Distribution network fault recovery two-stage robust modeling method considering mobile emergency power supply

Abstract

The invention relates to the field of electrical engineering, and discloses a distribution network fault recovery two-stage robust modeling method considering a mobile emergency power supply, and the method comprises the steps: constructing a distribution network system fault robust optimization model; constructing a deployment position constraint of the mobile emergency power supply, so that the mobile emergency power supply can be deployed at an optimal position in a preventive scheduling stage and arrives at a target position in an emergency response stage; a fuel tank driving path constraint is established to ensure that the mobile emergency power supply has enough fuel during operation; introducing a power-on path table to represent a load recovery path of the microgrid based on a virtual power-on agent concept, and constructing a power-on path table constraint to recover power supply of an affected region; power-on state modeling is completed, and it is ensured that power supply of all micro-grids is recovered; constructing an operation constraint of each part of the system; according to the method, the robust model related to mobile emergency power supply dispatching is introduced, so that the recovery process of power service is accelerated, and the power supply toughness in coping with a large-scale power failure event caused by extreme weather is remarkably improved.

Description

Technical Field

[0001] The invention relates to the field of electrical engineering, and in particular to a two-stage robust modeling method for distribution network fault recovery considering a mobile emergency power supply. Background Art

[0002] The impact of climate change on the power system is becoming more and more obvious. The frequent occurrence of extreme climate events makes the distribution system more vulnerable to disasters, and the frequency of large-scale power outages has increased significantly. This has not only caused serious losses to the social economy, but also put forward higher requirements on the recovery capacity of the power system. In this context, microgrids have received widespread attention as an important means to quickly achieve service restoration. By dividing the distribution system into multiple isolated microgrids, microgrids can effectively improve the resilience of the power grid. However, traditional microgrid recovery methods are insufficient in adaptability and dynamic adjustment capabilities, and it is difficult to fully cope with sudden failures caused by disasters.

[0003] Sequential service restoration methods can systematically expand the coverage of microgrids by optimizing switch switching sequences and operation scheduling, thereby solving the problem of missing intermediate operation sequences in traditional restoration methods. However, most current methods focus on fixed deployed energy resources and lack the ability to respond to random faults and dynamic demands.

[0004] In order to improve the resilience of power supply, the research on mobile emergency power supply has gradually increased in recent years. As a flexible energy scheduling resource, mobile emergency power supply can not only be optimally deployed before the fault, but also reach the target location according to actual needs. Existing research mainly focuses on the two-stage mobile emergency power supply scheduling framework: pre-deployment in the pre-fault stage and re-scheduling in the post-fault stage. However, these methods usually ignore the resilience of fuel supply. Due to the complexity of extreme events and the limitations of fuel storage, mobile emergency power supply may only be able to operate continuously for 2-3 hours under high load conditions. After the fuel is exhausted, it will face the risk of downtime, further leading to secondary power outages in the microgrid. Therefore, the coordinated scheduling of fuel distribution has also become a key link in improving supply resilience. In addition, when dealing with uncertainty problems in distribution systems and transportation networks, robust optimization has become an important modeling method. Summary of the invention

[0005] The purpose of the present invention is to propose a two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply, so as to solve the technical problems that the current distribution system has unreliable, unstable and low robustness service recovery after being impacted.

[0006] Specifically, the present invention provides a two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply, comprising the following steps:

[0007] S1. Construct a robust optimization model for distribution network faults;

[0008] S2. Construct the deployment position constraints of mobile emergency power supply so that the mobile emergency power supply can be deployed at the optimal position in the prevention and dispatching stage and reach the target position in the emergency response stage;

[0009] S3. Establish fuel tank driving path constraints to ensure that the mobile emergency power supply has sufficient fuel for operation;

[0010] S4. Based on the concept of virtual energizing agent, the energizing path table is introduced to represent the load recovery path of the microgrid, and the energizing path table constraints are constructed to restore the power supply to the affected areas;

[0011] S5. Complete the power-on status modeling to ensure that each microgrid restores power supply;

[0012] S6. Build operational constraints for each part of the system.

[0013] A storage medium stores instructions and data for implementing a two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply.

[0014] A two-stage robust modeling device for distribution network fault recovery considering mobile emergency power supplies comprises: a processor and the storage medium; the processor loads and executes instructions and data in the storage medium to implement a two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supplies.

[0015] The beneficial effects provided by the present invention are: by introducing a robust model involving the dispatch of mobile emergency power sources (MEGs), the present invention accelerates the restoration process of power services, significantly improves the power supply resilience in response to large-scale power outages caused by extreme weather, minimizes the social and economic losses caused by power outages, and provides innovative technical support for emergency management of power systems in disaster situations, and has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the process of the method of the present invention;

[0017] Figure 2 It is a schematic diagram of the topological structure of the simulated power-on state modeling implemented in the present invention;

[0018] Figure 3 It is a schematic diagram of the working of the hardware device of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described below with reference to the accompanying drawings.

[0020] Before formally describing the present invention, the scheme of the present invention is first generally described for easy understanding.

[0021] Please refer to Figure 1 The present invention provides a two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply, comprising:

[0022] S1. Construct a robust optimization model for distribution network faults;

[0023] The specific process of step S1 is as follows:

[0024] In order to cope with the uncertainty of distribution network failures, S1 established a robust optimization model with the objective function shown in (1), which is used to evaluate the power supply resilience of the distribution system recovery process under the worst scenario.

[0025]

[0026] Among them, S represents the scene set; N represents the node load set; T represents the time period set; p n Indicates the priority coefficient of load n; P n.t.s represents the restored active power of load n in time period t under scenario s; s represents the load reduction penalty coefficient for power outage after load recovery, which aims to minimize the power outage after load recovery; n.t.s is a 0-1 variable, indicating whether the restored load n is interrupted again in time period t under scenario s; f represents the fuel coefficient of the mobile emergency power supply, which aims to minimize the fuel consumption of the mobile emergency power supply; G represents the collection of mobile emergency power supplies; U g.t.s represents the fuel demand for mobile emergency power supply g in time period t under scenario s; t represents the correlation coefficient of fuel tank travel, which aims to minimize the ineffective travel of fuel tanks; F represents the set of fuel tanks; It represents the driving time that the fuel tank f needs to consume in time period t under scenario s.

[0027] S2. Construct the deployment position constraints of mobile emergency power supply so that the mobile emergency power supply can be deployed at the optimal position in the prevention and dispatching stage and reach the target position in the emergency response stage;

[0028] The specific process of step S2 is as follows:

[0029] S2.1 Before a fault occurs, the mobile emergency power supply will be deployed in advance to deal with the impending fault. The specific constraints are as follows:

[0030]

[0031] Among them, (2) ensure that each mobile emergency power supply can only be pre-deployed on one node, N MEG Indicates the set of nodes that allow mobile emergency power supply access. It is used to indicate whether the mobile emergency power supply g is pre-deployed at node i before the failure occurs; (3) It indicates that the number of pre-deployed mobile emergency power supplies at each node is affected by its capacity AN i constraints.

[0032] S2.2 After a fault occurs, the mobile emergency power supply is dispatched to a specific target location. The formula for the mobile emergency power supply deployment location model is as follows:

[0033]

[0034] Among them, (4) indicates that each mobile emergency power supply can be allocated to a node after a fault occurs. indicates whether the mobile emergency power supply g is located at node i in scenario s; (5) ensures that each candidate node is assigned at most one mobile emergency power supply, and (6) indicates that only the pre-deployed mobile emergency power supply can be rerouted after a failure occurs.

[0035] S3. Establish fuel tank driving path constraints to ensure that the mobile emergency power supply has sufficient fuel for operation;

[0036] The specific process of step S3 is as follows:

[0037] The S3 fuel tank driving path model establishes a fuel tank driving path to enhance the supply elasticity of mobile emergency power supply.

[0038]

[0039]

[0040] (7) ensures that each fuel tank can only be in one of the states of parking or driving at any time. f.i.t.s Indicates whether the fuel tank f stays at node i, g in time period t under scenario s f.i.t.s represents whether the fuel tank f goes to node i in time period t under scenario s; (8) represents the state transition between the parking state and the driving state of the fuel tank, H represents the total scheduling time; (9) represents the necessary driving time required for the fuel tank to travel, T f.ii'.s represents the time it takes for fuel tank f to travel from node i to node i' in scenario s; (10) represents the remaining travel time required for fuel tank f to travel, represents the remaining travel time of the fuel tank f in time period t under scenario s; (11) based on The value of constrains the driving state of the fuel tank; α is a sufficiently large constant; (12) ensures that the direction of the fuel tank remains unchanged during driving, σ f.t.s Indicates whether the fuel tank f travels from period t-1 to period t, and β represents a positive number in the range of (0,1]. Generally speaking, the travel time between two nodes after a fault occurs will exceed the travel time under normal traffic conditions. Parameter T f.ii'.s T represents the shortest time required to travel from node i to node i' when encountering traffic congestion in scenario s. f.ii'.s It is determined based on the principle of the shortest travel time path during congestion.

[0041] S4. Based on the concept of virtual energizing agent, the energizing path table is introduced to represent the load recovery path of the microgrid, and the energizing path table constraints are constructed to restore the power supply to the affected areas;

[0042] The specific process of step S4 is as follows:

[0043] The restoration path in S4 microgrid, based on the concept of virtual power-on agent, introduces the power-on path table to represent the restoration path of the microgrid. The power-on path table is a |N n |×|N n |Node association matrix, N c =|N n | represents the number of node cells. The element y in the table kl Indicates whether the virtual power-on agent moves from node cell k to node cell l. In order to ensure the radial structure of the microgrid and prevent fault propagation, the power-on path table modeling should satisfy the following constraints:

[0044]

[0045] y kl.s ≤DS kl.s ,(k,l)∈B SW ,s∈S (33)

[0046] y kl.s +y lk.s ≤1,(k,l)∈B SW ,s∈S (34)

[0047]

[0048] The matrix DS represents the connectivity between two nodes after fault isolation. Specifically, in scenario s, if branch b is not faulty, then DS b.s =1; In scenario s, if there is an unfaulted line between node cells k and l, then DS kl.s=1. It is worth noting that fault isolation is achieved by opening the switch on the line. Therefore, if there is no switch on the line, the power outage will spread. (13) and (14) require that the recovery path can only start from the node cell that meets two conditions: 1) contains a black start unit, 2) has a non-faulty line; where N bk represents the branch set in node cell k, N nk represents the node set in node cell k. (15) The agent is required to travel only through complete routes, B SW represents the set of branches with installed switches. (16) ensures that a node cell cannot be connected multiple times. (17) indicates that the agent can only connect to node cells without line faults. (18) indicates that the virtual power-on agent can only connect to the downstream node cell when its upstream node cell has been connected.

[0049] S5. Complete the power-on status modeling to ensure that each microgrid restores power supply;

[0050] The specific process of step S5 is as follows:

[0051] S5 power-on state modeling, in order to represent the power-on state of the node powered by the upstream network after the fault, a virtual power flow is introduced and an auxiliary binary variable o is used. Specifically, o kl.s =1 indicates that there is a virtual power flow from node k to node l in scenario s.

[0052] by Figure 2 Take the topology in the virtual power flow diagram as an example. Node 1 represents the upstream network and serves as the starting point of the virtual power flow. The corresponding matrix describing the virtual power flow is as follows: Figure 2 (b) As shown. Lines 1-2 and 2-4 are not faulty and are allowed to 12 =o 24 = 1, a virtual power flow is generated on these lines. Line 4-5 is interrupted, so o 45 Set to 0, indicating that there is no virtual power flow on this line. Since line 3-4 is interrupted and lacks a switch, o 23 =o 34 =0.

[0053] S5.1 Based on the above discussion, the constraints of the virtual power flow can be expressed as follows:

[0054]

[0055] Where (19) ensures the connectivity of the constructed spanning tree, Indicates whether the virtual power flow flows from node cell k to node cell l in scenario s; (20) indicates that the virtual power flow cannot flow to the node cell containing the line fault.

[0056] S5.2 Next, use formulas (21)-(25) to estimate the time when the virtual powered-on agent accesses the node cell.

[0057]

[0058] Where (21) generates the routing matrix of the mobile emergency power supply g; (22) calculates the time for the mobile emergency power supply g to arrive at the node i'; (23) indicates that when the node cell k is not connected by any virtual power-on agent, t k.s Will be set to the maximum recovery time where t k.s represents the time when the virtual power-on agent arrives at the node cell k; (24) represents the time when the node cell k is connected to the black start unit (such as y kk =1), then node cell k is powered on when the black start unit is started, represents the time when the mobile emergency power supply g arrives at node i' in scenario s; (25) If the virtual power-on agent moves from node cell h to node cell k, then represents the time required for the virtual powered agent to move from cell h to cell k. Similar to T f.ii'.s ,parameter represents the travel time of the mobile emergency power supply from node i to node i' in scenario s, which is also determined based on the shortest travel time route during traffic congestion.

[0059] S5.3 Next, we introduce binary variables To indicate whether the node cell k is powered on during time period t in scenario s.

[0060]

[0061] Among them, (26) indicates that the node cell can only be powered on if the virtual power-on agent is connected; (27) indicates that the node cell cannot be powered on before the virtual power-on agent reaches the cell; and (28) prevents the node cell from tripping after being powered on.

[0062] S5.4 Next, the power-on status of nodes and lines is formulated.

[0063]

[0064] Where (29) and (30) indicate that when a node cell is powered on, the nodes and non-switchable lines in the node cell can be powered on immediately, r i / b.t.s It is used to represent the power-on status of node i / branch b in time period t under scenario s; (31) indicates that the switchable line can be powered only when both node cells at both ends are powered and there is a virtual powered agent available to travel between node cells h and k, r hk.t.sIt is used to indicate the power status of the line (h, k) connecting node cells h and k in time period t under scenario s.

[0065] S5.5 Introducing the binary variable v d.t.s and v n.t.s To represent the power-on status of distributed generation d and load n in the node cell.

[0066] v d.t.s ≤r i.t.s ,d∈D i ,t∈T,s∈S (50)

[0067] v n.t.s ≤r i.t.s ,n∈N i ,t∈T,s∈S (51)

[0068] Where (32) and (33) indicate that the distributed generation and load can only be restored when the node i to which the distributed generation and load are connected is energized.

[0069] Therefore, the variable ι in (1) n.t.s It can be calculated by (34).

[0070]

[0071] S6. Build operational constraints for each part of the system.

[0072] It should be noted that the operating constraints of various parts of the system in step S6 include: distributed power generation constraints, load demand constraints, mobile emergency power supply fuel rate constraints, current fuel quantity and their respective fuel capacity ratio constraints, power balance, voltage constraints and line transmission power constraints.

[0073] As an embodiment, the specific process of step S6 is as follows:

[0074] S6.1 Generation of distributed generation is subject to the following constraints:

[0075]

[0076] Where (35) and (36) define the power generation of distributed generation d, represents the minimum power generation of distributed generation d, represents the maximum power generation of distributed power source d; (37) constrains the ramp active power of distributed power source d between two consecutive time periods. Indicates the upper / lower limit of the ramp rate of the distributed power source d. Specifically, the above model can also be applied to the generation of mobile emergency power.

[0077] S6.2 Load demand constraints are:

[0078]

[0079] Where (38) constrains the active load demand. When power is off, the active load demand is forced to 0. represents the lower / upper limit of the active power demand of load n in time period t; (39) calculates the reactive load demand, It indicates the ratio of reactive load to active load. It is worth noting that for adjustable load, the selection is based on the adjustment capacity. and For non-adjustable loads, and All are set to

[0080] S6.3 Fuel requirements of mobile emergency power supply gB g.t.s and power generation The relationship between can be formulated using piecewise linearization in (40). This is based on the manufacturer's practice of providing mobile emergency power supply fuel rates at various load levels (such as 1 / 4, 1 / 2, 3 / 4 and full load).

[0081]

[0082] Among them, b g.u represents the coefficient of the uth piecewise linear term of the mobile emergency power supply g fuel function, c g.u The coefficient of the u-th piecewise constant term of the mobile emergency power supply g fuel function, pg g.u Indicates the u-th split point of the mobile emergency power supply g.

[0083] S6.4 Fuel status of fuel tanks, mobile emergency power supplies and fuel depots (F S ) is used to represent the ratio of the current fuel amount to its respective fuel capacity. The relevant constraints are expressed as

[0084]

[0085]

[0086] Where (41) calculates the F of the mobile emergency power supply g S . B i.t.s Indicates that in scenario s, the mobile emergency power supply at node i is refueled within time period t; represents the fuel storage capacity of the mobile emergency power supply g. (42) Calculate the F of the fuel tank f S .W f.v.t.s It means that in scenario s, during time period t, fuel tank f is replenished in warehouse v; represents the fuel storage capacity of fuel tank f. (43) is the upper limit constraint of fuel replenishment at node i. f.i.t.s Indicates whether the fuel tank f stays at node i during time period t under scenario s. (44) Indicates the fuel replenishment constraint of the fuel tank f at warehouse v. (45)-(46) Indicates the mobile emergency power supply and fuel tank F S The upper and lower limit constraints.

[0087] S6.5 In each time period, power balance, voltage constraints and line transmission power constraints should also be met. A linearized DistFlow model is used to represent the relationship between power flow and voltage amplitudes of two adjacent nodes.

[0088] These constraints are summarized in a compact form as shown in (47)-(48).

[0089]

[0090] Among them, E EQ Represents the equality constraint; E IEQ Represents an inequality constraint.

[0091] See also Figure 3 , Figure 3 It is a working diagram of the hardware device of an embodiment of the present invention, and the hardware device specifically includes: a two-stage robust modeling device 401 for distribution network fault recovery considering mobile emergency power supply, a processor 402 and a storage medium 403.

[0092] A two-stage robust modeling device 401 for distribution network fault recovery considering mobile emergency power supply: The two-stage robust modeling device 401 for distribution network fault recovery considering mobile emergency power supply implements the two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply.

[0093] Processor 402: The processor 402 loads and executes the instructions and data in the storage medium 403 to implement the two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply.

[0094] Storage medium 403: The storage medium 403 stores instructions and data; the storage medium 403 is used to implement the two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply.

[0095] The beneficial effects of the present invention are as follows: the present invention accelerates the restoration process of power services by introducing a robust model involving the dispatch of mobile emergency power sources (MEGs), significantly improves the power supply resilience in response to large-scale power outages caused by extreme weather, minimizes the social and economic losses caused by power outages, and provides innovative technical support for emergency management of power systems in disaster situations, and has broad application prospects.

[0096] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply, characterized by: include: S1. Construct a robust optimization model for distribution network system failures; S2. Construct the deployment position constraints of mobile emergency power supply so that the mobile emergency power supply can be deployed at the optimal position in the prevention and dispatching stage and reach the target position in the emergency response stage; S3. Establish fuel tank driving path constraints to ensure that the mobile emergency power supply has sufficient fuel for operation; S4. Based on the concept of virtual energizing agent, the energizing path table is introduced to represent the load recovery path of the microgrid, and the energizing path table constraints are constructed to restore the power supply to the affected areas; S5. Complete the power-on status modeling to ensure that each microgrid restores power supply; S6. Build operational constraints for each part of the system.

2. A two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply as claimed in claim 1, characterized in that: The objective function of the distribution network fault robust optimization model in step S1 is as follows: Among them, S represents the scene set; N represents the node load set; T represents the time period set; p n Indicates the priority coefficient of load n; P n.t.s represents the restored active power of load n in time period t under scenario s; s represents the load reduction penalty coefficient for power outage after load recovery; n.t.s is a 0-1 variable, indicating whether the restored load n is interrupted again in time period t under scenario s; f represents the fuel coefficient consumed by the mobile emergency power supply; G represents the collection of mobile emergency power supplies; U g.t.s represents the fuel demand for mobile emergency power supply g in time period t under scenario s; t represents the correlation coefficient of fuel tank travel; F represents the set of fuel tanks s; It represents the driving time that the fuel tank f needs to consume in time period t under scenario s.

3. A two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply as described in claim 2, characterized in that: Step S2 is specifically as follows: S2.1: Before a fault occurs, deploy a mobile emergency power supply in advance, as constrained by the following formula: Among them, formula (2) ensures that each mobile emergency power supply can only be pre-deployed on one node, N MEG Indicates the set of nodes that allow mobile emergency power supply access. It is used to indicate whether the mobile emergency power supply g is pre-deployed at node i before the failure occurs; Formula (3) indicates that the number of pre-deployed mobile emergency power supplies at each node is affected by its capacity AN i Constraints; S2.2: After a fault occurs, the mobile emergency power supply is dispatched to a specific target location, as constrained by the following equation: Among them, formula (4) indicates that each mobile emergency power supply can be allocated to a node after a fault occurs. Indicates whether the mobile emergency power supply g is located at node i in scenario s; Formula (5) ensures that each candidate node is assigned at most one mobile emergency power supply, and Formula (6) indicates that only the pre-deployed mobile emergency power supply can be rerouted after a failure occurs.

4. A two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply as described in claim 3, characterized in that: In step S3, the fuel tank driving path constraint is established as follows: Formula (7) ensures that each fuel tank can only be in one of the states of parking or driving at any time. f.i.t.s Indicates whether the fuel tank f stays at node i, g in time period t under scenario s f.i.t.s Indicates whether the fuel tank f goes to node i in time period t under scenario s; Formula (8) represents the state transition between the parking state and the driving state of the fuel tank, H represents the total scheduling time; Formula (9) represents the necessary driving time required for the fuel tank to travel, T f.ii'.s represents the time it takes for the fuel tank f to travel from node i to node i' in scenario s; Formula (10) represents the remaining travel time that the fuel tank f needs to consume, represents the remaining travel time of the fuel tank f in the time period t under the scenario s; Formula (11) is based on The value of constrains the driving state of the fuel tank; α is a constant; Formula (12) ensures that the direction of the fuel tank remains unchanged during driving, σ f.t.s Indicates whether the fuel tank f travels from time period t-1 to time period t, β represents a positive number in the range of (0,1]; generally speaking, the travel time between two nodes after a fault occurs will exceed the travel time under normal traffic conditions; parameter T f.ii'.s T represents the shortest time required to travel from node i to node i' when encountering traffic congestion in scenario s. f.ii'.s It is determined based on the principle of the shortest travel time path during congestion.

5. A two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply as claimed in claim 4, characterized in that: The power-on path table constraints in step S4 are as follows: and kl.s ≤DS kl.s ,(k,l)∈B SW ,s∈S (15) and kl.s +y lk.s ≤1,(k,l)∈B SW ,s∈S (16) Among them, the power path table is a |N n |×|N n |Node association matrix, N c =|N n | represents the number of node cells; the element y in the table kl represents whether the virtual power-on agent moves from node cell k to node cell l; DS represents the connectivity between two nodes after fault isolation; Equations (13) and (14) require that the recovery path can only start from node cells that meet two conditions: 1) contain black start units, 2) have unfaulted lines; where N bk represents the branch set in node cell k, N nk represents the node set in node cell k; Formula (15) requires that the agent can only travel through complete routes, B SW represents the set of branches with installed switches; Formula (16) ensures that the node cell cannot be connected multiple times; Formula (17) indicates that the agent can only access the node cell without line faults; Formula (18) indicates that the virtual power-on agent can access the downstream node cell only when its upstream node cell has been connected.

6. A two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply as claimed in claim 5, characterized in that: The operating constraints of each part of the system in step S6 include: distributed power generation constraints, load demand constraints, mobile emergency power fuel rate constraints, current fuel quantity and their respective fuel capacity ratio constraints, power balance, voltage constraints and line transmission power constraints.

7. A storage medium, characterized in that: The storage medium stores instructions and data for implementing a two-stage robust modeling method for distribution network fault recovery taking into account mobile emergency power supplies as described in any one of claims 1 to 6.

8. A two-stage robust modeling device for distribution network fault recovery considering mobile emergency power supply, characterized in that: include: Processor and storage medium; the processor loads and executes instructions and data in the storage medium to implement a two-stage robust modeling method for distribution network fault recovery considering mobile emergency power supply as described in any one of claims 1 to 6.

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