An optimization method for distribution network post-disaster emergency repair operation strategy considering renewable energy and load uncertainty

Abstract

The present invention discloses a method for optimizing the post-disaster emergency repair operation strategy of a distribution network that considers renewable energy and load uncertainty. First, a two-stage robust optimization model for post-disaster emergency repair operation decisions is constructed, taking into account renewable energy and load uncertainty. The optimization model is then decomposed based on a column and constraint generation method, converting the two-layer structure problem into a single-layer problem. The optimized robust scheme for post-disaster emergency repair operation is then solved. The proposed scheme can consider the worst-case scenario of renewable energy and load uncertainty, ensure the effectiveness and safety of the post-disaster recovery process, accelerate the emergency repair and recovery process, and reduce load losses. The proposed scheme has both theoretical and engineering value.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grids, and in particular relates to a method for optimizing a distribution network post-disaster emergency repair operation strategy taking into account renewable energy and load uncertainty. Background Art

[0002] Distribution networks, at the end of the power system, are responsible for delivering electricity to users. However, due to their exposure to the outdoors, they are susceptible to extreme disasters and failures. To minimize losses from power outages, a rational emergency repair strategy is necessary to rapidly restore distribution network loads. During post-disaster recovery, in addition to dispatching construction teams to repair grid faults, distributed power sources (DGs) and mobile power supplies can also be utilized to provide power support and reduce load losses. Therefore, a post-disaster emergency repair strategy for distribution networks that incorporates DGs and mobile power supplies can help improve distribution network recovery efficiency and maximize the amount of power lost during power outages.

[0003] Due to the uncertainty of renewable energy output and load fluctuations during distribution network restoration, a series of safety issues may arise during distribution network load restoration. Therefore, it is necessary to study distribution network post-disaster emergency repair operation strategies that consider the uncertainty of renewable energy and load. Scenario probability methods can be used to deal with the uncertainty of renewable energy and load, but the probability distribution function is difficult to obtain, and the probability density function simulated by sampling methods is also difficult to guarantee accuracy. At the same time, to ensure the accuracy of uncertainty processing, a large number of possible scenarios need to be generated, which also increases the computational burden. Robust optimization is also a commonly used method for solving uncertainty. Compared with the scenario probability method, it only requires a bounded interval of the uncertainty quantity, the computational scale is smaller, and the optimization results are more conservative. Summary of the Invention

[0004] In view of the above problems, the purpose of the present invention is to provide a distribution network post-disaster repair operation strategy optimization method that takes into account renewable energy and load uncertainty during the distribution network post-disaster recovery process.

[0005] The specific technical solutions for achieving the purpose of the present invention are as follows:

[0006] A method for optimizing distribution network post-disaster emergency repair operation strategy considering renewable energy and load uncertainty includes the following steps:

[0007] Step 1: Construct a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty;

[0008] Step 2: Process the optimization model in step 1 and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method;

[0009] Step 3: Use the strong duality theorem to transform the double-layer structure problem of the sub-problem in step 2 into a single-layer problem;

[0010] Step 4: Iterate the main problem and subproblems of the two-stage robust optimization model and solve them using CPLEX to obtain the optimized robust solution for distribution network emergency repair operation.

[0011] Compared with the prior art, the present invention has the following significant advantages:

[0012] (1) The technical solution of the present invention fully considers the coordination and cooperation between distributed power sources and mobile power sources. Based on this, the optimization of the distribution network emergency repair strategy can speed up the distribution network emergency repair and recovery process and reduce load losses.

[0013] (2) The technical solution of the present invention does not require the uncertainty distribution of renewable energy and load fluctuations to be known. It only needs to determine the uncertainty interval to determine the optimal emergency repair strategy. The method is highly robust. The distribution network emergency repair operation plan obtained by the present invention can withstand all fluctuations within the uncertainty interval of renewable energy and load fluctuations.

[0014] (3) The technical solution of the present invention constructs a two-stage robust optimization model for distribution network post-disaster emergency repair operation decision-making, taking into account the dual uncertainty of distributed power output and load demand, and using column and constraint generation methods to solve it, ensuring the effectiveness and safety of the distribution network post-disaster recovery process, and has certain theoretical and engineering value.

[0015] The present invention is further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 The figure is a flow chart of the method steps of the present invention.

[0017] Figure 2 This is a flowchart of the iterative solution of the CCG algorithm of the present invention.

[0018] Figure 3 FIG. 3 is a schematic diagram of an IEEE 33 node in an embodiment of the present invention.

[0019] Figure 4 Schematic diagram of the dispatching path of mobile power supply and construction team in an embodiment of the present invention.

[0020] Figure 5 This is a routing diagram for dispatching mobile power supplies and construction teams in an embodiment of the present invention.

[0021] Figure 6 This is a comparison chart of recovery results considering different scheduling objects in an embodiment of the present invention.

[0022] Figure 73 is a comparison chart of the recovery results of this method and other solution methods in the embodiment of the present invention. DETAILED DESCRIPTION

[0023] Combine Figure 1 , a distribution network post-disaster emergency repair operation strategy optimization method considering renewable energy and load uncertainty, including the following steps:

[0024] Step 1: Construct a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty, specifically:

[0025] Step 1-1: Based on the robust optimization objective of the distribution network post-disaster repair operation strategy, the repair operation strategy under the worst-case scenario is constructed in the form of maximizing the minimax problem:

[0026]

[0027] Where x represents the distribution network emergency repair operation strategy, including construction team scheduling, mobile power scheduling and tie line operation plan, p represents the uncertainty variable, including distributed power output and load demand fluctuation, ρ i represents the load weight on node i, represents the load recovery amount of node i at time t;

[0028] Step 1-2: Determine the constraints in the optimization model, specifically:

[0029] Step 1-2-1: Determine the constraints on construction team scheduling during distribution network restoration;

[0030] (1) The starting and ending points of the construction team scheduling are constrained as follows:

[0031]

[0032]

[0033] in, represents the moving path of construction team c. If construction team c moves from point i to point j, it is equal to 1, D is the camp of construction team c, Φ F Represents a set of fault points;

[0034] (2) Construction team scheduling and movement constraints:

[0035]

[0036] The formula represents the logical order of construction team c moving from point j to point i and then to point k;

[0037] (3) Each fault is repaired by at most one construction team:

[0038]

[0039]

[0040] in, Indicates whether the fault point i is repaired by the construction team c. If yes, it is equal to 1. Φ C Indicates the assembly of the construction team;

[0041] (4) Time constraints for the construction team to arrive at the fault point:

[0042]

[0043]

[0044] Among them, AT i c represents the time when construction team c arrives at fault point i, represents the movement time of construction team c at fault point i and fault point j, represents the repair time required by construction team c to repair fault point j;

[0045] (5) Restoration time of the fault point and availability constraints of the faulty component:

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[0047]

[0048]

[0049] in, Indicates whether the fault point i is repaired at time t, if yes, it is 1, represents the availability status of the faulty component i. If the fault has been repaired before time t, it is 1. T represents the total time step of post-disaster repair.

[0050] Step 1-2-2: Determine the constraints on mobile power dispatch during distribution network restoration;

[0051] (1) Logical sequence constraints for mobile power scheduling:

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[0053]

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[0055]

[0056] in, represents the moving path of mobile power supply m. If m moves from node i to point j, it is equal to 1. d represents the initial node of mobile power supply m. Indicates whether the mobile power supply m is connected to the node i. If yes, it is equal to 1. Φ M Represents a collection of mobile power supplies;

[0057] (2) Constraints on the available state of mobile power supply:

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[0065] Among them, AT i m represents the time when mobile power source m arrives at node i, represents the movement time of mobile power source m at node i and node j, represents the residence time of mobile power source m at node j, M represents a very large number, only when When the mobile power source moves from node i to node j,

[0066] Step 1-2-3: Determine the constraints on the output of distributed generation during the distribution network restoration process;

[0067] (1) Output constraints of renewable energy:

[0068]

[0069]

[0070] in, represents the active power output of renewable energy k at time t, represents the reactive power output of renewable energy k at time t, represents the upper limit of active power output of renewable energy source k; represents the power factor of the renewable energy source k;

[0071] (2) Output constraints of mobile power supply:

[0072]

[0073]

[0074] in, represents the active output of the mobile power supply at node i at time t, represents the reactive power output of the mobile power supply at node i at time t, Indicates the upper limit of active output of mobile power supply m, Indicates the upper limit of reactive output of mobile power supply m, Indicates whether the mobile power supply is at node i at time t;

[0075] Step 1-2-4: Determine the constraints on the output of distributed generation during the distribution network restoration process;

[0076] (1) Line flow direction constraints:

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[0081] Among them, Z i,j,t represents the flow direction from node i to node j at time t, represents the available state of the fault line ij at time t, represents the available state of tie line ij at time t; N represents the set of grid nodes;

[0082] The above formulas respectively express the power flow direction constraints and open-loop operation constraints of normal lines, fault lines, and tie lines;

[0083] (2) Node load recovery constraint:

[0084]

[0085]

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[0087]

[0088]

[0089] Among them, v i,t Indicates whether node i belongs to the restored area at time t, Represents the load value of node i; M represents a very large number, only when Z i,j,t =1 means that when the flow direction is from node i to node j, v j,t =v i,t , otherwise the equation does not hold;

[0090] The above formulas represent the load recovery values of nodes located in the restored area and the unrestored area respectively;

[0091] (3) Node power balance constraint:

[0092]

[0093]

[0094]

[0095]

[0096] Among them, P i,t represents the active power flowing out of node i at time t, Q i,t represents the reactive power flowing out of node i at time t, represents the load recovery active power of node i at time t, represents the load recovery reactive power of node i at time t;

[0097] (4) Node voltage constraints:

[0098]

[0099]

[0100]

[0101] Among them, U i,t represents the voltage amplitude of node i at time t, R i,j represents the resistance of line ij, X i,j represents the reactance of line ij, represents the upper limit of the voltage amplitude of node i, U i Represents the lower limit of the voltage amplitude of node i; M represents a very large number, only when Z i,j,t =1 means that when the power flow direction is from node i to node j, the node voltage can be calculated, U j,t =U i,t -2(R i,j ·P i,t +X i,j Q i,t ), otherwise the equation does not hold;

[0102] Step 1-2-5: In actual operation, there is uncertainty in the output and load fluctuations of renewable energy. The output and load fluctuations of renewable energy can be constrained within a range without considering the specific distribution within the range. The uncertainty constraints of renewable energy output and load fluctuations are:

[0103]

[0104] in, represents the load fluctuation value of node i, P i L,E represents the load forecast value of node i, represents the lower deviation of the load forecast value of node i, represents the upper deviation of the load forecast value of node i, Represents the output fluctuation value of renewable energy i, P i G,E represents the output forecast value of renewable energy source i, represents the lower deviation of the output forecast value of renewable energy source i, It represents the upper deviation of the output forecast value of renewable energy source i.

[0105] Step 2: Process the optimization model in step 1 and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method. Specifically:

[0106] Step 2-1: The main problem is to solve the optimal emergency repair operation strategy under the uncertainty scenario, that is, given the renewable energy output and load fluctuations. The objective function is:

[0107]

[0108] Among them, x represents the distribution network emergency repair operation strategy, including the construction team, mobile power dispatch and tie line action plan, ρ i represents the load weight on node i, represents the load recovery amount of node i at time t;

[0109] The constraints of the objective function of the main problem are:

[0110] The main problem is that the renewable energy output and load fluctuation are given values. P i L，* To solve the optimal emergency repair operation strategy, we need to follow the construction team and mobile power scheduling constraints, distributed power output constraints, and distribution network operation constraints, which is:

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[0152] in, represents the moving path of construction team c. If construction team c moves from point i to point j, it is equal to 1, D is the camp of construction team c, Φ F Represents a set of fault points; Indicates whether the fault point i has been repaired by the construction team c, if so, it is equal to 1; Φ C Indicates the construction team is assembled; AT i c represents the time when construction team c arrives at fault point i, represents the movement time of construction team c at fault point i and fault point j, represents the repair time required by construction team c to repair fault point j; Indicates whether the fault point i is repaired at time t, if yes, it is 1, represents the availability status of faulty component i. If the fault has been repaired before time t, it is 1; T represents the total time for post-disaster repair; represents the moving path of mobile power supply m. If m moves from node i to point j, it is equal to 1. d represents the initial node of mobile power supply m. Indicates whether the mobile power supply m is connected to the node i. If yes, it is equal to 1. Φ MIndicates mobile power supply collection; AT i m represents the time when mobile power source m arrives at node i, represents the movement time of mobile power source m at node i and node j, represents the residence time of mobile power source m at node j, where M represents a very large number; represents the active power output of renewable energy k at time t, represents the reactive power output of renewable energy k at time t, represents the upper limit of active power output of renewable energy source k; represents the power factor of the renewable energy source k; represents the active output of the mobile power supply at node i at time t, represents the reactive power output of the mobile power supply at node i at time t, Indicates the upper limit of active output of mobile power supply m, Indicates the upper limit of reactive output of mobile power supply m, Indicates whether the mobile power supply is at node i at time t; Z i,j,t represents the flow direction from node i to node j at time t, represents the available state of the fault line ij at time t, represents the available state of the tie line ij at time t; N represents the set of grid nodes; v i,t Indicates whether node i belongs to the restored area at time t, represents the load value of node i; P i,t represents the active power flowing out of node i at time t, Q i,t represents the reactive power flowing out of node i at time t, represents the load recovery active power of node i at time t, represents the load recovery reactive power of node i at time t; U i,t represents the voltage amplitude of node i at time t, R i,j represents the resistance of line ij, X i,j represents the reactance of line ij, Indicates the upper limit of the voltage amplitude at node i, U i represents the lower limit of the voltage amplitude at node i;

[0153] Step 2-2: The sub-problem is to solve the worst-recovered renewable energy output and load fluctuation uncertainty scenario under the distribution network emergency repair operation strategy, i.e., the construction team and mobile power dispatch, and the tie line action plan. The objective function is:

[0154]

[0155] Where p represents the uncertainty variable, including the output of distributed generation and load demand fluctuations, ρ i represents the load weight on node i, represents the load recovery amount of node i at time t;

[0156] Among them, the constraints of the sub-problem objective function are:

[0157] After the distribution network emergency repair operation strategy is determined, the sub-problem is given as follows: and Solving the worst-recovery renewable energy output and load fluctuation uncertainty scenario requires following the distribution network operation constraints and uncertainty set constraints, which are:

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[0169] in, represents the determined value of the flow direction from node i to node j at time t; Indicates the recovery status of node i at time t; represents the load value of node i; P i,t represents the active power flowing out of node i at time t; Q i,t represents the reactive power flowing out of node i at time t; represents the load recovery active power of node i at time t; represents the load recovery reactive power of node i at time t; U i,t represents the voltage amplitude of node i at time t; R i,j represents the resistance of circuit ij; X i,jrepresents the reactance of line ij; represents the upper limit of the voltage amplitude of node i; U i represents the lower limit of the voltage amplitude at node i; represents the load fluctuation value of node i; P i L,E represents the load forecast value of node i; represents the lower deviation of the load forecast value of node i; represents the upper deviation of the load forecast value of node i; represents the output fluctuation value of renewable energy source i; P i G,E represents the output forecast value of renewable energy source i; represents the lower deviation of the output forecast value of renewable energy source i; It represents the upper deviation of the output forecast value of renewable energy source i.

[0170] Step 3: Use the strong duality theorem to transform the double-layer structure problem of the sub-problem in step 2 into a single-layer problem. Specifically:

[0171] Step 3-1: Based on the sub-problem objective function and constraints of step 2-2, use the strong duality to transform the two-layer model into a single-layer robust optimization model:

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[0173] st

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[0184] Where λ represents the dual multiplier;

[0185] Step 3-2: For the dual problem with bilinear variables Perform further linearization:

[0186] Since the worst scenario in the uncertainty set exists at the extreme point, the uncertainty set of renewable energy output and load fluctuation is expressed as:

[0187]

[0188] in, represents the load fluctuation value of node i; P i L represents the lower limit of the load fluctuation range of node i; ΔP i L represents the load fluctuation range of node i; represents the load fluctuation of node i; represents the output fluctuation value of renewable energy source i; P i G Represents the lower limit of the output fluctuation range of renewable energy i; ΔP i G Represents the output fluctuation range of renewable energy source i; represents the output fluctuation of renewable energy source i;

[0189] Using the Big M method, the bilinear term Replaced by ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8,

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[0197]

[0198] Where ω represents the auxiliary variable for linearization of bilinear terms; represents the load fluctuation of node i; where λ is the dual multiplier, and M represents a very large number, ensuring that when α=1, ω=λ, and when α=0, ω=0.

[0199] Step 4: The main problem and subproblems of the two-stage robust optimization model are iterated and solved using CPLEX to obtain the optimized robust solution for distribution network emergency repair operation.

[0200] Specifically:

[0201] First, CCG is used to iterate the main problem and sub-problems of the two-stage robust optimization model. The CCG algorithm flow chart is as follows: Figure 2 As shown;

[0202] Then, CPLEX is used to solve the problem. When the error between the two solutions is within the allowable range, a robust solution for distribution network emergency repair operation considering uncertainty is obtained.

[0203] A distribution network post-disaster emergency repair operation strategy optimization system considering renewable energy and load uncertainty includes the following steps:

[0204] Optimization model construction module: used to build a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty;

[0205] Model decomposition module: used to process the optimization model and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method;

[0206] Sub-problem conversion module: used to convert the double-layer structure of sub-problems into a single-layer problem;

[0207] Optimization solution module: It is used to iterate the main problem and subproblems of the two-stage robust optimization model, solve them using CPLEX, and obtain the optimized distribution network emergency repair operation robust solution.

[0208] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the following steps are implemented:

[0209] Step 1: Construct a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty;

[0210] Step 2: Process the optimization model in step 1 and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method;

[0211] Step 3: Use the strong duality theorem to transform the double-layer structure problem of the sub-problem in step 2 into a single-layer problem;

[0212] Step 4: Iterate the main problem and subproblems of the two-stage robust optimization model and solve them using CPLEX to obtain the optimized robust solution for distribution network emergency repair operation.

[0213] A computer storable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the computer program implements the following steps:

[0214] Step 1: Construct a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty;

[0215] Step 2: Process the optimization model in step 1 and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method;

[0216] Step 3: Use the strong duality theorem to transform the double-layer structure problem of the sub-problem in step 2 into a single-layer problem;

[0217] Step 4: Iterate the main problem and subproblems of the two-stage robust optimization model and solve them using CPLEX to obtain the optimized robust solution for distribution network emergency repair operation.

[0218] The present invention will be further described below with reference to the embodiments.

[0219] Example

[0220] This example takes the IEEE 33-node system as an example, and its topology is as follows Figure 3 The lightning icon represents the fault line, and the dotted line represents the tie line.

[0221] Three distributed generation units, consisting of wind power and energy storage, are installed at nodes 10, 23, and 33 of the system. Interfaces connecting the MPS units to the distribution grid are located at nodes 6, 12, 17, 20, and 24. In the example scenario, six lines fail during extreme weather, and two maintenance teams are deployed to repair the failures, assuming sufficient fuel is available for MPS operation.

[0222] The energy storage parameters are shown in Table 1. The prediction errors for power output and load demand are set to ±30% and ±35%, respectively. The MPS has a maximum output power of 500 kW / 400 kVar and is initially located at node 20. Furthermore, the substation bus voltage is set to 12.66 kV, and all bus voltages operate within the range of 0.9 to 1.1 pu. The expected fault repair time is shown in Table 2. The total repair time is approximately 5 hours, divided into 15 time steps of 20 minutes each.

[0223] Nodes 9, 23, and 33 are connected to three distributed power sources, with output information shown in Table 1. A mobile power source was deployed at Node 6 before the disaster. It is assumed to be a mobile emergency generator on a vehicle with an output of 190 kW / 100 kV and sufficient fuel.

[0224] Table 1 Energy storage coefficient

[0225]

[0226] Table 2 Fault repair time

[0227]

[0228] The proposed method coordinates the repair crews, MPS, and line switches to maximize the restored load. Figure 4 and Figure 5 The optimized repair sequence for the repair crews and the movement sequence of the MPS are shown, and Table 3 shows the status of the tie lines during the fault repair. After the fault occurred, all busbars in the distribution network experienced a power outage, except for nodes 2 and 3. Faults F1 and F2 were first repaired by two repair crews. In addition, tie lines 13-22 were put into operation after F1 was repaired to expand the repair area. Then, faults F3 and F6 were repaired by two groups of repair crews, restoring the areas powered by DG2 and DG3, respectively. In addition, tie lines 18-33 were put into operation to restore the entire power outage area. Finally, faults F4 and F5 were repaired, and the distribution operation system returned to normal.

[0229] Table 3 Tie line operation status

[0230]

[0231] Considering the load recovery results of different repair resources, Figure 6 As shown in the figure. For methods that do not consider grid reconfiguration, the load at the end of the feeder must be repaired before all faults are restored, which significantly reduces the recovery efficiency of this method. Although both MPS and DG can be used to supply power to critical loads during power outages, DG in the grid often has a larger capacity than MPS, so DG can significantly reduce load losses compared to MPS. By comparing different methods, the method proposed in this paper can most effectively restore loads by coordinating RC, MPS, DG, and grid reconfiguration, thereby improving the resilience of the distribution network.

[0232] To validate the effectiveness of the proposed robust approach, considering the uncertainty of wind power output and load demand, the robust model was compared with deterministic and stochastic approaches. In the deterministic approach, the forecast errors of wind power output and load demand were ignored. In the stochastic approach, wind power output was randomly generated based on the predicted output interval. Monte Carlo simulations were then used to generate 10,000 scenarios to select the worst-case scenario.

[0233] After obtaining the optimal coordination schemes of the three methods, 20 prediction errors of wind power output and load demand were randomly generated to test the safety of the three coordination schemes. In each test, wind power output and load demand fluctuated randomly within the allowed range. When the safety constraints were not met, the restored load was set to 0. The test results are shown in Figure 2. Figure 7 shown.

[0234] The test results show that while the optimal results of deterministic and stochastic methods are superior to those of the robust method, the robust method can still satisfy all safety constraints. During grid restoration, robustness is preferred over economic efficiency to ensure load power supply security. Therefore, considering the uncertainty of wind turbine output and load demand, the robust optimization method for distribution network post-disaster emergency repair operation strategy proposed in this patent has a safer recovery effect.

[0235] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

[0236] The above embodiments illustrate and describe the basic principles and main features of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications fall within the scope of the invention as claimed.

Claims

1. A distribution network post-disaster repair operation strategy optimization method considering renewable energy and load uncertainty, characterized by: The following steps are involved: Step 1: Construct a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty; Step 2: Process the optimization model in step 1 and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method: Step 2-1: The main problem is to solve the optimal emergency repair operation strategy under the uncertainty scenario, that is, given the renewable energy output and load fluctuations. The objective function is: Among them, x represents the distribution network emergency repair operation strategy, including the construction team, mobile power dispatch and tie line action plan, ρ i represents the load weight on node i, represents the load recovery amount of node i at time t; Step 2-2: The sub-problem is to solve the worst-recovered renewable energy output and load fluctuation uncertainty scenario under the distribution network emergency repair operation strategy, i.e., the construction team and mobile power dispatch, and the tie line action plan. The objective function is: Where p represents the uncertainty variable, including the output of distributed generation and load demand fluctuations, ρ i represents the load weight on node i, represents the load recovery amount of node i at time t; The constraints are: After the distribution network emergency repair operation strategy is determined, the sub-problem is given as follows: the grid topology, load recovery status and mobile power access status are given as follows: and Solving the worst-recovery renewable energy output and load fluctuation uncertainty scenario requires following the distribution network operation constraints and uncertainty set constraints, which are: in, represents the determined value of the flow direction from node i to node j at time t; Indicates the recovery status of node i at time t; represents the load value of node i; P i,t represents the active power flowing out of node i at time t; Q i,t represents the reactive power flowing out of node i at time t; represents the load recovery active power of node i at time t; represents the load recovery reactive power of node i at time t; U i,t represents the voltage amplitude of node i at time t; R i,j represents the resistance of circuit ij; X i,j represents the reactance of line ij; represents the upper limit of the voltage amplitude of node i; U i represents the lower limit of the voltage amplitude at node i; represents the load fluctuation value of node i; P i L ,E represents the load forecast value of node i; represents the lower deviation of the load forecast value of node i; represents the upper deviation of the load forecast value of node i; represents the output fluctuation value of renewable energy source i; P i G,E represents the output forecast value of renewable energy source i; represents the lower deviation of the output forecast value of renewable energy source i; represents the upper deviation of the output forecast value of renewable energy source i; Step 3: Use the strong duality theorem to transform the double-layer structure problem of the sub-problem in step 2 into a single-layer problem: Step 3-1: Based on the sub-problem objective function and constraints of step 2-2, use the strong duality to transform the two-layer model into a single-layer robust optimization model: st Where λ represents the dual multiplier; Step 3-2: For the existence of bilinear variables in the dual problem Perform further linearization: Since the worst scenario in the uncertainty set exists at the extreme point, the uncertainty set of renewable energy output and load fluctuation is expressed as: in, represents the load fluctuation value of node i; P i L represents the lower limit of the load fluctuation range of node i; ΔP i L represents the load fluctuation range of node i; represents the load fluctuation of node i; represents the output fluctuation value of renewable energy source i; P i G Represents the lower limit of the output fluctuation range of renewable energy i; ΔP i G Represents the output fluctuation range of renewable energy source i; represents the output fluctuation of renewable energy source i; Using the Big M method, the bilinear term Replaced by ω1, ω2, ω3, ω4, ω5, ω6, ω7, ω8, Where ω represents the auxiliary variable for linearization of bilinear terms; represents the load fluctuation of node i; where λ is the dual multiplier and M represents a very large number; Step 4: Iterate the main problem and subproblems of the two-stage robust optimization model and solve them using CPLEX to obtain the optimized robust solution for distribution network emergency repair operation.

2. The method for optimizing distribution network post-disaster emergency repair operation strategy considering renewable energy and load uncertainty according to claim 1 is characterized in that: The construction of the robust optimization model in step 1 is specifically as follows: Step 1-1: Based on the robust optimization objective of the distribution network post-disaster emergency repair operation strategy, construct the emergency repair operation strategy under the worst-case scenario: Where x represents the distribution network emergency repair operation strategy, including construction team scheduling, mobile power scheduling and tie line operation plan, p represents the uncertainty variable, including distributed power output and load demand fluctuation, ρ i represents the load weight on node i, represents the load recovery amount of node i at time t; Step 1-2: Determine the constraints in the optimization model.

3. The method for optimizing distribution network post-disaster emergency repair operation strategy considering renewable energy and load uncertainty according to claim 2 is characterized in that: The constraints in steps 1-2 are specifically: Step 1-2-1: Determine the constraints on construction team scheduling during distribution network restoration; (1) The starting and ending constraints of the construction team scheduling are: in, represents the moving path of construction team c. If construction team c moves from point i to point j, it is equal to 1, D is the camp of construction team c, Φ F Represents a set of fault points; (2) Construction team scheduling and movement constraints: (3) Each fault is repaired by at most one construction team: in, Indicates whether the fault point i has been repaired by the construction team c, if so, it is equal to 1; Φ C Indicates the assembly of the construction team; (4) Time constraints for the construction team to arrive at the fault point: Among them, AT i c represents the time when construction team c arrives at fault point i, represents the movement time of construction team c at fault point i and fault point j, represents the repair time required by construction team c to repair fault point j; (5) Restoration time of the fault point and availability constraints of the faulty component: in, Indicates whether the fault point i is repaired at time t, if yes, it is 1, represents the availability status of the faulty component i. If the fault has been repaired before time t, it is 1. T represents the total time step of post-disaster repair. Step 1-2-2: Determine the constraints on mobile power dispatch during distribution network restoration; (1) Logical sequence constraints for mobile power scheduling: in, represents the moving path of mobile power supply m. If m moves from node i to point j, it is equal to 1. d represents the initial node of mobile power supply m. Indicates whether the mobile power supply m is connected to the node i. If yes, it is equal to 1. Φ M Represents a collection of mobile power supplies; (2) Constraints on the available state of mobile power supply: Among them, AT i m represents the time when mobile power source m arrives at node i, represents the movement time of mobile power source m at node i and node j, represents the residence time of mobile power source m at node j, M represents a very large number, only when When the mobile power source moves from node i to node j, Step 1-2-3: Determine the constraints on the output of distributed generation during the distribution network restoration process; (1) Output constraints of renewable energy: in, represents the active power output of renewable energy k at time t, represents the reactive power output of renewable energy k at time t, represents the upper limit of active power output of renewable energy source k; represents the power factor of the renewable energy source k; (2) Output constraints of mobile power supply: in, represents the active output of the mobile power supply at node i at time t, represents the reactive power output of the mobile power supply at node i at time t, Indicates the upper limit of active output of mobile power supply m, Indicates the upper limit of reactive output of mobile power supply m, Indicates whether the mobile power supply is at node i at time t; Step 1-2-4: Determine the constraints on the output of distributed generation during the distribution network restoration process; (1) Line flow direction constraints: Among them, Z i,j,t represents the flow direction from node i to node j at time t, represents the available state of the fault line ij at time t, represents the available state of tie line ij at time t; N represents the set of grid nodes; (2) Node load recovery constraint: Among them, v i,t Indicates whether node i belongs to the restored area at time t, Represents the load value of node i; M represents a very large number, only when Z i,j,t =1 means that when the flow direction is from node i to node j, v j,t =v i,t , otherwise the equation does not hold; (3) Node power balance constraint: Among them, P i,t represents the active power flowing out of node i at time t, Q i,t represents the reactive power flowing out of node i at time t, represents the load recovery active power of node i at time t, represents the load recovery reactive power of node i at time t; (4) Node voltage constraints: Among them, U i,t represents the voltage amplitude of node i at time t, R i,j represents the resistance of line ij, X i,j represents the reactance of line ij, represents the upper limit of the voltage amplitude of node i, U i Represents the lower limit of the voltage amplitude of node i; M represents a very large number, only when Z i,j,t =1 means that when the power flow direction is from node i to node j, the node voltage can be calculated, U j,t =U i,t -2(R i,j ·P i,t +X i,j Q i,t ), otherwise the equation does not hold; Step 1-2-5: Constraints on renewable energy output and load fluctuations: in, represents the load fluctuation value of node i, P i L,E represents the load forecast value of node i, represents the lower deviation of the load forecast value of node i, represents the upper deviation of the load forecast value of node i, Represents the output fluctuation value of renewable energy i, P i G,E represents the output forecast value of renewable energy source i, represents the lower deviation of the output forecast value of renewable energy source i, It represents the upper deviation of the output forecast value of renewable energy source i.

4. The method for optimizing distribution network post-disaster emergency repair operation strategy considering renewable energy and load uncertainty according to claim 1 is characterized in that: The constraints of the objective function of the main problem in step 2-1 are: The main problem is that the renewable energy output and load fluctuation are given values. P i L，* To solve the optimal emergency repair operation strategy, we need to follow the construction team and mobile power scheduling constraints, distributed power output constraints, and distribution network operation constraints, which is: in, represents the moving path of construction team c. If construction team c moves from point i to point j, it is equal to 1, D is the camp of construction team c, Φ F Represents a set of fault points; Indicates whether the fault point i has been repaired by the construction team c, if so, it is equal to 1; Φ C Indicates the construction team is assembled; AT i c represents the time when construction team c arrives at fault point i, represents the movement time of construction team c at fault point i and fault point j, represents the repair time required by construction team c to repair fault point j; Indicates whether the fault point i is repaired at time t, if yes, it is 1, represents the availability status of faulty component i. If the fault has been repaired before time t, it is 1; T represents the total time for post-disaster repair; represents the moving path of mobile power supply m. If m moves from node i to point j, it is equal to 1. d represents the initial node of mobile power supply m. Indicates whether the mobile power supply m is connected to the node i. If yes, it is equal to 1. Φ M Indicates mobile power supply collection; AT i m represents the time when mobile power source m arrives at node i, represents the movement time of mobile power source m at node i and node j, represents the residence time of mobile power source m at node j, where M represents a very large number; represents the active power output of renewable energy k at time t, represents the reactive power output of renewable energy k at time t, represents the upper limit of active power output of renewable energy source k; represents the power factor of the renewable energy source k, represents the active output of the mobile power supply at node i at time t, represents the reactive power output of the mobile power supply at node i at time t, Indicates the upper limit of active output of mobile power supply m, Indicates the upper limit of reactive output of mobile power supply m, Indicates whether the mobile power supply is at node i at time t; Z i,j,t represents the flow direction from node i to node j at time t, represents the available state of the fault line ij at time t, represents the available state of the tie line ij at time t; N represents the set of grid nodes; v i,t Indicates whether node i belongs to the restored area at time t, represents the load value of node i; P i,t represents the active power flowing out of node i at time t, Q i,t represents the reactive power flowing out of node i at time t, represents the load recovery active power of node i at time t, represents the load recovery reactive power of node i at time t; U i,t represents the voltage amplitude of node i at time t, R i,j represents the resistance of line ij, X i,j represents the reactance of line ij, represents the upper limit of the voltage amplitude of node i, U i Indicates the lower limit of the voltage amplitude at node i.

5. A distribution network post-disaster emergency repair operation strategy optimization system considering renewable energy and load uncertainty, used to execute the method described in claim 1, characterized in that: The following steps are involved: Optimization model construction module: used to build a two-stage distribution network post-disaster emergency repair operation decision-making robust optimization model considering renewable energy and load uncertainty; Model decomposition module: used to process the optimization model and decompose it into the main problem of solving the emergency repair operation strategy and the sub-problem of solving the worst-case recovery uncertainty scenario based on the column and constraint generation method; Sub-problem conversion module: used to convert the double-layer structure of sub-problems into a single-layer problem; Optimization solution module: It is used to iterate the main problem and subproblems of the two-stage robust optimization model, solve them using CPLEX, and obtain the optimized distribution network emergency repair operation robust solution.

6. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A computer storable medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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