CN111832907A - Vulnerability assessment method of associated power-natural gas system under different faults - Google Patents
Vulnerability assessment method of associated power-natural gas system under different faults Download PDFInfo
- Publication number
- CN111832907A CN111832907A CN202010576780.XA CN202010576780A CN111832907A CN 111832907 A CN111832907 A CN 111832907A CN 202010576780 A CN202010576780 A CN 202010576780A CN 111832907 A CN111832907 A CN 111832907A
- Authority
- CN
- China
- Prior art keywords
- model
- dcpf
- gnf
- natural gas
- gas system
- 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.)
- Pending
Links
- 239000003345 natural gas Substances 0.000 title claims abstract description 116
- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 72
- 230000002596 correlated effect Effects 0.000 claims abstract description 10
- 230000008878 coupling Effects 0.000 claims abstract description 8
- 238000010168 coupling process Methods 0.000 claims abstract description 8
- 238000005859 coupling reaction Methods 0.000 claims abstract description 8
- 230000006378 damage Effects 0.000 claims description 50
- 239000007789 gas Substances 0.000 claims description 24
- 230000007123 defense Effects 0.000 claims description 19
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 230000001681 protective effect Effects 0.000 claims description 2
- VOPAGMQVZGBJDT-UHFFFAOYSA-N n'-(3,5-dichloro-4-prop-2-ynoxyphenyl)-n-methoxymethanimidamide Chemical compound CONC=NC1=CC(Cl)=C(OCC#C)C(Cl)=C1 VOPAGMQVZGBJDT-UHFFFAOYSA-N 0.000 claims 26
- 238000010187 selection method Methods 0.000 abstract description 2
- 238000013210 evaluation model Methods 0.000 abstract 1
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 88
- 238000010248 power generation Methods 0.000 description 7
- 238000006243 chemical reaction Methods 0.000 description 3
- 230000006872 improvement Effects 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 230000000875 corresponding effect Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004870 electrical engineering Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000004927 fusion Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000004445 quantitative analysis Methods 0.000 description 1
- 238000003860 storage Methods 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
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Marketing (AREA)
- Theoretical Computer Science (AREA)
- Development Economics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a vulnerability assessment method of a correlated power-natural gas system under different faults, and belongs to the field of toughness of key infrastructure. The method comprises the following steps: under different fault situations, selecting an operation model of the associated power-natural gas system according to expected calculation time and accuracy requirements, and establishing a system vulnerability evaluation model based on the selected operation model of the associated power-natural gas system and the fault situation model; and collecting structural information data, coupling information data, supply demand data, system operation constraint conditions and system component fault data of the associated power-natural gas system in real time, and inputting the structural information data, the coupling information data, the supply demand data, the system operation constraint conditions and the system component fault data into a vulnerability assessment model of the associated power-natural gas system to obtain a vulnerability assessment result. The invention qualitatively compares the existing models from two aspects of calculation time and calculation accuracy through quantitative calculation, and provides a model selection method suitable for different fault situations according to different fault situations and different calculation time/accuracy requirements.
Description
Technical Field
The invention belongs to the field of toughness of key infrastructure, and particularly relates to a vulnerability assessment method of a correlated power-natural gas system under different faults.
Background
With the development of society and the increasing of energy consumption, the problems of fossil energy shortage, environmental pollution and the like are not ignored. Therefore, various countries in the world propose and construct a new generation energy system taking the purposes of developing and utilizing new energy to the maximum extent and improving the comprehensive utilization efficiency of the energy to the maximum extent as a mission. The development of clean and efficient energy systems becomes a core strategy of energy policies of governments of all countries. In China, relevant scholars propose to construct a new generation of energy system with a power grid as a core, namely a related power-natural gas system. The system is a comprehensive system formed by coordinating and optimizing links such as generation, transmission, distribution, conversion, storage and the like of electric power and natural gas on the basis of considering the incidence relation between an electric power system and a natural gas system, aims to overcome the defects of single-row and block division of various energy plans, lack of effective energy market allocation and the like existing in China for a long time, realizes the mutual collaborative production, supply and planning of a multi-energy structure, and further achieves the comprehensive utilization, supply and demand interaction and efficient operation of various energy sources.
In contrast to conventional power systems, the association of power-gas systems is achieved by closely associating power and gas systems together. As a clean energy source, natural gas has the advantages of abundant reserves, high efficiency, environmental protection and the like, and is developed in a large amount worldwide. With the widespread application of gas turbine, Combined Heat and Power (CHP), power-to-gas (P2G) technologies, the physical relationship between the power system and the natural gas system in the power-natural gas system is becoming more and more compact. The increasingly close association relationship makes the associated power-natural gas system more vulnerable to sudden situations such as component failure and intentional destruction, so that the vulnerability of the associated power-natural gas system under the sudden situations is evaluated to ensure the safety and reliability of the associated power-natural gas system and the normal supply of energy in our country, and therefore, the improvement of the toughness of the associated power-natural gas system is particularly important.
The method comprises the steps of firstly constructing operation models of the power system and the natural gas system, and then modeling a coupling relation of the power system and the natural gas system to obtain the operation model of the associated power-natural gas system. The currently constructed power system operation models include a power system connectivity model (PNC), a power system network flow model (PNF) and a power system direct current power flow model (DCPF); the constructed natural gas system operation models comprise a natural gas system connectivity model (GNC), a natural gas system network flow model (GNF) and a natural gas system steady-state model (SGF).
Because the types of the respective operation models of the power system and the natural gas system are more, the precision degree of the description of the system by different models is different, and the model closer to the real system is higher in calculation accuracy and larger in calculation amount. Under different fault conditions, how to select a proper operation model of the associated power-natural gas system to evaluate the vulnerability of the system under the condition of simultaneously considering the requirements of calculation time and accuracy is a technical problem to be solved at present.
Disclosure of Invention
In view of the above defects or improvement requirements of the prior art, the present invention provides a method for evaluating the vulnerability of a correlated power-natural gas system under different faults, and aims to select a suitable operation model of the correlated power-natural gas system for vulnerability evaluation, so that the calculation time and the accuracy requirements are both considered.
In order to achieve the above object, the present invention provides a vulnerability assessment method of a correlated power-natural gas system under different faults, comprising:
s1, under different fault situations, selecting an operation model of a related power-natural gas system according to expected calculation time and accuracy requirements, and establishing a vulnerability assessment model of the related power-natural gas system based on the selected operation model of the related power-natural gas system and the fault situations; the different fault situations include random faults, deliberate subversion and deliberate subversion taking into account protection;
s2, collecting structural information data, coupling information data, supply demand data, system operation constraint conditions and system component fault data of the associated power-natural gas system in real time, and inputting the data into a vulnerability assessment model of the associated power-natural gas system to obtain a vulnerability assessment result; wherein the vulnerability represents a reduced magnitude of the functional level of the associated power-gas system when the fault occurs.
Further, for random failures, the system component failure data includes the failure probability of each component; for intentional corruption, system component failure data includes different attack costs; for deliberate subversion of the contemplated safeguards, the system component failure data includes different safeguarding costs and attack costs.
Further, the vulnerability assessment model of the associated power-natural gas system of step S1 is constructed based on different associated operation models.
Further, the different correlation operation models include a PNC & GNC model, a PNF & GNF model, a DCPF & GNF model, and a DCPF & SGF model.
Further, step S1 specifically includes:
s1.1, carrying out correlation comparison on the computed vulnerability of the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model under the random fault and the computed vulnerability of the DCPF & SGF model under the random fault, wherein the stronger the correlation is, the higher the accuracy of the correlation model is represented, and the accuracy sequence of the four correlation models under the random fault is obtained;
s1.2, comparing the optimal attack point calculated by the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model according to the vulnerability index under the condition of deliberate damage with the optimal attack point calculated by the DCPF & SGF model under the condition of deliberate damage, wherein the higher the coincidence degree is, the higher the accuracy of the correlation model is, and obtaining the accuracy sequence of the four correlation models under the deliberate damage;
s1.3, carrying out contact ratio comparison on the optimal defense points calculated by the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model according to the vulnerability indexes under protected deliberate destruction and the optimal defense points calculated by the DCPF & SGF model under deliberate destruction, wherein the higher the contact ratio is, the higher the accuracy of the correlation model is, and obtaining the accuracy sequence of the four correlation models under protected deliberate destruction;
s1.4, sequencing the calculation time required by the PNC & GNC model, the PNF & GNF model, the DCPF & GNF model and the DCPF & SGF model under different fault conditions;
s1.5, selecting an operation model of the associated power-natural gas system meeting the target requirement according to the expected calculation time and accuracy requirement.
Further, under random faults, four correlation models are sequentially as follows according to the calculation time from large to small: a DCPF & SGF model, a DCPF & GNF model, a PNF & GNF model, a PNC & GNC model; under the condition of deliberate destruction, the four correlation models are sequentially as follows according to the calculation time from large to small: a DCPF & SGF model, a PNC & GNC model, a DCPF & GNF model, a PNF & GNF model; under the protective deliberate destruction, the four correlation models are sequentially as follows according to the calculation time from large to small: a DCPF & SGF model, a PNC & GNC model, a DCPF & GNF model, a PNF & GNF model; the above calculation times are all obtained in the same operating environment.
Further, under random faults, the four correlation models are sequentially as follows according to the accuracy from large to small: a DCPF & SGF model, a DCPF & GNF model, a PNF & GNF model, a PNC & GNC model; under deliberate destruction, the four correlation models are in turn from large to small according to the accuracy: a DCPF & SGF model, a DCPF & GNF model, a PNF & GNF model and a PNC & GNC model; under the protected deliberate destruction, the four correlation models are as follows according to the accuracy from large to small: DCPF & SGF model, DCPF & GNF model, PNF & GNF model, PNC & GNC model.
Further, under random faults and deliberate damage, when the calculation time is not constrained, a DCPF & SGF model is selected to construct an operation model of the associated power-natural gas system; when the calculation time is limited, selecting a DCPF & GNF or PNF & GNF model to construct an operation model of the associated power-natural gas system;
under the protected deliberate destruction, when the calculation time is not constrained, a DCPF & SGF model is selected to construct an operation model of a related power-natural gas system; and when the calculation time is limited, selecting the DCPF & GNF model to construct an operation model of the associated power-natural gas system.
In general, the above technical solutions contemplated by the present invention can achieve the following advantageous effects compared to the prior art.
(1) Aiming at the problem of vulnerability assessment of the power-natural gas system, the invention provides a qualitative model selection scheme through quantitative analysis, compared with the traditional method of selecting a model according to intuition or experience, the invention quantitatively compares the existing models from two aspects of calculation time and calculation accuracy, and provides a proper model selection method for different fault situations and different calculation time/accuracy requirements.
(2) The invention considers three fault situations of random fault, deliberate attack and deliberate attack considering protection; and for random faults, different fault probabilities are considered; for deliberate attacks, different attack costs are considered; for deliberate attacks where protection is considered, different protection and attack costs are considered. Therefore, the vulnerability assessment method for the associated power-natural gas system is a universal method under different fault situations.
(3) The vulnerability assessment method of the associated power-natural gas system considers the coupling relationship between different power systems and natural gas systems, carries out overall vulnerability assessment and protection on the associated power-natural gas system, and can realize the minimization of the overall loss of the system compared with a model without considering the coupling relationship between the systems.
Drawings
FIG. 1 is a flow chart of a vulnerability assessment method of an associated power-natural gas system under different faults provided by the present invention;
FIG. 2 is a system block diagram of the associated IEEE39 power-20 node Belgium natural gas system provided by the present invention;
fig. 3 is a graph showing the variation of the correlation coefficient between the vulnerability calculated by the other three models and the vulnerability calculated by the DCPF & SGF model with the failure probability under the random failure condition provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
As shown in fig. 1, the present invention provides a vulnerability assessment method of a correlated power-natural gas system under different faults, including:
s1, under different fault situations, selecting an operation model of a related power-natural gas system according to expected calculation time and accuracy requirements, and establishing a vulnerability assessment model of the related power-natural gas system based on the selected operation model of the related power-natural gas system and the fault situations; the different fault situations comprise random faults, deliberate destruction and protected deliberate destruction;
and S2, collecting structural information data, supply demand data, coupling information of the power system and the natural gas system, system operation constraint conditions and node fault states of the associated power-natural gas system in real time, and inputting the data into a vulnerability assessment model of the associated power-natural gas system to obtain a vulnerability assessment result.
The vulnerability assessment model of the correlation power-natural gas system can be constructed based on different correlation operation models, wherein the calculation time and the accuracy of the different correlation operation models are different, and the calculation time of the model with higher accuracy is longer. In practical application, different users have different requirements on accuracy and calculation time, and how to select a model with calculation time within an acceptable range from a plurality of models and accuracy meeting the desired requirements is a main problem to be solved by the invention. The embodiment of the invention takes four common associated operation models as examples to explain the selection process of the models in detail, specifically comprises a PNC & GNC model, a PNF & GNF model, a DCPF & GNF model and a DCPF & SGF model, and introduces vulnerability assessment models based on the four operation models and a fault situation model.
PNC&GNC model: and the power network connectivity and the natural gas network connectivity model. The connectivity model considers an electric power system and a natural gas system as a topological network, only considers the connection degree of a source node (a power station and a gas supply station) and a sink node (a load or a user node), does not consider the physical characteristics between an energy source flow and the electric power system and the natural gas system, and is generally used for performance analysis of a lifeline system under natural disaster conditions. Model objective is to maximize the system functional level-FLPNC&GNCHere, the system functional level represents a demand node energy satisfaction ratio in the system, including a power demand satisfaction ratio of users in the power system and a natural gas demand satisfaction ratio of users in the natural gas system. The objective function is as follows:
the constraint conditions are as follows:
Constraints (P1) ((G1)) represent the actual demand of each power (gas) demand node. The actual demand is quantified by the product of the theoretical demand of the node and its connectivity level. The connectivity level refers to the ratio of the number of connections in the network under disruption to the number of connections under normal operation. Constraint (P2) ((G2)) means that if there is an edge between two uncorrupted power (gas) nodes m and n, they are tiedAnd forcibly connected. Constraint (P3) ((G3)) means that if node m in the power (gas) system has a neighboring node n, the constraint ensures that nodes m and n are connected. Constraint (I1) indicates for each node pair
On the other hand, if the gas node g has no source node, the thermal power generation unit p cannot normally operate.
PNF&GNF model: the power network flow model and the natural gas network flow model are different from the connectivity model, and the network flow model further considers energy flow. The actual supply of the source node, the actual acquisition demand of the sink node and the actual edge flow are used as decision variables, so that the system operation targets, such as the minimum operation cost, the maximum total demand and the like, are minimized or maximized. The method is generally used for performance analysis after large-scale system abstraction without considering the physical characteristics of the system. Model objective is to maximize the system functional level FLPNF&GNFHere, the system functional level represents a demand node energy satisfaction ratio in the system, including a power demand satisfaction ratio of users in the power system and a natural gas demand satisfaction ratio of users in the natural gas system.
The objective function is:
the constraint conditions are as follows:
wherein
Representing the maximum energy supply and the actual energy supply of the electrical (gas) source node n, respectively.
Representing the flow of the edge e in the electrical (gas) system,
indicating the corresponding maximum capacity.
Indicating the power generation conversion efficiency of one thermal power generation unit.
The constraint (P4) ((G4)) represents the balance of energy flows per node. The constraint (P5) ((G5)) represents a limit on the energy flow of the edge. Constraints (P6) ((G6)) represent capacity limits for each supply node. The constraint (P7) ((G7)) indicates that the actual demand of each demand node cannot exceed the theoretical demand. Constraint (I2): representing a constraint on the energy conversion efficiency of each thermal power generation unit.
DCPF&GNF modelType (2): the direct current power flow model of the power system is expanded, and some electrical engineering constraints are further considered. Therefore, the model adds two new constraints in the network flow model, including a linear power flow equation and an argument constraint, which describe the relationship between the line flow and the argument. Model objective is to maximize the system functional level FLDCPF&GNFHere, the system functional level represents a demand node energy satisfaction ratio in the system, including a power demand satisfaction ratio of users in the power system and a natural gas demand satisfaction ratio of users in the natural gas system. The objective function is:
the constraint conditions are as follows:
(P4)-(P7),(G4)-(G7),(I2)
where M represents infinity, [ theta ]nRepresenting the argument of node n, BeRepresenting the line impedance of edge e.
The constraint (P8) represents a linear equation between the flow of edge e and the node argument. Constraints (P9) represent the upper and lower limits of the node argument limit.
DCPF&SGF model: the direct current power flow and steady-state natural gas flow of the power system are combined into a model, the natural gas steady-state model considers the physical characteristics (node air pressure, pipeline coefficient, pressurization ratio and the like) of natural gas energy source flow on the basis of a network flow model, and the natural gas steady-state model is usually used for analyzing the steady-state performance of the natural gas system. In the natural gas steady-state model, pipelines are divided into passive pipelines and active pipelines. The pipeline with the compressor is an active pipeline, and the passive pipeline is a normal pipeline for conveying gas. Which is provided withAnd the node air pressure, the actual supply of the source node, the actual acquisition demand of the sink node and the actual edge flow are decision variables, so that the system operation target is minimized or maximized. Model objective is to maximize the system functional level FLDCPF&SGFHere, the system functional level represents a demand node energy satisfaction ratio in the system, including a power demand satisfaction ratio of users in the power system and a natural gas demand satisfaction ratio of users in the natural gas system. The objective function is:
the constraint conditions are as follows:
(P4)-(P9),(G4)-(G7),(I2)
wherein
Representing the pressure at node n and the lower and upper limits, respectively.
Representing the compression factor in the active pipe e. Ke: representing the Weymouth coefficient of the passive pipe e. EG,P: representing a passive pipeline in a natural gas system. EG,A: showing the active pipeline in a natural gas system.
The constraint (G8) represents a node pressure limit. The constraint (G9) represents the pressure relationship between the two endpoints of an active pipe. The constraint (G10) represents a restriction in the direction of natural gas flow in one active pipeline. Constraint (G11): the Weymouth equation is expressed which shows the relationship between the gas flow of the active pipe and the node pressures at its two end points.
Under three conditions of random fault, deliberate destruction and protected deliberate destruction, a system vulnerability assessment model is established respectively based on the four different associated operation models.
Vulnerability assessment model under random fault: when the associated power-gas system is randomly destroyed, the variable fpRepresenting the probability of each component being corrupted, where fp∈[0,1]. At this time, the vulnerability assessment model aims to minimize the system vulnerability Vrandom,OMHere, Vrandom,OM＝1-FOMWherein F isLOMRepresenting the system function level under different associated operation models, OM ∈ { PNC&GNC,PNF&GNF,DCPF&GNF,DCPF&SGF }. The objective function is:
min{1-FLOM}
constraint conditions are as follows: the constraint conditions are the same as those of the corresponding associated operation model OM
Vulnerability assessment model under deliberate disruption: the objective of the vulnerability assessment model is to maximize the system vulnerability V when the associated power-gas system is subjected to worst case deliberate attacksworst-case,OMHere, Vworst-case,OM＝1-FOM. The objective function is:
maxv,yVworst-case,OM,if OM＝PNC&GNC
besides the constraint conditions of the associated operation model OM, the constraint of the intentionally damaged model is also satisfied:
where y represents all of the associated power-natural gas system operational decision variables, v represents all of the attack decision variables,
this indicates that the index is 0 if the power (gas) node n is attacked, and 1 otherwise.
This indicates that the index is 0 if the power (gas) edge e is attacked, and 1 otherwise. B isARepresenting the number of components under attack.
The constraint (a1) represents a number limit of attack components. Constraints (A2) - (A3) represent constraints for conducting an attack. Constraints (C1) - (C4) indicate that a component is assumed to be completely destroyed after being attacked.
Vulnerability assessment model under protected deliberate disruption: the objective of the vulnerability assessment model is to minimize the system vulnerability V when the associated power-gas system is under worst case attack and has a protection strategyworst-case,OMHere, Vworst-case,OM＝1-FOM. The model objective function is:
besides the constraint conditions of the associated operation model OM, the constraint of the protected deliberate destruction model needs to be satisfied:
wherein w represents a protection policy, variable
Respectively represent the protection of a node n and an edge e in a power system and a node n and an edge e in a gas system, 0 represents the protection, and 1 represents the protection. B isDIndicating protectionThe number of components.
The constraint (D1) represents a limit on the number of components that are protected. Constraints (D2) - (D3) represent implementing binary defenses. Constraints (C5) - (C8) indicate that unprotected components will fail completely when under attack and protected components will not fail after attack.
Based on the vulnerability assessment model, vulnerability calculation is carried out on four different correlation operation models under the conditions of random faults, deliberate destruction and protected deliberate destruction, then the four models are compared and analyzed, and a proper model is selected as a correlation power-natural gas system operation model. The method specifically comprises the following steps:
s1.1, carrying out correlation comparison on the computed vulnerability of the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model under the random fault and the computed vulnerability of the DCPF & SGF model under the random fault, wherein the stronger the correlation is, the higher the accuracy of the correlation model is represented, and the accuracy sequence of the four correlation models under the random fault is obtained;
s1.2, comparing the optimal attack point calculated by the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model according to the vulnerability index under the condition of deliberate damage with the optimal attack point calculated by the DCPF & SGF model under the condition of deliberate damage, wherein the higher the coincidence degree is, the higher the accuracy of the correlation model is, and obtaining the accuracy sequence of the four correlation models under the deliberate damage;
s1.3, carrying out contact ratio comparison on the optimal defense points calculated by the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model according to the vulnerability indexes under protected deliberate destruction and the optimal defense points calculated by the DCPF & SGF model under deliberate destruction, wherein the higher the contact ratio is, the higher the accuracy of the correlation model is, and obtaining the accuracy sequence of the four correlation models under protected deliberate destruction;
s1.4, sequencing the calculation time required by the PNC & GNC model, the PNF & GNF model, the DCPF & GNF model and the DCPF & SGF model under different fault conditions;
s1.5, selecting an operation model of the associated power-natural gas system meeting the target requirement according to the expected calculation time and accuracy requirement.
Taking an example of a related system composed of an IEEE39 power system and a 20-node belgium natural gas system as an example, fig. 2 is a system structure diagram of a related IEEE39 power-20-node belgium natural gas system, wherein the related power-natural gas system includes two subsystems, a power system and a natural gas system.
For random failure scenarios, consider 21 failure probability values (failure probability f)p0:0.05:1), 1000 random fault cases were simulated for each fault probability, for a total of 21000 random fault cases. In each random failure case, the accuracy of the vulnerability results from the four models is calculated, as well as the calculation time.
From the aspect of calculation accuracy, since the DCPF & SGF model is the closest model to the real situation among the above four models, the vulnerability calculated by the DCPF & SGF model can be taken as a standard. In order to analyze the accuracy of the other three models, the embodiment of the present invention calculates the correlation coefficients between the vulnerabilities calculated by the other three models and the vulnerabilities calculated by the DCPF & SGF model, as shown in fig. 3, wherein the abscissa represents the system failure probability, the ordinate represents the correlation coefficients between the vulnerabilities calculated by the other three models and the vulnerabilities calculated by the DCPF & SGF model, and different curves represent different models. As can be seen from fig. 3, under the condition that the system node failure probabilities are the same, the correlation between the vulnerability calculated by the DCPF & GNF model and the vulnerability calculated by the DCPF & SGF model is strongest, and the correlation coefficient is always greater than 0.879; secondly, the correlation coefficient of the PNF & GNF model is always larger than 0.875; and the PNC & GNC model is the worst, the correlation with the DCPF & SGF model is weak, the correlation coefficients are all less than 0.8, and the minimum value is only 0.471. The above results show that for random faults, the accuracy of the 4 models is as follows from large to small: the accuracy of the DCPF & SGF model, the DCPF & GNF model, the PNF & GNF model and the PNC & GNC model is very close to that of the DCPF & SGF model, the DCPF & GNF model and the PNF & GNF model (the correlation coefficient is more than 0.8).
From the viewpoint of calculation time. The average calculated time for the four models in 21000 random failure cases is shown in table 3. As can be seen from the table, for random faults, the calculation time of the 4 models is as follows from large to small: the DCPF & SGF model, the DCPF & GNF model, the PNF & GNF model and the PNC & GNC model are adopted, wherein the calculation time of the DCPF & SGF model is 23 times of that of the DCPF & GNF model, 33 times of that of the PNF & GNF model and 98 times of that of the PNC & GNC model.
In conclusion, for random faults, when the calculation time is not constrained, the DCPF & SGF model is selected to construct an operation model of the associated power-natural gas system; and when the calculation time is limited, selecting a DCPF & GNF model or a PNF & GNF model to construct an operation model of the associated power-natural gas system.
For the case of deliberate destruction, consider the number of attacking nodes BAFive cases of deliberate destruction, 1,2,3,4, 5. In each case of deliberate destruction, the best set of attack nodes from the four models is solved (see table 1), and the computation times for the four models (see table 3).
From the aspect of calculation accuracy, the DCPF&The SGF model is the model closest to the real situation in the four models, so that the DCPF can be used&And taking the optimal attack node set calculated by the SGF model as a standard. As can be seen from Table 1, DCPF&GNF model and PNF&The optimal attack node sets of the GNF model are completely consistent; when attacking node number BA>1，DCPF&GNF model and PNF&Optimal attack node set and DCPF of GNF model&The optimal attack node sets of the SGF model are completely consistent; while PNC&GNC model and DCPF&The optimal attack node set of the SGF model is in the attack node number BAThe difference is large when different values are taken. In order to analyze the accuracy of the other three models, the optimal attack node set and the DCPF calculated by the other three models are calculated in the embodiment of the invention&Coincidence degree (coincidence node proportion) among optimal attack node sets calculated by SGF (generalized regression) model, DCPF (digital content fusion factor)&GNF model and DCPF&The overlap ratio between the best attack node sets of the SGF model is 93.3%, PNF&GNF model and DCPF&The contact ratio between the best attack node sets of the SGF model is 93.3 percent，PNC&GNC model and DCPF&The overlap ratio between the best attack node sets of the SGF model is 40%. The above results show that for deliberate destruction, the accuracy of the 4 models is, in order from large to small: DCPF&SGF model, DCPF&GNF model and PNF&GNF model, PNC&GNC model, among others, DCPF&SGF model, DCPF&GNF model, PNF&The three models of the GNF model are very close (93.3% overlap).
TABLE 1
| BA | PNC&GNC | | DCPF&GNF | DCPF&SGF | |
| 1 | G8 | | G8 | G2 | |
| 2 | P16,G8 | G2,G11 | G2,G11 | G2,G11 | |
| 3 | P16,G2,G8 | G2,G5,G11 | G2,G5,G11 | G2,G5,G11 | |
| 4 | P16,G2,G5,G14 | P16,G2,G5,G11 | P16,G2,G5,G11 | P16,G2,G5,G11 | |
| 5 | P16,G2,G5,G8,G14 | P2,P16,G2,G5,G11 | P2,P16,G2,G5,G11 | P2,P16,G2,G5,G11 |
From the calculation time point, the calculation time of the four models under 3 kinds of deliberate destruction is shown in table 3. As can be seen from the table, for the intentional destruction, the computation time increases as the number of attack nodes becomes larger, and particularly, in the DCPF & SGF model, the computation time increases by 6 times when the number of attack nodes changes from 1 to 3. Based on the calculation, the average calculation time (average value of calculation time under different attack node numbers) of the four models under the deliberate attack is calculated, and the calculation time of the 4 models is sequentially ordered from large to small: DCPF & SGF model (10s), PNC & GNC model (1.86s), DCPF & GNF model (1.8s), PNF & GNF model (0.42 s). Wherein, the calculation time of the DCPF & SGF model is 5.6 times of that of the DCPF & GNF model, 23.8 times of that of the PNF & GNF model and 5.4 times of that of the PNC & GNC model.
In conclusion, for the intentional destruction, when the calculation time is not constrained, the DCPF & SGF model is selected to construct an operation model of the associated power-natural gas system; and when the calculation time is limited, selecting a DCPF & GNF or PNF & GNF model to construct an operation model of the associated power-natural gas system.
For consideration of preventionProtecting the situation of intentional destruction, considering the number of attacking nodes B A1,2,3, number of attacking nodes BANine cases are 1,2 and 3. In each case, the set of best defense nodes from the four models was solved (see table 2), and the computation times for the four models (see table 3).
From the aspect of calculation accuracy, the DCPF&The SGF model is the model closest to the real situation in the four models, so that the DCPF can be used&And taking the optimal defense node set calculated by the SGF model as a standard. As can be seen from Table 2, when attacking node number BA>1，DCPF&Optimal defense node set of GNF model sum and DCPF&The optimal defense node sets of the SGF model are completely consistent; when attacking node number BA>1 and defending against node number BD<3，PNF&Optimal defense node set of GNF model sum and DCPF&The optimal defense node sets of the SGF model are completely consistent; while PNC&GNC model and DCPF&Optimal defense node set of SGF model in attack node number BAAnd the number of defending nodes BDThe values vary widely. In order to analyze the accuracy of the other three models, the optimal defense node set and the DCPF calculated by the other three models are calculated&Coincidence degree (coincidence node proportion) among optimal defense node sets calculated by SGF (generalized regression) model, DCPF (DCPF)&GNF model and DCPF&The overlap ratio between the best defense node sets of the SGF model is 72.2%, PNF&GNF model and DCPF&The overlap ratio between the best defense node sets of the SGF model is 66.7%, PNC&GNC model and DCPF&The overlap ratio between the best defense node sets of the SGF model is 0%. The above-mentioned results show that, for the deliberate destruction considering the protection, the accuracy of the 4 models is as follows from large to small: DCPF&SGF model, DCPF&GNF model, PNF&GNF model, PNC&The GNC model.
TABLE 2
From the viewpoint of calculation time. The calculated times for the four models in the case of 3 deliberate vandalism taking into account protection are shown in table 3. As can be seen from the table, the computation time increases as the number of attack nodes and the number of protection nodes become larger, and particularly, the DCPF & SGF model and the PNC & GNC model increase the computation time by 4 to 5 orders of magnitude when the number of attack nodes and protection nodes changes from 1 to 3. Based on the calculation, the average calculation time (the average value of the calculation time under different attack node numbers and protection node numbers) of the four models under the protection-considered intentional attack is calculated, and the calculation time of the 4 models is sequentially ordered from large to small: DCPF & SGF model (87640s), PNC & GNC model (15185s), DCPF & GNF model (4.97s), PNF & GNF model (2.13 s). The calculation time of the DCPF & SGF model is 17634 times that of the DCPF & GNF model, 41146 times that of the PNF & GNF model and 5.8 times that of the PNC & GNC model.
In conclusion, for the deliberate damage considering the protection, when the calculation time is not constrained, the DCPF & SGF model is selected to construct an operation model of the associated power-natural gas system; and when the calculation time is limited, selecting the DCPF & GNF model to construct an operation model of the associated power-natural gas system.
TABLE 3
In summary, under random faults and deliberate damage, when the calculation time is not constrained, the DCPF & SGF model is selected to construct an operation model of the associated power-natural gas system; when the calculation time is limited, selecting a DCPF & GNF or PNF & GNF model to construct an operation model of the associated power-natural gas system; under the protected deliberate destruction, when the calculation time is not constrained, a DCPF & SGF model is selected to construct an operation model of a related power-natural gas system; and when the calculation time is limited, selecting the DCPF & GNF model to construct an operation model of the associated power-natural gas system.
The embodiment of the invention further applies the model comparison method to other 4 associated power-natural gas systems, including: the system comprises an IEEE39 power system-48 node natural gas system, an associated IEEE39 power system-48 node natural gas system, an associated 6 node power system-7 node natural gas system and an artificial associated power-natural gas system which do not consider the association relationship. The model comparison results obtained in the 4 systems are completely consistent with the case system, and the practicability and universality of the method are verified.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for assessing vulnerability of a correlated power-gas system under different faults, comprising:
s1, under different fault situations, selecting an operation model of a related power-natural gas system according to expected calculation time and accuracy requirements, and establishing a vulnerability assessment model of the related power-natural gas system based on the selected operation model of the related power-natural gas system and the fault situations; the different fault situations include random faults, deliberate subversion and deliberate subversion taking into account protection;
s2, collecting structural information data, coupling information data, supply demand data, system operation constraint conditions and system component fault data of the associated power-natural gas system in real time, and inputting the data into a vulnerability assessment model of the associated power-natural gas system to obtain a vulnerability assessment result; wherein the vulnerability represents a reduced magnitude of the functional level of the associated power-gas system when the fault occurs.
2. The method of claim 1, wherein for random faults, the system component fault data includes a probability of failure for each component; for intentional corruption, system component failure data includes different attack costs; for deliberate subversion of the contemplated safeguards, the system component failure data includes different safeguarding costs and attack costs.
3. The method for vulnerability assessment of correlated power-natural gas system under different faults according to claim 1, wherein the vulnerability assessment model of correlated power-natural gas system of step S1 is constructed based on different correlated operation models.
4. The method of claim 3, wherein the different correlation operation models comprise a PNC & GNC model, a PNF & GNF model, a DCPF & GNF model, and a DCPF & SGF model.
5. The method for assessing vulnerability of power-gas system under different faults according to claim 4, wherein the step S1 specifically includes:
s1.1, carrying out correlation comparison on the computed vulnerability of the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model under the random fault and the computed vulnerability of the DCPF & SGF model under the random fault, wherein the stronger the correlation is, the higher the accuracy of the correlation model is represented, and the accuracy sequence of the four correlation models under the random fault is obtained;
s1.2, comparing the optimal attack point calculated by the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model according to the vulnerability index under the condition of deliberate damage with the optimal attack point calculated by the DCPF & SGF model under the condition of deliberate damage, wherein the higher the coincidence degree is, the higher the accuracy of the correlation model is, and obtaining the accuracy sequence of the four correlation models under the deliberate damage;
s1.3, carrying out contact ratio comparison on the optimal defense points calculated by the PNC & GNC model, the PNF & GNF model and the DCPF & GNF model according to the vulnerability indexes under protected deliberate destruction and the optimal defense points calculated by the DCPF & SGF model under deliberate destruction, wherein the higher the contact ratio is, the higher the accuracy of the correlation model is, and obtaining the accuracy sequence of the four correlation models under protected deliberate destruction;
s1.4, sequencing the calculation time required by the PNC & GNC model, the PNF & GNF model, the DCPF & GNF model and the DCPF & SGF model under different fault conditions;
s1.5, selecting an operation model of the associated power-natural gas system meeting the target requirement according to the expected calculation time and accuracy requirement.
6. The vulnerability assessment method of the correlation power-natural gas system under different faults according to claim 5, characterized in that under random fault, four correlation models are as follows according to calculation time from big to small: a DCPF & SGF model, a DCPF & GNF model, a PNF & GNF model, a PNC & GNC model; under the condition of deliberate destruction, the four correlation models are sequentially as follows according to the calculation time from large to small: a DCPF & SGF model, a PNC & GNC model, a DCPF & GNF model, a PNF & GNF model; under the protective deliberate destruction, the four correlation models are sequentially as follows according to the calculation time from large to small: a DCPF & SGF model, a PNC & GNC model, a DCPF & GNF model, a PNF & GNF model; the above calculation times are all obtained in the same operating environment.
7. The vulnerability assessment method of claim 5, characterized in that under random faults, four correlation models are in order from big to small according to accuracy: a DCPF & SGF model, a DCPF & GNF model, a PNF & GNF model, a PNC & GNC model; under deliberate destruction, the four correlation models are in turn from large to small according to the accuracy: a DCPF & SGF model, a DCPF & GNF model, a PNF & GNF model and a PNC & GNC model; under the protected deliberate destruction, the four correlation models are as follows according to the accuracy from large to small: DCPF & SGF model, DCPF & GNF model, PNF & GNF model, PNC & GNC model.
8. The method for assessing the vulnerability of the associated power-natural gas system under different faults according to any one of claims 1-7, characterized in that under random faults and deliberate destruction, when the calculation time is not constrained, the DCPF & SGF model is selected to establish the operation model of the associated power-natural gas system; when the calculation time is limited, selecting a DCPF & GNF or PNF & GNF model to establish an operation model of the associated power-natural gas system;
under the protected deliberate destruction, when the calculation time is not constrained, a DCPF & SGF model is selected to establish an operation model of a related power-natural gas system; and when the calculation time is limited, selecting the DCPF & GNF model to establish an operation model of the associated power-natural gas system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010576780.XA CN111832907A (en) | 2020-06-22 | 2020-06-22 | Vulnerability assessment method of associated power-natural gas system under different faults |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010576780.XA CN111832907A (en) | 2020-06-22 | 2020-06-22 | Vulnerability assessment method of associated power-natural gas system under different faults |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111832907A true CN111832907A (en) | 2020-10-27 |
Family
ID=72899424
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010576780.XA Pending CN111832907A (en) | 2020-06-22 | 2020-06-22 | Vulnerability assessment method of associated power-natural gas system under different faults |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111832907A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191643A (en) * | 2021-05-06 | 2021-07-30 | 四川大学 | Method for identifying fragile line of electric-gas interconnection system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107679716A (en) * | 2017-09-19 | 2018-02-09 | 西南交通大学 | Consider the risk assessment of interconnected network cascading failure and the alarm method of communication fragile degree |
| CN109936133A (en) * | 2019-02-28 | 2019-06-25 | 国网山东省电力公司青岛供电公司 | Consider the power system vulnerability analysis method that information physical gangs up against |
| CN110889594A (en) * | 2019-11-04 | 2020-03-17 | 华中科技大学 | Vulnerability assessment method for urban pipe gallery comprehensive energy system |
-
2020
- 2020-06-22 CN CN202010576780.XA patent/CN111832907A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107679716A (en) * | 2017-09-19 | 2018-02-09 | 西南交通大学 | Consider the risk assessment of interconnected network cascading failure and the alarm method of communication fragile degree |
| CN109936133A (en) * | 2019-02-28 | 2019-06-25 | 国网山东省电力公司青岛供电公司 | Consider the power system vulnerability analysis method that information physical gangs up against |
| CN110889594A (en) * | 2019-11-04 | 2020-03-17 | 华中科技大学 | Vulnerability assessment method for urban pipe gallery comprehensive energy system |
Non-Patent Citations (1)
| Title |
|---|
| HUI ZHANG等: "Simplified operation models of integrated power and gas systems for vulnerability analysis", PHYSICA A:STATISTICAL MECHANICS AND ITS APPLICATIONS, vol. 531, no. 3, pages 1 - 14 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113191643A (en) * | 2021-05-06 | 2021-07-30 | 四川大学 | Method for identifying fragile line of electric-gas interconnection system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Winkler et al. | Interface network models for complex urban infrastructure systems | |
| CN112507552A (en) | Power network vulnerability assessment method based on multiple attack strategies | |
| CN112633649A (en) | Power grid multi-attribute important node evaluation and planning method | |
| CN110096764B (en) | Method for identifying and optimizing fragile line of electric-gas coupling system | |
| CN114282855B (en) | Comprehensive protection method of electric-gas coupling system under heavy load distribution attack | |
| Han et al. | Quantifying the importance of elements of a gas transmission network from topological, reliability and controllability perspectives, considering capacity constraints | |
| CN111444593A (en) | Method for improving vulnerability of elements of electricity-gas comprehensive energy system | |
| CN107622360A (en) | A kind of critical circuits recognition methods for considering subjective and objective factor | |
| CN112149967A (en) | Power communication network vulnerability assessment method and system based on complex system theory | |
| CN115663922A (en) | Distributed power supply multi-degree-of-freedom optimal configuration method and system | |
| CN111832907A (en) | Vulnerability assessment method of associated power-natural gas system under different faults | |
| CN110266046B (en) | Electric heating micro-grid topology comprehensive diagnosis method and system based on complex network | |
| Jeong et al. | Minimizing the consequences of intentional attack on water infrastructure | |
| Sang et al. | Metrics and quantification of power‐line and pipeline resiliency in integrated gas and power systems | |
| CN109858822B (en) | Information power fusion system reliability assessment method based on flow correlation analysis | |
| Wu et al. | Strategies for Improving Resilience of Regional Integrated Energy Systems in the Prevention-Resistance Phase of Integration | |
| Zhang et al. | Simplified operation models of integrated power and gas systems for vulnerability analysis | |
| CN114567562B (en) | Method for identifying key nodes of coupling network of power grid and communication network | |
| CN114818506A (en) | Comprehensive energy system heat supply network fault hazard assessment method, system and device | |
| CN109167356B (en) | N-2 fault screening method | |
| Ouyang et al. | Does topological information matter for power grid vulnerability? | |
| Belle et al. | A mathematical framework for the optimal coupling of interdependent critical infrastructures | |
| CN113344743A (en) | Fault hazard index calculation and vulnerability assessment method for smart power grid | |
| Pani et al. | A Graph-Theoretic Approach to Assess the Power Grid Vulnerabilities to Transmission Line Outages | |
| CN114998051B (en) | Maximum flow theory-based key line identification method for electric-gas coupling system |
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 |