CN110675043A - Method and system for determining power grid power failure key line based on cascading failure model - Google Patents
Method and system for determining power grid power failure key line based on cascading failure model Download PDFInfo
- Publication number
- CN110675043A CN110675043A CN201910874650.1A CN201910874650A CN110675043A CN 110675043 A CN110675043 A CN 110675043A CN 201910874650 A CN201910874650 A CN 201910874650A CN 110675043 A CN110675043 A CN 110675043A
- Authority
- CN
- China
- Prior art keywords
- line
- simulation
- power grid
- power
- failure
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004088 simulation Methods 0.000 claims abstract description 108
- 238000004364 calculation method Methods 0.000 claims description 13
- 230000001186 cumulative effect Effects 0.000 claims description 10
- 238000012502 risk assessment Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 3
- 230000009191 jumping Effects 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000009826 distribution Methods 0.000 description 17
- 230000009466 transformation Effects 0.000 description 6
- 230000009286 beneficial effect Effects 0.000 description 4
- 239000011159 matrix material Substances 0.000 description 4
- 230000002354 daily effect Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 238000010248 power generation Methods 0.000 description 2
- 238000012614 Monte-Carlo sampling Methods 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012954 risk control Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/067—Enterprise or organisation modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02H—EMERGENCY PROTECTIVE CIRCUIT ARRANGEMENTS
- H02H7/00—Emergency protective circuit arrangements specially adapted for specific types of electric machines or apparatus or for sectionalised protection of cable or line systems, and effecting automatic switching in the event of an undesired change from normal working conditions
- H02H7/26—Sectionalised protection of cable or line systems, e.g. for disconnecting a section on which a short-circuit, earth fault, or arc discharge has occured
- H02H7/28—Sectionalised protection of cable or line systems, e.g. for disconnecting a section on which a short-circuit, earth fault, or arc discharge has occured for meshed systems
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Educational Administration (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a method for determining a power failure key line of a power grid based on a cascading failure model, which comprises the steps of obtaining a power grid model to be calculated and corresponding relevant model information thereof, and obtaining a topological structure of the power grid according to the power grid model and the corresponding relevant model information thereof; and determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining the line number of the disconnected line output by each day of simulation after the total number of days of simulation is finished, further counting the number of the line numbers of the disconnected line output by each day of simulation, and outputting the disconnected line with the maximum number of the line numbers as the power grid power failure key line. By implementing the method and the device, the key line which has important influence on the cascading failure of the power system can be identified, and data support is provided for line capacity expansion.
Description
Technical Field
The invention relates to the technical field of power risk control, in particular to a method and a system for determining a power grid power failure key line based on a cascading failure model.
Background
At present, a power grid enters a rapid development period of cross-large-area interconnection and ultra-high and extra-high voltage alternating current and direct current hybrid power transmission, and the complexity of the power grid is increased rapidly, so that the risk of large-area power failure accidents caused by cascading failures is increased gradually.
In order to facilitate the study of the cascading failures of the power grid, different cascading failure theoretical models and power failure risk indexes of the power system are provided. The model and the method are started from elements, the overall reliability of the system is measured through a certain computing technology, and the physical significance of the model and the method is clear.
However, for a large power grid system, on one hand, the severity of a blackout accident can be covered to a certain extent by a traditional probabilistic index, and on the other hand, the exponential increase of the calculation complexity is caused by the increase of the dimension, so that the online application is difficult. Therefore, the key lines which have important influence on the cascading failure of the power system cannot be identified, and the weak links of the power system cannot be reconstructed.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a method and a system for determining a power failure key line of a power grid based on a cascading failure model, which can identify a key line that has an important influence on cascading failures of a power system, provide data support for line capacity expansion, and implement transformation of weak links of the power system.
In order to solve the technical problem, an embodiment of the present invention provides a method for determining a power failure key line of a power grid based on a cascading failure model, where the method includes the following steps:
acquiring a power grid model to be calculated and relevant model information corresponding to the power grid model, and acquiring a topological structure of a power grid according to the power grid model and the relevant model information corresponding to the power grid model;
and determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining the line number of the disconnected line output by each day of simulation after the total number of days of simulation is finished, further counting the number of the line numbers of the disconnected line output by each day of simulation, and outputting the disconnected line with the maximum number of the line numbers as the power grid power failure key line.
The relevant model information comprises reference voltage of a power grid, node information, generator information, line information and/or transformer branch information.
The method comprises the following specific steps of determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, and obtaining the line number of a disconnected line output by each day of simulation after the total number of days of simulation is finished:
step S21, acquiring the total number of simulated days, and judging whether the total number of simulated days is 0;
step S22, if the total number of days of the simulation is not 0, entering a cascading failure simulation mode to carry out simulation, outputting a line number of a cut-off line, and returning to the step S21 after the total number of days of the simulation is reduced by one; the specific steps of entering a lock fault simulation mode for simulation and outputting a line number of a cut-off line comprise:
step S221, initializing the on-off probability of each line in the topological structure of the power grid to be P, initializing the number of the on-off lines to be 1, randomly sampling random numbers R which are uniformly distributed in a [0,1] interval, and determining the on-off lines with the on-off probability being greater than the random numbers R as the initial on-off lines; wherein P is between [0,1 ];
step S222, distinguishing each electric island in the topological structure of the power grid according to the initial cut-off line, and establishing a corresponding load flow calculation model for each electric island;
step S223, calculating a load flow calculation model of each electrical island by using an optimal direct current load flow method to obtain direct current load flows of lines contained in each electrical island;
step S224, judging whether the direct current power flow of one line in each electric island is larger than a preset heavy load threshold value; if yes, go to step S225; if not, jumping to step S227;
step S225, the lines with the direct current power flows larger than the preset heavy load threshold value are all cut off according to the probability of 1-beta, and the adjacent lines of each line with the direct current power flows larger than the preset heavy load threshold value are all cut off according to the random cut-off probability tau; wherein, beta and tau are both preset fixed values;
step S226, when the corresponding electrical island has an island after each line with the direct current power flow larger than the preset overloading threshold is cut off, resetting each island in the same electrical island as the electrical island, and returning to the step S223;
step S227, when the corresponding electric island does not have an island after each line with the direct current flow larger than the preset heavy load threshold value is cut off, taking each line with the direct current flow larger than the preset heavy load threshold value as a final cut-off line in the day and outputting the corresponding line number;
and step S23, if the total number of the simulated days is 0, outputting the line number of the broken line output by each day of simulation.
Wherein the method further comprises:
determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining a cascading failure accident chain and corresponding failure load loss corresponding to a broken line output by each day of simulation after the total number of days of simulation is finished, carrying out cumulative probability statistics according to the obtained failure load loss output by each day of simulation to obtain a probability density function of the power failure scale, and further calculating an index value for power failure risk evaluation according to the obtained probability density function of the power failure scale.
Wherein the index values include a risk value, a condition risk value, and a mathematical expectation of a power outage event.
The embodiment of the invention also provides a system for determining the power failure key line of the power grid based on the cascading failure model, which comprises the following steps:
the acquisition unit is used for acquiring a power grid model to be calculated and relevant model information corresponding to the power grid model, and acquiring a topological structure of a power grid according to the power grid model and the relevant model information corresponding to the power grid model;
the first output unit is used for determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining the line number of the disconnected line output by each day of simulation after the total number of days of simulation is finished, further counting the number of the obtained line number of the disconnected line output by each day of simulation, and outputting the disconnected line with the maximum number of the line number as the power grid power failure key line.
The relevant model information comprises reference voltage of a power grid, node information, generator information, line information and/or transformer branch information.
Wherein, still include:
and the second output unit is used for determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining a cascading failure accident chain and corresponding failure load loss corresponding to a cut-off line output in each day of simulation after the total number of days of simulation is finished, performing cumulative probability statistics according to the obtained failure load loss output in each day of simulation to obtain a probability density function of the power failure scale, and further calculating an index value for power failure risk assessment according to the obtained probability density function of the power failure scale.
Wherein the index values include a risk value, a condition risk value, and a mathematical expectation of a power outage event.
The embodiment of the invention has the following beneficial effects:
1. according to the method, the weak link of the power grid is pointed out through the accumulated on-off times of the line, the key line which has important influence on the cascading failure of the power system can be identified, data support is provided for line capacity expansion, and the transformation of the weak link of the power system is realized;
2. the method calculates index values (such as risk value, condition risk value and mathematical expectation of power failure accidents) for power failure risk assessment and carries out quantitative comparison to obtain a series of guidance measures beneficial to reducing power failure risk.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a flowchart of a method for determining a power outage critical line of a power grid based on a cascading failure model according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an IEEE118 node system in an application scenario of a method for determining a power outage critical line of a power grid based on a cascading failure model according to an embodiment of the present invention;
fig. 3 is a cumulative probability density distribution diagram of a blackout accident in an application scenario of a method for determining a power grid blackout critical line based on a cascading failure model according to an embodiment of the present invention;
fig. 4 is a fault scale distribution graph under standard operating parameters in an application scenario of the method for determining a power grid blackout key line based on a cascading failure model according to the embodiment of the present invention;
fig. 5 is a fault scale distribution graph of different initial load levels in an application scenario of the method for determining a power outage critical line of a power grid based on a cascading fault model according to the embodiment of the present invention;
fig. 6 is a fault scale distribution graph of different basic false operation probabilities in an application scenario of the method for determining a power outage critical line of a power grid based on a cascading failure model according to the embodiment of the present invention;
fig. 7 is a fault scale distribution graph of the present invention under different overload thresholds in an application scenario of a method for determining a power outage critical line of a power grid based on a cascading failure model according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a system for determining a power outage critical line of a power grid based on a cascading failure model according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
As shown in fig. 1, in an embodiment of the present invention, a method for determining a critical line of a power outage of a power grid based on a cascading failure model is provided, where the method includes the following steps:
step S1, acquiring a power grid model to be calculated and relevant model information corresponding to the power grid model, and acquiring a topological structure of a power grid according to the power grid model and the relevant model information corresponding to the power grid model;
the specific process is that a power grid model required to be calculated is imported, the model records relevant model information such as reference voltage, node information, generator information, line information and/or transformer branch information of a power grid in a matrix form, and the specific topological structure of the power grid can be determined according to the relevant model information.
In the embodiment of the invention, the IEEE118 node system in FIG. 2 is subjected to example calculation and analysis, so that the occurrence and propagation of the cascading failure of the power grid and the evolution process of the power grid can be visually reflected.
And step S2, determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining the line number of the disconnected line output by each day of simulation after the total number of days of simulation is finished, further counting the number of the obtained line number of the disconnected line output by each day of simulation, and outputting the disconnected line with the maximum number of the line number as the key line of power grid power failure.
The specific process is that firstly, the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid is determined, the simulation is carried out by taking each day as a unit, and after the total number of days to be simulated is finished, the specific steps of obtaining the line number of the cut-off line output by each day of simulation comprise:
step S21, acquiring the total number of simulated days (such as 350 days), and judging whether the total number of simulated days is 0;
step S22, if the total number of simulated days is not 0, entering a cascading failure simulation mode to carry out simulation, outputting the line number of the disconnected line, and returning to the step S21 after the total number of simulated days is reduced by one; the method comprises the following specific steps of entering a lock fault simulation mode for simulation, and outputting a line number of a cut-off line:
step S221, the disconnection probability of each line in the topological structure of the initial power grid is P, the number of the disconnected lines is 1, random sampling is carried out by random numbers R which obey [0,1] interval uniform distribution, and the disconnected lines with the disconnection probability larger than the random numbers R are determined to be the initial disconnected lines; wherein P is between [0,1 ]; it should be noted that, by using the monte carlo sampling technique, the probability P is used to determine whether a certain line will be disconnected during the cascading failure. When P is 0, indicating that a certain line in the topological structure of the power grid is not disconnected all the time; when P is 1, the fault disconnection of a certain line in the topological structure of the power grid can occur in the everyday evolution process. In order to simplify the processing and improve the simulation efficiency, all lines P are set to be 1 in the initial fault stage, and the upper limit of the number of the initial line breaks is 1, that is, in the initial stage, one line is necessarily randomly designated as the initial fault to break.
Step S222, distinguishing each electric island in a topological structure of a power grid according to an initial cut-off line, and establishing a corresponding load flow calculation model for each electric island; that is, an electrical island is generated after any one line is disconnected, data of all the electrical islands are separately stored into a power grid model in the same shape as that of the step S221, and the data are convenient to perform next-step load flow calculation respectively;
step S223, calculating a load flow calculation model of each electrical island by using an optimal direct current load flow method to obtain direct current load flows of lines contained in each electrical island; it should be noted that the power flow calculation model is existing in the field and is not described herein again, and the direct current power flow of the line includes a line load rate, and the line load rate is obtained by dividing the active power of all lines obtained through power flow calculation by the rated capacity of the corresponding line;
step S224, judging whether the direct current power flow of one line in each electric island is larger than a preset heavy load threshold value; if yes, go to step S225; if not, jumping to step S227; wherein the preset reloading threshold α is 0.99;
step S225, the lines with the direct current power flows larger than the preset heavy load threshold value are all cut off according to the probability of 1-beta, and the adjacent lines of each line with the direct current power flows larger than the preset heavy load threshold value are all cut off according to the random cut-off probability tau; wherein, beta and tau are both preset fixed values; beta is the false action rate beta of the protection device which is 0.001, and tau is the random breaking probability tau which is 0.0007;
step S226, when the corresponding electrical island has an island after each line with the direct current power flow larger than the preset overloading threshold is cut off, resetting each island in the same electrical island as the electrical island, and returning to the step S223; namely, the line is disconnected, the grid disconnection is caused, a new electrical island is formed, and the steps S222 to S224 need to be repeated again;
step S227, when the corresponding electric island does not have an island after each line with the direct current flow larger than the preset heavy load threshold value is cut off, taking each line with the direct current flow larger than the preset heavy load threshold value as a final cut-off line in the day and outputting the corresponding line number; if the island is not generated and the circuit is not disconnected, the same day cascading failure simulation mode is terminated;
and step S23, if the total number of the simulated days is 0, outputting the line number of the broken line output by each day of simulation.
It should be noted that, after the end of the daily cascading failure simulation mode and before the start of the next day cascading failure simulation mode, the increase of the load and the power generation capacity of the power grid and the transformation and upgrading of the line by the power grid can be simulated, and the transformation of the weak link of the power system is realized through the updating of the capacity of the load, the generator and the weak line. For example, the increase of the grid load and the power generation capacity are simulated by respectively multiplying the active load in the load information matrix in the grid model and the active output upper limit in the generator information matrix by an increase factor λ of 1.0005; for another example, the load rated capacity of the line information matrix corresponding to the line which is disconnected due to overload on the same day is multiplied by a line transmission capacity increase factor μ to be 1.005, so that the transformation and upgrading of the line by the power grid are simulated.
In the embodiment of the present invention, the simulation data further includes a daily cascading failure accident chain and a corresponding failure load loss, and the power outage risk assessment and the power grid weak link finding out may also be performed according to the daily failure load loss, so the method further includes:
determining the total number of days of cascading failure simulation mode simulation formed by a topological structure of a power grid, simulating by taking each day as a unit, obtaining a cascading failure accident chain and corresponding failure load loss corresponding to a broken line output by each day of simulation after the total number of days of simulation is finished, carrying out cumulative probability statistics according to the obtained failure load loss output by each day of simulation to obtain a probability density function of the power failure scale, and further calculating an index value for power failure risk evaluation according to the obtained probability density function of the power failure scale; wherein the index values include a risk value, a condition risk value, and a mathematical expectation of a power outage event.
It should be noted that, the cumulative probability statistics is to divide the fault load loss from the minimum loss value to the maximum loss value into finite small intervals, count the number of times of the power failure events falling into each interval respectively, and divide the number by the total number of the power failure events to obtain the probability density function p (x) of the power failure scale after statistics through fitting of the ksdensity function in Matlab.
The three index values can be calculated by using mathematical formulas (2) to (4), and the calculation is as follows:
where VaR is the Risk value, CVaR is the conditional Risk value, and Risk is the load loss expectation value.
As shown in fig. 3 to fig. 7, an application scenario of the method for determining a power outage critical line of a power grid based on a cascading failure model in the embodiment of the present invention is further described:
the cumulative probability density distribution of the blackout accident obtained through simulation calculation statistics is shown in fig. 3, and the fault scale distribution curve in fig. 3 obviously has the long tail characteristic of power law distribution, which means that the cascading failure model has the risk of the catastrophic accident with serious consequences because small probability cannot be ignored.
If the common coordinate axis of FIG. 3 is changed into a logarithmic coordinate axis, the power law characteristic of the simulation result is more visual and clear, and the fault scale distribution curve under the logarithmic coordinate axis is the fault scale distribution curve under the standard operation parameters shown in FIG. 4;
next, the confidence interval σ is taken to be 0.99, and the Risk value (VaR), the conditional Risk value (CVaR), and the load loss expectation value (Risk) are calculated by equations (2) to (4), with the results shown in table 1 below:
TABLE 1
At this time, the physical meaning of the risk value is that in 3500-day simulation results of the model, the power failure scale of 99.0% is less than 1036.257 MW; the physical implication of the conditional risk value is that the average excess loss for a power outage greater than 1036.257MW is 24.408 MW. The load loss expectation takes into account the mathematical expectation of the outage risk for all sizes, whereas CVaR reflects the risk of large-scale outage in comparison.
Fig. 5 shows the distribution curves of the fault sizes when the initial load levels are different and the other operation parameters are the same (all are standard operation parameters), and table 2 below is the power outage risk assessment calculated according to the three distribution curves:
TABLE 2
Fig. 5 and table 2 together show that the initial load level will largely affect the fault scale distribution, and the higher the initial load level is, the larger the scale and frequency of the power failure accident of the system will be, and the overall fault risk or the major power failure fault risk is obviously improved.
Fig. 6 shows the distribution curves of the fault scale with different basic false operation probabilities and the same other operation parameters, and table 3 below is the power outage risk assessment calculated according to the three distribution curves:
TABLE 3
The results in fig. 6 and table 3 show that the larger the basic false action probability is, the higher the outage Risk is, but the different basic false action probabilities mainly affect the values of VaR and Risk, and have little influence on CVaR, which means that the increase of the basic false action probability within a certain range does not increase the Risk of large outage accident, but increases the Risk of medium and small scale outage accident.
Fig. 7 shows the distribution curves of the fault sizes under different line heavy load thresholds, and table 4 below is the power outage risk assessment calculated according to three distribution curves:
TABLE 4
The results of fig. 7 and table 4 show that lowering the line reload threshold significantly increases the risk of a blackout event in the power system.
Finally, 5 lines with the highest cumulative number of disconnections in 3500-day simulation are counted, as shown in table 5 below
TABLE 5
The lines listed in table 5 are disconnected many times along with the evolution of the network, and particularly, the two lines 77-78 and 79-80 are disconnected up to 306 times in total, which indicates that the two lines are key lines causing major power failure of the IEEE118 node system and are weak links of the system. For an actual system, expansion of a relevant line needs to be considered preferentially.
As shown in fig. 8, in an embodiment of the present invention, a system for determining a critical line of a power outage of a power grid based on a cascading failure model includes:
the acquiring unit 10 is configured to acquire a power grid model to be calculated and relevant model information corresponding to the power grid model, and obtain a topology structure of a power grid according to the power grid model and the relevant model information corresponding to the power grid model;
the first output unit 20 is configured to determine the total number of days of the cascading failure simulation mode simulation formed by the topology structure of the power grid, perform simulation in units of each day, obtain the line number of the disconnected line that is output in the simulation on each day after the total number of days of the simulation is finished, further count the number of occurrences of the line number of the disconnected line that is obtained in the simulation on each day, and output the disconnected line with the largest number of occurrences of the line number as the power grid outage key line.
The relevant model information comprises reference voltage of a power grid, node information, generator information, line information and/or transformer branch information.
Wherein, still include:
the second output unit 30 is configured to determine a total number of days of cascading failure simulation mode simulation formed by the topology structure of the power grid, perform simulation on the total number of days per day, obtain a cascading failure accident chain and a corresponding failure load loss corresponding to a disconnected line that are output in each simulation after the total number of days of simulation is finished, perform cumulative probability statistics according to the obtained failure load loss that is output in each simulation, obtain a probability density function of the power outage scale, and further calculate an index value for power outage risk assessment according to the obtained probability density function of the power outage scale.
Wherein the index values include a risk value, a condition risk value, and a mathematical expectation of a power outage event.
The embodiment of the invention has the following beneficial effects:
1. according to the method, the weak link of the power grid is pointed out through the accumulated on-off times of the line, the key line which has important influence on the cascading failure of the power system can be identified, data support is provided for line capacity expansion, and the transformation of the weak link of the power system is realized;
2. the method calculates index values (such as risk value, condition risk value and mathematical expectation of power failure accidents) for power failure risk assessment and carries out quantitative comparison to obtain a series of guidance measures beneficial to reducing power failure risk.
It should be noted that, in the above system embodiment, each included unit is only divided according to functional logic, but is not limited to the above division as long as the corresponding function can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It will be understood by those skilled in the art that all or part of the steps in the method for implementing the above embodiments may be implemented by relevant hardware instructed by a program, and the program may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (9)
1. A method for determining a power grid blackout key line based on a cascading failure model is characterized by comprising the following steps:
acquiring a power grid model to be calculated and relevant model information corresponding to the power grid model, and acquiring a topological structure of a power grid according to the power grid model and the relevant model information corresponding to the power grid model;
and determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining the line number of the disconnected line output by each day of simulation after the total number of days of simulation is finished, further counting the number of the line numbers of the disconnected line output by each day of simulation, and outputting the disconnected line with the maximum number of the line numbers as the power grid power failure key line.
2. The method for determining grid blackout critical lines based on a cascading failure model of claim 1, wherein the relevant model information comprises a reference voltage of a grid, node information, generator information, line information, and/or transformer branch information.
3. The method for determining a critical line of a power failure of a power grid based on a cascading failure model according to claim 1, wherein the specific steps of determining the total number of days of a cascading failure simulation mode simulation formed by the topological structure of the power grid, performing simulation by taking each day as a unit, and obtaining the line number of the disconnected line output by each day of simulation after the total number of days of the simulation is over comprise:
step S21, acquiring the total number of simulated days, and judging whether the total number of simulated days is 0;
step S22, if the total number of days of the simulation is not 0, entering a cascading failure simulation mode to carry out simulation, outputting a line number of a cut-off line, and returning to the step S21 after the total number of days of the simulation is reduced by one; the specific steps of entering a lock fault simulation mode for simulation and outputting a line number of a cut-off line comprise:
step S221, initializing the on-off probability of each line in the topological structure of the power grid to be P, initializing the number of the on-off lines to be 1, randomly sampling random numbers R which are uniformly distributed in a [0,1] interval, and determining the on-off lines with the on-off probability being greater than the random numbers R as the initial on-off lines; wherein P is between [0,1 ];
step S222, distinguishing each electric island in the topological structure of the power grid according to the initial cut-off line, and establishing a corresponding load flow calculation model for each electric island;
step S223, calculating a load flow calculation model of each electrical island by using an optimal direct current load flow method to obtain direct current load flows of lines contained in each electrical island;
step S224, judging whether the direct current power flow of one line in each electric island is larger than a preset heavy load threshold value; if yes, go to step S225; if not, jumping to step S227;
step S225, the lines with the direct current power flows larger than the preset heavy load threshold value are all cut off according to the probability of 1-beta, and the adjacent lines of each line with the direct current power flows larger than the preset heavy load threshold value are all cut off according to the random cut-off probability tau; wherein, beta and tau are both preset fixed values;
step S226, when the corresponding electrical island has an island after each line with the direct current power flow larger than the preset overloading threshold is cut off, resetting each island in the same electrical island as the electrical island, and returning to the step S223;
step S227, when the corresponding electric island does not have an island after each line with the direct current flow larger than the preset heavy load threshold value is cut off, taking each line with the direct current flow larger than the preset heavy load threshold value as a final cut-off line in the day and outputting the corresponding line number;
and step S23, if the total number of the simulated days is 0, outputting the line number of the broken line output by each day of simulation.
4. The method for determining grid blackout critical lines based on a cascading failure model of claim 1, wherein the method further comprises:
determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining a cascading failure accident chain and corresponding failure load loss corresponding to a broken line output by each day of simulation after the total number of days of simulation is finished, carrying out cumulative probability statistics according to the obtained failure load loss output by each day of simulation to obtain a probability density function of the power failure scale, and further calculating an index value for power failure risk evaluation according to the obtained probability density function of the power failure scale.
5. The method for determining a critical line of a power grid outage based on a cascading failure model of claim 4, wherein the index values include a risk value, a conditional risk value, and a mathematical expectation of the outage event.
6. A system for determining a power grid blackout key line based on a cascading failure model is characterized by comprising:
the acquisition unit is used for acquiring a power grid model to be calculated and relevant model information corresponding to the power grid model, and acquiring a topological structure of a power grid according to the power grid model and the relevant model information corresponding to the power grid model;
the first output unit is used for determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining the line number of the disconnected line output by each day of simulation after the total number of days of simulation is finished, further counting the number of the obtained line number of the disconnected line output by each day of simulation, and outputting the disconnected line with the maximum number of the line number as the power grid power failure key line.
7. The cascading failure model-based grid blackout critical line determination system of claim 6, wherein the relevant model information comprises a reference voltage of a grid, node information, generator information, line information, and/or transformer branch information.
8. The cascading failure model-based system for determining grid blackout critical lines of claim 6, further comprising:
and the second output unit is used for determining the total number of days of cascading failure simulation mode simulation formed by the topological structure of the power grid, simulating by taking each day as a unit, obtaining a cascading failure accident chain and corresponding failure load loss corresponding to a cut-off line output in each day of simulation after the total number of days of simulation is finished, performing cumulative probability statistics according to the obtained failure load loss output in each day of simulation to obtain a probability density function of the power failure scale, and further calculating an index value for power failure risk assessment according to the obtained probability density function of the power failure scale.
9. The system for determining a critical line of a power outage of a power grid based on a cascading failure model of claim 8, wherein the index values include a risk value, a conditional risk value, and a mathematical expectation of a power outage event.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910874650.1A CN110675043B (en) | 2019-09-17 | 2019-09-17 | Method and system for determining power grid power failure key line based on cascading failure model |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN201910874650.1A CN110675043B (en) | 2019-09-17 | 2019-09-17 | Method and system for determining power grid power failure key line based on cascading failure model |
Publications (2)
Publication Number | Publication Date |
---|---|
CN110675043A true CN110675043A (en) | 2020-01-10 |
CN110675043B CN110675043B (en) | 2023-03-24 |
Family
ID=69077106
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN201910874650.1A Active CN110675043B (en) | 2019-09-17 | 2019-09-17 | Method and system for determining power grid power failure key line based on cascading failure model |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN110675043B (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111159922A (en) * | 2020-01-20 | 2020-05-15 | 浙江大学 | Key line identification method and device for cascading failure of power system |
CN113505458A (en) * | 2021-07-26 | 2021-10-15 | 中国电力科学研究院有限公司 | Cascading failure key trigger branch prediction method, system, equipment and storage medium |
Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN101013812A (en) * | 2007-02-07 | 2007-08-08 | 浙江大学 | Method for configuring PMU taking regard of high risk cascading failure path |
CN102214920A (en) * | 2011-06-10 | 2011-10-12 | 华北电力大学 | Circuit cluster-based method for analyzing grid cascading failure based on |
US20130080482A1 (en) * | 2003-04-09 | 2013-03-28 | Gary Charles Berkowitz | Virtual Supercomputer |
CN104376504A (en) * | 2014-11-06 | 2015-02-25 | 国家电网公司 | Power distribution system probability reliability assessing method based on analytical method |
CN104463376A (en) * | 2014-12-24 | 2015-03-25 | 贵州电网公司电力调度控制中心 | Power grid chain accident disaster prediction model modeling method based on CIM standard |
US20150331063A1 (en) * | 2014-05-13 | 2015-11-19 | Georgia Tech Research Corporation | Dynamic Modeling and Resilience for Power Distribution |
CN107093007A (en) * | 2017-03-31 | 2017-08-25 | 华南理工大学 | It is a kind of to consider the distribution network reliability evaluation method that light storage continues load capacity |
CN110070286A (en) * | 2019-04-19 | 2019-07-30 | 国网湖南省电力有限公司 | Disaster-ridden kind of coupling grid cascading failure analysis method of power grid and system |
-
2019
- 2019-09-17 CN CN201910874650.1A patent/CN110675043B/en active Active
Patent Citations (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20130080482A1 (en) * | 2003-04-09 | 2013-03-28 | Gary Charles Berkowitz | Virtual Supercomputer |
CN101013812A (en) * | 2007-02-07 | 2007-08-08 | 浙江大学 | Method for configuring PMU taking regard of high risk cascading failure path |
CN102214920A (en) * | 2011-06-10 | 2011-10-12 | 华北电力大学 | Circuit cluster-based method for analyzing grid cascading failure based on |
US20150331063A1 (en) * | 2014-05-13 | 2015-11-19 | Georgia Tech Research Corporation | Dynamic Modeling and Resilience for Power Distribution |
CN104376504A (en) * | 2014-11-06 | 2015-02-25 | 国家电网公司 | Power distribution system probability reliability assessing method based on analytical method |
CN104463376A (en) * | 2014-12-24 | 2015-03-25 | 贵州电网公司电力调度控制中心 | Power grid chain accident disaster prediction model modeling method based on CIM standard |
CN107093007A (en) * | 2017-03-31 | 2017-08-25 | 华南理工大学 | It is a kind of to consider the distribution network reliability evaluation method that light storage continues load capacity |
CN110070286A (en) * | 2019-04-19 | 2019-07-30 | 国网湖南省电力有限公司 | Disaster-ridden kind of coupling grid cascading failure analysis method of power grid and system |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN111159922A (en) * | 2020-01-20 | 2020-05-15 | 浙江大学 | Key line identification method and device for cascading failure of power system |
CN113505458A (en) * | 2021-07-26 | 2021-10-15 | 中国电力科学研究院有限公司 | Cascading failure key trigger branch prediction method, system, equipment and storage medium |
CN113505458B (en) * | 2021-07-26 | 2024-07-02 | 中国电力科学研究院有限公司 | Method, system, equipment and storage medium for predicting critical trigger branch of cascading failure |
Also Published As
Publication number | Publication date |
---|---|
CN110675043B (en) | 2023-03-24 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11333698B2 (en) | Method and system for fast search of cascading failures in hybrid AC/DC power systems | |
Ubeda et al. | Sequential simulation applied to composite system reliability evaluation | |
CN110675043B (en) | Method and system for determining power grid power failure key line based on cascading failure model | |
CN109687426B (en) | Fault rate parameter modeling method, device, equipment and storage medium | |
CN111507509A (en) | Risk assessment method for extreme events of power system | |
CN105117839A (en) | Power system weaknesses identification method based on cascading failure | |
Tu et al. | On self-organized criticality of the east China AC–DC power system—the role of DC transmission | |
CN115456410A (en) | Power grid risk assessment method and system | |
Rahnamay-Naeini et al. | Impacts of operating characteristics on sensitivity of power grids to cascading failures | |
CN110807172A (en) | Risk prediction method and device for relay protection of power system | |
CN109586281B (en) | Power distribution network reliability assessment method, device and medium based on node optimization number | |
CN103311925B (en) | Identification method of complex power grid self-organization critical state | |
CN114186849A (en) | Power system cascading failure risk assessment method considering secondary system influence and system thereof | |
CN113837625A (en) | Method, device and equipment for quantitatively evaluating risks of cascading failures of power grid | |
Zhu et al. | Propagation model and blackout risk quantitation analysis for cascading failures in AC/DC hybrid power systems | |
CN110021933B (en) | Power information system control function reliability assessment method considering component faults | |
CN112769124A (en) | Power system rapid operation risk assessment method based on power flow transfer and tracking | |
CN116341950A (en) | Comprehensive betting-based power system vulnerable line assessment method and device | |
CN115964951A (en) | Power system operation reliability assessment method and device and electronic equipment | |
CN110365047B (en) | Power grid short-circuit current probability evaluation method containing distributed photovoltaic power generation system | |
CN111049128B (en) | Power system reliability improving method considering load prediction power utilization influence | |
CN114117786A (en) | Stadium power supply system maintenance method, system, equipment and medium | |
CN112163314A (en) | Intelligent calculation method and device for transient stability simulation of power system | |
Das et al. | Estimating distribution of cascaded outages using observed utility data and simulation modeling | |
US11983472B2 (en) | Method for identifying fragile lines in power grids based on electrical betweenness |
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 |