CN109977577B - Gas-electricity coupling system reliability assessment method considering uncertainty of natural gas transmission and distribution pipe network - Google Patents
Gas-electricity coupling system reliability assessment method considering uncertainty of natural gas transmission and distribution pipe network Download PDFInfo
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Abstract
The invention discloses a gas-electricity coupling system reliability assessment method considering uncertainty of a natural gas pipeline network, which comprises the following steps: 1, simulating the running state of a natural gas pipeline network based on an analytic method; 2, transferring the failure probability of the natural gas pipe network to the power generation failure state of the gas turbine based on the proposed state probability transfer technology; 3, establishing a gas turbine reliability equivalent model considering uncertainty of a natural gas pipeline network based on a network equivalent technology; and 4, connecting the natural gas pipe network and the wind power plant into the RBTS system, and calculating the reliability of the coupling system. According to the invention, through researching the influence of different influence factors on the reliability of the coupling system, a gas-electric coupling system reliability model considering uncertainty of a natural gas pipeline network is established, and each influence factor is quantitatively analyzed according to a calculation example, so that a basis and a reference are provided for the engineering planning and the reliable operation of the actual gas-electric coupling system.
Description
Technical Field
The invention relates to the field of reliability evaluation, in particular to a gas-electricity coupling system reliability evaluation method considering uncertainty of a natural gas pipeline network.
Background
With the development of economy, the energy consumption amount continues to increase, and the share of natural gas as a clean fossil energy source in the global primary energy supply is rapidly increasing. With the breakthrough of the technologies such as gas turbine and shale gas development, gas power generation will become the dominant edition of natural gas consumption in China. The total amount of natural gas resources in China is abundant, however, the breadth of China is broad, the structure of a natural gas transmission and distribution pipeline network is complicated, and a plurality of users are provided, so that the reliability evaluation work difficulty of the natural gas pipeline network is increased. At present, the research on the reliability analysis of the natural gas network at home and abroad is still in an exploration stage, and the natural gas network is brought into a power system for reliability evaluation, and the research is more in a starting stage.
For the reliability assessment problem of a natural gas pipe network, a fault tree analysis method and a Monte Carlo simulation method are mainly used. The Monte Carlo method is also called as a statistical simulation method, and based on a probability and statistical theory method, the required calculation problem is solved by using random numbers, but the Monte Carlo method usually needs a large number of samples and simulation time to ensure the precision of the Monte Carlo method, and a large number of uncertain factors exist in a natural gas network system to cause certain difficulty to the simulation process; the fault tree analysis method needs to establish a fault tree for a research target, and obtains the failure probability of the research target according to the weight (determined by an empirical method and a semi-empirical method) of each fault event by using a minimum cut-set method, but the process of constructing the fault tree is high in logic and complex. For the coupling of the natural gas pipe network system and the power system, most of the existing research focuses on the joint operation and planning of the natural gas pipe network system and the power system, the reliability evaluation of the coupled system is rarely carried out, and the influence of various uncertain factors in the natural gas pipe network system on the system reliability can be considered in detail.
Disclosure of Invention
The invention aims to avoid the defects of the existing research, provides the gas-electric coupling system reliability assessment method considering the uncertainty of the natural gas pipe network, so that the complexity and the inaccuracy of assessment work caused by large-scale assessment of the whole gas-electric coupling system by using a Monte Carlo simulation method can be reduced, meanwhile, the reliability assessment can be independently carried out on the natural gas pipe network, and the simplicity and the accuracy of the assessment are realized.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
the invention relates to a gas-electricity coupling system reliability evaluation method considering uncertainty of a natural gas pipeline network, which is characterized by comprising the following steps of;
simulating the working state of a natural gas pipeline network by using an analytic method, and calculating the state probability;
step 1.1, obtaining reliability parameters of each element in a natural gas pipe network, comprising the following steps: failure rate λ of element i ng (i) And repair time γ ng (i) (ii) a And calculating a reliability index of the element i using equations (1) and (2), including: availability ratio A ng (i) And unavailability rate U ng (i)：
In the formulae (1) and (2), 1/γ ng (i) Representing the repair rate of the element i, T being the number of hours of a given time interval;
step 1.2, simulating the working state of the natural gas pipe network:
assuming that each element in the natural gas pipeline network is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, and the shutdown state is represented by 0, a random simulation state S of the element i is obtained by using an equation (3) ng (i) Thereby obtaining the random simulation state S of n elements in the natural gas pipe network ng ＝{s ng (1),s ng (2),…,s ng (n)}：
In the formula (3), r ng (i) Is the ith compliance interval [0,1 ] corresponding to the analog element i]Uniformly distributed sampling random numbers;
step 1.3, calculating the state probability p (S) of the natural gas pipe network by using the formula (4) ng )：
In the formula (4), omega A And Ω U Respectively indicate that the element is at positiveSet of Normal and Fault states, when S ng (i) If =0, i ∈ Ω U (ii) a When S is ng (i) If =1, i ∈ Ω A ；
Step two, establishing a natural gas pipe network reliability equivalent model based on a state probability transition equivalent method:
step 2.1, defining K as a sampling state sequence number of the natural gas pipe network, and setting the total sampling times as K; defining t as a number of gas load equipment, and initializing t =1;
step 2.2, initializing k =1;
step 2.3, sampling the random simulation state S of the natural gas pipe network under the kth simulation sequence according to the step 1.2 ng [k]And calculating its state probability p (S) ng [k])；
Step 2.4, traversing the connectivity of the natural gas pipe network corresponding to the kth simulation sequence through a depth search algorithm, judging whether the tth gas load equipment is connected with the natural gas pipe network, if so, calling that the tth gas load equipment is not affected by failure, and marking the tth gas load equipment as x (S) ng [k,t]) =0; otherwise, the tth air load equipment is called to have failure shutdown and marked as x (S) ng [k,t]) =1; thereby obtaining the probability p of the t-th air load equipment generating the accumulated shutdown under the k-th sampling sequence by using the formula (5) ng (k,t)：
p ng (k,t)＝p(S ng [k])×x(S ng [k,t]) (5)
Step 2.5, assigning k +1 to k, and judging k>If K is true, the cumulative accumulated outage probability p of the t-th gas load equipment is obtained by using the formula (6) ng (t); otherwise, returning to the step 2.3 for execution;
step 2.6, assigning t +1 to t, and judging t>N nd If yes, the cumulative outage probability p of all the gas load devices is calculated by using the formula (7) ng (ii) a Otherwise, returning to the step 2.2 to execute:
in the formula (7), N nd The total number of the gas loads for the natural gas pipe network;
and thirdly, performing probability equivalence on the uncertainty of the natural gas pipe network and the random fault of the gas turbine by adopting a series equivalence principle, so as to establish a gas turbine reliability model considering the uncertainty of the natural gas pipe network:
step 3.1, obtaining the reliability parameters of the gas turbine, including: failure rate λ of gas turbine m gt (m) and repair ratio γ gt (m); and calculates the availability ratio A of the gas turbine m using the equations (8) and (9) gt (m) and unavailability rate U gt (m)：
Step 3.2, obtaining the equivalent availability ratio p of the gas turbine m by using the formulas (10) and (11) Aeq (m) and equivalent unavailability p Ueq (m)：
p Aeq (m)＝(1-p ng (m))×A gt (m) (10)
p Ueq (m)＝1-(1-p ng (m))×A gt (m) (11)
Fourthly, evaluating the reliability of the gas-electric coupling system considering the uncertainty of the natural gas pipe network by utilizing a Monte Carlo simulation method:
step 4.1, setting H as Monte Carlo analog sampling number H max Is the total number of analog samples; initialization h =1;
and 4.2, assuming that the gas turbine is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, and the shutdown state is represented by 0, obtaining the h-th by using the formula (12)Stochastic simulation state S of gas turbine m in subsampling gt (M, h) to obtain the working states S of the M gas turbines under the h analog sampling sequence gt (h),＝{S gt (1,h),S gt (2,h),...,S gt (M,h)}：
In the formula (12), r gt (m, h) denotes the h analog sample sequence at [0,1 ]]The m-th random numbers are uniformly distributed in intervals; m =1,2, \ 8230, M is the number of gas turbines in the gas turbine set;
and 4.3, assuming that the traditional generator is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, the shutdown state is represented by 0, and the random simulation state S of the traditional generator j in the h-th sample is obtained by using a formula (13) tp (J, h) to obtain the working state S of J traditional generators under the h analog sampling sequence tp (h)＝{S tp (1,h),S tp (2,h),...,S tp (J,h)}：
In the formula (13), r tp (j, h) is the h analog sample sequence at [0, 1%]J random number, p, evenly distributed over the interval Utp (J, h) is the unavailability of a traditional generator J, J =1,2 \8230, J is the number of generators in the traditional generator set;
step 4.4, respectively calculating the generated power P of the gas turbine set under the h-th analog sampling sequence by using the formula (14) and the formula (15) tp (h) And the generated power P of the traditional generator set gt (h)：
In formula (14) -formula (15), P rtp (m, h) and P rgt (j, h) are the rated capacities of a single traditional generator and a single gas turbine respectively;
step 4.5, calculating the total generated power P of the gas-electric coupling system under the h analog sampling sequence by using the formula (16) sys (h)：
P sys (h)＝P tp (h)+P gt (h) (16)
Step 4.6, let f denote event: p is sys (h)＜P L (h) (ii) a Judgment of P sys (h)＜P L (h) If yes, indicating that the gas-electric coupling system is in a load loss state, and making a marking function I h (f) =1, otherwise, it indicates that the gas-electric coupling system is not unloaded, and let the marking function I h (f) =0; wherein, P H (h) The system load under the h analog sampling sequence is obtained;
step 4.7, assigning h +1 to h, and judging h>H max If yes, executing step 4.8; otherwise, returning to the step 4.2 for execution;
and 4.8, calculating the power shortage probability HOHP of the air-electric coupling system by using the formula (17):
and 4.9, calculating the expected electricity shortage time HOHE (h/yh) of the air-electric coupling system by using the formula (18):
step 4.10, calculating the power supply lack EDNS (MW) of the gas-electric coupling system by using the formula (19):
step 4.11, calculating the expected power shortage EENS (MWh) of the gas-electric coupling system by using the formula (19):
in formulae (17) to (20), H max Is the maximum sampling ordinal number;
step 4.12, calculating the direct contribution index B of the reliability of the natural gas power generation by using the formulas (21) and (22) respectively dr (x) And a unit contribution index B pu (x)：
B dr (x)＝x before -x after (21)
In the formulas (20) to (21), x represents any reliability index of the gas-electric coupling system, i.e., LOLP, LOLE, EDNS or EENS; x is the number of before And x after Reliability index values of the gas-electricity coupling system before and after the gas turbine is connected are respectively; p rgt (m) is the installed capacity of the gas turbine m.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention researches the reliability of the gas-electric coupling system considering the uncertainty of a natural gas pipe network system, and provides a reliability evaluation method of the gas-electric coupling system based on a probability transfer equivalent technology, wherein the method transfers the unreliability of a natural gas pipe network system to the affected outage of a gas unit, so that the complexity of the evaluation of the reliability of the combination of the gas-electric coupling system is reduced, and the reliability evaluation efficiency is improved;
2. according to the method, the influence of different influence factors on the reliability of the coupling system is researched, so that the influence of multiple factors such as multiple faults of a pipe network, isolation operation of a valve, multiple air pressure grades of the pipe network and the like on the reliability of the natural gas pipe network system can be effectively considered. The fault state possibly existing in the operation of the natural gas pipe network system is completely reflected in the model of the natural gas pipe network system, the corresponding state probability is calculated, a mathematical model is provided for mastering the operation state of the natural gas pipe network system, therefore, a large amount of sampling work required when Monte Carlo simulation is carried out on the natural gas pipe network can be simplified, and the calculation precision is improved.
3. Based on a network equivalent technology, a gas turbine reliability equivalent model considering uncertainty of a natural gas pipe network system is established, equivalent availability and equivalent unavailability after the gas turbine is connected are calculated, the coupling relation between the natural gas pipe network system and the gas turbine is quantized, and theoretical support can be provided for planning and reliable operation of a natural gas pipe network system;
4. the method calculates a series of classical indexes reflecting the reliability of the system, evaluates the reliability result from multiple aspects, provides a calculation formula for responding the reliability index, and reflects the reliability of the system from multiple aspects of probability, time and degree;
5. the method not only calculates the classical index of the reliability of the evaluation system, but also defines and calculates the direct reliability contribution index and the unit contribution index of the natural gas power generation, and can effectively depict the reliability contribution of the natural gas power generation, thereby being capable of making beneficial guidance for the management and planning of the natural gas power generation in the actual engineering.
Drawings
FIG. 1 is a schematic flow chart of the reliability evaluation method for the gas-electric coupling system according to the present invention.
Detailed Description
In this embodiment, as shown in fig. 1, a method for evaluating reliability of a gas-electric coupling system in consideration of uncertainty of a natural gas pipeline network includes first simulating an operation state of the natural gas pipeline network system based on an analysis technique and a network analysis technique and evaluating a state probability; secondly, transferring the unreliability of the natural gas pipe network system to a power generation failure state of the gas turbine set based on a probability transfer equivalent technology; finally, a gas turbine reliability equivalent model considering the uncertainty of a natural gas pipeline network system is established based on a network equivalent technology, the influence of various factors on the reliability of the gas-electricity coupling system is analyzed, and technical support can be provided for the engineering planning and the reliable operation of the actual gas-electricity coupling system; specifically, the method comprises the following steps:
simulating the working state of the natural gas pipe network system by using an analytic method, and calculating the state probability;
step 1.1, obtaining reliability parameters of each element in a natural gas pipe network, comprising the following steps: failure rate λ of element i ng (i) And rate of repair gamma ng (i) (ii) a And calculating a reliability index of the element i using equations (1) and (2), including: availability ratio A ng (i) And unavailability rate U ng (i)：
The availability and the unavailability are numerically complementary relationships, the availability representing an average of instantaneous availability in a given time interval, and the unavailability representing an average of instantaneous unavailability in a given time interval.
In the formulae (1) and (2), 1/γ ng (i) Denotes the repair rate of the element i, and T is the number of hours of a given time interval for numerically converting the unit of the repair rate. In this example, T is 8760, which represents 8760 hours of a year;
and step 1.2, simulating the working state of the natural gas pipe network system. Random simulation of System State x can be computer generated to obey [0,1 ]]Are associated with uniformly distributed random numbers. For example, let element i be a two-state element with an operating probability p i The failure probability is q i ，q i ＝1-p i Generating a obedience interval [0,1 ]]Random number u uniformly distributed i When u is i The primary sampling result satisfies that u is more than or equal to 0 i <p i The element i of the surface simulation test works normally; otherwise when p i ≤u i And (4) the surface element i is in a failure state when the surface element i is less than or equal to 1.
Assuming that each element in the natural gas pipe network adopts a two-state Markov modelWherein the operation state is represented by 1 and the shutdown state is represented by 0, the random simulation state S of the element i is obtained by the formula (3) ng (i) Thereby obtaining the random simulation state S of n elements in the natural gas pipe network ng ＝{s ng (1),s ng (2),…,s ng (n)}：
In the formula (3), r ng (i) Is the ith compliance interval [0,1 ] corresponding to the analog element i]Uniformly distributed sampling random numbers;
step 1.3, calculating the state probability p (S) of the natural gas pipe network by using the formula (4) ng )：
In the formula (4), omega A And Ω U Respectively representing the components in a normal state and a fault state, when S ng (i) If =0, i ∈ Ω U (ii) a When S is ng (i) If =1, i ∈ Ω A (ii) a T is taken for 8760 hours of a year.
Step two, establishing a natural gas pipe network system reliability equivalent model based on a state probability transfer equivalent technology:
step 2.1, defining K as a sampling state sequence number of the natural gas pipe network, and setting the total sampling times as K; defining t as a gas load equipment number, and initializing t =1;
step 2.2, initializing k =1;
step 2.3, sampling the random simulation state S of the natural gas pipe network under the kth simulation sequence according to the step 1.2 ng [k]And calculating its state probability p (S) ng [k])；
Step 2.4, defining the concept of 'accumulated outage state' of the gas load equipment, namely that the gas load equipment is called to be in the accumulated outage state when the gas load equipment cannot normally work due to equipment failure in the natural gas pipeline network system. Traversing the connectivity of a natural gas pipe network corresponding to the kth simulation sequence through a depth search algorithm, judging whether the tth gas load equipment is connected with the natural gas pipe network, if yes, calling that the tth gas load equipment is not affected and stopped, and marking the tth gas load equipment as x (S) ng [k,t]) =0; otherwise, the tth air load equipment is called to have failure shutdown and marked as x (S) ng [k,t]) And =1. Thereby obtaining the probability p of the t-th air load equipment generating the accumulated shutdown under the k-th sampling sequence by using the formula (5) ng (k,t)：
p ng (k,t)＝p(S ng [k])×x(S ng [k,t]) (5)
Step 2.5, assigning k +1 to k, and judging k>If K is true, the cumulative accumulated outage probability p of the t-th gas load equipment is obtained by using the formula (6) ng (t); otherwise, returning to the step 2.3 for execution;
step 2.6, assigning t +1 to t, and judging t>N nd If yes, the cumulative outage probability p of all the gas load devices is calculated by using the formula (7) ng (ii) a Otherwise, returning to the step 2.2 for execution;
in the formula (7), N nd The total number of gas loads for the natural gas pipe network system.
Step three, establishing a gas turbine reliability model considering uncertainty of a natural gas pipeline network system:
the invention accesses the tth gas turbine to the tth gas load point, wherein the gas load point is the gas load equipment in the step two, and the probability equivalence is carried out on the uncertainty of the natural gas pipeline network system and the random fault of the gas turbine by adopting the series equivalence principle.
Step 3.1, obtaining the reliability of the gas turbineSexual parameters, including: failure rate λ of gas turbine m gt (m) and repair ratio γ gt (m); and calculates the availability ratio A of the gas turbine m using the equations (8) and (9) gt (m) and unavailability rate U gt (m)：
In the formulas (8) to (9), T represents 8760 hours in one year.
Step 3.2, obtaining the equivalent availability ratio p of the gas turbine m by using the formulas (10) and (11) Aeq (m) and equivalent unavailability p Ueq (m)：
p Aeq (m)＝(1-p ng (m))×A gt (m) (10)
p Ueq (m)＝1-(1-p ng (m))×A gt (m) (11)
Fourthly, evaluating the reliability of the gas-electric coupling system considering the uncertainty of the natural gas pipe network system by utilizing a Monte Carlo simulation method:
methods commonly used for reliability assessment of power systems fall into two broad categories, namely analytical methods, also known as state enumeration methods, and simulation methods, which are further classified into sequential monte carlo methods and non-sequential monte carlo methods. Generally, if the failure probability of an element is very small or complicated operation conditions are not considered, the state enumeration method has a good effect; the Monte Carlo simulation is more convenient if the number of severe events is relatively large or complex conditions are accounted for. The reliability evaluation method adopts a Monte Carlo simulation method to carry out reliability evaluation on the gas-electricity coupling system.
Step 4.1, setting H as Monte Carlo analog sampling number H max Is the total number of analog samples; initialization h =1;
the monte carlo simulation method can also be divided into non-sequential random simulation and sequential random simulation. The present invention uses a non-sequential monte carlo simulation, often referred to as state sampling, to evaluate the reliability of an electro-pneumatic coupling system based on the fact that one system state is a combination of all the component states and each component state can be determined by sampling its probability of appearing in that state.
And 4.2, assuming that the gas turbine is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, and the shutdown state is represented by 0, obtaining the random simulation state S of the gas turbine m in the h sample by using the formula (12) gt (M, h) to obtain the working states S of the M gas turbines under the h analog sampling sequence gt (h),＝{S gt (1,h),S gt (2,h),…,S gt (M,h)}：
In the formula (12), r gt (m, h) denotes the h analog sample sequence at [0,1 ]]The m-th random numbers are uniformly distributed in intervals; m =1,2, \ 8230, M is the number of gas turbines in the gas turbine set;
and 4.3, assuming that the traditional generator is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, the shutdown state is represented by 0, and the random simulation state S of the traditional generator j in the h-th sample is obtained by using a formula (13) tp (J, h) to obtain the working state S of J traditional generators under the h analog sampling sequence tp (h)＝{S tp (1,h),S tp (2,h),…,S tp (J,h)}：
In the formula (13), r tp (j, h) is the h analog sample sequence at [0, 1%]J random number, p, evenly distributed over the interval Utp (J, h) is the unavailability of a traditional generator J, J =1,2 \8230, J is the number of generators in the traditional generator set; in the formula (12), r tp (j) Is the jth random number, p Utp (j) Is the jth oneUnavailability of conventional generators.
Step 4.4, respectively calculating the generated power P of the gas turbine set under the h-th analog sampling sequence by using the formula (14) and the formula (15) tp (h) And the generated power P of the traditional generator set gt (h)：
In formula (14) -formula (15), P rtp (m, h) and P rgt (j, h) are the rated capacities of a single traditional generator and a single gas turbine respectively;
step 4.5, calculating the total generated power P of the gas-electric coupling system under the h analog sampling sequence by using the formula (16) sys (h)：
P sys (h)＝P tp (h)+P gt (h) (16)
Step 4.6, let f denote event: p sys (h)＜P H (h) (ii) a Judgment of P sys (h)＜P H (h) If yes, indicating that the gas-electric coupling system is in a load loss state, and making a marking function I h (f) =1, otherwise, it indicates that the gas-electric coupling system is not unloaded, and let the marking function I h (f) =0; wherein PH (h) is the system load under the h analog sampling sequence;
step 4.7 assigns h +1 to h, judges h>H max If yes, executing step 4.8; otherwise, returning to the step 4.2 for execution;
4.8, the reliability indexes reflect the quality of the reliability of the system, the following 4 indexes are classical indexes in the reliability evaluation theory, and the larger the index value is, the worse the reliability of the system is; the smaller the index value, the better the system reliability. Calculating the power shortage probability LOLP (home of home) of the system by using the formula (17) under the condition of system load loss:
step 4.8, counting the expected LOLE (h/yr) of the system power shortage time by using a formula (17) in the system load loss state:
and 4.9, calculating the system power supply shortage EDNS (MW) (expected demand not supported) by using a formula (18) under the condition that the system is in a load loss state:
step 4.10, calculating the expected power shortage EENS (MWh) of the system by using the formula (19) under the condition of system load loss:
in formulae (17) to (20), H max Is the maximum sampling ordinal number; h is the number of hours of a given time interval;
and 4.11, besides the 4 classical indexes, formulas (20) and (21) are defined to respectively calculate a direct reliability contribution index and a unit reliability contribution index of the natural gas power generation, so that the reliability contribution of the natural gas power generation can be effectively described.
B dr (x)＝x before -x after (21)
In the formulas (20) to (21), x represents any one of the reliability indexes of the system, i.e., LOLP, LOLE, EDNS or EENS; x is the number of before And x after Before and after the gas turbine is connected respectivelyA system reliability index value of (a); p rgt (i) The installed capacity of the ith gas turbine.
Claims (1)
1. A gas-electricity coupling system reliability assessment method considering uncertainty of a natural gas pipeline network is characterized by comprising the following steps of;
simulating the working state of a natural gas pipeline network by using an analytic method, and calculating the state probability;
step 1.1, obtaining reliability parameters of each element in a natural gas pipe network, comprising the following steps: failure rate λ of element i ng (i) And repair time γ ng (i) (ii) a And calculating a reliability index of the element i using equations (1) and (2), including: availability ratio A ng (i) And unavailability rate U ng (i)：
In the formulae (1) and (2), 1/γ ng (i) Representing the repair rate of the element i, T being the number of hours of a given time interval;
step 1.2, simulating the working state of the natural gas pipe network:
assuming that each element in the natural gas pipeline network is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, and the shutdown state is represented by 0, a random simulation state S of the element i is obtained by using an equation (3) ng (i) Thereby obtaining the random simulation state S of n elements in the natural gas pipe network ng ＝{s ng (1),s ng (2),…,s ng (n)}：
In the formula (3), r ng (i) Is the ith compliance interval [0,1 ] corresponding to the analog element i]Uniformly distributed sampling random numbers;
step 1.3, calculating the state probability p (S) of the natural gas pipe network by using the formula (4) ng )：
In the formula (4), omega A And Ω U Respectively representing the components in a normal state and a fault state, when S ng (i) If =0, i ∈ Ω U (ii) a When S is ng (i) If =1, i ∈ Ω A ；
Step two, establishing a natural gas pipe network reliability equivalent model based on a state probability transition equivalent method:
step 2.1, defining K as a sampling state sequence number of the natural gas pipe network, and setting the total sampling times as K; defining t as a gas load equipment number, and initializing t =1;
step 2.2, initializing k =1;
step 2.3, sampling the random simulation state S of the natural gas pipe network under the kth simulation sequence according to the step 1.2 ng [k]And calculating its state probability p (S) ng [k])；
Step 2.4, traversing the connectivity of the natural gas pipe network corresponding to the kth simulation sequence through a depth search algorithm, judging whether the tth gas load equipment is connected with the natural gas pipe network, if so, calling that the tth gas load equipment is not affected by failure, and marking the tth gas load equipment as x (S) ng [k,t]) =0; otherwise, the tth air load equipment is called to have failure shutdown and marked as x (S) ng [k,t]) =1; thereby obtaining the probability p of the t-th air load equipment generating the accumulated shutdown under the k-th sampling sequence by using the formula (5) ng (k,t)：
p ng (k,t)＝p(S ng [k])×x(S ng [k,t]) (5)
Step 2.5, assigning k +1 to k, and judging k>If K is true, the cumulative effect of the t-th gas-using load equipment is obtained by using the formula (6)Accumulating accumulated outage probability p ng (t); otherwise, returning to the step 2.3 for execution;
step 2.6, assigning t +1 to t, and judging t>N nd If yes, the cumulative outage probability p of all the gas load devices is calculated by using the formula (7) ng (ii) a Otherwise, returning to the step 2.2 to execute:
in the formula (7), N nd The total number of the gas loads for the natural gas pipe network;
and thirdly, performing probability equivalence on the uncertainty of the natural gas pipe network and the random fault of the gas turbine by adopting a series equivalence principle, so as to establish a gas turbine reliability model considering the uncertainty of the natural gas pipe network:
step 3.1, obtaining the reliability parameters of the gas turbine, including: failure rate λ of gas turbine m gt (m) and repair ratio γ gt (m); and calculates the availability ratio A of the gas turbine m using the equations (8) and (9) gt (m) and unavailability rate U gt (m)：
Step 3.2, obtaining the equivalent availability ratio p of the gas turbine m by using the formulas (10) and (11) Aeq (m) and equivalent unavailability p Ueq (m)：
p Aeq (m)＝(1-p ng (m))×A gt (m) (10)
p Ueq (m)＝1-(1-p ng (m))×A gt (m) (11)
Fourthly, evaluating the reliability of the gas-electric coupling system considering the uncertainty of the natural gas pipe network by utilizing a Monte Carlo simulation method:
step 4.1, setting H as Monte Carlo analog sampling number H max Is the total number of analog samples; initialization h =1;
and 4.2, assuming that the gas turbine is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, and the shutdown state is represented by 0, obtaining the random simulation state S of the gas turbine m in the h-th sampling by using an equation (12) gt (M, h) to obtain the working states S of the M gas turbines under the h analog sampling sequence gt (h),＝{S gt (1,h),S gt (2,h),…,S gt (M,h)}：
In the formula (12), r gt (m, h) denotes the h analog sample sequence at [0,1 ]]The m-th random numbers are uniformly distributed in intervals; m =1,2, \ 8230, M is the number of gas turbines in the gas turbine set;
and 4.3, assuming that the traditional generator is represented by a two-state Markov model, wherein the running state of the two states is represented by 1, the shutdown state is represented by 0, and the random simulation state S of the traditional generator j in the h-th sample is obtained by using a formula (13) tp (J, h) to obtain the working state S of J traditional generators under the h analog sampling sequence tp (h)＝{S tp (1,h),S tp (2,h),…,S tp (J,h)}：
In the formula (13), r tp (j, h) is the h analog sample sequence at [0, 1%]J random number, p, evenly distributed over the interval Utp (J, h) is the unavailability of a traditional generator J, J =1,2 \8230, J is the number of generators in the traditional generator set;
step 4.4, respectively calculating the generated power P of the gas turbine set under the h-th analog sampling sequence by using the formula (14) and the formula (15) tp (h) And the generated power P of the traditional generator set gt (h)：
In formula (14) -formula (15), P rtp (m, h) and P rgt (j, h) are the rated capacities of a single traditional generator and a single gas turbine respectively;
step 4.5, calculating the total generated power P of the gas-electric coupling system under the h analog sampling sequence by using the formula (16) sys (h)：
P sys (h)＝P tp (h)+P gt (h) (16)
Step 4.6, let f denote event: p sys (h)<P L (h) (ii) a Judgment of P sys (h)<P L (h) If yes, indicating that the gas-electric coupling system is in a load loss state, and making a marking function I h (f) =1, otherwise, it indicates that the gas-electric coupling system is not unloaded, and let the marking function I h (f) =0; wherein, P H (h) The system load under the h analog sampling sequence is obtained;
step 4.7, assigning h +1 to h, and judging h>H max If yes, executing step 4.8; otherwise, returning to the step 4.2 for execution;
and 4.8, calculating the power shortage probability HOHP of the air-electric coupling system by using the formula (17):
and 4.9, calculating the expected electricity shortage time HOHE (h/yh) of the air-electric coupling system by using the formula (18):
step 4.10, calculating the power supply lack EDNS (MW) of the gas-electric coupling system by using the formula (19):
step 4.11, calculating the expected power shortage EENS (MWh) of the gas-electric coupling system by using the formula (20):
in formulae (17) to (20), H max Is the maximum sampling ordinal number;
step 4.12, calculating the direct contribution index B of the reliability of the natural gas power generation by using the formulas (21) and (22) respectively dr (x) And a unit contribution index B pu (x)：
B dr (x)＝x before -x after (21)
In the formulas (21) to (22), x represents any reliability index of the gas-electric coupling system, i.e., LOLP, LOLE, EDNS or EENS; x is a radical of a fluorine atom before And x after Reliability index values of the gas-electricity coupling system before and after the gas turbine is connected are respectively; p rgt (m) is the installed capacity of the gas turbine m.
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