Power grid congestion management method and system considering photovoltaic output uncertainty

Abstract

The invention discloses a power grid blocking management method and system considering photovoltaic output uncertainty, and the method obtains an optimal power grid blocking management scheme through constructing a power grid blocking management model and solving the constructed model. The power grid blocking management model constructed by the invention is a multi-stage distribution robust optimization model related to optimal line breaking, and takes into account the coordination of the power system operation constraint, the maximum allowable breaking line number constraint and the scene probability constraint considering the photovoltaic output uncertainty; therefore, the uncertainty of new energy output is handled on the aspect of power grid congestion management, the problem of line congestion is relieved, the new energy consumption capacity is improved, and the economic operation benefit of the power grid is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of power grid optimization and dispatching, and in particular relates to a power grid congestion management method and system taking into account the uncertainty of photovoltaic output. Background Art

[0002] With the proposal of the "dual carbon" strategic goal and the vigorous development of the national economy, human beings have an increasing demand for renewable clean energy. In recent years, renewable energy has developed rapidly, the proportion of wind power installed capacity and the total power generation have reached new highs, and thermal power units in the power system have gradually been replaced by new energy units. The problem that follows is that the inherent intermittent nature of new energy leads to serious congestion problems in the power grid. For example, the peak period of photovoltaic power generation usually occurs at noon, so the power grid in areas with a high proportion of photovoltaic power is prone to congestion during the noon period. Similarly, since the peak period of wind power generation usually occurs in the morning, the power grid in areas with a high proportion of wind power is prone to congestion in the morning. In the operation of traditional power systems, only the output of generators is adjusted for congestion management and line overload is eliminated, and network topology optimization methods are rarely considered, which will be detrimental to the safe and economic operation of the power system. At the same time, most of the existing optimal line disconnection models of some transmission networks use deterministic optimization models, ignoring the impact of photovoltaic output uncertainty on power system scheduling, and the scheduling results are too optimistic.

[0003] Therefore, how to propose a grid congestion management method and system that takes into account the uncertainty of photovoltaic output to deal with the uncertainty of new energy output, alleviate line congestion problems, increase new energy absorption capacity, and improve the economic operation efficiency of the power grid is an urgent problem to be solved by technical personnel in this field. Summary of the invention

[0004] In order to solve the problems existing in the prior art, the present invention proposes a grid congestion management method and system taking into account the uncertainty of photovoltaic output.

[0005] The technical solution of the present invention is as follows:

[0006] A grid congestion management method considering photovoltaic output uncertainty, comprising:

[0007] Taking the minimization of the total grid operation cost as the objective function, and taking the power system operation constraints, the maximum allowable number of disconnected lines constraints and the scenario probability constraints considering the uncertainty of photovoltaic output as constraints, a grid congestion management model with the grid line disconnection plan as the decision variable is constructed;

[0008] The power grid congestion management model is solved to obtain an optimal power grid congestion management solution.

[0009] Furthermore, the objective function of the grid congestion management model is expressed as follows:

[0010]

[0011] Where, subscript t represents the dispatching time; subscript g represents the conventional unit; Ω is the feasible domain of the real-time operation scenario; subscript k represents the photovoltaic output scenario; subscript pv represents the photovoltaic unit; T represents the time section; N G represents the number of conventional units; K is the number of typical scenarios; N PV Indicates the number of photovoltaic units; a g represents the variable operation and maintenance cost of conventional units; P g,t represents the active power of conventional unit g at time t; ρ k is the kth scene probability; The cost coefficient of the penalty for abandoning light; ΔP pv,t,k Indicates the abandoned light power of the photovoltaic unit pv at time t in the kth scenario; is the re-dispatch cost coefficient of conventional units; ΔP g,t,k is the re-dispatching power of conventional unit g at time t in the kth scenario.

[0012] Furthermore, the expression of the power system operation constraint is as follows:

[0013]

[0014] B ij (θ i,t,k -θ j,t.k )-P ij,t,k +(1-z ij )M≥0 (40)

[0015] B ij (θ i,t,k -θ j,t,k )-P ij,t,k -(1-z ij )M≤0 (41)

[0016] In the formula, subscripts i and j represent network nodes, represents the set of conventional generators connected to node i, represents the set of photovoltaic units connected to node i, σ(i) represents the set of lines connected to node i, represents the maximum active output of the photovoltaic unit pv in the kth scenario, D i,t represents the electrical load of node i at time t, P ij,t,k is the power flow of line ij at time t in the kth scenario, and They represent the upper and lower limits of the output of the traditional generator g at time t, θ i min ,θ imax Respectively represent the minimum and maximum values ​​of the voltage phase angle at node i, θ i,t,k represents the voltage phase angle of node i at time t in the kth scenario, is the minimum and maximum value of the power flow allowed to pass through line ij, z ij is a 0-1 variable representing the operating status of line ij, 0 indicates that the line is disconnected, 1 indicates that the line is operating, ij represents the imaginary part of the node admittance matrix, θ j,t,k represents the voltage phase angle of node j at time t in the kth scenario, and M is a constant used to relax the DC power flow equation when the line is disconnected.

[0017] Furthermore, the expression of the constraint on the maximum number of disconnected lines allowed is as follows:

[0018]

[0019] In the formula, w Indicates the maximum number of line disconnections allowed.

[0020] Furthermore, the expression of the scenario probability constraint considering the uncertainty of photovoltaic output is as follows:

[0021]

[0022] In the formula, θ 1 ,θ ∞ are the probability deviation limits allowed by 1-norm and ∞-norm respectively; S is the total number of historical samples; α 1 , α ∞ are the uncertainty probability confidence of 1-norm and ∞-norm respectively; ρ k0 is the initial probability of the kth scenario obtained from the actual running data.

[0023] Furthermore, the specific steps of solving the power grid congestion management model include:

[0024] The grid congestion management model is divided into a main problem for solving the unit output and line disconnection state under the finite bad probability distribution of the photovoltaic output scenario and a sub-problem for solving the worst probability distribution of the photovoltaic output scenario by using the constraint generation algorithm listed in the table, and the constraints of the main problem and the sub-problem are set respectively;

[0025] Based on the constraints of the main problem, the main problem is solved to obtain the unit output and line disconnection status under the finite severe probability distribution of the photovoltaic output scenario, and further obtain the lower bound of the grid congestion management model;

[0026] Based on the obtained finite worst probability distribution of the photovoltaic output scenario, the unit output and line disconnection state and the constraints of the sub-problem, the sub-problem is solved to obtain the worst probability distribution of the photovoltaic output scenario, and further obtain the upper bound of the grid congestion management model;

[0027] The worst probability distribution of the obtained photovoltaic output scenario is added to the constraints of the main problem, and the lower bounds of the new unit output and line disconnection state and the new grid congestion management model are calculated based on the constraints of the new main problem;

[0028] The worst probability distribution corresponding to the new photovoltaic output scenario and the upper bound of the new grid congestion management model are iterated from the new unit output and line disconnection state and the lower bound of the new grid congestion management model;

[0029] The iteration is terminated when the difference between the lower bound and the upper bound of the power grid congestion management model is less than or equal to a set threshold.

[0030] Furthermore, the main problem is

[0031]

[0032] The constraints of the main problem include:

[0033]

[0034] Where: L is the intermediate variable; l is the subscript of the bad probability distribution; is the bad probability distribution of the lth group in the kth scenario, It represents the abandoned light power of the lth photovoltaic group pv at time t in the kth scenario, represents the active output of the lth conventional unit g at time t in the kth scenario, represents the power flow of the lth group of lines ij at time t in the kth scenario, represents the voltage phase angle of the lth group of nodes i at time t in the kth scenario, represents the voltage phase angle of the lth group node j at time t in the kth scenario, and m is the number of iterations;

[0035] The sub-problems are:

[0036]

[0037] The constraints of the subproblem are:

[0038]

[0039] Where: and The conventional unit output and line status are obtained for the main problem respectively.

[0040] A power grid congestion management system considering the uncertainty of photovoltaic output, comprising a model building module and a model solving module;

[0041] The model building module is used to take the minimization of the total operation cost of the power grid as the objective function, and take the power system operation constraint, the maximum allowable number of disconnected lines constraint and the scenario probability constraint considering the uncertainty of photovoltaic output as constraints, to build a power grid congestion management model with the power grid line disconnection plan as the decision variable;

[0042] The model solving module is used to solve the power grid congestion management model to obtain an optimal power grid congestion management solution. An electronic device includes a memory and a processor, the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the above methods.

[0043] A computer-readable storage medium stores a computer program, wherein the computer program implements the steps of any of the above methods when executed by a processor.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] The present invention proposes a grid congestion management method and system considering the uncertainty of photovoltaic output. In the first stage of the model, the total cost of the comprehensive energy system is used as the objective function to formulate a scheduling plan including line disconnection and the planned output value of conventional units. Based on the one-stage scheduling plan, the second stage of the model responds to the uncertainty of photovoltaic output by re-adjusting the output of conventional units and dealing with abandoned light, and simultaneously constrains the uncertainty probability distribution confidence set by combining the 1-norm and the ∞-norm; the method obtains the optimal grid congestion management plan by constructing a grid congestion management model and solving the constructed model. The grid congestion management model constructed by the present invention is a multi-stage distributed robust optimization model related to the optimal line disconnection, and takes into account the coordination of the power system operation constraints, the maximum allowable number of disconnected lines constraints, and the scenario probability constraints considering the uncertainty of photovoltaic output. Therefore, the uncertainty of new energy output is handled in the grid congestion management problem, the line congestion problem is alleviated, the new energy absorption capacity is increased, and the economic operation efficiency of the grid is improved.

[0046] The power grid congestion management method of the present invention divides the constructed model into main problems and sub-problems, and through iterative solutions to the main problems and sub-problems, an accurate optimal power grid congestion management solution can be quickly obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 Flow chart of a grid congestion management method considering uncertainty of photovoltaic output in an embodiment;

[0048] Figure 2 It is a diagram of the improved IEEE118 system in the application embodiment;

[0049] Figure 3 It is a schematic diagram of active output of each conventional unit under different allowable numbers of disconnected lines in the application embodiment. DETAILED DESCRIPTION

[0050] The present invention is further explained below in conjunction with the accompanying drawings and specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. After reading the present invention, various equivalent modifications of the present invention by those skilled in the art all fall within the scope defined by the claims attached to this application.

[0051] Embodiment 1:

[0052] A grid congestion management method considering the uncertainty of photovoltaic output of the present invention is as follows: Figure 1 As shown, including:

[0053] Taking the minimization of the total grid operation cost as the objective function, and taking the power system operation constraints, the maximum allowable number of disconnected lines constraints and the scenario probability constraints considering the uncertainty of photovoltaic output as constraints, a grid congestion management model with the grid line disconnection plan as the decision variable is constructed;

[0054] The power grid congestion management model is solved to obtain an optimal power grid congestion management solution.

[0055] Embodiment 2:

[0056] This embodiment is further designed on the basis of the first embodiment in that the power grid congestion management model in this embodiment adopts a three-layer optimization model, and the expression of the objective function of the three-layer optimization model is as follows:

[0057]

[0058] Where, subscript t represents the dispatching time; subscript g represents the conventional unit; Ω is the feasible domain of the real-time operation scenario; subscript k represents the photovoltaic output scenario; subscript pv represents the photovoltaic unit; T represents the time section; N G represents the number of conventional units; K is the number of typical scenarios; N PV Indicates the number of photovoltaic units; a g represents the variable operation and maintenance cost of conventional units; P g,t represents the active power of conventional unit g at time t; ρ k is the kth scene probability; The cost coefficient of the penalty for abandoning light; ΔP pv,t,k Indicates the abandoned light power of the photovoltaic unit pv at time t in the kth scenario; is the re-dispatch cost coefficient of conventional units; ΔP g,t,k is the re-dispatching power of conventional unit g at time t in the kth scenario.

[0059] Embodiment three:

[0060] This embodiment is further designed on the basis of the second embodiment in that the expression of the power system operation constraint of the power grid congestion management model in this embodiment is as follows:

[0061]

[0062] In the formula, subscripts i and j represent network nodes, represents the set of conventional generators connected to node i, represents the set of photovoltaic units connected to node i, σ(i) represents the set of lines connected to node i, represents the maximum active output of the photovoltaic unit pv in the kth scenario, D i,t represents the electrical load of node i at time t, P ij,t,k is the power flow of line ij at time t in the kth scenario, and They represent the upper and lower limits of the output of the traditional generator g at time t, θ i min ,θ i max Respectively represent the minimum and maximum values ​​of the voltage phase angle at node i, θ i,t,k represents the voltage phase angle of node i at time t in the kth scenario, is the minimum and maximum value of the power flow allowed to pass through line ij, z ij is a 0-1 variable representing the operating status of line ij, 0 indicates that the line is disconnected, 1 indicates that the line is running, and the disconnected line is the line excluded from the optimal topology. ij represents the imaginary part of the node admittance matrix, θ j,t,k It represents the voltage phase angle of node j at time t in the kth scenario. M is a constant with a large value, which is used to relax the DC power flow equation when the line is disconnected.

[0063] Embodiment 4:

[0064] This embodiment is further designed on the basis of the second embodiment in that the expression of the constraint of the maximum number of disconnected lines allowed in the grid congestion management model in this embodiment is as follows:

[0065]

[0066] In the formula, w Indicates the maximum number of line disconnections allowed.

[0067] Embodiment five:

[0068] This embodiment is further designed on the basis of the second embodiment in that the grid congestion management model in this embodiment considers the scene probability constraint of the uncertainty of photovoltaic output as follows:

[0069]

[0070] In the formula, θ 1 ,θ ∞ are the probability deviation limits allowed by 1-norm and ∞-norm respectively; S is the total number of historical samples; α 1 , α ∞ are the uncertainty probability confidence of 1-norm and ∞-norm respectively; ρ k0 is the initial probability of the kth scenario obtained from the actual running data.

[0071] Embodiment six:

[0072] This embodiment is further designed on the basis of the second embodiment in that the specific steps of solving the power grid congestion management model by the power grid congestion management model in this embodiment include:

[0073] The grid congestion management model is divided into a main problem for solving the unit output and line disconnection state under the finite bad probability distribution of the photovoltaic output scenario and a sub-problem for solving the worst probability distribution of the photovoltaic output scenario by using the constraint generation algorithm listed in the table, and the constraints of the main problem and the sub-problem are set respectively;

[0074] The main problem solves the optimal one-stage dispatch decision (unit output and line disconnection status) under a finite worst probability distribution. Since the main problem may not contain the worst probability distribution of the photovoltaic scene, the lower bound of the model is obtained. The sub-problem obtains the worst probability distribution of the model when the variables in the first stage are known, and the upper bound of the model is obtained. As the number of iterations increases, the upper bound decreases, the lower bound gradually increases, and finally converges. Generally, the CPLEX solver is used for the following optimization solution.

[0075] Based on the constraints of the main problem, the main problem is solved to obtain the unit output and line disconnection status under the finite severe probability distribution of the photovoltaic output scenario, and further obtain the lower bound of the grid congestion management model;

[0076] Based on the obtained finite worst probability distribution of the photovoltaic output scenario, the unit output and line disconnection status and the constraints of the sub-problem, the sub-problem is solved to obtain the worst probability distribution of the photovoltaic output scenario, and further obtain the upper bound of the grid congestion management model;

[0077] The worst probability distribution of the obtained photovoltaic output scenario is added to the constraints of the main problem, and the lower bounds of the new unit output and line disconnection state and the new grid congestion management model are calculated based on the constraints of the new main problem;

[0078] The worst probability distribution corresponding to the new photovoltaic output scenario and the upper bound of the new grid congestion management model are iterated from the new unit output and line disconnection state and the lower bound of the new grid congestion management model;

[0079] The iteration is terminated when the difference between the lower bound and the upper bound of the power grid congestion management model is less than or equal to a set threshold.

[0080] Embodiment seven:

[0081] This embodiment is further designed on the basis of the sixth embodiment in that the main problem of the power grid congestion management model in this embodiment is:

[0082]

[0083] The constraints of the main problem include:

[0084]

[0085] Where: L is the intermediate variable; l is the subscript of the bad probability distribution; is the bad probability distribution of the lth group in the kth scenario, It represents the abandoned light power of the lth photovoltaic group pv at time t in the kth scenario, represents the active output of the lth conventional unit g at time t in the kth scenario, represents the power flow of the lth group of lines ij at time t in the kth scenario, represents the voltage phase angle of the lth group of nodes i at time t in the kth scenario, represents the voltage phase angle of the lth group node j at time t in the kth scenario, m is the number of iterations. In the first iteration, there is only one bad probability distribution, namely the initial probability distribution. After each iteration, the subproblem adds a bad probability distribution to the main problem, as well as the corresponding variables and constraints;

[0086] The sub-problems are:

[0087]

[0088] The constraints of the subproblem are:

[0089]

[0090]

[0091] Where: and The conventional unit output and line status are obtained for the main problem respectively.

[0092] The specific solution steps are as follows:

[0093] 1) Parameter initialization, set the next level UB of the model to +∞, the previous level LB of the model to -∞, and the number of iterations m to 1;

[0094] 2) Solve the main problem to obtain the one-stage optimal scheduling strategy under the constraint of finite bad probability distribution. The one-stage optimal scheduling strategy includes the unit output and line disconnection status And update the model lower bound,

[0095] 3) Based on the one-stage optimal scheduling strategy, solve the sub-problem and obtain the worst probability distribution corresponding to the one-stage scheduling strategy k = 1, 2, ... K, and update the upper bound of the model,

[0096] 4) Determine UB-LB ≤ 10 -3 Is it true? If so, stop the iteration; otherwise, m = m + 1, add a bad probability distribution to the main problem k=1,2,……K,generate variables And add corresponding constraints to the main problem and return to step 2).

[0097] In this example, the solution of the subproblem is further optimized. Since there is no coupling between the outer maximization optimization and the inner minimization optimization of the subproblem, this example further decomposes the subproblem into subproblem 1 and subproblem 2. In the solution of the subproblem, subproblem 1 is solved to obtain the optimal scheduling penalty cost for each typical photovoltaic output scenario. k=1,2,......K。

[0098] Again Substitute into subproblem 2 to obtain the first-stage scheduling strategy for the main problem The corresponding worst probability distribution k=1, 2,…K.

[0099] Specifically, the mathematical form of subproblem 1 is as follows:

[0100]

[0101] for:k=1,2,......K (109)

[0102] Where: f 2kIt is the sum of the optimal redispatching penalty cost and the abandonment penalty cost corresponding to the kth typical PV output scenario.

[0103] The mathematical form of sub-problem 2 is as follows:

[0104]

[0105]

[0106] Where: The optimal redispatching penalty and abandonment penalty corresponding to the kth typical PV output scenario obtained for sub-problem 1.

[0107] Embodiment eight:

[0108] A power grid congestion management system considering the uncertainty of photovoltaic output of the present invention comprises a model building module and a model solving module;

[0109] The model building module is used to take the minimization of the total operation cost of the power grid as the objective function, and take the power system operation constraint, the maximum allowable number of disconnected lines constraint and the scenario probability constraint considering the uncertainty of photovoltaic output as constraints, to build a power grid congestion management model with the power grid line disconnection plan as the decision variable;

[0110] The model solving module is used to solve the power grid congestion management model to obtain an optimal power grid congestion management solution.

[0111] Embodiment nine:

[0112] An electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is used to call and run the computer program stored in the memory to execute any of the methods described above.

[0113] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of any of the above methods are implemented.

[0114] Application examples:

[0115] This example simulates the implementation of the power grid congestion management method of the present invention. The simulation implementation object of this example is as follows Figure 2 The IEEE118 node transformation example shown in the figure has 19 traditional thermal power units and 5 photovoltaic units on nodes 79, 81, 96, 98 and 106. The rated capacity of each new energy unit is 500MW. The total installed capacity of the network is 8359.2MW, and renewable energy units and energy storage devices account for 29.9%. This example implements the grid congestion management method of the present invention through the GAMS optimization platform and uses the CPLEX solver to solve the problem.

[0116] Based on this example, the method of the present invention is used to simulate the system dispatch results under different allowable disconnection line numbers (results see Table 1) and the dispatch results under different confidence levels (results see Table 2), and the active output of each conventional unit under different allowable disconnection line numbers of the power system is given (results see Figure 3 ), where J=0 means that line disconnection is not allowed, J=1 means that at most one line is allowed to be disconnected, and J=2 means that at most two lines are allowed to be disconnected. Figure 3 It shows that as the number of lines allowed to be disconnected increases, the output of conventional units with higher power generation costs decreases, while the output of units with lower power generation costs increases, effectively reducing the system dispatch cost. Table 2 shows that as the confidence interval increases, the uncertainty contained increases, and more units need to be adjusted, so the cost increases slightly.

[0117] Table 1 System dispatch results under different allowed disconnection lines

[0118] Maximum allowed number of circuit breakers Disconnect Line 0 -- 1 82-83 2 76-77、82-83 3 75-77、76-77、82-83 4 75-77、76-77、77-78、82-83 5 25-26、75-77、76-77、77-78、82-83

[0119] Table 2 Comparative analysis of results under different confidence intervals

[0120]

[0121] In summary, the grid congestion management method considering the uncertainty of photovoltaic output of the present invention effectively handles the uncertainty of renewable energy output and mitigates the impact of line overload by constructing a two-stage distributed robust optimization model for optimal line disconnection considering the uncertainty of photovoltaic output.

[0122] The above description is only a specific implementation of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by a technician familiar with the technical field within the technical scope disclosed by the present invention should be covered by the protection scope of the present invention.

Claims

1. A grid congestion management method considering the uncertainty of photovoltaic output, characterized in that: include: Taking the minimization of the total grid operation cost as the objective function, and taking the power system operation constraints, the maximum allowable number of disconnected lines constraints and the scenario probability constraints considering the uncertainty of photovoltaic output as constraints, a grid congestion management model with the grid line disconnection plan as the decision variable is constructed; The power grid congestion management model is solved to obtain an optimal power grid congestion management solution.

2. The grid congestion management method considering the uncertainty of photovoltaic output according to claim 1 is characterized in that: The objective function of the grid congestion management model is expressed as follows: Where, subscript t represents the dispatching time; subscript g represents the conventional unit; Ω is the feasible domain of the real-time operation scenario; subscript k represents the photovoltaic output scenario; subscript pv represents the photovoltaic unit; T represents the time section; N G represents the number of conventional units; K is the number of typical scenarios; N PV Indicates the number of photovoltaic units; a g represents the variable operation and maintenance cost of conventional units; P g,t represents the active power of conventional unit g at time t; ρ k is the kth scene probability; The cost coefficient of the penalty for abandoning light; ΔP pv,t,k Indicates the abandoned light power of the photovoltaic unit pv at time t in the kth scenario; is the re-dispatch cost coefficient of conventional units; ΔP g,t,k is the re-dispatching power of conventional unit g at time t in the kth scenario.

3. The grid congestion management method considering the uncertainty of photovoltaic output according to claim 2 is characterized in that: The expression of the power system operation constraint is as follows: In the formula, subscripts i and j represent network nodes, represents the set of conventional generators connected to node i, represents the set of photovoltaic units connected to node i, σ(i) represents the set of lines connected to node i, represents the maximum active output of the photovoltaic unit pv in the kth scenario, D i,t represents the electrical load of node i at time t, P ij,t,k is the power flow of line ij at time t in the kth scenario, and They represent the upper and lower limits of the output of the traditional generator g at time t, Respectively represent the minimum and maximum values ​​of the voltage phase angle at node i, θ i,t,k represents the voltage phase angle of node i at time t in the kth scenario, is the minimum and maximum value of the power flow allowed to pass through line ij, z ij is a 0-1 variable representing the operating status of line ij, 0 indicates that the line is disconnected, 1 indicates that the line is operating, ij represents the imaginary part of the node admittance matrix, θ j,t,k represents the voltage phase angle of node j at time t in the kth scenario, and M is a constant used to relax the DC power flow equation when the line is disconnected.

4. The grid congestion management method considering the uncertainty of photovoltaic output according to claim 2 is characterized in that: The expression of the constraint of the maximum number of disconnected lines allowed is as follows: In the formula, w Indicates the maximum number of line disconnections allowed.

5. The grid congestion management method considering the uncertainty of photovoltaic output according to claim 2, characterized in that: The expression of the scenario probability constraint considering the uncertainty of photovoltaic output is as follows: In the formula, θ1, θ ∞ are the probability deviation limits allowed by the 1-norm and ∞-norm respectively; S is the total number of historical samples; α1, α ∞ are the uncertainty probability confidence of 1-norm and ∞-norm respectively; ρ k0 is the initial probability of the kth scenario obtained from the actual running data.

6. The grid congestion management method considering the uncertainty of photovoltaic output according to claim 2, characterized in that: The specific steps of solving the power grid congestion management model include: The grid congestion management model is divided into a main problem for solving the unit output and line disconnection state under the finite bad probability distribution of the photovoltaic output scenario and a sub-problem for solving the worst probability distribution of the photovoltaic output scenario by using the constraint generation algorithm listed in the table, and the constraints of the main problem and the sub-problem are set respectively; Based on the constraints of the main problem, the main problem is solved to obtain the unit output and line disconnection status under the finite severe probability distribution of the photovoltaic output scenario, and further obtain the lower bound of the grid congestion management model; Based on the obtained finite worst probability distribution of the photovoltaic output scenario, the unit output and line disconnection state and the constraints of the sub-problem, the sub-problem is solved to obtain the worst probability distribution of the photovoltaic output scenario, and further obtain the upper bound of the grid congestion management model; The worst probability distribution of the obtained photovoltaic output scenario is added to the constraints of the main problem, and the lower bounds of the new unit output and line disconnection state and the new grid congestion management model are calculated based on the constraints of the new main problem; The worst probability distribution corresponding to the new photovoltaic output scenario and the upper bound of the new grid congestion management model are iterated from the new unit output and line disconnection state and the lower bound of the new grid congestion management model; The iteration is terminated when the difference between the lower bound and the upper bound of the power grid congestion management model is less than or equal to a set threshold.

7. The grid congestion management method considering the uncertainty of photovoltaic output according to claim 6 is characterized in that: The main problem is The constraints of the main problem include: Where: L is the intermediate variable; l is the subscript of the bad probability distribution; is the bad probability distribution of the lth group in the kth scenario, It represents the abandoned light power of the lth photovoltaic group pv at time t in the kth scenario, represents the active output of the lth conventional unit g at time t in the kth scenario, represents the power flow of the lth group of lines ij at time t in the kth scenario, represents the voltage phase angle of the lth group of nodes i at time t in the kth scenario, represents the voltage phase angle of the lth group node j at time t in the kth scenario, m is the number of iterations; The sub-problems are: The constraints of the subproblem are: Where: and The conventional unit output and line status are obtained for the main problem respectively.

8. A grid congestion management system considering the uncertainty of photovoltaic output, characterized in that: It includes a model building module and a model solving module; The model building module is used to take the minimization of the total operation cost of the power grid as the objective function, and take the power system operation constraint, the maximum allowable number of disconnected lines constraint and the scenario probability constraint considering the uncertainty of photovoltaic output as constraints, to build a power grid congestion management model with the power grid line disconnection plan as the decision variable; The model solving module is used to solve the power grid congestion management model to obtain an optimal power grid congestion management solution.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor is configured to call and run the computer program stored in the memory to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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