressed as:

[0098]

[0099] In the formula, Indicates the qth + The ideal solution for a positive recovery capability index.

[0100] The negative ideal solutions for each negative recovery capability index are expressed as:

[0101]

[0102] In the formula, Indicates the qth - The negative ideal solution of a negative recovery capability index.

[0103] In specific implementation, the positive ideal Euclidean distance and the negative ideal Euclidean distance of each subregion are calculated based on the TOPSIS evaluation method, denoted as:

[0104]

[0105] In the formula, denotes the positive ideal Euclidean distance of the subregion i, denotes the negative ideal Euclidean distance of the subregion i, n + , n - denote the number of positive and negative recovery capability indexes respectively.

[0106] S23, based on the positive ideal Euclidean distance and the negative ideal Euclidean distance of each subregion, the recovery capability grade score of each subregion is obtained.

[0107] In specific implementation, the recovery capability grade score of each subregion is denoted as:

[0108]

[0109] In the formula, ρ i denotes the recovery capability grade score of the subregion i, the closer to 1, the better the subregion state, the stronger the power supply capability, and the higher the reliability, the closer to 0, the worse the subregion state, and there is a greater power supply risk; η z denotes the disaster threat coefficient.

[0110] Specifically, according to the recovery capability grade score of each subregion, different grade ranges are set to determine the corresponding recovery capability grade, and the grade division of each subregion is completed; for example, the recovery capability grade is set to three levels of low, medium and high, corresponding to the values 1, 2 and 3 respectively; the recovery capability grade score in the range of 0-0.3 is set to low grade, in the range of 0.3-0.7 is set to medium grade, and in the range of 0.7-0.1 is set to high grade.

[0111] In implementation, the subregion mutual aid support model includes a subregion capacity gap calculation model, denoted as:

[0112] ΔP i (t) = P i,total (t) - P i,av (t) (14)

[0113] wherein,

[0114]

[0115] P i,av (t) = P i,s (t) + P i,f (t) (16)

[0116] where ΔP i (t) denotes the capacity gap of zone i at time t, P i,total (t) denotes the total power demand of zone i at time t, P i,load (t) denotes the load power of zone i that needs to be supplied at time t, P i,c (t) denotes the charging demand power of the energy storage system in zone i at time t, P loss,i,e (t) denotes the power loss of zone i when transmitting through tie-line e at time t, E i denotes the set of all tie-lines of zone i, P i,av (t) denotes the available power of zone i at time t, P i,s (t) denotes the real-time power generation of the distributed power source in zone i at time t, P i,f (t) denotes the discharging power of the energy storage system in zone i at time t.

[0117] In implementation, the inter-zone mutual aid support model further comprises an actual support power calculation model between zones, denoted as:

[0118]

[0119] wherein,

[0120]

[0121] wherein, denotes the actual support power provided by zone j to zone i at time t, a i←j denotes the support contribution degree of zone j to zone i at time t, P i s (t) denotes the actual support power obtained by zone i at time t, P i←j (t) denotes the theoretical support power provided by zone j to zone i at time t, N i denotes the set of zones providing support to zone i, denotes the support power provided by zone j to zone i according to priority at time t, denotes the maximum support power that can be provided by zone j to zone i at time t, l i denotes the mutual aid demand priority coefficient of zone i, P j,av (t) denotes the available power of zone j at time t.

[0122] In specific implementation, the support contribution degree a i←j of zone j to zone i at time t is denoted as:

[0123]

[0124] wherein, Pj,m (t) represents the minimum power required for partition j to reserve.

[0125] In particular implementation, the mutual aid demand priority coefficient λ i is represented as:

[0126]

[0127] In the formula, L i represents the recovery ability level of partition i.

[0128] In particular implementation, the maximum support power that partition j can provide to partition i at time t is represented as:

[0129]

[0130] In the formula, V i (t) represents the voltage of partition i at time t, V j (t) represents the voltage of partition j at time t, X i,j represents the reactance between partition i and partition j, represents the maximum transmission capacity of the line between partition i and partition j.

[0131] In implementation, as Figure 2 shown, considering the influence of road travel time, node charging and discharging time and space on scheduling, the mobile energy storage space-time scheduling model is established, represented as:

[0132]

[0133] In the formula, represents the actual time of mobile energy storage i MESS traveling from partition j to partition i, D i←j (t) represents the space-time distance between partition j and partition i at time t, v i←j (t) represents the travel speed of mobile energy storage from partition j to partition i at time t, D i←j,0 (t) represents the actual location distance between partition j and partition i at time t, represents the actual travel speed of mobile energy storage i MESS at time t.

[0134] In particular, the actual travel speed of mobile energy storage i MESS at time t is represented as:

[0135]

[0136] In the formula, represents the actual travel speed of mobile energy storage i MESSwherein, v0 represents the initial speed of the vehicle, c represents the speed attenuation factor.

[0137] S3, based on the recovery ability level score of each partition, a collaborative recovery optimization objective function is constructed, the constraint conditions of the collaborative recovery optimization objective function are constructed based on the partition after the level division, the partition mutual aid support model and the mobile energy storage space-time scheduling model, and the solution is obtained, and the optimal collaborative support scheme is obtained to carry out system power supply recovery; wherein, the collaborative support scheme includes each supported partition, each selected tie line, and the path of the mobile energy storage.

[0138] In implementation, the collaborative recovery optimization objective function F is represented as:

[0139]

[0140] In the formula, α represents the load recovery weight coefficient; ρ i represents the recovery ability level score of the partition i; L i represents the recovered load amount of the partition i; N represents the set of all partitions; β represents the support capacity weight coefficient; x k represents the operating state of the switch of the tie line k, wherein, the value of 1 represents connection, and 0 represents disconnection; C k represents the support capacity of the tie line k; N k represents the set of all tie lines.

[0141] In implementation, the constraint conditions include mobile energy storage constraints, distributed power supply constraints, network reconstruction constraints and partition mutual aid constraints; wherein, the mobile energy storage constraints are constructed based on the mobile energy storage space-time scheduling model, and the partition mutual aid constraints are constructed based on the partition after the level division and the partition mutual aid support model.

[0142] In specific implementation,

[0143] The mobile energy storage constraint is represented as:

[0144]

[0145]

[0146] In the formula, represents the state of charge of the energy storage vehicle i MESS at time t, represents the discharge and charge power of the energy storage vehicle i MESS , represents the rated capacity of the energy storage vehicle i MESS , ESS represents the average driving speed of the energy storage vehicle; represents the state of charge of the energy storage vehicle i MESSwhere m' is the selected path, 1 means selected, 0 means not selected; M represents the total number of optional paths, denotes the energy storage vehicle i MESS the remaining distance to return to the base station, denotes the energy storage vehicle i MESS rated power; η f , η c respectively represent the discharge and charge coefficients of the energy storage vehicle, t max denotes the maximum driving time of the energy storage vehicle, Ω ESS denotes the set of energy storage vehicles, t end denotes the end time of the energy storage vehicle.

[0147] It can be understood that the SOC of the mobile energy storage needs to be limited within a reasonable range, the charging and discharging power cannot exceed its rated value, the moving time needs to consider the driving distance and speed, and the path constraint may need to ensure that the mobile energy storage will not be repeatedly dispatched.

[0148] The distributed power supply constraint is represented as:

[0149]

[0150] In the formula, denotes the minimum available output of DG, denotes the maximum available output of DG, denotes the output of distributed power supply i DG at time t, R DG denotes the maximum ramp rate of DG, denotes the recovery capacity of load node i jd , and denotes the output of distributed power supply i DG .

[0151] It can be understood that the output constraint of the distributed power supply includes the maximum and minimum output and the ramp rate limit, and at the same time, the distributed power supply needs to ensure to continuously bear a certain proportion of load recovery amount to avoid over-discharge of the energy storage.

[0152] The network reconstruction constraint is represented as:

[0153]

[0154] 0.9V ED ≤V l ≤1.1V ED (39)

[0155]

[0156] In the formula, δ l represents the connection state of branch l, where a value of 1 represents closed and a value of 0 represents open; Njd N represents the total number of nodes; m represents the number of partitions, I represents the rated current of branch l, l I represents the current of branch l, l V represents the voltage of branch l, ED V represents the rated voltage, B represents the set of all branches; wherein two nodes constitute a branch.

[0157] It can be understood that network reconstruction needs to ensure that the topology is radial, the branch is not overloaded, and the voltage is within the allowable range.

[0158] The partition mutual aid constraint is represented as:

[0159]

[0160]

[0161] In the formula, C represents the thermal stability capacity of the line between partition j and partition i, ΔP represents the upper limit of the dynamic support capacity of partition j to partition i, i adj (t) represents the adjusted partition i power gap at time t, ΔP i (t) represents the original partition i power gap at time t, T represents the total delay time of the mobile energy storage from partition j to partition i, Pmax represents the maximum support power in the same recovery capability level as partition j, Pmin represents the minimum support power in the same recovery capability level as partition j.

[0162] It can be understood that partition mutual aid needs to ensure that only high-level partitions support low-level partitions, and the support capacity does not exceed the capacity of the line and the energy storage.

[0163] Specifically, programming is performed through MATLAB, and an optimizer CPLEX in the YAMIP toolbox is called to solve the model.

[0164] In implementation, according to the recovery capability level score of each supporting partition in the cooperative support scheme, the corresponding tie line is gradually incorporated into the main network in order from high to low, and the mobile energy storage is moved based on the path of the mobile energy storage, to realize power supply recovery of the system.

[0165] Compared with the prior art, the network-road collaborative recovery method considering resilience improvement under extreme weather provided by the embodiment obtains each partition after the fault of the expressway power supply system, obtains the recovery ability grade score of each partition, and then obtains each partition after grade division, and then constructs a partition mutual aid support model, a partition mutual aid support model considering multiple factors such as energy utilization, load guarantee, and main network distance is constructed, accurate allocation of power supply resources in the fault recovery stage is realized, and the practicability and adaptability of the strategy are enhanced; the partition mutual aid constraint is constructed through the partition mutual aid mechanism and the power supply from the higher grade partition to the lower grade partition, the priority recovery of the key load is guaranteed, and the overall recovery ability and voltage stability of the system are improved; through the mobile energy storage constraint constructed based on the mobile energy storage space-time scheduling model and the distributed power supply constraint and network reconstruction constraint constructed, the comprehensive scheduling path, operation state and load gap are established, the energy storage vehicle driving and support strategy is optimized, the power supply flexibility and scheduling efficiency under the emergency scene are improved, and the load coverage ability and response speed in the partition recovery process are further guaranteed; through the constructed collaborative recovery optimization objective function and constraint condition, efficient power mutual aid is realized, the fault recovery ability of the power supply system is improved, and the power supply reliability of the power supply system after the disaster is enhanced, and the problems that the power grid fault recovery ignores voltage stability and power loss and the time-space dynamic characteristics of mobile energy storage in the prior art, and efficient power support is difficult to realize are solved.

[0166] Embodiment 2

[0167] In one specific embodiment of the application, a network-road collaborative recovery system considering resilience improvement under extreme weather is disclosed, comprising:

[0168] The partition acquisition module is configured to acquire each partition after the fault of the expressway power supply system.

[0169] The model construction module is configured to obtain the recovery ability grade score of each partition based on the acquired each partition, and then obtain each partition after grade division, and then construct a partition mutual aid support model and a mobile energy storage space-time scheduling model.

[0170] The power supply recovery module is configured to construct a collaborative recovery optimization objective function with the maximum mutual aid capacity as the target based on the recovery ability grade score of each partition, construct the constraint condition of the collaborative recovery optimization objective function based on each partition after grade division, the partition mutual aid support model and the mobile energy storage space-time scheduling model, and solve the collaborative recovery optimization objective function to obtain an optimal collaborative support scheme for system power supply recovery; wherein the collaborative support scheme comprises each supported partition, each selected tie line, and the path of the mobile energy storage.

[0171] The specific implementation process of the embodiment of the present application can refer to the method embodiment described above, which will not be repeated here.

[0172] Since the embodiment has the same principle as the above-mentioned method embodiment, the system also has the corresponding technical effects of the above-mentioned method embodiment.

[0173] Embodiment 3

[0174] In order to verify the correctness of embodiments 1 and 2 of the present application, a specific example is provided in this embodiment, which uses the improved 55-node power supply system diagram of a certain area in western China as the basis topology diagram as shown in Figure 3 , assuming that an extreme disaster weather of sandstorm occurs, node 3 and node 36 fail, the region needs to run independently for 8 hours after 10:00-18:00, and then connect to the main network as appropriate, and the 24-hour load prediction of the power system is as shown in Figure 4 .

[0175] Strategy one: adopt the fault recovery scheme proposed in embodiments 1 and 2, after partitioning, calculate the score of each partition according to the energy utilization rate and other indicators, and divide the partitions into grades according to the score, consider the space-time scheduling of mobile energy storage, establish a partition mutual aid model based on partition grade, solve the fault recovery scheme, and connect to the main network in order according to the partition grade.

[0176] Strategy two: without considering the evaluation score of each partition, according to the principle of proximity, the mobile energy storage scheduling is carried out, the fault recovery scheme is solved, and the partitions are connected to the main network according to the fault recovery situation.

[0177] Strategy one is based on the basic information of the power supply system, and the five partition evaluation indicators are calculated, all of which are normalized, and the final score of each partition is calculated considering the sandstorm disaster threat coefficient, as shown in Table 1.

[0178] Table 1 Partition evaluation score

[0179]

[0180] As can be seen from Table 1, partition 2 has the highest evaluation score because of the sufficient internal distributed power supply and mobile energy storage equipment, and no internal failure; although partition 1 has sufficient power supply, it has certain power loss area due to two node failures, so its evaluation score is lower than that of partition 2; partition 4 has only one distributed power supply inside and two failures, which has lost the ability to support power supply for the load, so its evaluation score is the lowest. The partition mutual aid scheme of strategy one obtained from this is as shown in Figure 5 , and the partition mutual aid scheme of strategy two is as shown in Figure 6 .

[0181] From Figure 7It can be seen that during the recovery process, Strategy 1 has diversified mutual assistance paths, avoiding the problem of overload on a single line, and the mutual assistance power is relatively balanced, ensuring the stability of power supply in each zone; the mutual assistance capacity of Strategy 2 is lower than that of Strategy 1 throughout the entire period.

[0182] Depend on Figure 8 It can be seen that in the entire period of Strategy 1, the level 1 and level 2 loads in partition 4 can be fully restored, and the level 3 load can also be mostly restored; overall, the restoration amount of level 2 and level 3 loads in Strategy 2 is much smaller than that in Strategy 1.

[0183] Depend on Figure 9 It can be seen that Strategy 1 achieves continuous recovery of multi-level loads through flexible allocation of distributed power sources and mobile energy storage, as well as mutual assistance between different zones; Strategy 2 emphasizes load priority management, sacrificing tertiary loads to ensure power supply to critical loads when system capacity is tight.

[0184] like Figure 10 As shown, overall, Strategy 1 has a better load recovery effect than Strategy 2, Strategy 1 has an earlier grid connection time than Strategy 2, and Strategy 1 has higher voltage stability during the recovery process than Strategy 2.

[0185] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0186] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A network-road coordination recovery method considering resilience improvement under extreme weather, characterized in that, The method comprises the following steps: obtaining each partition after the power supply system of the expressway fails; based on the obtained each partition, obtaining the recovery ability grade score of each partition, and then obtaining each partition after grade division, and then constructing a partition mutual aid support model and a mobile energy storage space-time scheduling model; based on the recovery ability grade score of each partition, constructing a collaborative recovery optimization objective function with the maximum mutual aid support capacity as the target, constructing the constraint condition of the collaborative recovery optimization objective function based on each partition after grade division, the partition mutual aid support model and the mobile energy storage space-time scheduling model, and solving to obtain an optimal collaborative support scheme for system power supply recovery; wherein the collaborative support scheme comprises each supported partition, each selected tie line, and the path of the mobile energy storage.

2. The network-road collaborative recovery method considering resilience enhancement under extreme weather according to claim 1, characterized in that, The recovery ability grade score of each partition is obtained in the following manner: selecting a recovery ability index of the partition to obtain each recovery ability index data of each partition, and then constructing a recovery ability index matrix; wherein the types of the recovery ability index include positive and negative; based on the recovery ability index matrix, calculating the positive ideal solution of each positive recovery ability index and the negative ideal solution of each negative recovery ability index, and then obtaining the positive ideal Euclidean distance and the negative ideal Euclidean distance of each partition; based on the positive ideal Euclidean distance and the negative ideal Euclidean distance of each partition, obtaining the recovery ability grade score of each partition.

3. The network-road collaborative recovery method for resilience enhancement under extreme weather according to claim 2, characterized in that, The positive recovery ability index includes energy comprehensive utilization rate and mobile energy storage utilization rate; The negative recovery ability index includes load power shortage rate, expressway important equipment power outage rate and partition distance from the main network.

4. The network-road collaborative recovery method with resilience enhancement under extreme weather according to claim 1, characterized in that, The partition mutual aid support model comprises a partition capacity gap calculation model, which is represented as: ΔP i (t) = P i,total (t) - P i,av (t) wherein, P i,av (t) = P i,s (t) + P i,f (t) where ΔP i (t) denotes the capacity gap of zone i at time t, P i,total (t) denotes the total power demand of zone i at time t, P i,load (t) denotes the load power of zone i that needs to be supplied at time t, P i,c (t) denotes the charging demand power of the energy storage system within zone i at time t, P loss,i,e (t) denotes the power loss of zone i when transmitting through tie-line e at time t, E i denotes the set of all tie-lines of zone i, P i,av (t) denotes the available power of zone i at time t, P i,s (t) denotes the real-time generation power of the distributed power source within zone i at time t, P i,f (t) denotes the discharging power of the energy storage system within zone i at time t.

5. The network-road collaborative recovery method for resilience enhancement under extreme weather according to claim 4, characterized in that, The partition mutual aid support model further comprises an actual support power calculation model between partitions, which is represented as: wherein, wherein, P (t) represents the actual support power provided by partition j to partition i at time t, α i←j P (t) represents the support contribution degree of partition j to partition i at time t, P i s P (t) represents the actual support power obtained by partition i at time t, P i←j P (t) represents the theoretical support power provided by partition j to partition i at time t, N i P (t) represents the set of partitions providing support to partition i, P (t) represents the support power provided by partition j to partition i according to priority at time t, P (t) represents the maximum support power that can be provided by partition j to partition i at time t, λ i P (t) represents the priority coefficient of the mutual aid demand of partition i, P j,av P (t) represents the available power of partition j at time t.

6. The network-road collaborative recovery method for resilience enhancement under extreme weather according to claim 5, wherein, The mobile energy storage space-time scheduling model is represented as: where T iMESS,i←j (t) represents the moving energy storage i MESS D (t) represents the actual time to travel from zone j to zone i i←j v (t) represents the space-time distance between zone j and zone i at time t i←j v (t) represents the travel speed of the moving energy storage from zone j to zone i at time t i←j,0 v (t) represents the actual location distance between zone j and zone i at time t iMESS (t) represents the moving energy storage i MESS at time t.

7. The network-road collaborative recovery method with resilience enhancement under extreme weather according to claim 1, characterized in that, The collaborative recovery optimization objective function F is represented as: wherein, α represents a load recovery weight coefficient; ρ i represents the restoration capability level score of partition i; L i represents the restored load amount of partition i; N represents the set of all partitions; β represents the support capacity weight coefficient; x k represents the operating state of the switch of tie line k, wherein the value of 1 represents connection and 0 represents disconnection; C k represents the support capacity of tie line k; N k represents the set of all tie lines.

8. The network-road collaborative recovery method with resilience enhancement under extreme weather according to claim 1, characterized in that, The constraint condition comprises a mobile energy storage constraint, a distributed power supply constraint, a network reconstruction constraint and a partition mutual aid constraint; wherein the mobile energy storage constraint is constructed based on the mobile energy storage space-time scheduling model, and the partition mutual aid constraint is constructed based on each partition after grade division and the partition mutual aid support model.

9. The network-road collaborative recovery method with resilience enhancement under extreme weather according to claim 1, wherein, According to the recovery ability grade score of each supported partition in the collaborative support scheme, each supported partition is gradually incorporated into the main network in order from high to low based on the corresponding tie line, and the mobile energy storage is moved based on the path of the mobile energy storage, so as to realize the power supply recovery of the system.

10. A network-road coordination recovery system considering resilience enhancement under extreme weather, characterized in that, It comprises: a partition obtaining module for obtaining each partition after the power supply system of the expressway fails; a model construction module for obtaining the recovery ability grade score of each partition based on the obtained each partition, and then obtaining each partition after grade division, and then constructing a partition mutual aid support model and a mobile energy storage space-time scheduling model; The power supply recovery module is configured to construct a collaborative recovery optimization objective function with a target of maximizing mutual aid capacity based on the recovery capability level scores of the subareas, construct constraint conditions of the collaborative recovery optimization objective function based on the subareas after the level division, a mutual aid model of the subareas, and a mobile energy storage space-time scheduling model, and solve the collaborative recovery optimization objective function to obtain an optimal collaborative aid scheme for system power supply recovery. The collaborative aid scheme includes the subareas for aid, selected tie lines, and paths of the mobile energy storage.

Images