CN112310962A - Optimal network reconstruction method and system based on wind power plant - Google Patents

Optimal network reconstruction method and system based on wind power plant Download PDF

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CN112310962A
CN112310962A CN202011142247.9A CN202011142247A CN112310962A CN 112310962 A CN112310962 A CN 112310962A CN 202011142247 A CN202011142247 A CN 202011142247A CN 112310962 A CN112310962 A CN 112310962A
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张璨
张华一
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Nanjing Power Supply Co of State Grid Jiangsu 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
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    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
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    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract

A method and a system for optimal network reconstruction based on a wind power plant are provided. Analyzing a network structure of a power grid system and topological relations of all nodes and lines by using a topological graph of a power grid, and determining a skeleton network by using the network structure and the nodes; after power failure, the hybrid power supply based on the wind power plant is used as a black start power supply to supply power to a power grid system; calculating a recovery risk index of the skeleton network; calculating the importance of the contracted line; optimizing the skeleton network by using a particle swarm algorithm and taking the recovery risk index of the minimized network reconstruction and the line importance of the maximized skeleton network as optimization targets; determining a skeleton network by using a network reconstruction method based on the support degree; and according to the skeleton network, starting a black start power supply. Compared with the existing method through example analysis, the feasibility and the superiority of the model and the method are remarkable.

Description

Optimal network reconstruction method and system based on wind power plant
Technical Field
The invention belongs to the technical field of power systems, and relates to a method and a system for optimal network reconstruction based on a wind power plant.
Background
At present, relevant research works are carried out at home and abroad aiming at the problem of power system recovery after heavy power failure.
The existing research on system recovery only considers that the conventional hydroelectric or thermal power unit is used as a black-start power supply, and the research on the renewable energy power unit (such as a wind power unit) as the black-start power supply is rarely reported. In addition, the traditional method needs experts to give weights to the indexes when the optimal black start strategy is selected, and the traditional method is inevitably influenced by human subjective factors.
In the prior art, prior art document 1 (Zhang Guosong, Liu Junyong, Wei seismogram, etc. (Zhang Junyong, Wei Zhenbo, et al) discloses a framework network reconfiguration (skeeleton-network configuration based on priority and path electrical effects) [ J ] Power System Protection and Control (Power System Protection and Control),2011,38(17):1-6.) proposes a network reconfiguration method taking topology priority and path electrical effects into account based on a node dielectric theory, and comprehensively considers the importance of nodes and the influence of recovery path optimization on network reconfiguration.
Prior art document 2 (Wang Liang, liuyan, consider snow, etc. (Wang Liang, Liu Yan, Gu xue ping, et al). network reconfiguration (skeeleton-network reconfiguration based on node importance and line between) taking node importance and line between into account [ J ]. Power system Automation (Automation of Electric Power Systems),2010,34(12):29-33.) proposes a network reconfiguration optimization strategy taking line importance and node importance into account, and optimizes a target grid frame by using a discrete particle swarm algorithm.
Document 3(Gu X, Zhong h. optimization of network configuration based on a two-layer unit-recovery frame for power system recovery [ J ]. IET Generation, Transmission and Distribution,2012,6(7): 693:700.) proposes a two-layer recovery architecture including network layer unit recovery and power plant layer unit recovery, recovers as many power Generation nodes as possible at the network layer, recovers as many units as possible at the power plant layer, and considers the importance of the load to be recovered.
Prior art document 4(Wang C, Vital V, Kolluri V, et al. PTDF-Based automatic recovery path selection [ J ]. IEEE Trans on Power Systems,2010,25(3): 1686-.
Prior art document 5(Chou Y, Liu C, Wang Y, et al. development of a black start determination support system for isolated Power Systems [ J ]. IEEE Trans on Power Systems,2013,28(3):2202-2210.) developed a black start recovery strategy support system with a graphical user interface and applied to the formulation of Taiwan electric company black start schemes.
In the prior art document 6(Sun W, Liu C, Zhang l. optimal generator start-up for bulk Power system recovery [ J ]. IEEE Trans on Power Systems,2011,26(3): 1357-.
In prior art document 7 (Liu Yan, Gao Qian, Gu xue), a target planning-based grid reconstruction path Optimization method (Optimization of restoration sequence of network based on good planning) [ J ] Power system Automation (Automation of Electric Power Systems,2010, 34(11):33-37.) uses network reconstruction time and unit output restoration degree as Optimization targets, and optimizes the restoration sequence of target nodes by using a cross particle swarm algorithm. The existing research on system recovery only considers that the conventional hydroelectric or thermal power unit is used as a black-start power supply, and the research on the renewable energy power unit (such as a wind power unit) as the black-start power supply is rarely reported.
Prior art documents 8-11(Lin Z, Wen F, Huang J, et al. evaluation of black-start schemes applied with accuracy weight-based determination-making the order [ J ]. Journal of Energy Engineering-ASCE,2010,136(2): 42-49;
forest Intelligence, Wenfu, Xue Yu, etc. (Lin Zhenzhi, Wen Fushu, Xue Yusheng, et al.) Smart grid Black Start-up decision based on multiple Attribute group decision feature root method (Black-start decision-making in smart grid using Multi-attribute group Electric utility method) [ J ] Power System Automation (Automation of Electric Power Systems),2010,34(5): 18-23;
forest wisdom, fuwen bolt, schuman, etc. (Lin Zhenzhi, Wen fushu, Xue Yusheng, et al.) Reliability analysis of smart grid black start group decision (Reliability analysis on the group decisions-creating results of black-start strategies in smart grids) [ J ] Power system Automation (Automation of Electric Power Systems),2010,34(9): 17-22;
liu W, Lin Z, Wen F, et al, analysis and optimization of the preferences in black-start group determination-mapping [ J ] IET Generation, Transmission & Distribution,2013,7(1):14-23.) the proposed methods all require experts to weight the indices, which is inevitably affected by human subjective factors.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a method for reconstructing an optimal network based on a wind power plant. The hybrid power supply of the wind power plant is taken as a black start power supply, meanwhile, the influence of the uncertainty of the wind power output on the optimization of the skeleton network is also considered, a line importance evaluation method based on line shrinkage is provided, a network reconstruction strategy optimization method based on support is finally developed, and the influence of artificial subjective factors is effectively avoided.
The invention adopts the following technical scheme:
a method for optimal network reconstruction based on a wind farm comprises the following steps:
step 1: analyzing a network structure of a power grid system and topological relations of all nodes and lines by using a topological graph of a power grid, and determining a skeleton network by using the network structure and the nodes;
step 2: after power failure, the hybrid power supply based on the wind power plant is used as a black start power supply to supply power to a power grid system;
and step 3: calculating a recovery risk index of the skeleton network;
and 4, step 4: calculating the importance of the contracted line;
and 5: according to the recovery risk index obtained in the step 3 and the line importance obtained in the step 4, optimizing the skeleton network by using a particle swarm algorithm and taking the recovery risk index of the minimum network reconstruction and the line importance of the maximum skeleton network as optimization targets;
step 6: determining the skeleton network by using a network reconstruction method based on the support degree according to the skeleton network obtained in the step 5;
and 7: and 6, starting a black start power supply according to the skeleton network obtained in the step 6.
In the step 2, the black start power supply comprises a wind power plant, a hydroelectric generating set or a thermal generating set, and the proportion of the black start power supply in the hybrid power supply is any proportion.
The proportion of the wind power plant, the hydroelectric generating set and the thermal power generating set in the hybrid power supply is 5:3: 2.
In the step 2, after the power failure, the wind power field in the hybrid power supply, the hydroelectric generating set and the thermal power generating set are started as the black start power supply in the sequence of starting the wind power field firstly, then starting the thermal power generating set and finally starting the hydroelectric generating set.
In the step 3, in the framework network optimization stage, the power-off unit is recovered through the black start power supply so as to recover the backbone network frame, and the node voltage amplitude constraint and the line stability limit constraint are met:
Vi min≤Vi≤Vi max i∈ΨN
Figure BDA0002738600830000041
in the formula, psiNRepresenting a set of all nodes, ViIs the voltage amplitude, V, of node ii minAnd Vi maxLower and upper voltage level limits, Ψ, respectively allowed for node iLRepresenting a set of all lines, PlbAnd
Figure BDA0002738600830000042
active power passing through the line b and its allowable upper limit。
Under the condition that the power generation output of the wind power plant is determined, the recovery risk index of the framework network scheme is evaluated and recovered on the basis of the node voltage amplitude out-of-limit condition and the line power out-of-limit condition, and the expression is
Figure BDA0002738600830000043
In the formula, RsFor the risk of recovery of the skeleton network s, Nb,sNumber of lines in s, Nn,sIs the number of nodes in s, NeFor Monte Carlo sampling times of wind speed, Ee,b,sAnd Ee,i,sRespectively the out-of-range percentage of the line power and the node voltage amplitude in the mth line and the ith node in s in the e time of Monte Carlo sampling;
Ee,b,sand Ee,i,sAre respectively as
Figure BDA0002738600830000051
Figure BDA0002738600830000052
In the formula, Vi rateIs the nominal voltage of node i, ΨL,sAnd ΨN,sRespectively, a line set and a node set, P, in a skeleton network slbAnd
Figure BDA0002738600830000053
respectively the active power passing on the line b and its allowable upper limit, ViIs the voltage amplitude, V, of node ii minAnd Vi maxRespectively, the lower limit and the upper limit of the voltage amplitude allowed by the node i.
In step 4, the line importance based on the line shrinkage method is calculated according to the following formula:
Figure BDA0002738600830000054
wherein:
Figure BDA0002738600830000055
Figure BDA0002738600830000056
in the formula, alphabFor line importance, nbDegree of the same node into which the nodes at both ends of the line b converge after shrinkage, dbIs the average shortest distance of the network after the contraction of the line b, A is the adjacency matrix of the network containing N nodes before the contraction of the line, the nodes g and p are the nodes at the two ends of the line b, IgAnd IpN-dimensional column vectors of the g-th element and the p-th element being 1, and the remaining elements being 0,
Figure BDA0002738600830000057
and
Figure BDA0002738600830000058
are respectively IgAnd IpI is an N-dimensional column vector with all elements 1, Nb,iAnd Ψb,NRespectively the number of nodes in the network after the contraction of the line b and the collection of the nodes,
Figure BDA0002738600830000059
is the shortest distance between node pair (i, j).
In the step 5, the recovery risk index for minimizing network reconstruction and the line importance of maximizing the skeleton network are used as optimization targets for determining the skeleton network,
Figure BDA0002738600830000061
in the formula Ee,bAnd Ee,iRespectively, the b-th line andout-of-bounds percentage of line power and node voltage amplitude, R, in the ith node in the e-th Monte Carlo samplingbaseAnd alphabaseRespectively a recovery risk reference value and an importance reference value, N, of the skeleton networkb,sNumber of lines in s, NeFor Monte Carlo sampling times of wind speed, NbFor the number of nodes in the network after the contraction of line b, NnFor the number of nodes in the network after the shrinkage of the line N, Nb,iFor the ith node, n, in the network after the contraction of the line bbThe degree of the same node that the two end nodes of the line b are converged into after shrinkage,
Figure BDA0002738600830000062
is the shortest distance, Ψ, between node pairs (i, j)b,sFor a set of lines in the skeleton network s, Ψb,NIs a collection of nodes in the skeleton network N.
In the step 6, the data obtained by simultaneously measuring the same measured parameter by m sensors is x1,x2,x3,…,xk…,xmThe set of which is Ω x,
to characterize xkIs x bygDegree of support, support function SkgComprises the following steps:
Figure BDA0002738600830000063
measurement data x from m sensors1,x2,x3,…,xk…,xmThe support matrix is obtained as follows:
Figure BDA0002738600830000064
the comprehensive support degree passes through the support degree Sk1,Sk2,…,SkmIs synthesized to obtaink＝Z1Sk1+Z2Sk2+…+ZmSkmIs xkComprehensive support of (3), Z in the eigenvector Z1,Z2,…,ZmIs a group of nonnegative numbers as the weight of each support function value when calculating the comprehensive support value,
H＝SZ
wherein H ═ H1,h2,…,hk,…,hm]T，Z＝[Z1,Z2,…,Zk,…,Zm]T。
Solving the S maximum characteristic root lambda max according to the Perron-Frobenius theorem, wherein the corresponding characteristic vector Z is a positive characteristic vector,
due to the fact that
SZ＝λmaxZ
Then
H＝λmaxZ
Normalizing the elements in Z to obtain the weight vector of the measurement data as
Figure BDA0002738600830000071
Solving according to the multi-target framework network optimization model to obtain a group of Pareto optimal solutions;
assuming that w strategies including r indexes exist in the Pareto optimal solution set, the index value matrix is represented as
Figure BDA0002738600830000072
In the formula, xu,qDefining a value of a qth index representing the u-th strategy, defining a modified index value Xu,qComprises the following steps:
Figure BDA0002738600830000073
the modified index value matrix is:
Figure BDA0002738600830000074
from X1,q,X2,q,…,Xu,q,…,Xw,qObtaining a support matrix of the qth index:
Figure BDA0002738600830000075
maximum characteristic root for calculating support matrix and characteristic vector Z thereofqAnd normalizing the q index value to obtain a weight vector F corresponding to the q index valueq＝[F1,q,F2,q,…,Fw,q]And sequentially calculating a weight vector for each index to obtain an index value weight matrix as follows:
Figure BDA0002738600830000081
and obtaining the support degree of each network reconstruction strategy by the index value matrix:
Figure BDA0002738600830000082
in the formula DuReconfiguration of policy support for the u-th network, IuIs a w-dimensional column vector with the u-th element being 1 and the other elements being 0.
A system of a method for reconstructing an optimal network based on a wind power plant comprises a skeleton network construction module, a black start power module, a risk index recovery module, a line importance module and a support module,
the skeleton network construction module analyzes a network structure of the power grid system and the node distribution condition in the network structure to construct a skeleton network;
after the black start power supply module is powered off, the hybrid power supply based on the wind power plant is used as a black start power supply to supply power to a power grid system;
the risk indicator recovery module optimizes the skeleton network by minimizing the risk indicators;
the line importance module optimizes the skeleton network by maximizing the importance of the skeleton network by using the line importance of line contraction;
the support degree module optimizes the skeleton network by using a network reconstruction strategy based on the support degree.
Equivalent to the prior art, the invention can obtain the following beneficial technical effects:
the skeleton network obtained by the method has smaller recovery risk and higher skeleton network importance, and the number of lines required to be recovered is small, namely the required line operation is small. In the network reconstruction stage, the power-off station is recovered through fewer lines, so that on one hand, the switching operation times can be reduced, and the recovery process is accelerated; on the other hand, the risk possibly brought by the line operation is also reduced. The main purpose of the network reconstruction stage is to quickly recover a power-off station through a black-start power supply, establish a stable grid structure and lay a solid foundation for the next load recovery.
Drawings
FIG. 1 is a relationship curve between the output power Pw and the wind speed v of a wind turbine;
FIG. 2 is a schematic diagram of a line shrinkage of a scaleless network;
fig. 3 is a system of new england 10 machines 39 nodes;
FIG. 4 is the final restored net mount;
FIG. 5 is a schematic workflow diagram of a system of a method for optimal network reconfiguration based on a wind farm.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. The embodiments described herein are only some embodiments of the invention, and not all embodiments. All other embodiments obtained by a person skilled in the art without making any inventive step on the basis of the spirit of the present invention are within the scope of protection of the present invention.
Step 1: analyzing a network structure of a power grid system and topological relations of all nodes and lines by using a topological graph of a power grid, and determining a skeleton network by using the network structure and the nodes;
step 2: after power failure, the hybrid power supply based on the wind power plant is used as a black start power supply to supply power to a power grid system;
the black start power supply comprises a wind power plant, a hydroelectric generating set or a thermal power generating set, and the proportion of the black start power supply in the hybrid power supply is any proportion.
In the preferred embodiment, the proportion of the wind power plant, the hydroelectric generating set and the thermal generating set in the hybrid power supply is 5:3: 2.
After power failure, the wind power field in the hybrid power supply, the hydroelectric generating set and the thermal power generating set are used as black start power supplies, and the starting sequence comprises the steps of starting the wind power field firstly, then starting the thermal power generating set, and finally starting the hydroelectric generating set.
Wind power output has intermittency and randomness, and how to stabilize the fluctuation of the generated output is one of the main problems to be solved by wind power generation, which can be solved from three aspects:
1) utilizing the output complementary effect of the large wind power plant;
2) constructing a matched energy storage system;
3) and if necessary, adopting a waste air management measure.
Compared with the traditional thermal power black start unit, the wind power black start unit has high wind power start speed and can rapidly provide start power for the system; in addition, the wind power plant is generally located in a region with rich wind resources, and the condition of no wind is relatively rare, so that the wind power plant can be considered to be used as an auxiliary black start power supply to help the system to recover quickly after a major power failure occurs.
Voltage problems in power systems are mainly related to reactive power. The doubly-fed induction wind driven generator widely adopted at present controls reactive output by using rotor current, and decouples active and reactive control through independent exciting current. Besides the wind turbine itself for reactive power control, the wind farm is generally equipped with reactive power compensation devices, such as static reactive power compensators, parallel capacitors, etc., during the construction of the wind farm, and these devices can be used for controlling the reactive power output of the wind farm and further controlling the voltage. With the continuous development of power electronic technology and the like, the control capability of the wind power plant on reactive power can be gradually enhanced, and the problem of voltage fluctuation possibly caused by the wind power plant can be relieved.
The wind power generation output has uncertainty, and the output is influenced by wind power change. The probability density function of wind speed is often described by a Weibull distribution:
Figure BDA0002738600830000101
wherein v is the wind speed; k and c are respectively the shape parameter and the scale parameter of Weibull distribution; f (v) is the probability density function of v.
Output power P of wind turbinewThe relationship with the wind speed v is shown in fig. 1, and the functional relationship is as follows:
Figure BDA0002738600830000102
in the formula, vwsTo cut into the wind speed; v. ofwoCutting out the wind speed; v. ofwrRated wind speed; pwrThe rated power of the wind turbine generator is obtained. The wind turbine generator output power corresponding to Monte Carlo wind speed sampling based on the formula (1) each time can be calculated through the formula (2), and probability distribution sampling is specifically adopted.
If only the wind power plant is used as a black start power supply after the system has a major power failure, due to uncertainty of wind power output, the black start power supply can not be provided for the system in time and other non-black start units can not be started, so other conventional black start power supplies such as hydroelectric generating sets or thermal generating sets are needed. Therefore, the invention aims at the condition that the hybrid power supply comprising the wind power plant is used as the black start power supply, wherein the hybrid power supply comprises at least two power supplies, such as the wind power plant, the hydroelectric generating set and the fuel generating set, which have the black start condition. The invention does not research which configuration is best, the previous research is only rarely used for researching the utilization of the wind power plant as the black start power supply, and the invention provides a method for utilizing the black start of the wind power plant.
And step 3: the influence of wind power output uncertainty on optimization is considered, and a recovery risk index is constructed and minimized to optimize a skeleton network;
the main task of the network frame reconstruction stage is to recover the power-off unit and further recover the backbone network frame through the black start power supply, and in the framework network optimization stage, the power-off unit and further recover the backbone network frame through the black start power supply, so that node voltage amplitude constraint and line stability limit constraint are met:
Vi min≤Vi≤Vi maxi∈ΨN (3)
Figure BDA0002738600830000111
in the formula, psiNRepresenting a set of all nodes, ViIs the voltage amplitude, V, of node ii minAnd Vi maxLower and upper voltage level limits, Ψ, respectively allowed for node iLRepresenting a set of all lines, PlbAnd
Figure BDA0002738600830000112
respectively the active power passing on line b and its allowable upper limit.
Uncertainty and intermittence of the generated output of the wind power plant can cause the change of the system tide, namely the change of the voltage of each node and the active power passing through the line. On the other hand, even under the condition that the generated output of the wind power plant is determined, the node voltage amplitude and the line power caused by recovering different skeleton networks are different. The recovery risk associated with recovering the skeletal network scheme is evaluated based on node voltage magnitude violations and line power violations and is described by equation (5).
Figure BDA0002738600830000113
In the formula, RsFor the risk of recovery of the skeleton network s, Nb,sNumber of lines in s, Nn,sIs the number of nodes in s, NeFor Monte Carlo sampling times of wind speed, Ee,b,sAnd Ee,i,sRespectively the line power and the node in the e-th Monte Carlo sampling of the b-th line and the i-th node in sOut-of-range percentage of voltage amplitude;
Ee,b,sand Ee,i,sDetermined by equations (6) and (7), respectively:
Figure BDA0002738600830000121
Figure BDA0002738600830000122
in the formula, Vi rateIs the nominal voltage of node i, ΨL,sAnd ΨN,sRespectively, a line set and a node set, P, in a skeleton network slbAnd
Figure BDA0002738600830000123
respectively the active power passing on the line b and its allowable upper limit, ViIs the voltage amplitude, V, of node ii minAnd Vi maxRespectively, the lower limit and the upper limit of the voltage amplitude allowed by the node i.
And 4, step 4: optimizing the skeleton network by utilizing a line importance evaluation method of line shrinkage and maximizing the importance of the skeleton network;
and (4) evaluating the importance of the line in the scale-free network by utilizing the idea of a contraction node. Fig. 2 shows a line shrinking process for a small system. The thick line segments in fig. 2 represent busbars in the power network, which in the topology may also be seen as nodes. As can be seen from fig. 2, after the contraction of the line b, the nodes 6 and 5 at both ends converge to the same node 6'.
The line importance based on the line shrinkage idea is defined as:
Figure BDA0002738600830000124
wherein:
Figure BDA0002738600830000125
Figure BDA0002738600830000126
in the formula, alphabFor line importance, nbDegree of the same node into which the nodes at both ends of the line b converge after shrinkage, dbIs the average shortest distance of the network after the contraction of the line b, A is the adjacency matrix of the network containing N nodes before the contraction of the line, the nodes g and p are the nodes at the two ends of the line b, IgAnd IpN-dimensional column vectors of the g-th element and the p-th element being 1, and the remaining elements being 0,
Figure BDA0002738600830000131
and
Figure BDA0002738600830000132
are respectively IgAnd IpI is an N-dimensional column vector with all elements 1, Nb,iAnd Ψb,NRespectively the number of nodes in the network after the contraction of the line b and the collection of the nodes,
Figure BDA0002738600830000133
is the shortest distance between node pair (i, j).
As can be seen from equation (10), the importance of a line is related to the connection between its two end nodes and other nodes and the location of the line in the network structure:
1) establishing a link between the line importance evaluation and the node importance evaluation through line shrinkage, wherein the greater the importance of the node formed after the line shrinkage is, the more lines directly connected with the line are, and the more important the line is;
2) the importance of a line located at a "critical location" in the network structure is generally high and the critical location only takes into account the characteristics of the topology, since it is located in the shortest path between many pairs of nodes, so the contraction of the line will cause the average shortest distance of the pairs of nodes in the network to decrease, and thus the importance value of the line will be greater in correspondence with equation (10).
Taking line b in the network shown in FIG. 2 as an example, n thereofb＝5,Nb,i＝7,
Figure BDA0002738600830000134
Then its importance is alphab＝2.625。
And 5: by utilizing a particle swarm algorithm, the minimized network reconstruction recovery risk and the maximized skeleton network importance are used as optimization targets for determining the skeleton network, and the following results are obtained:
Figure BDA0002738600830000135
in the formula Ee,bAnd Ee,iThe out-of-range percentages, R, of line power and node voltage amplitude in the e-th Monte Carlo sampling of the b-th line and the i-th node, respectivelybaseAnd alphabaseRespectively a recovery risk reference value and an importance reference value, N, of the skeleton networkb,sNumber of lines in s, NeFor Monte Carlo sampling times of wind speed, NbFor the number of nodes in the network after the contraction of line b, NnFor the number of nodes in the network after the shrinkage of the line N, Nb,iFor the ith node, n, in the network after the contraction of the line bbThe degree of the same node that the two end nodes of the line b are converged into after shrinkage,
Figure BDA0002738600830000136
is the shortest distance, Ψ, between node pairs (i, j)b,sFor a set of lines in the skeleton network s, Ψb,NIs a collection of nodes in the skeleton network N.
Particle swarm optimization simulates birds in a bird swarm by designing a particle without mass, and the particle only has two attributes: speed, which represents how fast the movement is, and position, which represents the direction of the movement. And each particle independently searches an optimal solution in a search space, records the optimal solution as a current individual extremum, shares the individual extremum with other particles in the whole particle swarm, finds the optimal individual extremum as a current global optimal solution of the whole particle swarm, and adjusts the speed and the position of each particle in the particle swarm according to the found current individual extremum and the current global optimal solution shared by the whole particle swarm.
In the system recovery process, the system operation constraint can be properly relaxed, and the constraint condition can be met by adjusting the output and the load; when the constraint condition can not be met, measures such as adjusting the output of the generator and the input load amount can be adopted. In the optimization model described in equation (11), the candidate restoration line is an optimization variable, so it is also a multi-objective optimization problem with discrete variables. The multi-objective optimization problem generally does not have a unique optimal solution, and the optimal solution is generally a solution set, namely a so-called Pareto optimal solution or a non-inferior solution. The multi-objective optimization problem can be solved by adopting a traditional operation research method, an intelligent optimization method or a heuristic optimization method to obtain a group of Pareto optimal solutions. The particle swarm algorithm is adopted to solve the problem, and the details of the algorithm are not described here in consideration of the fact that the algorithm is a mature technology in the prior art.
Step 6: two links of network reconstruction optimization and decision making are considered, and a network reconstruction strategy optimization method based on support degree is utilized.
The support in the information field is mainly used for processing data fusion of multiple sensors, and the basic concept of the data fusion is briefly introduced here. Setting the data obtained by simultaneously measuring the same measured parameter by m sensors as x1,x2,x3,…,xk…,xmThe set of which is Ω x. Since the "true value" of the actual parameter is not known, xkCan only be measured to obtain data x1,x2,x3,…,xk…,xmIs determined by the information contained therein. If xkThe higher the support for obtaining other measurement data, the higher the likelihood that it is a "true value". Let x bekAnd xgFor two data in the set Ω x, xkIs x bygThe degree of support is from xgAngle of (1) view xkTo the extent possible for the true value. To characterize xkIs x bygDegree of support, defining a support function SkgComprises the following steps:
Figure BDA0002738600830000141
if xkDistance xgThe closer the distance of (2), the support function SkgThe greater the value of (A), i.e. xkIs x bygThe greater the degree of support. Measurement data x from m sensors1,x2,x3,…,xk…,xmThe support matrix can be obtained as follows:
Figure BDA0002738600830000151
Skgonly reflect xkIs x bygDegree of support, which does not reflect xkThe degree of comprehensive support of other data, which is passed through Sk1,Sk2,…,SkmAnd (4) synthesizing. Let hk＝Z1Sk1+Z2Sk2+…+ZmSkmIs xkComprehensive support of (3), Z in the eigenvector Z1,Z2,…,ZmIs a set of nonnegative numbers, Z is the maximum characteristic root λ of SmaxThe corresponding feature vector is used as the weight of each support function value when calculating the comprehensive support value.
Further can obtain
H＝SZ (14)
Wherein H ═ H1,h2,…,hk,…,hm]T；Z＝[Z1,Z2,…,Zk,…,Zm]T。hkThe larger, the measured value xkThe greater the degree of support, the greater the likelihood that it will be a true value. S is a non-negative irreducible matrix as can be seen from the definition of the support degree matrix, and S has a maximum characteristic root lambda according to the Perron-Frobenius theoremmaxThe corresponding eigenvector Z is a positive eigenvector. Due to the fact that
SZ＝λmaxZ (15)
Then
H＝λmaxZ (16)
The size of each element in the feature vector Z represents the relative reliability of the corresponding measurement data. Normalizing the elements in Z to obtain the weight vector of the measurement data
Figure BDA0002738600830000152
Finally, a group of Pareto optimal solutions can be obtained through solving according to the multi-objective framework network optimization model, and then the problem to be solved is how to select a proper solution from the group of Pareto optimal solutions. In the existing method, a power expert generally needs to give a weight to a relevant index of each recovery strategy, and then selects a proper strategy according to each known index value and index weight, so that artificial/subjective factors are inevitably introduced. The support degree is introduced into the optimization of the network reconstruction strategy, the final network reconstruction strategy is optimized by analyzing the internal mutual support relationship among the index values of the strategies in the Pareto solution set, and the influence of artificial subjective factors on the decision result is avoided as much as possible.
Assuming that there are w strategies in the Pareto optimal solution set, including r indexes, the index value matrix can be expressed as
Figure BDA0002738600830000161
In the formula, xu,qThe value of the qth index representing the u-th strategy. Considering that some optimization problems have the condition that part of index values need to be maximized and other index values need to be minimized, in order to unify all optimization targets and take the maximized modification index value as a target, a modification index value Xu is defined, and q is as follows:
Figure BDA0002738600830000162
the modified index value matrix is:
Figure BDA0002738600830000163
from X1,q,X2,q,…,Xu,q,…,Xw,qA support matrix of the qth index can be obtained:
Figure BDA0002738600830000164
and calculating the maximum characteristic root of the support matrix and a characteristic vector Zq thereof, and normalizing the maximum characteristic root and the characteristic vector Zq to obtain a weight vector Fq corresponding to the q-th index value [ F1, q, F2, q, …, Fw, q ]. And calculating a weight vector for each index in sequence to obtain an index value weight matrix which is as follows:
Figure BDA0002738600830000165
and obtaining the support degree of each network reconstruction strategy by the index value matrix:
Figure BDA0002738600830000171
in the formula DuReconstructing the support degree of the strategy for the u network; i isuIs a w-dimensional column vector with the u-th element being 1 and the other elements being 0. And after the support degree of each network reconstruction strategy is calculated, the network reconstruction strategy with the maximum support degree is the final network reconstruction strategy.
And 7: and 6, starting a black start power supply according to the skeleton network obtained in the step 6.
The present application further provides an embodiment, which is to take the new england 10 machine 39 node system shown in fig. 3 as an example to illustrate the method proposed by the present invention to take account of the optimal network reconfiguration of the wind farm.
The calculation results of the importance of each line are shown in table 1. Assuming that the node 33 and the node 37 are nodes where a conventional black start unit is located, the node 34 is a node where a wind farm is located, the rated power of the wind farm is 100MW, and the units located at the three nodes are used as a black start power supply for system recovery. Current calculation time samplingUsing document [12 ]](Zhou Min Liu Yan (Zhou Yang) and a grid parallel recovery strategy (A parallel recovery strategy for power network processing) of thermal power unit starting process) [ J parallel recovery strategy (J recovery strategy for power network processing of thermal units) [ J recovery strategy for power network processing of thermal units ]]The parameters given in Automation of Electric Power Systems (2011, 35(10):30-34, 93); rbaseAnd alphabaseRespectively a recovery risk reference value and an importance reference value of the skeleton network, and the related parameters for calculating the recovery risk and the importance are given as Rbase＝1；α base1 is ═ 1; ne 1000. Table 1 shows 4 Pareto optimal solution sets of the network reconfiguration policy obtained by using the formula (11) as the optimization target and related parameters thereof.
TABLE 1 line importance parameters
Figure BDA0002738600830000172
Figure BDA0002738600830000181
From the two index values of each network reconfiguration policy in table 2, namely, the restoration risk and the network importance, the support degree matrices of the two indexes can be calculated as follows:
TABLE 2 Pareto optimal solution parameters for network reconfiguration strategies
Figure BDA0002738600830000182
Figure BDA0002738600830000183
Figure BDA0002738600830000191
Further, a weight matrix of the index value can be obtained
Figure BDA0002738600830000192
FXThe size of each element in (A) reflects the degree of mutual support between strategies (F)XThe larger the value of the element in (b), the greater the degree to which the corresponding policy is supported by other policies. When the index values of the strategies are different, the support degrees of the strategies by other strategies are different, and the mutual support degrees are finally reflected in the weight matrix. Finally, the support degree of each network reconfiguration strategy shown in table 2 is obtained. As can be seen from table 3, the policy 3 with the largest support degree is the optimal network reconfiguration policy, and fig. 3 is the corresponding recovery net rack, in which the solid line represents the recovered line, and the nodes connected to both ends thereof are the recovered nodes; the dashed line represents an unrecovered line.
Fig. 4 is the final restored net mount.
To illustrate the effectiveness of the proposed model and method, a comparison was made with the method of document [13 ]. The framework networks obtained using these two methods are given in table 4.
TABLE 3 support degree parameters for network reconfiguration policies
Figure BDA0002738600830000193
TABLE 4 framework network optimized by two methods
Figure BDA0002738600830000194
Figure BDA0002738600830000201
As Can be seen from table 3, compared with the method of document [13] (Zhang Can, Lin Zhenzhi, Wen fusuman, et al.). network reconfiguration two-step strategy based on regret thought (a two-stage strategy for network reconfiguration based on the regret) [ J ]. Power system Automation (Automation of Electric Power Systems),2013,37(8):46-52,75.), the skeleton network obtained by the method of the present invention has smaller recovery risk and higher skeleton network importance, and the number of lines to be recovered is also smaller, and the required line operation is less. In the network reconstruction stage, the power-off station is recovered through fewer lines, so that on one hand, the switching operation times can be reduced, and the recovery process is accelerated; on the other hand, the risk possibly brought by the line operation is also reduced. The main purpose of the network reconstruction stage is to quickly recover a power-off station through a black-start power supply, establish a stable grid structure and lay a solid foundation for the next load recovery. Therefore, compared with the method of the document [13], the framework network optimized by the method of the present invention is more favorable for system recovery after a blackout.
The application also discloses a system of the optimal network reconstruction method based on the wind power plant, and the specific working flow is shown in FIG. 5.
The system of the optimal network reconstruction method comprises a skeleton network construction module, a black start power supply module, a risk index recovery module, a line importance module and a support module, and specifically comprises the following steps:
the framework network construction module analyzes the network structure of the power grid system and the node distribution condition in the network structure to construct a framework network;
after power failure, the black start power supply module takes a hybrid power supply based on the wind power plant as a black start power supply to supply power to a power grid system;
the recovery risk index module optimizes the skeleton network by minimizing the recovery risk index;
the line importance module optimizes the skeleton network by maximizing the importance of the skeleton network by using the line importance of line contraction;
the support degree module optimizes the skeleton network by using a network reconstruction strategy based on the support degree.
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 (14)

1. A method for optimal network reconstruction based on a wind farm is characterized by comprising the following steps:
step 1: analyzing a network structure of a power grid system and topological relations of all nodes and lines by using a topological graph of a power grid, and determining a skeleton network by using the network structure and the nodes;
step 2: after power failure, the hybrid power supply based on the wind power plant is used as a black start power supply to supply power to a power grid system;
and step 3: calculating a recovery risk index of the skeleton network;
and 4, step 4: calculating the importance of the contracted line;
and 5: according to the recovery risk index obtained in the step 3 and the line importance obtained in the step 4, optimizing the skeleton network by using a particle swarm algorithm and taking the recovery risk index of the minimum network reconstruction and the line importance of the maximum skeleton network as optimization targets;
step 6: determining the skeleton network by using a network reconstruction method based on the support degree according to the skeleton network obtained in the step 5;
and 7: and 6, starting a black start power supply according to the skeleton network obtained in the step 6.
2. The method for optimal network reconfiguration based on wind farms according to claim 1, characterized in that:
in the step 2, the black start power supply comprises a wind power plant, a hydroelectric generating set or a thermal generating set, and the proportion of the black start power supply in the hybrid power supply is any proportion.
3. Method for optimal network reconfiguration based on wind farms according to claim 2, characterized in that:
the proportion of the wind power plant, the hydroelectric generating set and the thermal power generating set in the hybrid power supply is 5:3: 2.
4. The method for optimal network reconfiguration based on wind farms according to claim 1, characterized in that:
in the step 2, after the power failure, the wind power field in the hybrid power supply, the hydroelectric generating set and the thermal power generating set are started as the black start power supply in the sequence of starting the wind power field firstly, then starting the thermal power generating set and finally starting the hydroelectric generating set.
5. The method for optimal network reconfiguration based on wind farms according to claim 1, characterized in that:
in the step 3, in the framework network optimization stage, the power-off unit is recovered through the black start power supply so as to recover the backbone network frame, and the node voltage amplitude constraint and the line stability limit constraint are met:
Vi min≤Vi≤Vi max i∈ΨN
Figure FDA0002738600820000021
in the formula, psiNRepresenting a set of all nodes, ViIs the voltage amplitude, V, of node ii minAnd Vi maxLower and upper voltage level limits, Ψ, respectively allowed for node iLRepresenting a set of all lines, PlbAnd
Figure FDA0002738600820000022
respectively the active power passing on line b and its allowable upper limit.
6. Method for optimal network reconfiguration based on wind farms according to claim 5, characterized in that:
under the condition that the power generation output of the wind power plant is determined, the recovery risk index of the framework network scheme is evaluated and recovered on the basis of the node voltage amplitude out-of-limit condition and the line power out-of-limit condition, and the expression is
Figure FDA0002738600820000023
In the formula, RsFor the risk of recovery of the skeleton network s, Nb,sNumber of lines in s, Nn,sIs the number of nodes in s, NeFor Monte Carlo sampling times of wind speed, Ee,b,sAnd Ee,i,sRespectively the out-of-range percentage of the line power and the node voltage amplitude in the mth line and the ith node in s in the e time of Monte Carlo sampling;
Ee,b,sand Ee,i,sAre respectively as
Figure FDA0002738600820000024
Figure FDA0002738600820000025
In the formula, Vi rateIs the nominal voltage of node i, ΨL,sAnd ΨN,sRespectively, a line set and a node set, P, in a skeleton network slbAnd
Figure FDA0002738600820000031
respectively the active power passing on the line b and its allowable upper limit, ViIs the voltage amplitude, V, of node ii minAnd Vi maxRespectively, the lower limit and the upper limit of the voltage amplitude allowed by the node i.
7. The method for optimal network reconfiguration based on wind farms according to claim 1, characterized in that:
in step 4, the line importance based on the line shrinkage method is calculated according to the following formula:
Figure FDA0002738600820000032
wherein:
Figure FDA0002738600820000033
Figure FDA0002738600820000034
in the formula, alphabFor line importance, nbDegree of the same node into which the nodes at both ends of the line b converge after shrinkage, dbIs the average shortest distance of the network after the contraction of the line b, A is the adjacency matrix of the network containing N nodes before the contraction of the line, the nodes g and p are the nodes at the two ends of the line b, IgAnd IpN-dimensional column vectors of the g-th element and the p-th element being 1, and the remaining elements being 0,
Figure FDA0002738600820000035
and
Figure FDA0002738600820000036
are respectively IgAnd IpI is an N-dimensional column vector with all elements 1, Nb,iAnd Ψb,NRespectively the number of nodes in the network after the contraction of the line b and the collection of the nodes,
Figure FDA0002738600820000037
is the shortest distance between node pair (i, j).
8. The method for optimal network reconfiguration based on wind farms according to claim 1, characterized in that:
in the step 5, the recovery risk index for minimizing network reconstruction and the line importance of maximizing the skeleton network are used as optimization targets for determining the skeleton network,
Figure FDA0002738600820000038
in the formula Ee,bAnd Ee,iThe out-of-range percentages, R, of line power and node voltage amplitude in the e-th Monte Carlo sampling of the b-th line and the i-th node, respectivelybaseAnd alphabaseRespectively a recovery risk reference value and an importance reference value, N, of the skeleton networkb,sNumber of lines in s, NeFor Monte Carlo sampling times of wind speed, NbFor the number of nodes in the network after the contraction of line b, NnFor the number of nodes in the network after the shrinkage of the line N, Nb,iFor the ith node, n, in the network after the contraction of the line bbThe degree of the same node that the two end nodes of the line b are converged into after shrinkage,
Figure FDA0002738600820000041
is the shortest distance, Ψ, between node pairs (i, j)b,sFor a set of lines in the skeleton network s, Ψb,NIs a collection of nodes in the skeleton network N.
9. The method for optimal network reconfiguration based on wind farms according to claim 1, characterized in that:
in the step 6, the data obtained by simultaneously measuring the same measured parameter by m sensors is x1,x2,x3,…,xk…,xmThe set of which is Ω x,
characterization of xkIs x bygDegree of support, support function SkgComprises the following steps:
Figure FDA0002738600820000042
measurement data x from m sensors1,x2,x3,…,xk…,xmObtaining the support degreeThe matrix is:
Figure FDA0002738600820000043
10. the method for optimal network reconfiguration based on wind farms according to claim 9, characterized in that:
the comprehensive support degree passes through the support degree Sk1,Sk2,…,SkmIs synthesized to obtaink＝Z1Sk1+Z2Sk2+…+ZmSkmIs xkComprehensive support of (3), Z in the eigenvector Z1,Z2,…,ZmIs a group of nonnegative numbers as the weight of each support function value when calculating the comprehensive support value,
H＝SZ
wherein H ═ H1,h2,…,hk,…,hm]T，Z＝[Z1,Z2,…,Zk,…,Zm]T。
11. Method for optimal network reconfiguration based on wind farms according to claim 10, characterized in that:
solving the S maximum characteristic root lambda max according to the Perron-Frobenius theorem, wherein the corresponding characteristic vector Z is a positive characteristic vector,
due to the fact that
SZ＝λmaxZ
Then
H＝λmaxZ
Normalizing the elements in Z to obtain the weight vector of the measurement data as
Figure FDA0002738600820000051
12. The method for optimal network reconfiguration based on wind farms according to claim 11, characterized in that:
solving according to the multi-target framework network optimization model to obtain a group of Pareto optimal solutions;
assuming that w strategies including r indexes exist in the Pareto optimal solution set, the index value matrix is represented as
Figure FDA0002738600820000052
In the formula, xu,qDefining a value of a qth index representing the u-th strategy, defining a modified index value Xu,qComprises the following steps:
Figure FDA0002738600820000053
the modified index value matrix is:
Figure FDA0002738600820000054
13. the method for optimal network reconfiguration based on wind farms according to claim 12, characterized in that:
from X1,q,X2,q,…,Xu,q,…,Xw,qObtaining a support matrix of the qth index:
Figure FDA0002738600820000061
maximum characteristic root for calculating support matrix and characteristic vector Z thereofqAnd normalizing the q index value to obtain a weight vector F corresponding to the q index valueq＝[F1,q,F2,q,…,Fw,q]And sequentially calculating a weight vector for each index to obtain an index value weight matrix as follows:
Figure FDA0002738600820000062
and obtaining the support degree of each network reconstruction strategy by the index value matrix:
Figure FDA0002738600820000063
in the formula DuReconfiguration of policy support for the u-th network, IuIs a w-dimensional column vector with the u-th element being 1 and the other elements being 0.
14. A system using the method for optimal network reconfiguration based on the wind farm according to any one of claims 1 to 13, wherein the system of the method for optimal network reconfiguration comprises a skeleton network construction module, a black start power module, a risk index recovery module, a line importance module and a support module, and is characterized in that:
the skeleton network construction module analyzes a network structure of the power grid system and the node distribution condition in the network structure to construct a skeleton network;
after the black start power supply module is powered off, the hybrid power supply based on the wind power plant is used as a black start power supply to supply power to a power grid system;
the risk indicator recovery module optimizes the skeleton network by minimizing the risk indicators;
the line importance module optimizes the skeleton network by maximizing the importance of the skeleton network by using the line importance of line contraction;
the support degree module optimizes the skeleton network by using a network reconstruction strategy based on the support degree.
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CN112818489A (en) * 2021-02-23 2021-05-18 三一重能股份有限公司 Wind turbine generator design method and system
CN112884607A (en) * 2021-03-11 2021-06-01 国网陕西省电力公司电力科学研究院 Computing method and computing system for in-zone black start recovery network

Cited By (4)

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Publication number Priority date Publication date Assignee Title
CN112818489A (en) * 2021-02-23 2021-05-18 三一重能股份有限公司 Wind turbine generator design method and system
CN112818489B (en) * 2021-02-23 2023-09-22 三一重能股份有限公司 Wind turbine generator design method and system
CN112884607A (en) * 2021-03-11 2021-06-01 国网陕西省电力公司电力科学研究院 Computing method and computing system for in-zone black start recovery network
CN112884607B (en) * 2021-03-11 2023-08-01 国网陕西省电力公司电力科学研究院 Computing method and computing system for black start restoration network in zone

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