CN108985561B - Active power distribution network island division method based on opportunity constraint - Google Patents

Active power distribution network island division method based on opportunity constraint Download PDF

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CN108985561B
CN108985561B CN201810588310.8A CN201810588310A CN108985561B CN 108985561 B CN108985561 B CN 108985561B CN 201810588310 A CN201810588310 A CN 201810588310A CN 108985561 B CN108985561 B CN 108985561B
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赵金利
张孟珍
王成山
冀浩然
宋关羽
李鹏
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Abstract

An active power distribution network island division method based on opportunity constraint comprises the following steps: according to the selected power distribution system, input line parameters, load levels, network topology connection relations, system operation voltage levels, branch active power limits, confidence parameters, controllable and uncontrollable distributed power access positions and capacities, a load prediction curve, system fault moments, reference voltages and reference power initial values; discretizing the power generation power of the uncontrollable distributed power supply to obtain a discrete probability distribution parameter of the power generation power of the uncontrollable distributed power supply; establishing an active power distribution network island division model based on opportunity constraint; converting system power flow constraint, node voltage probability constraint and branch active power probability constraint to obtain a mixed integer nonlinear model; solving the mixed integer nonlinear model by adopting an interior point method; and outputting a solving result. The invention can work out the island division strategy meeting different requirements, and ensures the safe operation of the system while ensuring the reliable power supply of part of important loads.

Description

Active power distribution network island division method based on opportunity constraint
Technical Field
The invention relates to an active power distribution network island division method. In particular to an active power distribution network island division method based on opportunity constraint.
Background
With the high-proportion wide access of distributed power supplies, the operation and scheduling modes of the power distribution system are changed profoundly and durably. Island operation is a special operation mode of an active power distribution system, and can supply power for important loads in the system by means of a distributed power supply under the condition of extreme faults, so that the power supply reliability and the power supply elasticity of the power distribution system are effectively improved.
However, after a large number of uncontrollable distributed power sources such as fans and photovoltaic power sources are connected to an active power distribution network, the operating characteristics of the uncontrollable distributed power sources are greatly influenced by the environment and have obvious randomness and volatility, and many problems such as node voltage out-of-limit, branch current overload and the like can be brought to the safe operation of the power distribution network. In the island operation process of the power distribution network, when the output of the uncontrollable distributed power supply is different from a predicted value, the controllable distributed power supply is limited in capacity and is often difficult to ensure power balance in an island region through self power regulation, so that a preset island strategy is difficult to normally operate, the problems of voltage out-of-limit and current overload in the power distribution network are more obvious, and the operation safety and the power supply reliability of the power distribution system are seriously influenced. Therefore, the influence of the output uncertainty on the island operation strategy is fully considered when the island division is carried out on the power distribution network containing the uncontrollable distributed power supply.
At present, the existing island division method considering the uncertainty of the distributed power output can be roughly divided into a robust optimization algorithm and a random programming method. The robust optimization algorithm replaces the probability exact distribution of the power generation power of the uncontrollable distributed power source with the uncertain set, takes the optimized operation strategy in the worst scene as the operation strategy of the system, and obtains a conservative result when the method is used for ensuring that the load in the system is supplied with power continuously as much as possible. The stochastic programming method is characterized in that uncertain information of the uncontrollable distributed power supply is mostly described in a scene probability distribution mode, when an island is divided by the stochastic programming method, an opportunity constraint model is mostly adopted, the model allows that an island operation strategy does not meet constraint conditions to a certain extent, but the probability of the constraint conditions being met is not lower than a certain confidence level. However, the existing active power distribution network island division method based on opportunity constraint mostly adopts a heuristic/intelligent algorithm + check two-stage algorithm, and the method has more iteration times, complicated calculation process, difficulty in finding a global optimal solution and certain limitation.
Therefore, a mathematical programming method capable of being directly solved is urgently needed, and is used for solving an active power distribution network island division model based on opportunity constraint, so that an island division strategy capable of meeting different requirements is worked out, the confidence level and the load recovery level of safe operation of a system are fully coordinated, and the system is enabled to operate safely while reliable power supply of part of important loads is ensured.
Disclosure of Invention
The invention aims to solve the technical problem of providing an active power distribution network island division method based on opportunity constraint, which can meet different requirements of an active power distribution network.
The technical scheme adopted by the invention is as follows: an active power distribution network island division method based on opportunity constraint comprises the following steps:
1) according to the selected power distribution system, input line parameters, load levels, network topology connection relations, system operation voltage levels, branch active power limits, confidence parameters, controllable and uncontrollable distributed power access positions and capacities, a load prediction curve, system fault moments, reference voltages and reference power initial values;
2) discretizing the power generation power of the uncontrollable distributed power supply to obtain a discrete probability distribution parameter of the power generation power of the uncontrollable distributed power supply;
3) establishing an active power distribution network island division model based on opportunity constraint, comprising the following steps: setting the maximum active load quantity recovered by a power distribution system in a period of time as an objective function, and respectively considering radial constraint, system power flow constraint, network reconstruction constraint, node voltage probability constraint, branch active power probability constraint, uncontrollable distributed power supply operation constraint and controllable distributed power supply operation constraint;
4) converting system power flow constraint, node voltage probability constraint and branch active power probability constraint to obtain a mixed integer nonlinear model;
5) solving the mixed integer nonlinear model obtained in the step 4) by adopting an interior point method;
6) and outputting the solving result of the step 5), including the power distribution system active load recovery quantity, the sectionalizing switch and interconnection switch states and the recovery load coefficient of each node which meet any confidence level.
The method for discretizing the power generation power of the uncontrollable distributed power supply in the step 2) comprises the following steps:
Figure BDA0001689932420000021
wherein, f (x) is a probability density function of the generated power of the uncontrollable distributed power supply; x is the generated power of the uncontrollable distributed power supply; m is a scene of discrete power generation power of the uncontrollable distributed power supply; p (m) is the generated power of the uncontrollable distributed power supply under the scene of m; f [ P (m)]Representing the probability that the power generation power of the uncontrollable distributed power source is P (m); q is a discretization step length; omegasIs a collection of scenes m.
The system power flow constraint in the step 3) is as follows:
Figure BDA0001689932420000022
Figure BDA0001689932420000023
Figure BDA0001689932420000024
Figure BDA0001689932420000025
Figure BDA0001689932420000026
in the formula, omegabIs a set of branches; pt,ji,m、Qt,ji,mRespectively the active power and the reactive power flowing through the m scene branch ji in the t time period; pt,i,m、Qt,i,mThe sum of active power and reactive power injected at the time t and the m scene node iSum of rates;
Figure BDA0001689932420000027
respectively the active power and the reactive power consumed by the load on the node i in the t period; lambda [ alpha ]iFor the recovery of the load on node i, λi∈{0,1}，λ i1 denotes the load recovery on node i, λ i0 indicates that the load on node i is not recovered;
Figure BDA0001689932420000028
respectively the active power and the reactive power injected by the controllable distributed power supply at the time period t and the scene node m;
Figure BDA0001689932420000029
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the m scene node i in the t time period; u. oft,i,mThe square of the voltage amplitude on the m scene node i in the time period t; i.e. it,ij,mThe current amplitude of the m scene branch ij is squared at the time t; rijIs the resistance, X, of branch ijijIs the reactance of branch ij.
The node voltage probability constraint in step 3) is as follows:
Figure BDA00016899324200000210
in the formula, Pr{. denotes the probability that an event holds; epsilon is a confidence parameter;
Figure BDA00016899324200000211
respectively the upper and lower limits of the voltage amplitude; u. oft,i,mThe square of the voltage amplitude on the m scene node i in the time period t.
Step 3) the branch active power probability constraint is as follows:
Figure BDA0001689932420000031
in the formula, Pr{. represents a certain factProbability of a condition being established; epsilon is a confidence parameter;
Figure BDA0001689932420000032
the upper limit and the lower limit of the active power of the branch circuit are respectively set; pt,ij,mThe active power flowing through the m scene branch ij in the time period t.
The transformation in the step 4) comprises the following steps:
introduction of the variable z from 0 to 1t,mAnd jointly converting system power flow constraint, node voltage probability constraint and branch active power probability constraint into a mixed integer nonlinear model:
Figure BDA0001689932420000033
Figure BDA0001689932420000034
Figure BDA0001689932420000035
Figure BDA0001689932420000036
Figure BDA0001689932420000037
Figure BDA0001689932420000038
Figure BDA0001689932420000039
Figure BDA00016899324200000310
Figure BDA00016899324200000311
Figure BDA00016899324200000312
Figure BDA00016899324200000313
Figure BDA00016899324200000314
Figure BDA00016899324200000315
Figure BDA00016899324200000316
in the formula, zt,mFor the introduced binary variable, zt,mThe expression 0 includes the scenario m, z when solving the optimal island operation strategy t,m1 represents that a scene m is not taken into account when solving the optimal island operation strategy; pit,mIs the probability of occurrence of the scene m at the time t; m represents a very large constant; omegabIs a set of branches; pt,ji,m、Qt,ji,mRespectively the active power and the reactive power flowing through the m scene branch ji in the t time period; pt,i,m、Qt,i,mRespectively being the sum of active power and reactive power injected at the t time period and the m scene node i;
Figure BDA00016899324200000317
respectively the active power and the reactive power consumed by the load on the node i in the t period; lambda [ alpha ]iFor the recovery of the load on node i, λi∈{0,1}，λ i1 denotes the load recovery on node i, λ i0 indicates that the load on node i is not recovered;
Figure BDA00016899324200000318
respectively the active power and the reactive power injected by the controllable distributed power supply at the time period t and the scene node m;
Figure BDA00016899324200000319
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the m scene node i in the t time period; u. oft,i，mThe square of the voltage amplitude on the m scene node i in the time period t; i.e. it，ij，mThe current amplitude of the m scene branch ij is squared at the time t; rijIs the resistance, X, of branch ijijReactance for branch ij; epsilon is a confidence parameter;
Figure BDA00016899324200000320
respectively the upper and lower limits of the voltage amplitude;
Figure BDA00016899324200000321
respectively the upper and lower limits of the active power of the branch.
The invention discloses an active power distribution network island division method based on opportunity constraint, which is based on solving the island division problem of an active power distribution network containing an uncontrollable distributed power supply, fully considering network topology constraint, system power flow constraint, node voltage probability constraint, branch active power probability constraint, uncontrollable distributed power supply operation constraint and controllable distributed power supply operation constraint, establishing an active power distribution network island division model based on opportunity constraint, solving by adopting an interior point method, and obtaining an island division strategy meeting node voltage and branch active power probability confidence level, wherein the mathematical essence of the model is a mixed integer nonlinear programming problem. The invention can work out the island division strategy meeting different requirements, fully coordinate the confidence level and the load recovery level of the safe operation of the system, and ensure the reliable power supply of part of important loads and ensure the safe operation of the system.
Drawings
FIG. 1 is a flow chart of an active power distribution network islanding method based on opportunity constraint according to the invention;
FIG. 2 is a diagram of an improved IEEE33 node algorithm;
FIG. 3 is a load prediction curve;
FIG. 4 is a probability density function distribution plot of the generated power of the photovoltaic unit at 9;
FIG. 5 is a discrete probability distribution graph of the power generated by the photovoltaic generator at 9;
FIG. 6 is a schematic diagram of an islanding strategy of scenario 1 when a system fails at 9;
FIG. 7 is a schematic diagram of an islanding strategy of scenario 2 when a system fails at 9;
FIG. 8 is a schematic diagram of an islanding strategy of scenario 3 when a system fails at 9;
FIG. 9 is a schematic diagram of the relationship between the load recovery level and the confidence level when the system fails at 9.
Detailed Description
The invention provides an active power distribution network islanding method based on opportunity constraint, which is described in detail below with reference to embodiments and drawings.
As shown in fig. 1, the method for dividing an island of an active power distribution network based on opportunity constraint of the present invention includes the following steps:
1) according to the selected power distribution system, input line parameters, load levels, network topology connection relations, system operation voltage levels, branch active power limits, confidence parameters, controllable and uncontrollable distributed power access positions and capacities, a load prediction curve, system fault moments, reference voltages and reference power initial values;
2) discretizing the power generation power of the uncontrollable distributed power supply to obtain a discrete probability distribution parameter of the power generation power of the uncontrollable distributed power supply; the method for discretizing the power generation power of the uncontrollable distributed power supply comprises the following steps:
Figure BDA0001689932420000041
wherein, f (x) is a probability density function of the generated power of the uncontrollable distributed power supply; x is the generated power of the uncontrollable distributed power supply; m is a scene of discrete power generation power of the uncontrollable distributed power supply; p (m) is the generated power of the uncontrollable distributed power supply under the scene of m; f [ P (m)]Representing the probability that the power generation power of the uncontrollable distributed power source is P (m); q is a discretization step length; omegasIs a collection of scenes m.
3) Establishing an active power distribution network island division model based on opportunity constraint, comprising the following steps: setting the maximum active load quantity recovered by a power distribution system in a period of time as an objective function, and respectively considering radial constraint, system power flow constraint, network reconstruction constraint, node voltage probability constraint, branch active power probability constraint, uncontrollable distributed power supply operation constraint and controllable distributed power supply operation constraint; wherein,
(1) the maximum recovery active load capacity of the power distribution system in a period of time is expressed as an objective function
Figure BDA0001689932420000042
In the formula, omegaτA set of power distribution system island operation times; omeganIs the collection of all nodes of the power distribution system; lambda [ alpha ]iFor the recovery of the load on node i, λi∈{0，1}，λ i1 denotes the load recovery on node i, λ i0 indicates that the load on node i is not recovered;
Figure BDA0001689932420000051
is the active load on node i during time t.
(2) The radial constraint is expressed as
αij＝βij+βji，ij∈Ωb (3)
Figure BDA0001689932420000052
Figure BDA0001689932420000053
αij∈{0，1} (6)
0≤βij≤1，0≤βji≤1 (7)
In the formula, omegabRepresenting a collection of all branches of the power distribution system; omegarRepresenting a node set for supporting island voltage and frequency when the power distribution system operates in an island; alpha is alphaijRepresenting the open state of the switch on branch ij, alpha ij1 denotes switch closed, α ij0 indicates that the switch is open; beta is aijRepresents the relationship of node i and node j, β ij1 means that node j is the parent node of node i, otherwise βij＝0。
(3) The system flow constraint is as follows:
Figure BDA0001689932420000054
Figure BDA0001689932420000055
Figure BDA0001689932420000056
Figure BDA0001689932420000057
Figure BDA0001689932420000058
in the formula, Pt，ji，m、Qt，ji，mRespectively the active power and the reactive power flowing through the m scene branch ji in the t time period; pt，i，m、Qt,i，mRespectively t period and m fieldThe sum of active power and reactive power injected at a scene node i;
Figure BDA0001689932420000059
respectively the active power and the reactive power consumed by the load on the node i in the t period;
Figure BDA00016899324200000510
respectively the active power and the reactive power injected by the controllable distributed power supply at the time period t and the scene node m;
Figure BDA00016899324200000511
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the m scene node i in the t time period; u. oft，i，mThe voltage amplitude of the m scene node i is squared in the t time period; i.e. it，ij，mThe current amplitude of the m scene branch ij is squared at the time t; rijIs the resistance, X, of branch ijijIs the reactance of branch ij.
(4) The network reconfiguration constraint is expressed as
-Mαij≤Pt,ij，m≤Mαij (13)
-Mαij≤Qt，ij,m≤Mαij (14)
0≤it,ji,m≤Mαij (15)
Figure BDA00016899324200000512
Figure BDA00016899324200000513
In the formula, M represents a very large constant.
(5) The node voltage probability constraint is expressed as
Figure BDA00016899324200000514
In the formula, Pr{. denotes the probability that an event holds; epsilon is a confidence parameter;
Figure BDA00016899324200000515
respectively the upper and lower limits of the voltage amplitude.
(6) The branch active power probability constraint is expressed as
Figure BDA0001689932420000061
In the formula,
Figure BDA0001689932420000062
respectively the upper and lower limits of the active power of the branch.
(7) The controllable distributed power supply operation constraint is expressed as
Figure BDA0001689932420000063
Figure BDA0001689932420000064
Figure BDA0001689932420000065
In the formula,
Figure BDA0001689932420000066
representing the capacity of the controllable distributed power supply on the node i;
Figure BDA0001689932420000067
is the minimum power factor for the operation of the distributed power supply on node i.
(8) The uncontrollable distributed power supply operation constraint is expressed as
Figure BDA0001689932420000068
Figure BDA0001689932420000069
Figure BDA00016899324200000610
In the formula,
Figure BDA00016899324200000611
representing the capacity of the uncontrollable distributed power supply on node i.
4) Converting system power flow constraint, node voltage probability constraint and branch active power probability constraint to obtain a mixed integer nonlinear model; the transformation comprises the following steps:
introduction of the variable z from 0 to 1t,mAnd jointly converting system power flow constraint, node voltage probability constraint and branch active power probability constraint into a mixed integer nonlinear model:
Figure BDA00016899324200000612
Figure BDA00016899324200000613
Figure BDA00016899324200000614
Figure BDA00016899324200000615
Figure BDA00016899324200000616
Figure BDA00016899324200000617
Figure BDA00016899324200000618
Figure BDA00016899324200000619
Figure BDA00016899324200000620
Figure BDA00016899324200000621
Figure BDA00016899324200000622
Figure BDA00016899324200000623
Figure BDA00016899324200000624
Figure BDA0001689932420000071
in the formula, zt,mFor the introduced binary variable, zt,mThe expression 0 includes the scenario m, z when solving the optimal island operation strategy t,m1 represents that a scene m is not taken into account when solving the optimal island operation strategy; pit,mIs the probability of occurrence of the scene m at the time t; m represents a very large constant.
5) Solving the mixed integer nonlinear model obtained in the step 4) by adopting an interior point method;
6) and outputting the solving result of the step 5), including the power distribution system active load recovery quantity, the sectionalizing switch and interconnection switch states and the recovery load coefficient of each node which meet any confidence level.
For the embodiment of the invention, firstly, the impedance value of a line element in an IEEE33 node system, the active power and the reactive power of a load element, the network topology connection relation, the confidence parameter, a load prediction curve and the distributed power supply parameter are input, the arithmetic structure is shown in figure 2, and the detailed parameters are shown in tables 1, 2 and 3; assuming that a permanent three-phase fault occurs between the branches 1-2 at 9 am, the fault isolation time is 1 hour; setting the reference voltage of the system to be 12.66kV and the reference power to be 1 MVA; to verify the validity of the method, the following 3 scenarios were used for analysis.
Scene 1: the island division strategy with the confidence level of 100 percent;
scene 2: the island division strategy corresponding to the confidence level of 90 percent;
scene 3: the island division strategy corresponds to the confidence level of 80%;
it is assumed that the uncontrollable distributed power sources accessed by the power distribution system are all photovoltaic units, and the probability density function of the generated power of the photovoltaic units is shown in fig. 4 at 9 am. The generated power of the photovoltaic unit is dispersed by the discretization method provided by the invention, and the obtained generated power discrete probability distribution is shown in figure 5. Solving is carried out by adopting the method, active power distribution network island division strategies under 3 scenes are shown in figures 6, 7 and 8, wherein solid nodes represent load recovery of the nodes, and hollow nodes represent load non-recovery of the nodes; the correlation between the load recovery amount and the confidence level is shown in fig. 9, and the load recovery conditions of different confidence levels are detailed in table 4; in order to verify the effectiveness of the invention, monte carlo tests are performed on the islanding strategy of 3 scenes, and the results are shown in table 5.
The computer hardware environment for executing the optimization calculation is Intel ICoreII5-3470CPU, the dominant frequency is 3.20GHz, and the memory is 4 GB; the software environment is the Windows 7 operating system.
As can be seen from fig. 6, 7 and 8, according to the invention, a corresponding islanding strategy of the active power distribution network can be formulated according to the node voltage and the probability confidence level that the branch active power constraint condition is established. When the confidence levels are different, the islanding strategies of the power distribution network are different, the load recovery amount is also different, the specific correlation between the load recovery level and the confidence level is shown in fig. 9, and the specific load recovery condition is shown in table 3. It can be seen that the active load recovery level of the power distribution system gradually decreases with the increase of the confidence level, because the higher the confidence level is, the more scenes the island operation strategy is suitable for are, and besides the scene that the generated power of the photovoltaic unit is higher, the island operation strategy must also meet the scene that the generated power of the photovoltaic unit is lower, so that the load recovery amount is reduced. To verify the effectiveness of the present invention, Monte Carlo tests were performed on 3 scenarios and the results are shown in Table 5. As can be seen from table 5, the active power distribution network islanding method based on opportunity constraint provided by the present invention can ensure that the obtained islanding operation strategy safely operates with a probability higher than a certain confidence level. The method can effectively coordinate the relationship between the confidence level of the safe operation of the system and the load recovery level, and formulate the corresponding island division strategy according to different requirements, thereby having important significance for the safe operation and reliable power supply of the power distribution system.
TABLE 1 IEEE33 node sample load access location and Power
Figure BDA0001689932420000081
TABLE 2 IEEE33 node exemplary line parameters
Figure BDA0001689932420000082
Figure BDA0001689932420000091
TABLE 3 distributed Power supply configuration
Figure BDA0001689932420000092
TABLE 4 load recovery scenarios with different confidence levels
Figure BDA0001689932420000093
Figure BDA0001689932420000101
TABLE 5 Monte Carlo test results
Figure BDA0001689932420000102

Claims (5)

1. An active power distribution network island division method based on opportunity constraint is characterized by comprising the following steps:
1) according to the selected power distribution system, input line parameters, load levels, network topology connection relations, system operation voltage levels, branch active power limits, confidence parameters, controllable and uncontrollable distributed power access positions and capacities, a load prediction curve, system fault moments, reference voltages and reference power initial values;
2) discretizing the power generation power of the uncontrollable distributed power supply to obtain a discrete probability distribution parameter of the power generation power of the uncontrollable distributed power supply;
3) establishing an active power distribution network island division model based on opportunity constraint, comprising the following steps: setting the maximum active load quantity recovered by a power distribution system in a period of time as an objective function, and respectively considering radial constraint, system power flow constraint, network reconstruction constraint, node voltage probability constraint, branch active power probability constraint, uncontrollable distributed power supply operation constraint and controllable distributed power supply operation constraint;
4) converting system power flow constraint, node voltage probability constraint and branch active power probability constraint to obtain a mixed integer nonlinear model; the transformation comprises the following steps:
introduction of the variable z from 0 to 1t,mAnd jointly converting system power flow constraint, node voltage probability constraint and branch active power probability constraint into a mixed integer nonlinear model:
Figure FDA0003113520140000011
Figure FDA0003113520140000012
Figure FDA0003113520140000013
Figure FDA0003113520140000014
Figure FDA0003113520140000015
Figure FDA0003113520140000016
Figure FDA0003113520140000017
Figure FDA0003113520140000018
Figure FDA0003113520140000019
Figure FDA00031135201400000110
Figure FDA00031135201400000111
Figure FDA00031135201400000112
Figure FDA00031135201400000113
Figure FDA00031135201400000114
in the formula, zt,mFor the introduced binary variable, zt,mThe expression 0 includes the scenario m, z when solving the optimal island operation strategyt,m1 represents that a scene m is not taken into account when solving the optimal island operation strategy; pit,mIs the probability of occurrence of the scene m at the time t; m represents a very large constant; omegabIs a set of branches; pt,ji,m、Qt,ji,mRespectively the active power and the reactive power flowing through the m scene branch ji in the t time period; pt,i,m、Qt,i,mRespectively being the sum of active power and reactive power injected at the t time period and the m scene node i;
Figure FDA00031135201400000115
respectively the active power and the reactive power consumed by the load on the node i in the t period;λifor the recovery of the load on node i, λi∈{0,1}，λi1 denotes the load recovery on node i, λi0 indicates that the load on node i is not recovered;
Figure FDA0003113520140000021
respectively the active power and the reactive power injected by the controllable distributed power supply at the time period t and the scene node m;
Figure FDA0003113520140000022
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the m scene node i in the t time period; u. oft,i,mThe square of the voltage amplitude on the m scene node i in the time period t; i.e. it,ij,mThe current amplitude of the m scene branch ij is squared at the time t; rijIs the resistance, X, of branch ijijReactance for branch ij; epsilon is a confidence parameter;
Figure FDA0003113520140000023
respectively the upper and lower limits of the voltage amplitude;
Figure FDA0003113520140000024
the upper limit and the lower limit of the active power of the branch circuit are respectively set;
5) solving the mixed integer nonlinear model obtained in the step 4) by adopting an interior point method;
6) and outputting the solving result of the step 5), including the power distribution system active load recovery quantity, the sectionalizing switch and interconnection switch states and the recovery load coefficient of each node which meet any confidence level.
2. The opportunity constraint-based active power distribution network islanding method according to claim 1, wherein the method for discretizing the power generation power of the uncontrollable distributed power source in the step 2) is as follows:
Figure FDA0003113520140000025
wherein, f (x) is a probability density function of the generated power of the uncontrollable distributed power supply; x is the generated power of the uncontrollable distributed power supply; m is a scene of discrete power generation power of the uncontrollable distributed power supply; p (m) is the generated power of the uncontrollable distributed power supply under the scene of m; f [ P (m)]Representing the probability that the power generation power of the uncontrollable distributed power source is P (m); q is a discretization step length; omegasIs a collection of scenes m.
3. The active power distribution network islanding method based on opportunity constraint according to claim 1, wherein the system power flow constraint in step 3) is as follows:
Figure FDA0003113520140000026
Figure FDA0003113520140000027
Figure FDA0003113520140000028
Figure FDA0003113520140000029
Figure FDA00031135201400000210
in the formula, omegabIs a set of branches; pt,ji,m、Qt,ji,mRespectively the active power and the reactive power flowing through the m scene branch ji in the t time period; pt,i,m、Qt,i,mRespectively being the sum of active power and reactive power injected at the t time period and the m scene node i;
Figure FDA00031135201400000211
respectively the active power and the reactive power consumed by the load on the node i in the t period; lambda [ alpha ]iFor the recovery of the load on node i, λi∈{0,1}，λi1 denotes the load recovery on node i, λi0 indicates that the load on node i is not recovered;
Figure FDA00031135201400000212
respectively the active power and the reactive power injected by the controllable distributed power supply at the time period t and the scene node m;
Figure FDA00031135201400000213
respectively the active power and the reactive power injected by the uncontrollable distributed power supply on the m scene node i in the t time period; u. oft,i,mThe square of the voltage amplitude on the m scene node i in the time period t; i.e. it,ij,mThe current amplitude of the m scene branch ij is squared at the time t; rijIs the resistance, X, of branch ijijIs the reactance of branch ij.
4. The opportunity constraint-based active power distribution network islanding method according to claim 1, wherein the node voltage probability constraint in step 3) is as follows:
Figure FDA00031135201400000214
in the formula, Pr{. denotes the probability that an event holds; epsilon is a confidence parameter;
Figure FDA00031135201400000215
respectively the upper and lower limits of the voltage amplitude; u. oft,i,mThe square of the voltage amplitude on the m scene node i in the time period t.
5. The method for islanding an active power distribution network based on opportunity constraint according to claim 1, wherein the branch active power probability constraint in step 3) is as follows:
Figure FDA0003113520140000031
in the formula, Pr{. denotes the probability that an event holds; epsilon is a confidence parameter;
Figure FDA0003113520140000032
the upper limit and the lower limit of the active power of the branch circuit are respectively set; pt,ij,mThe active power flowing through the m scene branch ij in the time period t.
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