CN110034581B - Interval electrical medium number vulnerability assessment method for power system under wind power grid-connected condition - Google Patents
Interval electrical medium number vulnerability assessment method for power system under wind power grid-connected condition Download PDFInfo
- Publication number
- CN110034581B CN110034581B CN201910331619.3A CN201910331619A CN110034581B CN 110034581 B CN110034581 B CN 110034581B CN 201910331619 A CN201910331619 A CN 201910331619A CN 110034581 B CN110034581 B CN 110034581B
- Authority
- CN
- China
- Prior art keywords
- node
- interval
- power
- line
- vulnerability
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 41
- 238000012163 sequencing technique Methods 0.000 claims abstract description 13
- 230000010354 integration Effects 0.000 claims abstract description 12
- 239000011159 matrix material Substances 0.000 claims description 22
- 230000005540 biological transmission Effects 0.000 claims description 15
- 238000005192 partition Methods 0.000 claims description 14
- 238000010248 power generation Methods 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 7
- 150000001875 compounds Chemical class 0.000 claims description 6
- 238000011160 research Methods 0.000 claims description 6
- 238000002347 injection Methods 0.000 claims description 5
- 239000007924 injection Substances 0.000 claims description 5
- 238000010586 diagram Methods 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000013178 mathematical model Methods 0.000 claims description 2
- 238000011161 development Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- QERYCTSHXKAMIS-UHFFFAOYSA-M thiophene-2-carboxylate Chemical compound [O-]C(=O)C1=CC=CS1 QERYCTSHXKAMIS-UHFFFAOYSA-M 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 238000012038 vulnerability analysis Methods 0.000 description 1
Images
Classifications
-
- 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
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
-
- H02J3/386—
-
- 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
- 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
- H02J3/48—Controlling the sharing of the in-phase component
-
- 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]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses a method for evaluating interval electrical medium number vulnerability of a power system under wind power grid-connected condition. The method adopts interval numbers to describe the fluctuation of wind power grid-connected power, provides an interval electrical medium number method, sequences line and node interval electrical medium numbers respectively based on a possibility interval sequencing method, and identifies fragile lines and nodes of wind power grid-connected. The method can effectively and accurately identify the fragile lines and nodes of the wind power integration, compared with the traditional electric power system vulnerability assessment method, the method is more in line with the actual operation condition of the electric power system after the wind power integration, and has important guiding significance for avoiding the blackout of the wind power integration electric power system.
Description
Technical Field
The invention relates to the field of new energy grid-connected power systems, in particular to a method for evaluating interval electrical energy medium vulnerability of a power system under wind power grid-connected condition.
Background
With the rapid development of the power grid, the topological structure and the physical characteristics of the power grid become increasingly complex, so that the power grid blackout probability and risk are greatly increased. For example, 11/10/2009, brazil suffered from a large-scale power outage nationwide, with a loss load exceeding 2400 ten thousand kilowatts, causing severe impact and huge economic losses. In 2015, 31 months and 3 days, turkish has a serious blackout accident. In 2018, 3, 21 and the large-area power failure of the Brazil power grid is caused by chain reaction caused after the overload protection action of the circuit breaker. Research shows that the major power failure accidents are mostly caused by cascading failures caused by a few key nodes and line failures in the system, and the key nodes and the lines play a key role in stable operation of the power system. Therefore, identifying the fragile nodes and lines and performing grid vulnerability assessment are of great significance for ensuring safe and stable operation of the grid.
At present, a plurality of scholars conduct extensive research on a power grid vulnerability assessment method, and provide a series of indexes for assessing the vulnerability of a power system, such as risk indexes, tidal current entropy indexes, vulnerability indexes defined based on a static energy function method and the like. In order to research the power grid cascading failure mechanism, a cascading failure model is widely applied to assessment of power grid vulnerability by combining power grid flow transfer and distribution characteristics. The complex network theory is widely applied to power grid vulnerability assessment, and the medium degree and betweenness of the complex network theory are used for identifying central nodes and key lines. On the basis, students propose electrical betweenness and network efficiency indexes to identify vulnerable lines and nodes of the power grid while considering network topology and electrical characteristics. The above studies are mainly directed to conventional power systems without considering fluctuating new energy.
In recent years, with the continuous and rapid development of wind power, the penetration rate of the wind power in a power grid is continuously improved. However, because wind power has randomness and volatility, the electrical performance of the connection node such as injection power, node voltage, power flow of the power grid and the like will fluctuate correspondingly. And the grid vulnerability assessment usually depends on the result of the power flow, so the grid vulnerability indexes of each line and each node also change along with the fluctuation of the power flow. Therefore, after the wind power is connected into the power grid, how to describe the fluctuation and influence of the wind power and how to accurately and quickly identify the fragile lines and nodes in the power grid are very important.
Disclosure of Invention
Aiming at the problems in the technical background, the invention provides a method for evaluating the interval electrical factor vulnerability of an electric power system under wind power integration.
The technical scheme for solving the problems is as follows: the interval number is adopted to describe the fluctuation of the wind power grid-connected power, the vulnerability index of the electrical medium number between the line and the node of the power grid is provided, and the electrical medium number between the line and the node is sorted respectively based on a probability interval sorting method to identify the vulnerable line and the node.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
step 1: providing an interval electrical medium number vulnerability index;
the method comprises the following steps of (1) representing a fluctuation interval of wind power by using an interval number, and providing an electric medium number vulnerability index between a power grid line and a node interval, wherein the step 1 specifically comprises the following steps:
1-1: establishing an interval direct current power flow model of wind power grid connection;
uncertainty of wind power generationFluctuations in the grid power flow distribution can be caused. The output of the fan is affected by wind direction, wind speed, temperature and other factors, and the real output power is difficult to be represented by accurate numbers. The number of intervals has good performance when the uncertainty problem is processed, and uncertainty information of the number of intervals is expressed in the form of the number of intervals. Therefore, the invention outputs the interval output power of the fan
Described for formula (1):
in the formula (I), the compound is shown in the specification,
and
the lower limit and the upper limit of the output power of the fan interval are respectively.
In order to calculate the power flow of the transmission line and the nodes, the system injection power P is expressed by interval numbers in consideration of the fluctuation of wind power. Therefore, an interval direct current power flow model is adopted. The invention describes a mathematical model of direct current power flow between system sections with N nodes and M branches,
in the formula (I), the compound is shown in the specification,
active power in an interval of the injection node comprises a load node and a generator node; b is a node admittance matrix of the network;
is the node voltage phase angle vector; i is an Nx 1 order vector with all 1 elements, I T Represents the transpose of matrix I; b is -1 Is the inverse of matrix B;
representing the active power of an interval from the node i to the node j; b is L Is a diagonal matrix of matrix B; a is an M × N order connection matrix;
and
respectively, the lower limit and the upper limit of the active power value of the branch interval.
Representing the maximum transmission power limit of the branch from node i to node j.
1-2: providing interval electrical medium number vulnerability indexes considering wind power access;
1-2-1: providing a line interval electrical medium number vulnerability index considering wind power access; (ii) a
In order to research the line vulnerability of the wind power grid-connection, it is proposed that the line interval electrical medium number from the node i to the node j (hereinafter, reference line i-j) is defined as (3).
Where G and L are the set of generator nodes and load nodes, respectively.
Represents the section active power generated on the line i-j when injecting the unit active power (P =1 into the generation node m, P = -1 into the load node n). W is a group of mn Is a weighting coefficient representing the maximum available transmission power between node m and node n. And W mn ＝min(S m ,S n ) Wherein S is m Represents the rated power generation capacity, S, of the node m n Representing the maximum load demand of node n. If the power generation node m contains a fan, then
At this time, W mn Expressed in terms of the number of intervals, then,
if the load node n contains a fan, then
Then W mn In order to realize the purpose,
1-2-2: providing a node interval electrical medium vulnerability index considering wind power access;
in order to better identify the fragile node of the wind power integration, firstly, an index which can reflect the topology and the electrical characteristics of the power grid at the same time must be selected, and the difference between the fragile node and other nodes is highlighted. Therefore, according to the mathematical relationship between node betweenness and edge betweenness in the complex network and the interval direct current power flow model, the proposed node interval electrical betweenness can be expressed as,
in the formula (I), the compound is shown in the specification,
represents the interval electrical permittivity of the node k;
is the interval electrical permittivity of the line k-l; f (k) is a line set connected to node k; w is a kn The transmission power weight is the transmission power weight when unit active power is injected between a power generation node k and an arbitrary load node n; w is a mk Is at any power generation node m and negativeAnd the transmission power weight when unit active power is injected between the charge nodes k.
Step 2: an interval number sequencing method based on the possibility degree is provided, the line and node interval electrical medium numbers are sequenced respectively, and the fragile line and the node are identified; the step 2 specifically comprises:
2-1: providing a section electric medium number ordering method based on the possibility degree;
the vulnerability indexes obtained by the formulas (3) and (4) are the number of intervals. The number of intervals is not suitable for direct comparison and must be converted before comparison can be performed. Therefore, the invention adopts a probability interval number sequencing method to sequence the vulnerability indexes of all lines and nodes.
Note the book
Balance with scale
Is a number of intervals; when the temperature is higher than the set temperature
When the number of the intervals is the same or one is the number of the intervals, the
And records
Then
The probability of (c) is:
for the interval vulnerability index value in question,
i belongs to {1,2, ·, N }, and the interval numbers are compared two by two,the likelihood matrix P = (P) formed by matrix elements using the value obtained by equation (5) ij ) N×N And calculating the ranking vector omega of the possibility degree matrix P by using the ranking formula (6) i And sorts its size.
2-2: the vulnerability assessment process of the interval electrical medium method;
to sum up, the vulnerability assessment process of the interval electrical betweenness method comprises the following steps: first, the output power of the fan is expressed as the number of intervals. Solving the interval active power of each branch according to the interval direct current power flow equation
The system partition is determined by analyzing network connectivity, and in the same partition, a pair of 'power generation-load node pairs' (m, n) is arbitrarily selected and unit active power is injected between them. And (4) calculating the sum of the interval active power generated by the power generation-load node pair on the line according to the step (3). And finally, sequencing the obtained interval electrical betweenness by an interval number sequencing method based on the possibility. The interval electrical betweenness-based line and node vulnerability assessment process is consistent. The detailed sequencing process of the interval electrical dielectric constant is as follows:
1) And solving the interval active power of each branch circuit based on the interval direct current power flow network model. Simplifying the power system into an authorized network diagram consisting of N nodes and M branches to form an adjacency matrix and obtain the power grid topology model.
2) And judging the network connectivity and determining the partition of each node of the power grid.
3) A node admittance matrix for each partition is formed.
4) A 'generation-load node pair' (m, n) is arbitrarily selected, and whether the generation-load node pair is in the same partition or not is judged. If the (m, n) is not in the same partition, reselecting; if (m, n) is in the same partition, active power P =1 and P = -1 are injected into the power generation node m and the load node n, respectively, and the section active power generated on all lines and nodes is calculated according to equations (2) and (3).
5) And (5) after traversing all the power generation-load node pairs, calculating the interval electrical betweenness according to the formulas (3) and (4).
6) And sequencing the obtained interval electrical permittivity results by adopting an interval number sequencing method based on the possibility.
7) And outputting a final sequencing result, and terminating the algorithm to obtain a vulnerability index result of the wind power grid-connected power system.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the method considers the vulnerability of the power grid under the uncertain wind power grid connection, accords with the energy development direction of new energy grid connection at the present stage, and is the safety requirement for evaluating the future development of the intelligent power grid of new energy grid connection;
2. the method overcomes the limitation that the power grid vulnerability analysis is generally only limited to the flow direction of the tidal current between lines along the shortest path in the traditional complex network model, and meanwhile, the method comprehensively considers the topological structure, the power generation capacity, the load capacity and the distribution characteristics of the power grid, and can more truly and effectively evaluate the power grid vulnerability.
3. The system key nodes and lines discovered by the research of the method under the condition of fluctuating wind power integration have certain guiding significance for avoiding the power system blackout accident under the condition of large-scale high wind power permeability integration.
Drawings
FIG. 1 is a flow chart of the present invention.
FIG. 2 is a flowchart of the interval electrical interface arrangement of the present invention.
FIG. 3 is a topology diagram of an IEEE-39 node system.
FIG. 4 is a diagram illustrating the result of electrical betweenness between the IEEE-39 node system lines.
FIG. 5 is a graph showing the results of electrical betweenness between nodes in an IEEE-39 node system.
Detailed Description
The invention is further described below with reference to the figures and examples.
By taking an IEEE-39 example node system in the attached figure 3 as an example, comprehensive analysis is carried out on the line vulnerability and the node vulnerability of the wind power grid connection, and the effectiveness of the method applied in the vulnerability assessment field is verified. The method comprises the following specific steps:
step 1: simulating the weak line identification of the wind power integration according to the topological graph of the IEEE-39 node system;
the IEEE-39 node system comprises 46 transmission lines and 39 nodes, of which there are 10 generators, 19 load nodes and 10 transmission nodes, and furthermore, node 31 is a balanced node.
The invention sets the line capacity to be 1.4 times of the initial power flow, and assumes that the fan is connected to the node 32, and the fluctuation interval of the injected active power is [300,900] MW in consideration of the fluctuation of the wind power. According to the line interval electric betweenness model and the interval number-based sorting method, the sorting result is shown in the table 1, wherein the line with the top 20 is ranked; the results of the electrical interface between the circuit sections are shown in FIG. 3.
TABLE 1 IEEE-39 node system line interval electrical betweenness (first twenty)
As can be seen from table 1, the lines with higher vulnerability are mainly lines connecting multiple nodes, connecting generator outlets, and connecting heavy-load nodes, and these lines have higher topological importance, and are responsible for larger power transmission and loading tasks. Once these lines are protected from being broken, the lines will stop functioning, which will severely affect multiple nodes, resulting in extensive blackouts of the grid and more power flow interruptions affecting the grid. In the top ten of the ranking results, lines 29-38, 22-35, 19-33 and 10-32 are the exit critical lines for the generators or fans, and these line failures will result in a portion of the generators or fans outputting power and an insufficient supply of power to a portion of the system. The lines 16-17 are located at the middle position of the system topology, and the lines connecting a plurality of nodes have a high degree, and if the lines fail, the power of the generators 33, 34, 35 and 36 cannot be normally transmitted, so that the power supply of the system power supply is insufficient. In a word, the vulnerability identification method provided by the invention can not only reflect the physical properties of the power grid topology, but also well reflect the electrical performance of the fluctuating wind power grid.
Step 2: simulating the weak node identification of the wind power integration according to the topological graph of the IEEE-39 node system;
in order to further study the vulnerability assessment of the wind power grid-connected power system, the vulnerability of the node of a plurality of fan units connected into the power grid is analyzed and considered. Assuming that the fan is connected to the node 2 and the node 14, the active power of the fluctuation interval is [100,300] MW and [300,500] MW respectively. Calculating the active power of the interval according to the step (2), and performing simulation analysis to obtain a sequencing result, wherein the distribution of the first twenty nodes is shown in a table 2; the results of the electrical parameters between the node sections are shown in FIG. 4.
TABLE 2 IEEE-39 node system node interval electrical betweenness number (first twenty)
As is clear from table 2, the nodes 39, 9, 1, 4 and 16 are the most important five nodes. Where node 39 is an important generator node and nodes 9, 1 and 4 are nodes near the power supply, generator or fan, these nodes are particularly important in terms of power supply, where a fault will result in an under-supply of grid power, thereby affecting the balance of system power. Node 16 is a significant hub node that serves as a significant power delivery node for generator nodes 33, 34, 35 and 36 and has a high degree; if the system fails, the power flow distribution of the system can cause great changes, which causes power imbalance of adjacent nodes and even cascading failure of the system.
In conclusion, the vulnerability node identification method disclosed by the invention not only comprises the structural vulnerability nodes in the network topology, but also comprises the electrical performance nodes in the system, so that the vulnerability of the wind power grid-connected power system is more comprehensively and effectively evaluated. The method provided by the invention can be used for quickly and accurately identifying the weak nodes and lines of the system after wind power integration, and has a certain guiding function for efficiently screening weak links of the system and judging the weakness degree of the system by power operation managers and improving the robustness and safety of the system.
Claims (1)
1. The interval electrical medium number vulnerability assessment method for the power system under the grid-connected wind power comprises the following steps:
step 1: describing the fluctuation of the wind power grid-connected power by using the interval number, and determining the vulnerability index of the electrical betweenness between the power grid line and the node interval;
step 2: sorting the line and node interval electrical betweenness respectively based on a possibility degree interval number sorting method, and identifying a fragile line and a fragile node;
the step 1 specifically comprises:
1-1: establishing an interval direct current power flow model of wind power integration;
wind power uncertainty may cause fluctuation of power flow distribution of a power grid, the output of a fan is influenced by wind direction, wind speed, temperature and other factors, and the output real power is difficult to be represented by an accurate numerical value; the number of intervals has good performance when the uncertainty problem is processed, and uncertainty information of the number of intervals is expressed in the form of the number of intervals; therefore, the interval output power of the fan is adjusted
Described for formula (1):
in the formula (I), the compound is shown in the specification,
and
respectively the lower limit and the upper limit of the output power of the fan interval;
in order to calculate the power flow of the transmission line and the nodes, the fluctuation of wind power is considered, and the system injection power P is represented by interval numbers; therefore, by adopting an interval direct current power flow model, a mathematical model of the interval direct current power flow of the wind power grid-connected power system with N nodes and M branches is described as follows,
in the formula (I), the compound is shown in the specification,
active power in an interval of the injection node comprises a load node and a generator node; b is a node admittance matrix of the network;
is the node voltage phase angle vector; i is an Nx 1 order vector with all 1 elements, I T Represents the transpose of matrix I; b is -1 Is the inverse of matrix B;
representing the active power of the interval from the node i to the node j; b is L Is a diagonal matrix of matrix B; a is a M × N order connection matrix;
and
respectively is the lower limit and the upper limit of the active power value of the branch interval;
represents the maximum transmission power limit of the branch from node i to node j;
1-2: determining a line interval electrical medium number vulnerability index considering wind power access;
in order to research the line vulnerability of the wind power grid-connection, the electrical medium number of a line interval from a node i to a node j is defined as (3), and the line from the node i-j is simply referred to as a reference line in the following;
wherein G and L are the set of generator nodes and load nodes, respectively;
representing the interval active power generated on the line i-j when the unit active power is injected, injecting P =1 into the power generation node m and P = -1 into the load node n; w mn Is a weighting coefficient representing the maximum available transmission power between node m and node n; and W mn ＝min(S m ,S n ) Wherein S is m Represents the rated power generation capacity, S, of the node m n Representing the maximum load demand of node n; if the power generation node m contains a fan, then S m ＝[P w - ,P w + ]At this time, W mn Expressed in terms of the number of intervals, then,
if the load node n contains a fan, then S n ＝[P w - ,P w + ]Then W is mn In order to realize the purpose,
1-3: determining a node interval electrical medium vulnerability index considering wind power access;
in order to better identify the fragile node of the wind power integration, firstly, an index capable of reflecting the topology and the electrical characteristics of the power grid at the same time needs to be selected, and the difference between the fragile node and other nodes is highlighted; therefore, according to the mathematical relationship between node betweenness and edge betweenness in the complex network and the interval direct current power flow model, the node interval electrical betweenness can be expressed as,
in the formula (I), the compound is shown in the specification,
the interval electrical medium number of the node k is represented;
is the interval electrical permittivity of the line k-l; f (k) is a line set connected to node k; w is a kn The transmission power weight is the transmission power weight when unit active power is injected between a power generation node k and any load node n; w is a mk Is the transmission power weight when unit active power is injected between any power generation node m and load node k;
the step 2 specifically comprises:
2-1: an interval electric betweenness ordering method based on the possibility degree;
the vulnerability index obtained by the formulas (3) and (4) is the number of intervals; the interval number is not suitable for direct comparison, and the interval number can be compared only by conversion; therefore, the vulnerability indexes of all lines and nodes are sequenced by adopting a probability interval number method;
note the book
Balance with scale
Is an interval electrical number; when the temperature is higher than the set temperature
When the number of the intervals is the same or one is the number of the intervals, the
And record
Then
The probability of (c) is:
with respect to the indicator of the vulnerability,
the interval numbers are compared two by two, and the value obtained by the formula (5) is a probability matrix P = (P) formed by matrix elements ij ) N×N And calculating the rank vector omega of the probability matrix P by using the rank formula (6) i Sorting the sizes of the components;
2-2: the vulnerability assessment process of the interval electrical medium method;
the vulnerability assessment process of the interval electrical betweenness method comprises the following steps: firstly, expressing the output power of a fan as the number of intervals; solving the interval active power of each branch according to the interval direct current power flow equation
Determining the partition of the system by analyzing the network connectivity, randomly selecting a pair of 'power generation-load node pairs' (m, n) in the same partition, and injecting unit active power between the pair of 'power generation-load node pairs' (m, n); according to (3) calculatingThe sum of the interval active power generated by the 'generation-load node pair' on the line; finally, sequencing the obtained interval electrical betweenness number based on an interval number sequencing method of the possibility degree; the line and node vulnerability assessment process based on the interval electrical betweenness is consistent; the detailed flow of interval electrical medium vulnerability assessment is as follows:
1) Solving the interval active power of each branch circuit based on an interval direct current power flow network model; simplifying the power system into an authorized network diagram consisting of N nodes and M branches to form an adjacency matrix and obtain a power grid topology model;
2) Judging the network connectivity and determining the partition of each node of the power grid;
3) Forming a node admittance matrix of each partition;
4) Randomly selecting a 'power generation-load node pair' (m, n), and judging whether the nodes are in the same partition or not; if the (m, n) is not in the same partition, reselecting; if (m, n) is in the same partition, injecting active power P =1 and P = -1 to the power generation node m and the load node n respectively, and calculating interval active power generated on all lines and nodes according to equations (2) and (3);
5) After traversing all the power generation-load node pairs, calculating the interval electrical betweenness according to the formulas (3) and (4);
6) Sorting the obtained interval electrical betweenness results by adopting an interval number sorting method based on the possibility;
7) And outputting a final sequencing result, and terminating the algorithm to obtain a vulnerability index result of the wind power grid-connected power system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910331619.3A CN110034581B (en) | 2019-04-23 | 2019-04-23 | Interval electrical medium number vulnerability assessment method for power system under wind power grid-connected condition |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910331619.3A CN110034581B (en) | 2019-04-23 | 2019-04-23 | Interval electrical medium number vulnerability assessment method for power system under wind power grid-connected condition |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110034581A CN110034581A (en) | 2019-07-19 |
| CN110034581B true CN110034581B (en) | 2023-01-06 |
Family
ID=67239915
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910331619.3A Active CN110034581B (en) | 2019-04-23 | 2019-04-23 | Interval electrical medium number vulnerability assessment method for power system under wind power grid-connected condition |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110034581B (en) |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111092453B (en) * | 2019-12-30 | 2023-04-07 | 南京工程学院 | Power grid key line identification method for multipoint access photovoltaic power supply |
| CN111313408B (en) * | 2020-03-05 | 2022-04-29 | 西南交通大学 | Power grid fragile line identification method considering transient energy correlation |
| CN111523252B (en) * | 2020-06-11 | 2021-02-05 | 西华大学 | Vulnerable stream transmission capturing method fusing fault transfer factors |
| CN112231705B (en) * | 2020-09-23 | 2023-07-28 | 四川中电启明星信息技术有限公司 | Information system reliability improving method based on secondary division |
| CN112487658B (en) * | 2020-12-14 | 2022-09-16 | 重庆邮电大学 | Method, device and system for identifying key nodes of power grid |
| CN113191643B (en) * | 2021-05-06 | 2022-04-01 | 四川大学 | Method for identifying fragile line of electric-gas interconnection system |
| CN113725855B (en) * | 2021-09-03 | 2022-09-16 | 湘潭大学 | Electric DebtRank algorithm-based electric power system fragile line identification method |
| CN115173413B (en) | 2022-08-10 | 2023-06-06 | 湖南科技大学 | Electric-betting-based power grid fragile line identification method |
| CN117134502B (en) * | 2023-10-23 | 2023-12-22 | 国网四川雅安电力(集团)股份有限公司荥经县供电分公司 | Power data monitoring method and monitoring system |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103066620B (en) * | 2012-12-24 | 2014-10-22 | 中国电力科学研究院 | Design method of automatic generation control model under intermittent energy grid-connection |
| CN107846014B (en) * | 2017-11-09 | 2021-03-30 | 电子科技大学 | Power grid probability vulnerability assessment method based on random features and series calculation |
| CN108667005B (en) * | 2018-04-13 | 2021-11-23 | 湘潭大学 | Power grid static and dynamic combination vulnerability assessment method considering new energy influence |
-
2019
- 2019-04-23 CN CN201910331619.3A patent/CN110034581B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110034581A (en) | 2019-07-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110034581B (en) | Interval electrical medium number vulnerability assessment method for power system under wind power grid-connected condition | |
| JP7269592B2 (en) | Vulnerable Node Identification Method in Active Grids Considering New Energy Impacts | |
| Fang et al. | Power system structural vulnerability assessment based on an improved maximum flow approach | |
| Rocchetta et al. | Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions | |
| CN108899904A (en) | A kind of alternating current-direct current large power grid cascading failure method for fast searching and system | |
| CN105656040B (en) | A kind of power system restoration power computational methods for considering black starting-up process | |
| CN106529791B (en) | The appraisal procedure of branch different degree in a kind of electric system | |
| CN109687438B (en) | Power grid fragile line identification method considering high-speed rail impact load effect | |
| CN104901308B (en) | Power system critical circuits discrimination method | |
| CN110350522B (en) | Electric power system fragile line identification method based on weighted H index | |
| CN113722868A (en) | Multi-index power grid node vulnerability assessment method fusing structure hole characteristics | |
| Su et al. | Identification of critical nodes for cascade faults of grids based on electrical PageRank | |
| Chen et al. | Real‐time risk assessment of cascading failure in power system with high proportion of renewable energy based on fault graph chains | |
| Ahmed et al. | Deep Neural Network Based Fault Classification and Location Detection in Power Transmission Line | |
| Al Ameri et al. | Genetic algorithm for optimal sizing and location of multiple distributed generations in electrical network | |
| CN117726191A (en) | Method, system and medium for generating multiple fault sets of power grid by considering external disasters | |
| Karapidakis | Machine learning for frequency estimation of power systems | |
| CN113077150B (en) | Reliability evaluation method and system for offshore multi-platform interconnected power system | |
| CN111313408B (en) | Power grid fragile line identification method considering transient energy correlation | |
| Latorre et al. | A stochastic-robust approach to hierarchical generation-transmission expansion planning | |
| Li et al. | Interval Electrical Betweenness Method for Power Grid Vulnerability Assessment Considering Wind Power | |
| Nasiruzzaman et al. | A new model of centrality measure based on bidirectional power flow for smart and bulk power transmission grid | |
| Zhu et al. | Cascading failure chains screening model of AC-DC hybrid power system | |
| CN111092453A (en) | Power grid key line identification method for multipoint access photovoltaic power supply | |
| Zhao et al. | Comprehensive Identification of Critical Line in Power System with Large-Scale Wind Farms Integration |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |