CN106374453B - Power system reconstruction method - Google Patents

Power system reconstruction method Download PDF

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CN106374453B
CN106374453B CN201610801024.6A CN201610801024A CN106374453B CN 106374453 B CN106374453 B CN 106374453B CN 201610801024 A CN201610801024 A CN 201610801024A CN 106374453 B CN106374453 B CN 106374453B
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subsystem
node
recovery
path
nodes
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CN106374453A (en
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张志强
唐晓骏
李群炬
郑超
吴涛
徐友平
邵德军
王承民
赵腾
陈得治
王青
吴丽华
丁剑
吉平
宋云亭
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Shanghai Jiaotong University
China Electric Power Research Institute Co Ltd CEPRI
Central China Grid Co Ltd
Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
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Shanghai Jiaotong University
China Electric Power Research Institute Co Ltd CEPRI
Central China Grid Co Ltd
Electric Power Research Institute of State Grid Jibei Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention relates to a power system reconstruction method, which comprises the following steps: dividing a system to be reconstructed into x subsystems according to a subsystem division rule, wherein x is a positive integer; recovering the backbone channel of the subsystem by using the backbone channel recovery model of the subsystem and the constraint conditions thereof; restoring the local power grid of the subsystem by using the objective function of the local power grid restoration path optimization of the subsystem and the constraint conditions thereof; performing intersystem networking recovery on the subsystem by using the regional networking recovery function and the constraint condition thereof; the method provided by the invention combines serial recovery and parallel recovery algorithms, takes the coordination of backbone channels, local power grids and regional networking into account, and improves the recovery speed of the power system.

Description

Power system reconstruction method
Technical Field
The invention relates to the field of power system control, in particular to a power system reconstruction method.
Background
After a large-scale power failure of the system occurs, the recovery process of the system lasts longer, needs several hours if short, even days if long, and has different recovery emphasis in each time period. From the time perspective, the overall process is generally divided into three phases according to the difference between the characteristics of the system recovery process in different time periods and the main recovery objects: black start phase, net rack recovery phase and load recovery phase.
(1) Black start phase
Generally, the time is 30-60 minutes. At this stage, the starting power supply is firstly provided for the tripped power supply with critical time limit respectively, so that the power generation capacity of the tripped power supply is recovered and is re-merged into the power grid, and a subsystem which runs in isolation is formed. The starting power supply of the system can be a water wheel generator, a gas wheel generator, a generator (such as a generator with self service power after tripping) remained in the system after an accident or the support of an isolated subsystem and an adjacent system after disconnection. The main problems involved at this stage are: the starting and running characteristics of the unit, the self-excitation and overvoltage problems caused by charging to a no-load line and a transformer, the parallel resonance problem caused by transformer saturation, the starting of a large motor, the frequency modulation and voltage regulation problems of an isolated small system and the like. This phase is the recovery process from electromagnetic transients, electromechanical transients to quasi-steady state.
(2) Net frame recovery stage
Usually for 3 to 4 hours. In the stage, the large-scale unit with the basic load is started and the main power transmission line is put into the main network to gradually recover the main network, so that on one hand, the connection between power plants is strengthened to improve the power supply reliability of the station power, and on the other hand, some subsystems are arranged in parallel, so that a stable network is established, and a foundation is laid for comprehensively recovering the load in the next stage. Of course, for longer links between systems in some regions, investing can be suspended and left after load recovery to avoid stability problems and reduce stress on dispatchers. In addition, lines supplying power to less important loads in remote areas may not be required to be used temporarily. The main problem involved in this phase is to avoid the reactive power absorbed by the generator exceeding its phase-advance capacity and the voltage rise that results from a large amount of reactive power flowing through the unloaded line. Sometimes, a certain amount of load is required to be input in order to absorb reactive power generated by the line capacitor and reduce the no-load overvoltage of the line.
(3) Load recovery phase
After the thermal power generating unit is started and has certain power generation capacity and a relatively stable grid frame is established, the load can be gradually recovered due to the fact that active power and reactive power which can be supplied by the system are greatly increased. The main problem in this stage is how to keep the system frequency and voltage within the allowed range and not overload the line. Because the load increase rate of the thermal power generating unit has a certain limit, the biggest factor for limiting the load recovery is that the frequency of the system is not reduced too much (such as not exceeding 0.5Hz), and the low-frequency load reduction action cannot be caused.
Disclosure of Invention
The invention provides a power system reconstruction method, which aims to adopt the combination of serial recovery and parallel recovery algorithms, take the coordination and cooperation of backbone channels, a local power grid and regional networking into account and improve the recovery speed of a power system.
The purpose of the invention is realized by adopting the following technical scheme:
in a power system reconfiguration method, the improvement comprising:
dividing a system to be reconstructed into x subsystems according to a subsystem division rule, wherein x is a positive integer;
recovering the backbone channel of the subsystem by using the backbone channel recovery model of the subsystem and the constraint conditions thereof;
restoring the local power grid of the subsystem by using the objective function of the local power grid restoration path optimization of the subsystem and the constraint conditions thereof;
and performing intersystem networking recovery on the subsystems by using the regional networking recovery function and the constraint conditions thereof.
Preferably, the subsystem comprises: the system comprises at least one black start power supply, a started power supply, a power transmission line, a transformer substation and a load node.
Preferably, the subsystem partitioning rule includes:
the number x of the subsystems is smaller than the number of black start power supplies in the system to be reconstructed;
the difference of the total installed capacity or the maximum load capacity of the generator sets among the subsystems is larger than a partition size threshold value y;
the load and the output in each subsystem are balanced;
the number of the tie lines among the subsystems is less than or equal to 4, and the voltage grade of the tie lines is 110-220 kV;
the subsystem takes a black start power supply as a center;
and the subsystems can be connected with the grid.
Preferably, the recovering the backbone channel of the subsystem by using the backbone channel recovery model of the subsystem and the constraint condition thereof includes:
defining the net rack coverage rate and net rack dispersion rate of the backbone channel of the subsystem;
constructing a backbone channel recovery model of the subsystem and a constraint condition thereof by using the net rack coverage rate and the net rack dispersion rate;
and optimizing and selecting a target node, optimizing a target node recovery sequence and a recovery path according to the backbone channel recovery model of the subsystem, wherein the backbone channel formed by the target node needs to meet the constraint condition.
Further, the defining of the grid coverage and the grid dispersion rate of the subsystem backbone channel includes:
defining the network frame coverage rate C of the backbone network frame K of the subsystem backbone channel according to the following formula (1)kAnd the minimum value d of the shortest path from the node j to each node in the backbone net rack Kj-K：
Figure BSA0000134271400000031
In the formula (1), omegakIs the set of nodes in the backbone network frame K of the subsystem,
Figure BSA0000134271400000032
is a set of nodes, alpha, outside the backbone network frame K of the subsystemiThe overall importance of the node i, αjIs the overall importance of node j, dj-iThe distance from the node j to the node i, namely the number of lines passing through the shortest path from the node j to the node i;
defining the grid discrete rate D of the subsystem backbone channel and recovering the network discrete rate at the ith target node according to the following formula (2)Degree Di：
Figure BSA0000134271400000033
In the formula (2), nkIs the total number of target nodes, ΩiGTo restore the set of power nodes in the backbone network frame at the time of the ith target node,
Figure BSA0000134271400000034
to recover the set of non-power nodes in the backbone network frame at the time of the i-th target node,
Figure BSA0000134271400000035
the number of restored non-power source target nodes.
Further, the constructing a backbone channel recovery model of the subsystem and its constraint conditions by using the net rack coverage rate and the net rack dispersion rate includes:
constructing a backbone channel recovery model of the subsystem according to the following formula (3):
Figure BSA0000134271400000036
in the formula (3), f is a backbone channel recovery efficiency function of the subsystem, tau is a backbone net rack coverage rate weight of the subsystem, and CKNet frame coverage, L, of a backbone net frame K, which is a subsystemKTo recover the set of lines, omega, contained by the backbone networkiThe weight of the recovered line is T, and the time required by the system recovery is T;
determining a power flow constraint of the subsystem according to the following formula (4):
Figure BSA0000134271400000037
in the formula (4), nGFor the number of recovered generator nodes in the subsystem, nKFor the number of repeated nodes in the subsystem, nKIFor the number of lines that have been duplicated in the subsystem,
Figure BSA0000134271400000041
the minimum active power output of the ith generator node in the subsystem,
Figure BSA0000134271400000042
is the maximum active power output, P, of the ith generator node in the subsystemGiThe active output of the i-th generator node in the subsystem,
Figure BSA0000134271400000043
the minimum reactive power output of the ith generator node in the subsystem,
Figure BSA0000134271400000044
is the maximum reactive power output, Q, of the ith generator node in the subsystemGiFor the reactive power output of the i-th generator node in the subsystem,
Figure BSA0000134271400000045
is the voltage minimum value of the j node in the subsystem, VjIs the voltage at the jth node in the subsystem,
Figure BSA0000134271400000046
is the maximum voltage value of the j node in the subsystem, IkFor the current of the kth line in the subsystem,
Figure BSA0000134271400000047
the maximum value of the current of the kth line in the subsystem;
determining a non-power node parallel recovery constraint function of the subsystem according to the following formula (5):
Figure BSA0000134271400000048
in the formula (5), L*The total load of the nodes is restored in parallel for the subsystems,
Figure BSA0000134271400000049
for the lowest frequency that the subsystem is allowed to occur, it is determined as follows (6)
Figure BSA00001342714000000410
And TJ：
Figure BSA00001342714000000411
In the formula (6), the reaction mixture is,
Figure BSA00001342714000000412
is the ramp rate, T, of the ith generator in the subsystemJiIs the inertia time constant of the ith generator in the subsystem,
Figure BSA00001342714000000413
is the rated capacity of the ith generator in the subsystem.
Further, the optimally selecting a target node according to the backbone channel recovery model of the subsystem includes:
determining a reduction function of a backbone channel recovery model of the subsystem according to the following formula (7):
Figure BSA00001342714000000414
in the formula (7), f 'is a backbone channel recovery efficiency function reduction value of the subsystem, and T' is recovery required time C 'after the backbone channel recovery efficiency function reduction of the subsystem'KThe net rack coverage rate of the backbone net rack K after the recovery efficiency function of the backbone channel of the subsystem is simplified;
wherein, the recovery required time T' after the backbone channel recovery efficiency function of the subsystem is simplified is determined according to the following formula (8):
T′＝∑T′i i∈ΩD (8)
in formula (8), T'iThe time, omega, required for recovering the ith node in the target node set after the backbone channel recovery efficiency function of the subsystem is simplifiedDIs a target nodeGathering;
determining the net rack coverage rate C 'of the backbone net rack K after the backbone channel recovery efficiency function simplification of the subsystem according to the following formula (9)'K：
CK′＝∑α′j j∈ΩD (9)
In the formula (9), alpha'jThe comprehensive importance of the jth node in the target node set after the recovery efficiency function of the backbone channel of the subsystem is simplified;
further, the time T' required for the ith node in the target node set to recover after the backbone channel recovery efficiency function of the subsystem is simplified is determined according to the following formula (10):
Figure BSA0000134271400000051
in the formula (10), the compound represented by the formula (10),
Figure BSA0000134271400000052
for the set of shortest paths in the path of node i to other destination nodes,
Figure BSA0000134271400000053
for the set of secondary short paths in the path from node i to other target nodes,
Figure BSA0000134271400000054
the required recovery time for line j;
the step of optimally selecting the target node specifically comprises the following steps:
a. setting all nodes of the system to be target nodes, and acquiring function values according to the formula (7);
b. the node with the minimum importance degree in the target nodes is listed as a non-target node;
c. transferring the comprehensive importance degree of the non-target node, wherein the comprehensive importance degree alpha of the node i in the subsystemiAnd if the minimum value is reached, the node i is a non-target node, and the comprehensive importance degree alpha 'of the node i is converted'i0, and dividing the comprehensive importance degree alphaiTransfer to the target node j closest to node i, then pressEquation (11) below converts the overall importance α of the target node jj：
Figure BSA0000134271400000055
In formula (11), is'jTo the overall importance of the translated target node j,
Figure BSA0000134271400000056
as the set of nodes closest to the ith non-target node, di-DIs node i to
Figure BSA0000134271400000057
The distance of (a) to (b),
Figure BSA0000134271400000058
is composed of
Figure BSA0000134271400000059
The number of middle target nodes;
d. obtaining a function value according to the formula (7), if the function value is larger than the function value in the step a, returning to the step b, and if the function value is smaller than the function value in the step a, turning to the step e;
e. if the function value in the step d is continuously reduced for the second time, if so, turning to the step f, otherwise, saving the current target node set X, and turning to the step b;
f. outputting a current target node set;
and f, acquiring a target node set, wherein the target node set acquired in the step f is an optimal target node set.
Further, the optimizing a target node restoration sequence according to the backbone channel restoration model of the subsystem includes:
acquiring an optimal recovery sequence of the target nodes by adopting a cross particle swarm optimization algorithm;
in the execution process of the cross particle swarm optimization algorithm, dividing target nodes into two types according to the distance between the target nodes and a black start power supply in a subsystem, and initializing the target nodes according to an initialization principle, wherein the initialization principle comprises the following steps: a. target nodes belonging to different classes have unchanged sequence; b. target nodes in the same class can be randomly ordered;
and in the execution process of the cross particle swarm optimization algorithm, the target function is a backbone channel recovery model of the subsystem.
Further, the optimizing a target node restoration path according to the backbone channel restoration model of the subsystem includes:
a. restoring the target nodes one by one according to the restoring sequence of the target nodes;
b. c, judging whether the current target node is a non-power source node or not, if so, turning to the step c; otherwise, turning to the step f;
c. judging whether a subsequent node of the current target node is a non-power node, if so, further judging whether the subsequent node and the current target node can be recovered simultaneously, and if so, turning to the step d; if not, turning to the step e;
d. acquiring a recovery path of the target node according to the recovery sequence of the target node, setting the simultaneously recoverable line as a simultaneously recovered line, and turning to the step g;
e. acquiring a recovery path of the target node according to the recovery sequence of the target node, and turning to the step g;
f. acquiring a recovery path of the target node according to the recovery sequence of the target node, and if the node cannot be recovered within the starting time constraint, setting the importance of the node to be 0;
g. judging whether all target nodes are recovered, if so, finishing the algorithm; otherwise, restoring the next node according to the restoring sequence of the target node, and turning to the step b.
Preferably, the recovering the local grid of the subsystem by using the objective function of the local grid recovery path optimization of the subsystem and the constraint condition thereof includes:
defining the network main path length, the average path length and the dynamic discrete rate of a local power grid of a subsystem;
constructing an objective function and a constraint condition of the local power grid restoration path optimization of the subsystem;
and restoring the local power grid of the subsystem according to the objective function of the local power grid restoration path optimization of the subsystem.
Further, the defining of the network main path length, the average path length and the dynamic dispersion ratio of the local power grid of the subsystem includes:
defining the network main path length T of the local power grid of the subsystem according to the following formula (12):
Figure BSA0000134271400000061
in the formula (12), dijThe shortest distance between a node i and a node j in the local power grid is represented by the number of lines;
defining the average path length of the local grid of the subsystem according to the following equation (13): f
Figure BSA0000134271400000071
In the formula (13), N is the number of nodes in the local power grid;
defining the dynamic dispersion rate E of the local grid of the subsystem according to the following formula (14)c：
Figure BSA0000134271400000072
In formula (14), TnewAnd FnewRespectively the network main path length and the network average path length T of the new net rack formed after the subsystem is changedmaxThe network main path of the whole system before the major power failure; fmaxAnd the maximum network average path length occurring in the system reconstruction process.
Further, the constructing an objective function and constraint conditions of the optimization of the local grid restoration path of the subsystem includes:
determining the comprehensive weight M of the restoration path in the local power grid according to the following formula (15)ij：
Figure BSA0000134271400000073
In the formula (15), μ is the number of times of passing through the transformer in the recovery path, CijCharging capacitors for converting to lines of the same voltage class, CmaxMaximum value of charging capacitance, xi, for a single line in a system11 or 0, indicating whether the network dispersion is taken into account, EcThe dynamic discrete rate of the local power grid of the subsystem is more than 0 and less than or equal to 1, and the two addend dimensions are both 1;
constructing an objective function of the local grid restoration path optimization of the subsystem according to the following formula (16):
Figure BSA0000134271400000074
in the formula (16), f is a local power grid path restoration optimization function, and β is the sum of the importance of each node in the restoration path;
determining constraints of an objective function of the local grid restoration path optimization of the subsystem according to the following formula (17):
Figure BSA0000134271400000075
in the formula (17), G is a recovered power source node, L is a set of load nodes, Pj is an active load quantity recovered by the node, and QjAmount of reactive load, P, restored for a nodei，maxFor maximum active injected power, P, of a nodei，minMinimum active injected power, Q, for a nodei，maxMaximum reactive injection power, Q, for a nodei，minIs the minimum reactive injected power for the node.
Further, the recovering the local grid of the subsystem according to the objective function of the optimization of the local grid recovery path of the subsystem and the constraint condition thereof includes:
a. acquiring the power supply quantity and the load quantity of a local power grid to be restored and the ground of each lineCapacitor Cij *Taking the backbone channel of the recovered subsystem as the starting point of a search path, taking the rest part of the subsystem as an unrecovered network frame in a region, and defining an initial value k to be 1;
b. equivalent recovered net frame to a node OkWith OkAcquiring all power supply nodes and load nodes belonging to unrecovered net racks in the area within radius r for the center, and putting the power supply nodes and the load nodes into a target node set Ik；
c. Let the weight of the line in the path be the capacitance to ground, let IkIntermediate destination node to center node OkPut the shortest path into the shortest path set Sk；
d. Respectively determine SkRestoring the optimized objective function of the path of the local power grid of each path, and deleting the paths which do not meet the constraint condition;
e. from SkSelecting a path L with the minimum objective function value of the local power grid restoration path optimization of the subsystem, and merging the path L into the recovered power network;
f. carrying out power flow verification on the recovered power network, and turning to the step g if the power flow is converged; flow non-convergence at SkAnd deleting the path L in the recovered power network, and turning to the step e;
g. if the load recovery rate R of the local power gridk> 90%, the algorithm stops; otherwise, turning to the step b.
Preferably, the performing inter-system networking recovery on the subsystem by using the regional networking recovery function and the constraint condition thereof includes:
constructing a regional networking recovery model and constraint conditions thereof;
and performing intersystem networking recovery on the subsystems by using the regional networking recovery function and the constraint conditions thereof.
Further, the building of the regional networking recovery function and the constraints thereof includes:
determining the composite weight M of the inter-subsystem restoration path in area networking according to the following formula (18)ij：
Figure BSA0000134271400000081
In the formula (18), μ is the number of times of passing through the transformer in the recovery path, CijCharging capacitors for converting to lines of the same voltage class, CmaxMaximum value of charging capacitor for single line in system, and Δ D is sensitivity of partition distance, ξ11 or 0, indicating whether the network dispersion is taken into account, EcThe dynamic discrete rate of the local power grid of the subsystem is more than 0 and less than or equal to 1, and the three addend terms are all 1;
the regional networking recovery function is constructed as follows (19):
Figure BSA0000134271400000082
in the formula (19), f is a regional networking recovery function, and β is the sum of the importance of each node in the recovery path;
determining constraints of the area networking recovery function as follows (20):
Figure BSA0000134271400000091
in the formula (20), G is the recovered power node, L is the set of load nodes, PjAmount of active load, Q, restored for a nodejAmount of reactive load, P, restored for a nodei，maxFor maximum active injected power, P, of a nodei，minMinimum active injected power, Q, for a nodei，maxMaximum reactive injection power, Q, for a nodei，minIs the minimum reactive injected power for the node.
Further, the performing inter-system networking restoration on the subsystem by using the regional networking restoration function and the constraint condition thereof includes:
a. acquiring the power supply quantity and the load quantity in the n subsystems and the ground capacitance C of each lineij *Using each subsystem black start power supply as a recovered net rack in the area as a starting point of a search path, and using the rest as an unrecovered net rack in the areaAnd initializing k to 1; initializing ξ in each subsystem1＝1，ξ2＝0；
b. The recovered net rack in the area of the subsystem k is equivalent to a node OkIf xi is2When the value is 0, then O is addedkObtaining a radius r for the centerkAll power supply nodes and load nodes belonging to unrecovered net racks in the region, and putting the power supply nodes and the load nodes into a target node set IkIf xi is21, then OkObtaining a radius r for the centerkAll power nodes and load nodes which are not recovered are arranged in the target node set Ik；
c. Using the earth capacitance as the weight of the line in the path, calling Dijkstra algorithm to obtain IkThe shortest path of each target node in the network is put into a shortest path set Sk；
d. Xi is a1If 1, acquiring the main path T of the network of the subsystem kkAnd rate of load recovery R1kIf T isk＞2/3TmaxThen xi will be1Set 0 if R1kGreater than 50%, will ξ21, placing;
e. respectively determine SkRestoring function values of the area networking of each path, and deleting the paths which do not meet the constraint conditions;
f. from SkSelecting a path L with the minimum regional networking recovery function value, and merging the path L into a subsystem k;
g. performing power flow verification on the subsystem k, and turning to the step h if the power flow is converged; flow non-convergence at SkDeleting the path L in the subsystem k, and turning to the step f;
h. checking whether the frequency difference, the voltage amplitude difference and the phase angle difference of the subsystem k and the adjacent subsystem m on two sides of a parallel point meet the parallel requirement or not, if so, combining the subsystems k and m, wherein n is n-1, and k is 1; if not, k ═ k + 1)% n;
i. if the load recovery rate R of the subsystem kkIf the number of the subsystems is more than 70 percent, and the number n of the subsystems is 1, stopping the algorithm; otherwise, turning to the step b.
The invention has the beneficial effects that:
the invention provides a power system reconstruction method, which is characterized in that a subsystem capable of being recovered in parallel is determined based on the number of black start power supplies in a recovered system; the method comprises the steps of optimizing target node selection, recovery sequence and recovery path by combining indexes such as network coverage, network dispersion rate and recovery time, and then completing the recovery of a backbone channel in a subsystem; after the coverage rate of the net rack reaches the condition, the recovery of the local power grid of the subsystem is completed by combining the network dynamic discrete rate index and the step-by-step optimization algorithm after the load recovery rate reaches the condition; the technical scheme provided by the invention firstly provides the important degree of dynamically considering the nodes in the system, measures the optimization progress of the grid structure by using the dynamic discrete rate index, and also considers the matching problem of the reconstruction process among the subsystems. The power system reconstruction method considering coordination and coordination of the backbone channel, the local power grid and the regional networking is beneficial to rapidly completing restoration of the backbone channel, the local power grid and the regional networking and has great significance for restoration of power supply of the power grid.
Drawings
Fig. 1 is a flow chart of a power system reconfiguration method of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The reconstruction method of the power system provided by the invention is based on the background that the system needs to be recovered as soon as possible after a power failure, adopts the combination of serial recovery and parallel recovery algorithms, and takes the coordination of backbone channels, a local power grid and regional networking into consideration, thereby achieving the purpose of recovering the system as soon as possible.
Generally, serial recovery refers to that after a local power failure accident or a complete stop accident occurs in a system, a black start power supply or external support in a power failure area is used for charging a main network and a possible secondary network, a main network is recovered first, then a small network is recovered, a start power supply is provided for a unit without self-starting capability, and then the normal operation of the system is gradually recovered in a layered mode under the condition that power supply and load balance is kept.
The parallel recovery means that after a major power failure accident, an original system is decomposed into a plurality of independent subsystems with black start capability to be recovered respectively, and then the power grid scale is gradually enlarged and the network structure is reinforced through interconnection among the subsystems and reconstruction of a main network frame until the normal operation of the system is comprehensively recovered.
The technical scheme provided by the invention, as shown in figure 1, comprises the following steps:
101. dividing a system to be reconstructed into x subsystems according to a subsystem division rule, wherein x is a positive integer;
at present, the domestic practical power grid is subjected to hierarchical and partitioned management, and the power grid of each area is divided perfectly. The inside of the regional power grid is generally provided with a black start power supply, and the arrangement of the contact points and the tie lines between the regional power grids is also very clear. Therefore, on the basis of meeting the division principle, the division of the power grid subsystems in China is mature.
102. Recovering the backbone channel of the subsystem by using the backbone channel recovery model of the subsystem and the constraint conditions thereof;
103. restoring the local power grid of the subsystem by using the objective function of the local power grid restoration path optimization of the subsystem and the constraint conditions thereof;
104. and performing intersystem networking recovery on the subsystems by using the regional networking recovery function and the constraint conditions thereof.
Wherein the subsystem comprises: the system comprises at least one black start power supply, a started power supply, a power transmission line, a transformer substation and a load node.
The subsystem partitioning rule includes:
the number x of the subsystems is smaller than the number of black start power supplies in the system to be reconstructed;
the difference of the total installed capacity or the maximum load capacity of the generator sets among the subsystems is larger than a partition size threshold value y;
the load and the output in each subsystem are balanced;
the number of the tie lines among the subsystems is less than or equal to 4, and the voltage grade of the tie lines is 110-220 kV;
the subsystem takes a black start power supply as a center;
and the subsystems can be connected with the grid.
Specifically, the step 102 includes:
defining the net rack coverage rate and net rack dispersion rate of the backbone channel of the subsystem;
constructing a backbone channel recovery model of the subsystem and a constraint condition thereof by using the net rack coverage rate and the net rack dispersion rate;
and optimizing and selecting a target node, optimizing a target node recovery sequence and a recovery path according to the backbone channel recovery model of the subsystem, wherein the backbone channel formed by the target node needs to meet the constraint condition.
Further, the defining of the grid coverage and the grid dispersion rate of the subsystem backbone channel includes:
defining the network frame coverage rate C of the backbone network frame K of the subsystem backbone channel according to the following formula (1)kAnd the minimum value d of the shortest path from the node j to each node in the backbone net rack Kj-K：
Figure BSA0000134271400000111
In the formula (1), omegakIs the set of nodes in the backbone network frame K of the subsystem,
Figure BSA0000134271400000112
is that it isSet of nodes, alpha, outside the backbone K of the subsystemiThe overall importance of the node i, αjIs the overall importance of node j, dj-iThe distance from the node j to the node i, namely the number of lines passing through the shortest path from the node j to the node i;
defining the grid discrete rate D of the subsystem backbone channel and the network discrete rate D when recovering the ith target node according to the following formula (2)i：
Figure BSA0000134271400000121
In the formula (2), nkIs the total number of target nodes, ΩiGTo restore the set of power nodes in the backbone network frame at the time of the ith target node,
Figure BSA0000134271400000122
to recover the set of non-power nodes in the backbone network frame at the time of the i-th target node,
Figure BSA0000134271400000123
the number of restored non-power source target nodes.
The method for constructing the backbone channel recovery model of the subsystem and the constraint conditions thereof by utilizing the net rack coverage rate and the net rack dispersion rate comprises the following steps:
constructing a backbone channel recovery model of the subsystem according to the following formula (3):
Figure BSA0000134271400000124
in the formula (3), f is a backbone channel recovery efficiency function of the subsystem, tau is a backbone net rack coverage rate weight of the subsystem, and CKNet frame coverage, L, of a backbone net frame K, which is a subsystemKTo recover the set of lines, omega, contained by the backbone networkiThe weight of the recovered line is T, and the time required by the system recovery is T;
determining a power flow constraint of the subsystem according to the following formula (4):
Figure BSA0000134271400000125
in the formula (4), nGFor the number of recovered generator nodes in the subsystem, nKFor the number of repeated nodes in the subsystem, nKlFor the number of lines that have been duplicated in the subsystem,
Figure BSA0000134271400000126
the minimum active power output of the ith generator node in the subsystem,
Figure BSA0000134271400000127
is the maximum active power output, P, of the ith generator node in the subsystemGiThe active output of the i-th generator node in the subsystem,
Figure BSA0000134271400000128
the minimum reactive power output of the ith generator node in the subsystem,
Figure BSA0000134271400000129
is the maximum reactive power output, Q, of the ith generator node in the subsystemGiFor the reactive power output of the i-th generator node in the subsystem,
Figure BSA00001342714000001210
is the voltage minimum value of the j node in the subsystem, VjIs the voltage at the jth node in the subsystem,
Figure BSA0000134271400000131
is the maximum voltage value of the j node in the subsystem, IkFor the current of the kth line in the subsystem,
Figure BSA0000134271400000132
the maximum value of the current of the kth line in the subsystem;
determining a non-power node parallel recovery constraint function of the subsystem according to the following formula (5):
Figure BSA0000134271400000133
in the formula (5), L*The total load of the nodes is restored in parallel for the subsystems,
Figure BSA0000134271400000134
for the lowest frequency that the subsystem is allowed to occur, it is determined as follows (6)
Figure BSA0000134271400000135
And TJ：
Figure BSA0000134271400000136
In the formula (6), the reaction mixture is,
Figure BSA0000134271400000137
is the ramp rate, T, of the ith generator in the subsystemJiIs the inertia time constant of the ith generator in the subsystem,
Figure BSA0000134271400000138
is the rated capacity of the ith generator in the subsystem.
The optimizing and selecting the target node according to the backbone channel recovery model of the subsystem comprises the following steps:
because the net rack coverage rate C in the backbone channel recovery model of the formula (3)KAnd the line weight omega is only related to the final shape of the backbone network frame; the time T for network reconstruction is related to the final scale of the backbone network frame and the node parallel recovery rate during reconstruction; the net rack dispersion rate D is only related to the recovery sequence of the nodes in the construction process of the backbone net rack. Therefore, in this stage, the influence of the grid discrete rate can be temporarily ignored, and meanwhile, considering that the grid coverage rate weight τ in the equation (3) is large in value and the influence of the line weight ω on the objective function is small, the objective function in this stage can be temporarily simplified, and the reduction function of the backbone channel recovery model of the subsystem is determined according to the following equation (7):
Figure BSA0000134271400000139
in the formula (7), f 'is a backbone channel recovery efficiency function reduction value of the subsystem, and T' is recovery required time C 'after the backbone channel recovery efficiency function reduction of the subsystem'KThe net rack coverage rate of the backbone net rack K after the recovery efficiency function of the backbone channel of the subsystem is simplified;
since the influence of the synchronization restoration of the non-power source node on the reduction of the system restoration time is linear, and the linear reduction of the time T can be ignored in the optimization of the objective function shown in equation (7), the restoration required time T' after the backbone channel restoration efficiency function of the subsystem is simplified is determined according to equation (8):
T′＝∑T′i i∈ΩD (8)
in formula (8), T'iThe time, omega, required for recovering the ith node in the target node set after the backbone channel recovery efficiency function of the subsystem is simplifiedDA target node set is obtained;
determining the net rack coverage rate C 'of the backbone net rack K after the backbone channel recovery efficiency function simplification of the subsystem according to the following formula (9)'K：
CK′＝∑α′j j∈ΩD (9)
In the formula (9), alpha'jThe comprehensive importance of the jth node in the target node set after the recovery efficiency function of the backbone channel of the subsystem is simplified;
further, the time T' required for the ith node in the target node set to recover after the backbone channel recovery efficiency function of the subsystem is simplified is determined according to the following formula (10):
Figure BSA0000134271400000141
in the formula (10), the compound represented by the formula (10),
Figure BSA0000134271400000142
is a section ofThe set of shortest paths from point i to other destination node paths,
Figure BSA0000134271400000143
for the set of secondary short paths in the path from node i to other target nodes,
Figure BSA0000134271400000144
the required recovery time for line j;
from the above formula, it can be found that when the target node is too small, the net rack coverage rate CK' too low, low value of the objective function; when the target nodes are too many, the system recovery time T' will be too long, and the target function value will still not be ideal. It is foreseen that when the objective function is maximum, the number of target nodes must be at an intermediate value, therefore, in the technical scheme provided by the present invention, the target nodes are optimally selected, it is assumed that all the nodes are target nodes, then the nodes with the minimum importance degree in the target nodes are gradually removed, the objective function is gradually increased initially, after a period of time, the target function has a peak value, and then gradually decreased, the corresponding target node when the target function has the peak value is the optimal target node, and the specific steps include:
a. setting all nodes of the system to be target nodes, and acquiring function values according to the formula (7);
b. the node with the minimum importance degree in the target nodes is listed as a non-target node;
c. transferring the comprehensive importance degree of the non-target node, wherein the comprehensive importance degree alpha of the node i in the subsystemiAnd if the minimum value is reached, the node i is a non-target node, and the comprehensive importance degree alpha 'of the node i is converted'i0, and dividing the comprehensive importance degree alphaiWhen the node is transferred to the target node j nearest to the node i, the comprehensive importance degree alpha of the target node j is converted according to the following formula (11)j：
Figure BSA0000134271400000145
In formula (11), is'jFor the composite of converted target node jThe degree of importance is that,
Figure BSA0000134271400000146
as the set of nodes closest to the ith non-target node, di-DIs node i to
Figure BSA0000134271400000147
The distance of (a) to (b),
Figure BSA0000134271400000148
is composed of
Figure BSA0000134271400000149
The number of middle target nodes;
d. obtaining a function value according to the formula (7), if the function value is larger than the function value in the step a, returning to the step b, and if the function value is smaller than the function value in the step a, turning to the step e;
e. if the function value in the step d is continuously reduced for the second time, if so, turning to the step f, otherwise, saving the current target node set X, and turning to the step b;
f. outputting a current target node set;
and f, acquiring a target node set, wherein the target node set acquired in the step f is an optimal target node set.
After the selection of the target node is completed, if the recovery sequence of the target node can be determined, the optimal recovery paths of the target node can be searched one by one according to the sequence, all the target nodes are recovered, and therefore system recovery is completed, and a system recovery scheme is obtained. The optimization problem of target node recovery sequence is an optimization problem of sequence, and aiming at the problem, a document [ noble, Yangyjing ] group intelligent algorithm and an application thereof [ M ]. Beijing: chinese water conservancy and hydropower press, 2006: 89-91] in the cross-particle swarm optimization.
In addition, in order to improve the efficiency of the cross particle swarm optimization algorithm, the initial particle quality can be optimized. Due to the existence of the grid discrete rate D in the target function formula (3), the optimal grid reconstruction scheme inevitably restores the target nodes which are closer to the black starting point first and then restores the target nodes which are far away. Therefore, in order to improve the quality of the initial particles, the target nodes can be classified according to the distance between the target nodes and the black starting point, and the following principles are observed when the particles are initialized: 1) target nodes belonging to different classes have unchanged sequence; 2) the target nodes in the same class can be randomly ordered. The optimizing the target node recovery sequence according to the backbone channel recovery model of the subsystem comprises:
the specific steps of obtaining the optimal recovery sequence of the target node by adopting a cross particle swarm optimization algorithm comprise:
a) and classifying the target nodes according to the distance from the black starting point to the black starting point, and initializing the particles according to the initial particle quality optimization method.
b) And calculating an objective function of the system recovery scheme corresponding to each particle, and finding out the historical optimal sequence Pxbest and the global optimal sequence Gbest of each particle.
c) The particles are interleaved with Pxbest and Gtest, respectively.
d) Whether the algorithm is likely to fall into the locally optimal solution is analyzed. If yes, taking self-escaping measures for the particles and then turning to the step e); otherwise, directly turning to the step e).
e) Judging whether the algorithm meets a convergence condition, if so, outputting Gtest and finishing the calculation; otherwise, turning to the step b).
After the optimal recovery sequence of the target nodes is obtained, the optimal recovery paths of the target nodes are searched one by one according to the sequence, and the system recovery scheme can be formed. When an optimal recovery path of a target node is found, attention needs to be paid to the problem of recovery time limitation of a thermal power generating unit and the problem of synchronous recovery of non-power nodes, and the specific step of optimizing the recovery path of the target node according to the backbone channel recovery model of the subsystem comprises the following steps:
a. restoring the target nodes one by one according to the restoring sequence of the target nodes;
b. c, judging whether the current target node is a non-power source node or not, if so, turning to the step c; otherwise, turning to the step f;
c. judging whether a subsequent node of the current target node is a non-power node, if so, further judging whether the subsequent node and the current target node can be recovered simultaneously, and if so, turning to the step d; if not, turning to the step e;
d. acquiring a recovery path of the target node according to the recovery sequence of the target node, setting the simultaneously recoverable line as a simultaneously recovered line, and turning to the step g;
e. acquiring a recovery path of the target node according to the recovery sequence of the target node, and turning to the step g;
f. acquiring a recovery path of the target node according to the recovery sequence of the target node, and if the node cannot be recovered within the starting time constraint, setting the importance of the node to be 0;
g. judging whether all target nodes are recovered, if so, finishing the algorithm; otherwise, restoring the next node according to the restoring sequence of the target node, and turning to the step b.
After the node of the subsystem backbone channel is restored, the local power grid of the subsystem needs to be restored, and step 103 includes:
defining the network main path length, the average path length and the dynamic discrete rate of a local power grid of a subsystem; wherein, the length of the network main path is as follows: refers to the maximum value of the shortest distance between any two nodes in the network. Network main path: the shortest path between two nodes corresponding to the length of the main path of the network is referred to. Network average path length: refers to the arithmetic mean of the shortest distance between any two nodes in the network.
Constructing an objective function and a constraint condition of the local power grid restoration path optimization of the subsystem;
and restoring the local power grid of the subsystem according to the objective function of the local power grid restoration path optimization of the subsystem.
The network main path length, the average path length and the dynamic dispersion ratio of the local power grid of the subsystem are defined, and the method comprises the following steps:
defining the network main path length T of the local power grid of the subsystem according to the following formula (12):
Figure BSA0000134271400000161
in the formula (12)，dijThe shortest distance between a node i and a node j in the local power grid is represented by the number of lines;
defining the average path length of the local grid of the subsystem according to the following equation (13): f
Figure BSA0000134271400000162
In the formula (13), N is the number of nodes in the local power grid;
defining the dynamic dispersion rate E of the local grid of the subsystem according to the following formula (14)c：
Figure BSA0000134271400000163
In formula (14), TnewAnd FnewRespectively the network main path length and the network average path length T of the new net rack formed after the subsystem is changedmaxThe network main path of the whole system before the major power failure; fmaxAnd the maximum network average path length occurring in the system reconstruction process.
The method for constructing the objective function and the constraint conditions of the optimization of the local power grid restoration path of the subsystem comprises the following steps:
the method adopts an extended step-by-step optimization method for system recovery, wherein the most important is the selection of the optimal recovery path in each stage, and therefore, the comprehensive weight M of the recovery path in the local power grid is determined according to the following formula (15)ij：
Figure BSA0000134271400000171
In the formula (15), μ is the number of times of passing through the transformer in the recovery path, CijCharging capacitors for converting to lines of the same voltage class, CmaxMaximum value of charging capacitance, xi, for a single line in a system11 or 0, indicating whether the network dispersion is taken into account, EcThe dynamic discrete rate of the local power grid of the subsystem is that Ec is more than 0 and less than or equal to 1 and two are addedThe dimensions are all 1;
the restoration path is composed of several nodes and lines. The recovery of a node depends on the power transmission of the line; the operation of the line is not separated from the voltage support of the node. The system reconstruction can be smoothly performed due to the inseparable cooperative relationship between the two, and in view of this, an objective function of the subsystem for local power grid restoration path optimization is constructed according to the following formula (16):
Figure BSA0000134271400000172
in the formula (16), f is a local power grid path restoration optimization function, β is the sum of the importance of each node in the restoration path, and the formula of the comprehensive importance of the node j is as follows: beta is aj＝cRk，l+(1-c)Rk，sC is more than 0 and less than 1, wherein: rk，1And Rk，sRespectively representing the load recovery rate and the power supply standby rate of the subsystem k after the node j is recovered; c is a regulatory factor. The higher the importance of the node is, the more obvious the improvement effect on the load recovery rate or the power supply standby rate is shown. The importance of the node is not only related to the characteristics of the node, namely the power supply and the load, but also related to the recovery process of the system. According to the formula of the comprehensive importance of the node j, even if the adjustment factor c is not changed, the importance of the same node is changed at different periods of system recovery, the dynamic change reflects the difference of the demand degree of the system for the power supply and the load, and the demand difference can be properly amplified or reduced by changing the size of the c, so that the purpose of screening the demand nodes is achieved.
While finding the optimal restoration path, taking care to ensure dynamic balancing of the power supply and the load, the constraints of the objective function of the local grid restoration path optimization of the subsystem are determined according to the following formula (17):
Figure BSA0000134271400000173
in formula (17), G is the recovered power supply nodePoint, L is the set of load nodes, PjThe amount of active load recovered for a node, Qj is the amount of reactive load recovered for a node, Pi，maxFor maximum active injected power, P, of a nodei，minMinimum active injected power, Q, for a nodei，maxMaximum reactive injection power, Q, for a nodei，minIs the minimum reactive injected power for the node.
Aiming at the coordination and coordination problem of a backbone channel and a local power grid, the recovery of the local power grid is completed based on a local power grid recovery optimization algorithm, and the recovery of the local power grid of a subsystem according to an objective function of the local power grid recovery path optimization of the subsystem and a constraint condition thereof comprises the following steps:
a. acquiring the power supply quantity and the load quantity of a local power grid to be recovered and the ground capacitance C of each lineij *Taking the backbone channel of the recovered subsystem as the starting point of a search path, taking the rest part of the subsystem as an unrecovered network frame in a region, and defining an initial value k to be 1;
b. equivalent recovered net frame to a node OkWith OkAcquiring all power supply nodes and load nodes belonging to unrecovered net racks in the area within radius r for the center, and putting the power supply nodes and the load nodes into a target node set Ik；
c. Let the weight of the line in the path be the capacitance to ground, let IkIntermediate destination node to center node OkPut the shortest path into the shortest path set Sk；
d. Respectively determine SkRestoring the optimized objective function of the path of the local power grid of each path, and deleting the paths which do not meet the constraint condition;
e. from SkSelecting a path L with the minimum objective function value of the local power grid restoration path optimization of the subsystem, and merging the path L into the recovered power network;
f. carrying out power flow verification on the recovered power network, and turning to the step g if the power flow is converged; flow non-convergence at SkAnd deleting the path L in the recovered power network, and turning to the step e;
g. if the load recovery rate R of the local power gridk> 90%, algorithmStopping; otherwise, turning to the step b.
And similarly, an expanded step-by-step optimization method is adopted for regional networking recovery, the comprehensive weight of the line in the recovery path introduces dual indexes of network dispersion and partition distance sensitivity, the recovery path of the recovery path deviates to the direction of an adjacent subsystem with strong complementarity, and the net racks of the adjacent subsystem have the same recovery trend. Coordinating the coordination of the recovery progress between the partitions and the selection of the parallel opportunity, wherein the step 104 includes:
constructing a regional networking recovery model and constraint conditions thereof;
and performing intersystem networking recovery on the subsystems by using the regional networking recovery function and the constraint conditions thereof.
The method for constructing the regional networking recovery function and the constraint conditions thereof comprises the following steps:
the method of expanding step-by-step optimization is adopted to carry out system recovery, and the comprehensive weight M of the recovery path among the subsystems in the area networking is determined according to the following formula (18)ij：
Figure BSA0000134271400000181
In the formula (18), μ is the number of times of passing through the transformer in the recovery path, CijCharging capacitors for converting to lines of the same voltage class, CmaxMaximum value of charging capacitor for single line in system, and Δ D is sensitivity of partition distance, ξ11 or 0, indicating whether the network dispersion is taken into account, EcThe dynamic discrete rate of the local power grid of the subsystem is more than 0 and less than or equal to 1, and the three addend terms are all 1;
the regional networking recovery function is constructed as follows (19):
Figure BSA0000134271400000191
in the formula (19), f is a regional networking recovery function, and β is the sum of the importance of each node in the recovery path;
while finding the optimal restoration path, taking care to ensure dynamic balancing of power and load, the constraints of the area networking restoration function are determined as follows (20):
Figure BSA0000134271400000192
in the formula (20), G is the recovered power node, L is the set of load nodes, PjAmount of active load, Q, restored for a nodejAmount of reactive load, P, restored for a nodei，maxFor maximum active injected power, P, of a nodei，minMinimum active injected power, Q, for a nodei，maxMaximum reactive injection power, Q, for a nodei，minIs the minimum reactive injected power for the node.
Aiming at the coordination problem of local power grid and regional networking, the recovery of the regional networking is completed based on a regional networking recovery optimization algorithm, and the intersystem networking recovery is performed on the subsystem by using a regional networking recovery function and a constraint condition thereof, and the recovery method comprises the following steps:
a. acquiring the power supply quantity and the load quantity in the n subsystems and the ground capacitance C of each lineij *Taking each subsystem black start power supply as a recovered net rack in the area as a starting point of a search path, taking the rest part as an unrecovered net rack in the area, and initializing k to be 1; initializing ξ in each subsystem1＝1，ξ2＝0；
b. The recovered net rack in the area of the subsystem k is equivalent to a node OkIf xi is2When the value is 0, then O is addedkObtaining a radius r for the centerkAll power supply nodes and load nodes belonging to unrecovered net racks in the region, and putting the power supply nodes and the load nodes into a target node set IkIf xi is21, then OkObtaining a radius r for the centerkAll power nodes and load nodes which are not recovered are arranged in the target node set Ik；
c. Using the earth capacitance as the weight of the line in the path, calling Dijkstra algorithm to obtain IkEach ofThe shortest path of the target node is put into the shortest path set Sk；
d. Xi is a1If 1, acquiring the main path T of the network of the subsystem kkAnd rate of load recovery R1kIf T isk＞2/3TmaxThen xi will be1Set 0 if R1kGreater than 50%, will ξ21, placing;
e. respectively determine SkRestoring function values of the area networking of each path, and deleting the paths which do not meet the constraint conditions;
f. from SkSelecting a path L with the minimum regional networking recovery function value, and merging the path L into a subsystem k;
g. performing power flow verification on the subsystem k, and turning to the step h if the power flow is converged; flow non-convergence at SkDeleting the path L in the subsystem k, and turning to the step f;
h. checking whether the frequency difference, the voltage amplitude difference and the phase angle difference of the subsystem k and the adjacent subsystem m on two sides of a parallel point meet the parallel requirement or not, if so, combining the subsystems k and m, wherein n is n-1, and k is 1; if not, k ═ k + 1)% n;
i. if the load recovery rate R of the subsystem kkIf the number of the subsystems is more than 70 percent, and the number n of the subsystems is 1, stopping the algorithm; otherwise, turning to the step b.
The parallel requirement usually means that the phase sequence, phase, frequency and voltage are the same, but different grids may have their own special specifications.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (15)

1. A method of power system reconfiguration, the method comprising:
dividing a system to be reconstructed into x subsystems according to a subsystem division rule, wherein x is a positive integer;
recovering the backbone channel of the subsystem by using the backbone channel recovery model of the subsystem and the constraint conditions thereof;
restoring the local power grid of the subsystem by using the objective function of the local power grid restoration path optimization of the subsystem and the constraint conditions thereof;
performing intersystem networking recovery on the subsystem by using the regional networking recovery function and the constraint condition thereof;
the subsystem partitioning rule includes:
the number x of the subsystems is smaller than the number of black start power supplies in the system to be reconstructed;
the difference of the total installed capacity or the maximum load capacity of the generator sets among the subsystems is larger than a partition size threshold value y;
the load and the output in each subsystem are balanced;
the number of the tie lines among the subsystems is less than or equal to 4, and the voltage grade of the tie lines is 110-220 kV;
the subsystem takes a black start power supply as a center;
and the subsystems can be connected with the grid.
2. The method of claim 1, wherein the subsystem comprises: the system comprises at least one black start power supply, a started power supply, a power transmission line, a transformer substation and a load node.
3. The method of claim 1, wherein the recovering the backbone channel of the subsystem using the backbone channel recovery model of the subsystem and its constraints comprises:
defining the net rack coverage rate and net rack dispersion rate of the backbone channel of the subsystem;
constructing a backbone channel recovery model of the subsystem and a constraint condition thereof by using the net rack coverage rate and the net rack dispersion rate;
and optimizing and selecting a target node, optimizing a target node recovery sequence and a recovery path according to the backbone channel recovery model of the subsystem, wherein the backbone channel formed by the target node needs to meet the constraint condition.
4. The method of claim 3, wherein the defining the rack coverage and rack dispersion rate for the subsystem backbone channels comprises:
defining the network frame coverage rate C of the backbone network frame K of the subsystem backbone channel according to the following formula (1)kAnd the minimum value d of the shortest path from the node j to each node in the backbone net rack Kj-K：
Figure FDA0003307871140000011
In the formula (1), omegakIs a set of nodes, omega, in the backbone network frame K of the subsystemKIs a set of nodes, alpha, outside the backbone network frame K of the subsystemiThe overall importance of the node i, αjIs the overall importance of node j, dj-iThe distance from the node j to the node i, namely the number of lines passing through the shortest path from the node j to the node i;
defining the grid discrete rate D of the subsystem backbone channel and the network discrete rate D when recovering the ith target node according to the following formula (2)i：
Figure FDA0003307871140000021
In the formula (2), nkIs the total number of target nodes, ΩiGTo restore the set of power nodes in the backbone network frame at the time of the ith target node,
Figure FDA0003307871140000022
to recover the set of non-power nodes in the backbone network frame at the time of the i-th target node,
Figure FDA0003307871140000023
to have recoveredA number of complex non-power source target nodes.
5. The method of claim 3, wherein the constructing the backbone channel recovery model of the subsystem and its constraints using the net rack coverage and net rack dispersion comprises:
constructing a backbone channel recovery model of the subsystem according to the following formula (3):
Figure FDA0003307871140000024
in the formula (3), f is a backbone channel recovery efficiency function of the subsystem, tau is a backbone net rack coverage rate weight of the subsystem, and CKNet frame coverage, L, of a backbone net frame K, which is a subsystemKTo recover the set of lines, omega, contained by the backbone networkiThe weight of the recovered line is T, and the time required by the system recovery is T;
determining a power flow constraint of the subsystem according to the following formula (4):
Figure FDA0003307871140000025
in the formula (4), nGFor the number of recovered generator nodes in the subsystem, nKFor the number of repeated nodes in the subsystem, nKlFor the number of lines that have been duplicated in the subsystem,
Figure FDA0003307871140000026
the minimum active power output of the ith generator node in the subsystem,
Figure FDA0003307871140000027
is the maximum active power output, P, of the ith generator node in the subsystemGiThe active output of the i-th generator node in the subsystem,
Figure FDA0003307871140000028
is aThe minimum reactive power output of the ith generator node in the system,
Figure FDA0003307871140000029
is the maximum reactive power output, Q, of the ith generator node in the subsystemGiFor the reactive power output of the i-th generator node in the subsystem,
Figure FDA00033078711400000210
is the voltage minimum value of the j node in the subsystem, VjIs the voltage at the jth node in the subsystem,
Figure FDA0003307871140000031
is the maximum voltage value of the j node in the subsystem, IkFor the current of the kth line in the subsystem,
Figure FDA0003307871140000032
the maximum value of the current of the kth line in the subsystem;
determining a non-power node parallel recovery constraint function of the subsystem according to the following formula (5):
Figure FDA0003307871140000033
in the formula (5), L*The total load of the nodes is restored in parallel for the subsystems,
Figure FDA0003307871140000034
for the lowest frequency that the subsystem is allowed to occur, it is determined as follows (6)
Figure FDA0003307871140000035
And TJ：
Figure FDA0003307871140000036
In the formula (6), the reaction mixture is,
Figure FDA0003307871140000037
is the ramp rate, T, of the ith generator in the subsystemJiIs the inertia time constant of the ith generator in the subsystem,
Figure FDA0003307871140000038
is the rated capacity of the ith generator in the subsystem.
6. The method of claim 3, wherein said optimally selecting a target node according to the backbone channel restoration model of the subsystem comprises:
determining a reduction function of a backbone channel recovery model of the subsystem according to the following formula (7):
Figure FDA0003307871140000039
in the formula (7), f 'is a backbone channel recovery efficiency function reduction value of the subsystem, and T' is recovery required time C 'after the backbone channel recovery efficiency function reduction of the subsystem'KThe net rack coverage rate of the backbone net rack K after the recovery efficiency function of the backbone channel of the subsystem is simplified;
wherein, the recovery required time T' after the backbone channel recovery efficiency function of the subsystem is simplified is determined according to the following formula (8):
T′＝∑Ti′i∈ΩD (8)
in the formula (8), Ti' time, omega, required for recovering ith node in target node set after backbone channel recovery efficiency function simplification of subsystemDA target node set is obtained;
determining the net rack coverage rate C 'of the backbone net rack K after the backbone channel recovery efficiency function simplification of the subsystem according to the following formula (9)'K：
CK′＝∑α'jj∈ΩD (9)
In the formula (9), alpha'jThe comprehensive importance of the jth node in the target node set after the recovery efficiency function of the backbone channel of the subsystem is simplified;
further, the time T' required for the ith node in the target node set to recover after the backbone channel recovery efficiency function of the subsystem is simplified is determined according to the following formula (10):
Figure FDA0003307871140000041
in the formula (10), the compound represented by the formula (10),
Figure FDA0003307871140000042
for the set of shortest paths in the path of node i to other destination nodes,
Figure FDA0003307871140000043
for the set of secondary short paths in the path from node i to other target nodes,
Figure FDA0003307871140000044
the required recovery time for line j;
the step of optimally selecting the target node specifically comprises the following steps:
a. setting all nodes of the system to be target nodes, and acquiring function values according to the formula (7);
b. the node with the minimum importance degree in the target nodes is listed as a non-target node;
c. transferring the comprehensive importance degree of the non-target node, wherein the comprehensive importance degree alpha of the node i in the subsystemiAnd if the minimum value is reached, the node i is a non-target node, and the comprehensive importance degree alpha 'of the node i is converted'i0, and dividing the comprehensive importance degree alphaiWhen the node is transferred to the target node j nearest to the node i, the comprehensive importance degree alpha of the target node j is converted according to the following formula (11)j：
Figure FDA0003307871140000045
In formula (11), is'jTo the overall importance of the translated target node j,
Figure FDA0003307871140000046
as the set of nodes closest to the ith non-target node, di-DIs node i to
Figure FDA0003307871140000047
The distance of (a) to (b),
Figure FDA0003307871140000048
is composed of
Figure FDA0003307871140000049
The number of middle target nodes;
d. obtaining a function value according to the formula (7), if the function value is larger than the function value in the step a, returning to the step b, and if the function value is smaller than the function value in the step a, turning to the step e;
e. if the function value in the step d is continuously reduced for the second time, if so, turning to the step f, otherwise, saving the current target node set X, and turning to the step b;
f. outputting a current target node set;
and f, acquiring a target node set, wherein the target node set acquired in the step f is an optimal target node set.
7. The method of claim 3, wherein optimizing a target node recovery order according to a backbone channel recovery model of the subsystem comprises:
acquiring an optimal recovery sequence of the target nodes by adopting a cross particle swarm optimization algorithm;
in the execution process of the cross particle swarm optimization algorithm, dividing target nodes into two types according to the distance between the target nodes and a black start power supply in a subsystem, and initializing the target nodes according to an initialization principle, wherein the initialization principle comprises the following steps: a. target nodes belonging to different classes have unchanged sequence; b. target nodes in the same class can be randomly ordered;
and in the execution process of the cross particle swarm optimization algorithm, the target function is a backbone channel recovery model of the subsystem.
8. The method of claim 3, wherein optimizing a target node restoration path according to a backbone channel restoration model of the subsystem comprises:
a. restoring the target nodes one by one according to the restoring sequence of the target nodes;
b. c, judging whether the current target node is a non-power source node or not, if so, turning to the step c; otherwise, turning to the step f;
c. judging whether a subsequent node of the current target node is a non-power node, if so, further judging whether the subsequent node and the current target node can be recovered simultaneously, and if so, turning to the step d; if not, turning to the step e;
d. acquiring a recovery path of the target node according to the recovery sequence of the target node, setting the simultaneously recoverable line as a simultaneously recovered line, and turning to the step g;
e. acquiring a recovery path of the target node according to the recovery sequence of the target node, and turning to the step g;
f. acquiring a recovery path of the target node according to the recovery sequence of the target node, and if the node cannot be recovered within the starting time constraint, setting the importance of the node to be 0;
g. judging whether all target nodes are recovered, if so, finishing the algorithm; otherwise, restoring the next node according to the restoring sequence of the target node, and turning to the step b.
9. The method of claim 1, wherein the restoring the local grid of the subsystem using the objective function of the subsystem's local grid restoration path optimization and its constraints comprises:
defining the network main path length, the average path length and the dynamic discrete rate of a local power grid of a subsystem;
constructing an objective function and a constraint condition of the local power grid restoration path optimization of the subsystem;
and restoring the local power grid of the subsystem according to the objective function of the local power grid restoration path optimization of the subsystem.
10. The method of claim 9, wherein the defining a network major path length, an average path length, and a dynamic dispersion ratio of a local grid of a subsystem comprises:
defining the network main path length T of the local power grid of the subsystem according to the following formula (12):
Figure FDA0003307871140000051
in the formula (12), dijThe shortest distance between a node i and a node j in the local power grid is represented by the number of lines;
defining the average path length of the local grid of the subsystem according to the following equation (13): f
Figure FDA0003307871140000052
In the formula (13), N is the number of nodes in the local power grid;
defining the dynamic dispersion rate E of the local grid of the subsystem according to the following formula (14)c：
Figure FDA0003307871140000061
In formula (14), TnewAnd FnewRespectively the network main path length and the network average path length T of the new net rack formed after the subsystem is changedmaxThe network main path of the whole system before the major power failure; fmaxAnd the maximum network average path length occurring in the system reconstruction process.
11. The method of claim 9, wherein constructing the objective function and its constraints for the optimization of the local grid restoration path of the subsystem comprises:
determining the comprehensive weight M of the restoration path in the local power grid according to the following formula (15)ij：
Figure FDA0003307871140000062
In the formula (15), μ is the number of times of passing through the transformer in the recovery path, CijCharging capacitors for converting to lines of the same voltage class, CmaxMaximum value of charging capacitance, xi, for a single line in a system11 or 0, indicating whether the network dispersion is taken into account, EcThe dynamic discrete rate of the local power grid of the subsystem is more than 0 and less than or equal to 1, and the two addend dimensions are both 1;
constructing an objective function of the local grid restoration path optimization of the subsystem according to the following formula (16):
Figure FDA0003307871140000063
in the formula (16), f is a local power grid path restoration optimization function, and β is the sum of the importance of each node in the restoration path;
determining constraints of an objective function of the local grid restoration path optimization of the subsystem according to the following formula (17):
Figure FDA0003307871140000064
in the formula (17), G is the recovered power node, L is the set of load nodes, PjAmount of active load, Q, restored for a nodejAmount of reactive load, P, restored for a nodei,maxFor maximum active injected power, P, of a nodei,minMinimum active injected power, Q, for a nodei,maxMaximum reactive injection power, Q, for a nodei,minIs the minimum reactive injected power for the node.
12. The method of claim 9, wherein recovering the local grid of the subsystem according to the objective function of the subsystem's local grid recovery path optimization and its constraints comprises:
a. acquiring the power supply quantity and the load quantity of a local power grid to be recovered and the ground capacitance C of each lineij *Taking the backbone channel of the recovered subsystem as the starting point of a search path, taking the rest part of the subsystem as an unrecovered network frame in a region, and defining an initial value k to be 1;
b. equivalent recovered net frame to a node OkWith OkAcquiring all power supply nodes and load nodes belonging to unrecovered net racks in the area within radius r for the center, and putting the power supply nodes and the load nodes into a target node set Ik；
c. Let the weight of the line in the path be the capacitance to ground, let IkIntermediate destination node to center node OkPut the shortest path into the shortest path set Sk；
d. Respectively determine SkRestoring the optimized objective function of the path of the local power grid of each path, and deleting the paths which do not meet the constraint condition;
e. from SkSelecting a path L with the minimum objective function value of the local power grid restoration path optimization of the subsystem, and merging the path L into the recovered power network;
f. carrying out power flow verification on the recovered power network, and turning to the step g if the power flow is converged; flow non-convergence at SkAnd deleting the path L in the recovered power network, and turning to the step e;
g. if the load recovery rate R of the local power gridk>90%, the algorithm stops; otherwise, turning to the step b.
13. The method of claim 1, wherein the using the regional networking recovery function and its constraints for intersystem networking recovery for subsystems comprises:
constructing a regional networking recovery model and constraint conditions thereof;
and performing intersystem networking recovery on the subsystems by using the regional networking recovery function and the constraint conditions thereof.
14. The method of claim 13, wherein the constructing the area networking recovery function and its constraints comprises:
determining the composite weight M of the inter-subsystem restoration path in area networking according to the following formula (18)ij：
Figure FDA0003307871140000071
In the formula (18), μ is the number of times of passing through the transformer in the recovery path, CijCharging capacitors for converting to lines of the same voltage class, CmaxMaximum value of charging capacitor for single line in system, and Δ D is sensitivity of partition distance, ξ11 or 0, indicating whether the network dispersion is taken into account, EcThe dynamic discrete rate of the local power grid of the subsystem is more than 0 and less than or equal to 1, and the three addend terms are all 1;
the regional networking recovery function is constructed as follows (19):
Figure FDA0003307871140000072
in the formula (19), f is a regional networking recovery function, and β is the sum of the importance of each node in the recovery path;
determining constraints of the area networking recovery function as follows (20):
Figure FDA0003307871140000081
in the formula (20), G is the recovered power node, L is the set of load nodes, PjRecovering for a nodeActive load amount of (2), QjAmount of reactive load, P, restored for a nodei,maxFor maximum active injected power, P, of a nodei,minMinimum active injected power, Q, for a nodei,maxMaximum reactive injection power, Q, for a nodei,minIs the minimum reactive injected power for the node.
15. The method of claim 13, wherein the using the regional networking recovery function and its constraints for intersystem networking recovery for subsystems comprises:
a. acquiring the power supply quantity and the load quantity in the n subsystems and the ground capacitance C of each lineij *Taking each subsystem black start power supply as a recovered net rack in the area as a starting point of a search path, taking the rest part as an unrecovered net rack in the area, and initializing k to be 1; initializing ξ in each subsystem1＝1，ξ2＝0；
b. The recovered net rack in the area of the subsystem k is equivalent to a node OkIf xi is2When the value is 0, then O is addedkObtaining a radius r for the centerkAll power supply nodes and load nodes belonging to unrecovered net racks in the region, and putting the power supply nodes and the load nodes into a target node set IkIf xi is21, then OkObtaining a radius r for the centerkAll power nodes and load nodes which are not recovered are arranged in the target node set Ik；
c. Using the earth capacitance as the weight of the line in the path, calling Dijkstra algorithm to obtain IkThe shortest path of each target node in the network is put into a shortest path set Sk；
d. Xi is a1If 1, acquiring the main path T of the network of the subsystem kkAnd rate of load recovery RlkIf T isk>2/3TmaxThen xi will be1Set 0 if Rlk>50%, then xi21, placing;
e. respectively determine SkRestoring function value of area networking of each path and deleting the condition that the function value is not satisfiedA path of a constraint;
f. from SkSelecting a path L with the minimum regional networking recovery function value, and merging the path L into a subsystem k;
g. performing power flow verification on the subsystem k, and turning to the step h if the power flow is converged; flow non-convergence at SkDeleting the path L in the subsystem k, and turning to the step f;
h. checking whether the frequency difference, the voltage amplitude difference and the phase angle difference of the subsystem k and the adjacent subsystem m on two sides of a parallel point meet the parallel requirement or not, if so, combining the subsystems k and m, wherein n is n-1, and k is 1; if not, k ═ k + 1)% n;
i. if the load recovery rate R of the subsystem kk>70%, and the number n of subsystems is equal to 1, stopping the algorithm; otherwise, turning to the step b.
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