CN115549067A - Power distribution network sequence recovery optimization method considering area division, recovery path and power supply sequence cooperation - Google Patents

Power distribution network sequence recovery optimization method considering area division, recovery path and power supply sequence cooperation Download PDF

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CN115549067A
CN115549067A CN202211008381.9A CN202211008381A CN115549067A CN 115549067 A CN115549067 A CN 115549067A CN 202211008381 A CN202211008381 A CN 202211008381A CN 115549067 A CN115549067 A CN 115549067A
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蔡胜
谢云云
吴昊
时涵
卜京
殷明慧
邹云
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Nanjing University of Science and Technology
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Abstract

The invention discloses a power distribution network sequence recovery optimization method considering area division, recovery path and power supply sequence cooperation. According to the technical scheme, optimization of the micro-grid structure is considered in the sequence recovery process, synchronous sequence recovery of a plurality of micro-grids which run independently and have self-starting capability is achieved, the local distributed power supply can be fully utilized after the power distribution network loses the power supply of the main grid, and the power failure time of important loads is effectively reduced.

Description

Power distribution network sequence recovery optimization method considering area division, recovery path and power supply sequence cooperation
Technical Field
The invention belongs to the technical field of power grids, and particularly relates to a power distribution network sequence recovery optimization method considering cooperation of region division, recovery paths and a power supply sequence.
Background
In recent years, extreme natural disasters frequently occur, which brings great challenges to the safe and stable operation of a power system. Especially, the power distribution network is very easy to be damaged by natural disasters to cause large-area power failure accidents due to the weak infrastructure such as the net rack and the like and the insufficient reserve capacity. In order to reduce social influence and economic loss caused by blackout accidents, the construction of an elastic power grid becomes a research hotspot. Along with novel electric power system's construction, distributed generator inserts distribution system on a large scale, can establish the little electric wire netting of island through distributed generator and provide emergency power supply for important load when the major network takes place to have a power failure accident to promote the elasticity of distribution network. Therefore, the elastic power distribution network can quickly respond and recover partial area power supply after a disaster occurs, and the elastic power distribution network has important significance for reducing load loss, shortening power failure time and improving the operation reliability of a power system under an extreme natural disaster.
In order to realize power restoration of a power distribution network after power failure, scholars propose a power distribution network elastic operation method based on a micro-grid, but the existing research mainly focuses on static island division of the micro-grid after disaster and energy dispatching among micro-grids. However, in actual operation, islanded microgrids may not be successfully generated due to uncertainty in distributed power and load demand. Therefore, for the micro-grid area which cannot establish an island successfully after the fault, scholars propose to sequentially restore the power distribution network by taking an island division result as a target. The sequential recovery is to gradually establish an island micro-grid which stably runs by generating sequential operation instructions of each device under a power distribution network topology structure, so as to realize power supply recovery of a non-fault power failure area.
Disclosure of Invention
In order to solve the problems, the invention provides a sequential recovery decision method considering the cooperative optimization of island microgrid partition, recovery path and power supply state. Firstly, modeling is carried out on a recovery path, on the basis, a sequential recovery problem considering microgrid partition optimization is modeled into a mixed integer nonlinear programming model, and nonlinear constraints in the model are processed to facilitate solving. Simulation examples show that the method can realize synchronous sequential recovery of a plurality of island micro-grids and improve the problem of low efficiency of the traditional sequential recovery method.
The specific technical scheme for realizing the purpose of the invention is as follows:
a power distribution network sequence recovery optimization method considering cooperation of region division, recovery paths and power supply sequences comprises the following steps:
step 1, constructing a recovery path and a recovery time model;
step 2, constructing a source-network-load-storage coordination optimization model in the sequence recovery process;
and 3, carrying out linearization processing and solving on the models constructed in the steps 1 and 2 to obtain a power distribution network sequence recovery optimization result.
Compared with the prior art, the invention has the remarkable advantages that:
according to the technical scheme, optimization of the micro-grid structure is considered in the sequence recovery process, synchronous sequence recovery of a plurality of micro-grids which run independently and have self-starting capability is achieved, the local distributed power supply can be fully utilized after the power distribution network loses the power supply of the main grid, and the power failure time of important loads is effectively reduced.
The present invention is described in further detail below with reference to the attached drawing figures.
Drawings
FIG. 1 is a flow chart of the steps of the present invention.
Fig. 2 is a schematic diagram of an IEEE37 power grid topology according to a preferred embodiment of the present invention.
FIG. 3 is a diagram illustrating a recovery path in accordance with a preferred embodiment of the present invention.
FIG. 4 is a schematic diagram of a power-step diagram according to a preferred embodiment of the present invention.
FIG. 5 is a diagram illustrating a weighted load recovery amount under different sequential recovery concepts in an embodiment of the present invention.
FIG. 6 is a comparison of utilization of distributed power sources under different sequence recovery concepts in a preferred embodiment of the invention.
Detailed Description
With reference to fig. 1, a power distribution network sequence recovery optimization method considering cooperation of region division, recovery paths and power supply sequence includes the following steps:
step 1, constructing a recovery path and a recovery time model, specifically:
step 1-1, modeling a restoration path in the power distribution network through a motion path table, and in order to ensure a radial structure of the power distribution network, when the restoration path is generated, meeting the following constraints:
Figure BDA0003809885680000021
Figure BDA0003809885680000022
Figure BDA0003809885680000023
Figure BDA0003809885680000031
Figure BDA0003809885680000032
Figure BDA0003809885680000033
Figure BDA0003809885680000034
wherein the content of the first and second substances,
Figure BDA0003809885680000035
representing the state of the restoration path from node i to node j in the microgrid m,
Figure BDA0003809885680000036
indicating that there is a restoration path in the microgrid m from node i to node j, and vice versa,
Figure BDA0003809885680000037
indicates the absence of a restoration path, N B Indicating the node, L, where the distributed power supply with black start capability is located ij Representing a line connectivity matrix in the distribution network, N representing a set of nodes in the distribution network, B representing a set of lines in the distribution network, M representing a set of generated micro-grids, N c Representing the number of nodes in the distribution network;
step 1-2, determining the time when the recovery path reaches each node:
Figure BDA0003809885680000038
Figure BDA0003809885680000039
Figure BDA00038098856800000310
wherein, t i Indicating the time of recovery of the node i,
Figure BDA00038098856800000311
indicating a black start power supply connected to node iThe self-starting duration of phi denotes a large constant, T R.max The power failure duration of the power distribution network is shown,
Figure BDA00038098856800000312
the time length required for the recovery path to start from the node h and reach the node i is represented;
step 1-3, the node power supply state and the line power supply state are expressed as follows:
Figure BDA00038098856800000313
Figure BDA00038098856800000314
Figure BDA00038098856800000315
Figure BDA00038098856800000316
wherein the content of the first and second substances,
Figure BDA00038098856800000317
indicating the recovery state of the node i in the microgrid m at time t,
Figure BDA00038098856800000318
representing the recovery state of the line (h, i) in the microgrid m at the time t;
1-4, the power supply state of the power supply and the load is represented as follows:
Figure BDA0003809885680000041
in the formula, N G Represents the node set, N, where the distributed power source is located E Representing the set of nodes at which the energy is stored, N L Representing the set of nodes at which the load is located，
Figure BDA0003809885680000042
Represents the load or power supply state connected with the node i in the micro-grid m at the time t,
Figure BDA0003809885680000043
indicating that the equipment connected with the node i in the microgrid m recovers power supply at the time t, and conversely,
Figure BDA0003809885680000044
indicating that power is not restored.
Step 2, constructing a source-network-load-storage coordination optimization model in the sequence recovery process, which specifically comprises the following steps:
according to the actual power generation amount of the distributed power supply, constructing an optimization model by taking the maximum weighted load recovery amount in the fault duration as a sequential recovery target:
Figure BDA0003809885680000045
where T represents a sequential recovery time step set, w i Represents the weight coefficient of the node i,
Figure BDA0003809885680000046
the active load quantity of the power supplied by the micro-grid m on the node i at the moment t is represented, and delta t represents the duration time length in an optimization time step.
Furthermore, considering safety conditions such as power output, load requirements, system power flow and the like to be met in the power distribution network sequence recovery process, and determining constraint conditions to be considered;
the constraint conditions of the optimization model comprise:
(1) The black start power supply output constraint, during the sequence recovery process, the distributed power supply operation with self-starting capability needs to satisfy the following operation constraint:
Figure BDA0003809885680000047
Figure BDA0003809885680000048
Figure BDA0003809885680000049
wherein the content of the first and second substances,
Figure BDA00038098856800000410
representing the minimum value of the active power of the black start power supply b,
Figure BDA00038098856800000411
representing the maximum active power of the black start power supply b,
Figure BDA00038098856800000412
represents the minimum value of the reactive power of the black start power supply b,
Figure BDA00038098856800000413
representing the maximum value of the reactive power of the black start power supply b,
Figure BDA00038098856800000414
representing the active power of the black start power supply b at time t,
Figure BDA00038098856800000415
representing the reactive power of the black start power supply b at time t,
Figure BDA00038098856800000416
indicating the allowed turndown power of the black start power supply b,
Figure BDA00038098856800000417
indicating the allowed power up-regulation of the black start power supply b;
(2) The non-black start power supply output constraint needs to satisfy the following operation constraint in the operation process for the distributed power supply without self-starting: :
Figure BDA00038098856800000418
Figure BDA0003809885680000051
Figure BDA0003809885680000052
Figure BDA0003809885680000053
wherein the content of the first and second substances,
Figure BDA0003809885680000054
representing the minimum value of the active power of the non-black start power supply g,
Figure BDA0003809885680000055
representing the maximum value of the active power of the non-black start power supply g,
Figure BDA0003809885680000056
represents the minimum value of the reactive power of the non-black start power source g,
Figure BDA0003809885680000057
representing the maximum value of the reactive power of the non-black start power g,
Figure BDA0003809885680000058
representing the active power output of the non-black start power supply g in the micro-grid m at the moment t,
Figure BDA0003809885680000059
the reactive power of the non-black start power source g in the micro-grid m at the moment t is shown,
Figure BDA00038098856800000510
indicating the allowed turndown power of the non-black start power supply g,
Figure BDA00038098856800000511
indicating allowable up-regulated power, p, of a non-black start power supply g g Represents the rated power factor of the non-black start power supply g;
(3) The energy storage output is restrained, and the energy storage equipment in the system needs to meet the following operation restraint in the operation process:
Figure BDA00038098856800000512
Figure BDA00038098856800000513
Figure BDA00038098856800000514
Figure BDA00038098856800000515
Figure BDA00038098856800000516
Figure BDA00038098856800000517
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00038098856800000518
representing the state of charge of the stored energy e in the microgrid m at the moment t,
Figure BDA00038098856800000519
to representThe discharge state of the stored energy e in the microgrid m at the moment t,
Figure BDA00038098856800000520
representing the active charging power of the energy storage e in the microgrid m at the moment t,
Figure BDA00038098856800000521
representing the active discharge power of the stored energy e in the micro-grid m at the moment t,
Figure BDA00038098856800000522
representing the minimum value of the active charging power of the stored energy e,
Figure BDA00038098856800000523
representing the minimum value of the active discharge power of the stored energy e,
Figure BDA00038098856800000524
the maximum value of the active charging power of the stored energy e is shown,
Figure BDA00038098856800000525
representing the maximum value of the active discharge power of the stored energy E, E e.0 Representing the initial state of charge of the stored energy e,
Figure BDA00038098856800000526
indicating the state of charge of the stored energy e at time t,
Figure BDA00038098856800000527
represents the minimum value of the allowable state of charge of the stored energy e,
Figure BDA00038098856800000528
represents the maximum allowable state of charge of the stored energy e,
Figure BDA00038098856800000529
the charging efficiency of the stored energy e is indicated,
Figure BDA00038098856800000530
the discharge efficiency of the stored energy e is represented;
(4) And (3) load power demand constraint, wherein for the controllable load, the power demand of the controllable load meets the following constraint:
Figure BDA00038098856800000531
Figure BDA0003809885680000061
Figure BDA0003809885680000062
wherein the content of the first and second substances,
Figure BDA0003809885680000063
indicating that the load i has an active power modulation value at time t,
Figure BDA0003809885680000064
representing the maximum value of the active power demand of the load/at time t,
Figure BDA0003809885680000065
representing the minimum value of the active power demand of the load/at time t,
Figure BDA0003809885680000066
representing the reactive load, sigma, supplied by the microgrid m at a node l at time t l A power demand factor representing a load l;
(5) The micro-grid power flow constraint is based on a direct current power flow model in the micro-grid, and the power balance constraint and the node voltage constraint can be described as follows:
Figure BDA0003809885680000067
Figure BDA0003809885680000068
Figure BDA0003809885680000069
Figure BDA00038098856800000610
Figure BDA00038098856800000611
Figure BDA00038098856800000612
Figure BDA00038098856800000613
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA00038098856800000614
represents the active power flowing through the line (h, i) in the microgrid m at the moment t,
Figure BDA00038098856800000615
represents the reactive power flowing through the line (h, i) in the microgrid m at the moment t,
Figure BDA00038098856800000616
a set of sub-nodes representing node i in the microgrid m,
Figure BDA00038098856800000617
representing the square of the voltage amplitude of node i in the microgrid m at time t,
Figure BDA00038098856800000618
representing the maximum value of the squared voltage magnitude at node i,
Figure BDA00038098856800000619
represents the minimum value of the square of the voltage magnitude at node i,
Figure BDA00038098856800000620
represents the maximum value of the active power allowed to flow on the lines (h, i),
Figure BDA00038098856800000621
represents the minimum value of the active power allowed to flow on the line (h, i),
Figure BDA00038098856800000622
represents the maximum value of the reactive power allowed to flow on the line (h, i),
Figure BDA00038098856800000623
represents the minimum value of reactive power allowed to flow on the line (h, i).
Step 3, carrying out linearization processing and solving on the models constructed in the step 1 and the step 2 to obtain a power distribution network sequence recovery optimization result, which specifically comprises the following steps:
step 3-1, carrying out linearization processing on the constraint of the line power supply state;
for line-powered state constraints, variables
Figure BDA00038098856800000624
The constraint condition is obtained by multiplying two discrete variables, so that a nonlinear variable exists in the constraint condition, and therefore the nonlinear variable needs to be linearized, and the line power state constraint can be expressed as
Figure BDA0003809885680000071
Step 3-2, the active load amount is adjusted
Figure BDA0003809885680000072
The load power demand constraint of (2) is linearized;
for load power demand constraints, variables
Figure BDA0003809885680000073
The load power demand constraint is obtained by multiplying a discrete variable and a continuous variable, so that a nonlinear variable exists in a constraint condition, and therefore the nonlinear variable needs to be linearized, and can be expressed as:
Figure BDA0003809885680000074
and 3-3, solving the models constructed in the steps 1 and 2 based on the linearized constraint conditions to obtain the optimal power distribution network sequence recovery result considering the cooperation of the area division, the recovery path and the power supply sequence.
A power distribution network sequence recovery optimization system considering cooperation of region division, recovery paths and power supply sequences comprises the following modules:
a restoration path and restoration time model building module: the recovery path model and the recovery time model are constructed;
the source-network-load-storage coordination optimization model construction module comprises: the method is used for constructing a source-network-load-storage coordination optimization model in the sequential recovery process;
a solution module: and the method is used for carrying out linearization processing and solving on the constructed model to obtain an optimal power distribution network sequence recovery result.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
step 1, constructing a recovery path and a recovery time model;
step 2, constructing a source-network-load-storage coordination optimization model in the sequence recovery process;
and 3, carrying out linearization processing and solving on the models constructed in the steps 1 and 2 to obtain a power distribution network sequence recovery optimization result.
A computer-storable medium having stored thereon a computer program which, when executed by a processor, performs the steps of:
step 1, constructing a recovery path and a recovery time model;
step 2, constructing a source-network-load-storage coordination optimization model in the sequence recovery process;
and 3, carrying out linearization processing and solving on the models constructed in the steps 1 and 2 to obtain a power distribution network sequence recovery optimization result.
The present invention will be further described with reference to the following examples.
Examples
The effectiveness of the power distribution network sequential recovery method provided by the invention is verified by taking an improved IEEE37 node power distribution system as an example, and the power distribution network topology is shown in figure 2.
Suppose that the main network has power failure due to extreme natural disasters, the power distribution network is in a complete power loss state, and 5 lines have disconnection faults. The system is provided with 5 distributed power supplies, wherein G1, G2 and G3 have self-starting capability, and G4 and G5 do not have self-starting capability; relevant parameters of the distributed power supply are shown in table 1, wherein a DG state of "1" indicates that the power supply has a self-starting capability; "0/1" indicates that the power supply does not have self-starting capability and can be started when the starting power is met; a "0" indicates that the power source is not involved in the power distribution grid sequence recovery process. In addition, the system is also provided with 2 energy storage auxiliary power distribution networks for sequential recovery, and relevant parameters of energy storage equipment are shown in table 2.
TABLE 1 distributed Power parameters
Tab.1 Parameters of distributed generators
Figure BDA0003809885680000081
TABLE 2 energy storage device-related parameters
Tab.2 Parameters of energy storages
Figure BDA0003809885680000082
Figure BDA0003809885680000091
In the decision process of the sequential recovery scheme, variables and constraints of each time step need to be defined, so that the selection of the number of the time steps affects the decision efficiency of the sequential recovery. When the number of steps is too small, the generation of a local optimal solution can be caused; when the time step length is too large, the calculation speed is slow. In this example, it is assumed that the interval Δ t between two consecutive time steps in the sequential recovery process is set to 3 minutes, and the total recovery time is 10 time steps.
Through optimization decision, the power supply range of the three black-start power supplies is gradually enlarged, 3 independently-operated micro-grids are finally generated, and nodes contained in the finally-generated micro-grids are shown in a table 3.
Table 3 nodes included in the microgrid finally generated
Tab.3 The formed MGs and correspondingnodes contained
Figure BDA0003809885680000092
Taking MG1 as an example, a path recovery situation in the microgrid is shown, and a recovery path in MG1 is shown in fig. 3. It can be seen that the microgrid is maintained in a radial configuration throughout the sequential recovery process.
The power supply device output in MG1 is shown in fig. 4.
The node N738 where the distributed power supply G4 is located recovers power supply at the 5 th time step, so that G4 participates in sequential recovery from the 5 th time step and gradually increases power output. The node N733 where the energy storage device ES1 is located recovers power supply at the 4 th time step, so that the ES1 starts to participate in power supply recovery from the 4 th time step, when power support in the microgrid is insufficient, the ES1 is in a discharging state, and when power in the microgrid is excessive, the ES1 is in a charging state, so that power supply and demand balance in the microgrid is maintained. With the gradual recovery of important loads, the power output of the micro-grid gradually increases to meet the power supply requirement of loads in the sequential recovery process.
The sequence recovery method provided by the invention simultaneously considers the collaborative optimization of the region division, the recovery path and the power supply sequence. In order to compare and explain the advantages of the method provided in this section, this section compares the sequential recovery strategy based on the flexible structure microgrid with the sequential recovery strategy based on the fixed structure microgrid.
In the sequential recovery strategy based on the fixed structure microgrid partition scheme, the microgrid structure and the sequential recovery scheme are decoupled and optimized. The method comprises the steps of designing an island microgrid operation scheme before a fault occurs, executing a corresponding emergency control scheme when a blackout occurs, and generating an optimized recovery path and a power supply sequence inside each microgrid.
And randomly generating 20 microgrid partition schemes by adopting a Monte Carlo sampling method, deciding an optimized sequential recovery scheme on the basis, and selecting an optimal solution for comparison. Fig. 5 compares the weighted load recovery amounts in the two sequential recovery ideas. In the sequential recovery scheme presented herein that considers microgrid partition fabric optimization, the total weighted load recovery amount is 4453.2, whereas when microgrid fabric optimization is not considered, the total weighted load recovery amount is 3316. Taking the 10 th time step as an example, the utilization rate of the distributed power supplies under the two sequential recovery methods is shown in fig. 6.
As can be seen from fig. 5 and 6, optimization of the microgrid partition strategy is considered in the sequential recovery process, so that the utilization efficiency of the distributed power supply can be improved, and the power supply recovery capability of the system can be further improved. The optimized region division strategy can effectively improve the electric energy supply and demand balance level of the microgrid and realize the full utilization of limited power generation resources.
The foregoing embodiments illustrate and describe the general principles and principal features of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.

Claims (8)

1. A power distribution network sequence recovery optimization method considering cooperation of region division, recovery paths and power supply sequences is characterized by comprising the following steps:
step 1, constructing a recovery path and a recovery time model;
step 2, constructing a source-network-load-storage coordination optimization model in the sequence recovery process;
and 3, carrying out linearization processing and solving on the models constructed in the steps 1 and 2 to obtain a power distribution network sequence recovery optimization result.
2. The method for optimizing the sequential restoration of the power distribution network in consideration of the cooperation of the area division, the restoration path and the power supply sequence according to claim 1, wherein the step 1 of constructing the restoration path and the restoration time model specifically comprises the following steps:
step 1-1, in order to ensure the radial structure of the power distribution network, the following constraints are required to be met when a recovery path is generated:
Figure FDA0003809885670000011
Figure FDA0003809885670000012
Figure FDA0003809885670000013
Figure FDA0003809885670000014
Figure FDA0003809885670000015
Figure FDA0003809885670000016
Figure FDA0003809885670000017
wherein the content of the first and second substances,
Figure FDA0003809885670000018
representing the state of a restoration path from node i to node j in the microgrid m,
Figure FDA0003809885670000019
indicating that there is a restoration path in the microgrid m from node i to node j, and vice versa,
Figure FDA00038098856700000110
indicates the absence of a restoration path, N B Indicating the node, L, where the distributed power supply with black start capability is located ij Representing a line connectivity matrix in the distribution network, N representing a set of nodes in the distribution network, B representing a set of lines in the distribution network, M representing a set of generated micro-grids, N c Representing the number of nodes in the power distribution network;
step 1-2, determining the time when the recovery path reaches each node:
Figure FDA00038098856700000111
Figure FDA00038098856700000112
Figure FDA0003809885670000021
wherein, t i Representing nodesi at the moment of the recovery of the packet,
Figure FDA0003809885670000022
denotes the self-start-up duration of the black start power supply connected to node i, phi denotes a large constant, T R.max The power failure duration of the power distribution network is shown,
Figure FDA0003809885670000023
the time length required for the recovery path to start from the node h and reach the node i is represented;
step 1-3, the node power supply state and the line power supply state are expressed as follows:
Figure FDA0003809885670000024
Figure FDA0003809885670000025
Figure FDA0003809885670000026
Figure FDA0003809885670000027
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003809885670000028
indicating the recovery state of the node i in the microgrid m at time t,
Figure FDA0003809885670000029
representing the recovery state of the line (h, i) in the microgrid m at the time t;
1-4, the power supply state of the power supply and the load is represented as follows:
Figure FDA00038098856700000210
in the formula, N G Represents the node set, N, where the distributed power source is located E Representing the set of nodes at which the energy is stored, N L Representing the set of nodes at which the load is located,
Figure FDA00038098856700000211
represents the load or power supply state of the micro-grid m connected with the node i at the time t,
Figure FDA00038098856700000212
indicating that the equipment connected with the node i in the microgrid m recovers power supply at the time t, and conversely,
Figure FDA00038098856700000213
indicating that power is not restored.
3. The method for optimizing the sequential recovery of the power distribution network in consideration of the cooperation of the area division, the recovery path and the power supply sequence according to claim 1, wherein the method for optimizing the sequential recovery of the power distribution network in step 2 comprises the following steps:
according to the actual power generation amount of the distributed power supply, constructing an optimization model by taking the maximum weighted load recovery amount in the fault duration as a sequential recovery target:
Figure FDA00038098856700000214
wherein T represents a sequential recovery time step set, w i Represents the weight coefficient of the node i,
Figure FDA00038098856700000215
the active load quantity of the power supplied by the micro-grid m on the node i at the moment t is represented, and delta t represents the duration time length in an optimization time step.
4. The method according to claim 3, wherein the constraint conditions of the optimization model include:
(1) Black start power supply output constraint:
Figure FDA0003809885670000031
Figure FDA0003809885670000032
Figure FDA0003809885670000033
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003809885670000034
representing the minimum value of the active power of the black start power supply b,
Figure FDA0003809885670000035
representing the maximum value of the active power of the black start power supply b,
Figure FDA0003809885670000036
represents the minimum value of the reactive power of the black start power supply b,
Figure FDA0003809885670000037
representing the maximum value of the reactive power of the black start power supply b,
Figure FDA0003809885670000038
representing the active power of the black start power supply b at time t,
Figure FDA0003809885670000039
representing the reactive power of the black start power supply b at time t,
Figure FDA00038098856700000310
indicating the allowed turndown power of the black start power supply b,
Figure FDA00038098856700000311
indicating the allowed power up-regulation of the black start power supply b;
(2) And (3) non-black start power output restraint:
Figure FDA00038098856700000312
Figure FDA00038098856700000313
Figure FDA00038098856700000314
Figure FDA00038098856700000315
wherein the content of the first and second substances,
Figure FDA00038098856700000316
represents the minimum value of the active power of the non-black start power supply g,
Figure FDA00038098856700000317
representing the maximum value of the active power of the non-black start power supply g,
Figure FDA00038098856700000318
representing reactive power of non-black start power gThe minimum value of the sum of the values of,
Figure FDA00038098856700000319
representing the maximum value of the reactive power of the non-black start power g,
Figure FDA00038098856700000320
representing the active power output of the non-black start power supply g in the micro-grid m at the moment t,
Figure FDA00038098856700000321
representing the reactive power output of the non-black start power source g in the micro-grid m at the moment t,
Figure FDA00038098856700000322
indicating the allowed turndown power of the non-black start power supply g,
Figure FDA00038098856700000323
indicating allowable up-regulated power, p, of a non-black start power supply g g A rated power factor representing the non-black start power supply g;
(3) Energy storage output restraint:
Figure FDA00038098856700000324
Figure FDA00038098856700000325
Figure FDA00038098856700000326
Figure FDA0003809885670000041
Figure FDA0003809885670000042
Figure FDA0003809885670000043
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003809885670000044
representing the state of charge of the stored energy e in the microgrid m at the moment t,
Figure FDA0003809885670000045
representing the discharge state of the stored energy e in the microgrid m at the moment t,
Figure FDA0003809885670000046
representing the active charging power of the energy storage e in the microgrid m at the moment t,
Figure FDA0003809885670000047
the active discharge power of the stored energy e in the microgrid m at the moment t is shown,
Figure FDA0003809885670000048
the minimum value of the active charging power of the stored energy e is shown,
Figure FDA0003809885670000049
representing the minimum value of the active discharge power of the stored energy e,
Figure FDA00038098856700000410
the maximum value of the active charging power of the stored energy e is shown,
Figure FDA00038098856700000411
representing the maximum value of the active discharge power of the stored energy E, E e.0 Representing the initial state of charge of the stored energy e,
Figure FDA00038098856700000412
indicating the state of charge of the stored energy e at time t,
Figure FDA00038098856700000413
represents the minimum value of the allowable state of charge of the stored energy e,
Figure FDA00038098856700000414
represents the maximum allowable state of charge of the stored energy e,
Figure FDA00038098856700000415
the charging efficiency of the stored energy e is indicated,
Figure FDA00038098856700000416
represents the discharge efficiency of the stored energy e;
(4) Load power demand constraint:
Figure FDA00038098856700000417
Figure FDA00038098856700000418
Figure FDA00038098856700000419
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00038098856700000420
indicating that the load i has an active power modulation value at time t,
Figure FDA00038098856700000421
representing the maximum value of the active power demand of the load/at time t,
Figure FDA00038098856700000422
representing the minimum value of the active power demand of the load i at time t,
Figure FDA00038098856700000423
representing the reactive load, sigma, supplied by the microgrid m at a node l at time t l Represents the power demand factor of the load l;
(5) Micro-grid power flow constraint:
Figure FDA00038098856700000424
Figure FDA00038098856700000425
Figure FDA00038098856700000426
Figure FDA00038098856700000427
Figure FDA00038098856700000428
Figure FDA00038098856700000429
Figure FDA0003809885670000051
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003809885670000052
represents the active power flowing through the line (h, i) in the microgrid m at the moment t,
Figure FDA0003809885670000053
represents the reactive power flowing through the line (h, i) in the microgrid m at the time t,
Figure FDA0003809885670000054
a set of sub-nodes representing node i in the microgrid m,
Figure FDA0003809885670000055
representing the square of the voltage amplitude of node i in the microgrid m at time t,
Figure FDA0003809885670000056
representing the maximum value of the squared voltage magnitude at node i,
Figure FDA0003809885670000057
represents the minimum value of the squared voltage magnitude at node i,
Figure FDA0003809885670000058
represents the maximum value of the active power allowed to flow on the lines (h, i),
Figure FDA0003809885670000059
represents the minimum value of the active power allowed to flow on the line (h, i),
Figure FDA00038098856700000510
represents the maximum value of the reactive power allowed to flow on the line (h, i),
Figure FDA00038098856700000511
represents the minimum value of reactive power allowed to flow on the line (h, i).
5. The method for optimizing the sequential restoration of the power distribution network in consideration of the cooperation of the area division, the restoration path and the power supply sequence according to claim 2, wherein the step 3 of performing the linearization processing and solving on the constructed model specifically comprises the steps of:
step 3-1, carrying out linearization treatment on the constraint of the line power supply state:
Figure FDA00038098856700000512
step 3-2, the active load amount is adjusted
Figure FDA00038098856700000513
The load power demand constraint of (2) is linearized:
Figure FDA00038098856700000514
and 3-3, solving the models constructed in the steps 1 and 2 based on the linearized constraint conditions to obtain an optimal power distribution network sequence recovery result considering the cooperation of region division, a recovery path and a power supply sequence.
6. A distribution network sequence recovery optimization system considering cooperation of region division, recovery paths and power supply sequences is characterized by comprising the following modules:
a restoration path and restoration time model building module: the method is used for constructing a recovery path and a recovery time model;
the source-network-load-storage coordination optimization model construction module comprises: the method is used for constructing a source-network-load-storage coordination optimization model in the sequential recovery process;
a solving module: and the method is used for carrying out linearization processing and solving on the constructed model to obtain an optimal power distribution network sequence recovery result.
7. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method as claimed in any one of claims 1-5 are implemented by the processor when executing the computer program.
8. A computer-storable medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to claims 1 to 5.
CN202211008381.9A 2022-08-22 2022-08-22 Power distribution network sequence recovery optimization method considering area division, recovery path and power supply sequence cooperation Pending CN115549067A (en)

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Publication number Priority date Publication date Assignee Title
CN116845888A (en) * 2023-09-01 2023-10-03 国网江苏省电力有限公司苏州供电分公司 Active power distribution network fault recovery method and system based on dynamic island division

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116845888A (en) * 2023-09-01 2023-10-03 国网江苏省电力有限公司苏州供电分公司 Active power distribution network fault recovery method and system based on dynamic island division
CN116845888B (en) * 2023-09-01 2024-01-23 国网江苏省电力有限公司苏州供电分公司 Active power distribution network fault recovery method and system based on dynamic island division

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