CN111898239A - Distributed residual voltage power generation system energy supply reliability evaluation method based on Monte Carlo simulation method - Google Patents
Distributed residual voltage power generation system energy supply reliability evaluation method based on Monte Carlo simulation method Download PDFInfo
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Abstract
The invention discloses a distributed excess pressure power generation system energy supply reliability evaluation method based on a Monte Carlo simulation method. Based on the method, the energy supply reliability index of the distributed residual voltage power generation system is solved by adopting a Monte Carlo simulation method, the method randomly samples the duration time of each device or part in the current state based on the probability theory principle in statistics, recombines the sampling states of all the devices or parts, judges whether the system in each state meets the requirement of normal operation, and finally obtains the energy supply reliability index of the distributed residual voltage power generation system. The invention adopts a Monte Carlo simulation method to evaluate the energy supply reliability of the distributed residual pressure power generation system and optimize the structure and equipment layout of the distributed residual pressure power generation system.
Description
Technical Field
The invention relates to the field of reliability evaluation of distributed excess pressure power generation systems, in particular to energy supply reliability evaluation of a distributed excess pressure power generation system based on a Monte Carlo simulation method.
Background
The energy production and consumption in China are all in the forefront of the world, but a series of outstanding problems exist in the energy utilization mode: the energy structure is unreasonable, the energy utilization rate is not high, and the development and utilization ratio of renewable energy is low. In recent years, active recycling of secondary energy has become an effective measure for reducing energy consumption, saving energy, and reducing cost. The recycling of the residual pressure and the residual heat is one of the effective important means. The distributed residual pressure power generation system mainly utilizes the pressure difference energy and the heat energy of natural gas or steam in the pressure reduction and temperature reduction processes to drive the turboexpander to do work, converts the work into mechanical energy, and drives the generator to generate power so as to realize energy conversion and output electric energy. The system can save energy, improve the utilization rate of resources and does not cause any pollution to the environment.
According to field resources and requirements, the topology or structural form of the distributed residual voltage power generation system is various: 1. the medium at the input end can be water vapor or natural gas, different working media, and different devices or parts in the system; 2. the input ends can be connected in parallel or in series to the system; 3. the turbine system can be connected into the system in parallel or in series, and different access methods cause different equipment and components in the system; 4. the output end can output electric energy and heat energy, and different energy supply modes can be adopted. The distributed residual pressure power generation system is flexible in topological structure and energy supply form, and can greatly improve the energy efficiency of the system. However, the novel distributed residual pressure system lacks an effective evaluation method and technology, and the development of the distributed residual pressure power generation system is greatly hindered.
The Monte Carlo Simulation method (Monte Carlo Simulation-MCS) is a computer Simulation method for obtaining a reliability index by using a statistical method, and is characterized in that the probability theory principle in statistics is used for randomly sampling the duration time of each device or part in the current state, the sampling states of all elements are recombined, whether the system meets the requirement of normal operation in each state is judged, and finally the system energy supply reliability index is obtained. The Monte Carlo simulation method randomly simulates various possible states based on the original reliability parameters of each device or component in the distributed residual pressure power generation system, and calculates the reliability index of the system by adopting a large number of simulation experiment results. The method does not count the system scale and the model dimension, so the algorithm and the program structure are generally simpler, the convergence rate is relatively higher, and the probability distribution form of the reliability index can be obtained. However, the analog method has the disadvantages that large-scale random analog sampling is required, and the calculation time is much longer than that of the analytic method.
In summary, the distributed residual voltage power generation system with a large number of types of devices or components is a multi-input multi-output complex nonlinear system with strong randomness, and the monte carlo simulation method is applicable to the reliability index calculation of the complex system with multiple dimensions and strong randomness, so that the application of the monte carlo simulation method to the reliability index calculation of the distributed residual voltage power generation system is feasible.
Disclosure of Invention
The invention aims to solve the energy supply reliability of different distributed residual pressure power generation systems, thereby optimizing the structure and equipment layout of the distributed residual pressure power generation system and providing a reference basis for the design of a new distributed residual pressure power generation system and the reconstruction of an established distributed residual pressure power generation system.
The technical scheme adopted by the invention for solving the problems is as follows: a distributed residual pressure power generation system energy supply reliability assessment method based on a Monte Carlo simulation method is characterized in that the Monte Carlo simulation method is used for calculating the energy supply reliability of the distributed residual pressure power generation system. The distributed residual pressure power generation system mainly comprises a turboexpander, a generator, a frequency converter, a drain valve, an electric pressure regulating valve, an electric stop valve, a steam pipeline, a turbine outlet pipeline, a bearing, an electric power circuit and the like. The evaluation method adopted by the invention can effectively calculate the energy supply reliability of the complex and variable distributed residual pressure power generation system, and the energy supply reliability evaluation result of the distributed residual pressure power generation system can effectively guide the construction planning of a new distributed residual pressure power generation system and the optimization and reconstruction of the established distributed residual pressure power generation system.
The invention utilizes the characteristic that vapor pressure parameters of both supply and supply sides are not matched, adopts the turbo expander to convert the partial pressure difference energy into mechanical energy, and drives the generator to generate electricity by the mechanical energy, thereby realizing energy conversion and outputting electric energy. The distributed residual pressure power generation system converts originally wasted differential pressure energy into green high-quality electric energy to be on-line or consumed by users on the spot, and can greatly improve the energy efficiency of a distributed energy station/cogeneration user in the heat supply process, thereby improving the economic benefit of the distributed energy station/cogeneration user.
According to the method, the time of the current state of each device or component in the system is randomly sampled according to the probability theory in statistics, the sampling states of all the devices or components are recombined, and whether the system meets the energy supply condition in each state is judged, so that the energy supply reliability index of the whole distributed residual voltage power generation system is obtained.
The method is based on the original reliability parameters of each device or component in the distributed residual pressure power generation system, and the reliability index of the whole distributed residual pressure power generation system is calculated according to various states possibly occurring in random simulation and a large number of simulation experiment results.
The reliability of the energy supply of the distributed residual pressure power generation system mainly depends on the reliability index of the energy supply at the user side, the reliability index of the energy supply at the user side is obtained based on the reliability parameters of the equipment, and the reliability parameters of the equipment are obtained based on the historical data statistics of the operation of each component of the system. The user-side energy supply reliability index reflects the reliability degree of continuous energy supply for a user, mainly comprises an average fault rate, an average fault repair time and an average outage time, which are probability indexes and reflect expected values under certain probability distribution. Since all the components between the energy using side and the energy supplying side are in series relationship, the condition for the normal energy supply by the energy using side is that all the components between them are normally operated.
The energy utilization side reliability indexes mainly comprise average fault rate, average outage time and average fault repair time.
(1) Average failure rate: the energy utilization side refers to the number of times of stopping energy supply caused by the failure of equipment elements in the distributed residual voltage power generation system within a given time interval (usually 1 year), and the energy utilization side uses lambdaiExpressed in units of times per year.
Wherein λ isjIs the failure rate of element j;
(2) average downtime: the power supply stopping time of a user within a given time (usually 1 year) is indicated, and U is usediExpressed in units of hours/year.
Wherein, γjThe time to repair the failure for element j;
(3) mean time to failure repair: means the time average value of energy utilization from the occurrence of energy supply stoppage to the restoration of energy supply, using gammaiExpressed in units of hours/time.
The reliability index of the energy supply of the whole distributed excess pressure power generation system can be established based on the reliability index of the energy utilization side, and the reliability index is used for reflecting the reliability degree of the energy supply of the whole system.
According to different reliability evaluation contents, the energy supply reliability indexes of the distributed residual voltage power generation system comprise energy supply stopping frequency and time indexes and energy supply stopping load energy indexes, and the method is specifically divided into the following items:
(1) the system average outage frequency index (SAIFI) refers to the average outage frequency experienced by each user powered by the system in the unit of times/(user-year).
Counting, and recording as SAIFI-1 when all reliability indexes caused by stopping energy supply are counted; when the reliability index caused by external power supply stopping is not counted, the reliability index is marked as SAIFI-2; when the reliability index of stopping energy supply caused by planned energy supply stopping, energy supply limiting and the like due to insufficient energy supply is not counted, the SAIFI-3 is marked.
(2) The system average outage duration index (SAIDI) refers to the average outage duration experienced by each user powered by the system in one year, in hours/(user-year).
Similarly, during statistics, when reliability indexes caused by stopping energy supply are counted, the reliability indexes are recorded as SAIDI-1; when the reliability index caused by external energy supply stopping is not counted, the reliability index is marked as SAIDI-2; when the reliability index of stopping energy supply caused by planned energy supply stopping, energy supply limiting and the like due to insufficient energy supply is not counted, the SAIDI-3 is marked.
(3) The user average energization interruption duration index (CAIDI) refers to an average energization interruption duration experienced by a user whose energization is interrupted in one year, and is expressed in units of hours/(energization interruption user · year).
(4) The average power availability index (ASAI) is a ratio of the total number of non-stop power hours experienced by a user in a year to the total number of power hours required by the user.
(5) An energy shortage expectation (EENS) refers to the shortage of energy provided to a user in mega joules per year due to component down time.
EENS＝∑La(i)Ui
The Monte Carlo simulation method is based on the premise of original data of the reliability of each equipment element of the distributed residual voltage power generation system, a computer is used for sampling to simulate the operation state which can randomly appear, and the required reliability index is calculated by a probability statistical method. The sampling method is generally divided into sequential and non-sequential Monte Carlo methods. The method mainly utilizes a sequential Monte Carlo simulation algorithm to evaluate the reliability of the energy supply of the distributed residual voltage power generation system.
The monte carlo sampling method is also called a random sampling method and is essentially a probability simulation method. The process of evaluating the energy supply reliability of the distributed residual voltage power generation system by adopting the Monte Carlo simulation method can be roughly divided into three steps of system state sampling, system state analysis and system index statistics. The basic idea is as follows: selecting the state by a sampling method; then, carrying out state estimation on the extracted state, and judging whether each parameter of the state is in a required range; and finally, obtaining the reliability index of the system by adopting a statistical method.
In the monte carlo simulation method, the states of various device elements in a system are sampled, wherein the system elements comprise various system device components, auxiliary device components, a topological pipe network and the like. The essence of randomly sampling the various device elements of the system is the transition of the state of the system, in which two parameters are determined, one being the duration of the state and the other being the determination of which element has a change in state. Assuming that the system is already in state i, a series of events occur that cause a change in the state of the system, of which events the event that causes a change in the state of the energy supply system is in fact the first event to occur. Thus requiring the system to be in state i to find the first occurring event.
One theorem is given below:
From theorem 1, it is assumed that the probability of the state change of the element in the system to make the system enter the state i is PiAnd the system continues in state i for a time TiTime T after the system enters state iiThe system element state changes, and the system enters the next state (i + 1). The time T that the known system lasts in state iiSubject to an exponential distribution, i.e.From this, the time T can be determinedi。
In the system entry state i, via TiAfter time, the system state changes due to the change of the state of the elements in the system, so that the system enters the state (i +1) from the state i. Since the state changes of the various elements in the system are random, it is also randomly determined which occurs first. Theorem 2 is given below:
theorem 2, if the system is in state i and the arrival time of all events is exponential distribution, event eiThe expectation of arrival time is 1/aeiThen event eiThe probability of first occurrence is:
a random number between [0, 1] can be generated by using theorem 2, and the change of system elements can be simulated randomly.
Setting the fault rate of any element i in the distributed residual voltage power generation system as fiIt is an operating state, then XiProbability function P (X)i) Comprises the following steps:
for a system comprising n elements, Xi＝(Xi1，Xi2，…，Xin) Is a sample of the system running state, and the joint distribution function P (X) can be determined according to the forced outage rate and the cross-correlation of each equipment elementi)。
Compared with the prior art, the invention has the following advantages and effects:
the invention provides a method for solving the energy supply reliability index of a distributed residual voltage power generation system by adopting a Monte Carlo simulation method, which randomly samples the duration of each device or component in the current state based on the probability theory principle in statistics; and the sampling states of all the devices or parts are recombined, and whether the system meets the requirement of normal operation in each state is judged, so that the energy supply reliability index of the distributed residual voltage power generation system is obtained finally.
The Monte Carlo simulation method adopted by the invention is used for evaluating the energy supply reliability of the distributed residual pressure power generation system, so as to optimize the structure and equipment layout of the distributed residual pressure power generation system and provide reference basis for the design of a new distributed residual pressure power generation system and the reconstruction of the established distributed residual pressure power generation system. The feasible energy supply reliability evaluation of the distributed residual pressure power generation system can become a routine work for planning and operating the distributed residual pressure power generation system, and the development of the novel residual pressure resource utilization system can be greatly promoted.
Drawings
FIG. 1 is a flow chart for evaluating the reliability of the energy supply of a distributed residual voltage power generation system based on a Monte Carlo simulation method.
Fig. 2 is a schematic structural diagram of a distributed excess pressure power generation system.
In the figure: the system comprises a high steam pressure user 1, a first electric pressure regulating valve 2, a drain valve 3, a flowmeter 4, a first electric stop valve 5, an electric flow regulating valve 6, a second electric stop valve 7, a turbine expander 8, a second electric pressure regulating valve 9, a backpressure valve 10, a generator 11, a frequency converter 12, a low steam pressure user 13, a steam pipeline 14, a turbine outlet pipeline 15, an emergency stop bypass 16, a bearing 17 and an electric power circuit 18.
Detailed Description
The present invention will be described in further detail below with reference to the accompanying drawings by way of embodiments, where the present embodiment is mainly described in the reliability evaluation process of power supply in a single-input single-output distributed residual voltage power generation system, and the following embodiments are illustrative of the present invention and the present invention is not limited to the following embodiments, and may also be applied to the reliability evaluation of power supply such as thermal energy in a multi-energy complementary system.
Examples are given.
Referring to fig. 2, in the present embodiment, the distributed residual pressure power generation system includes a turboexpander 8, a generator 11, a frequency converter 12, a steam pipe 14, a turbine outlet pipe 15, a bearing 17, and an electric power line 18; the tail end of the steam transmission of the steam pipeline 14 is connected with the turbo expander 8, the turbo expander 8 is connected with one end of a bearing 17, the other end of the bearing 17 is connected with a generator 11, the generator 11 is connected with one end of an electric power circuit 18, the other end of the electric power circuit 18 is connected with a frequency converter 12, one end of a turbine outlet pipeline 15 is connected with an outlet of the turbo expander 8, and the other end of the turbine outlet pipeline 15 is communicated with a user; the steam pipeline 14 is sequentially provided with a first electric pressure regulating valve 2, a flowmeter 4, a first electric stop valve 5 and an electric flow regulating valve 6 according to the steam conveying direction; and a second electric pressure regulating valve 9 and a back pressure valve 10 are sequentially arranged on the turbine outlet pipeline 15 according to the steam conveying direction. A drain valve 3 is also installed on the steam pipeline 14, and the drain valve 3 is positioned between the first electric pressure regulating valve 2 and the flowmeter 4. The distributed residual pressure power generation system further comprises an emergency stop bypass 16, a second electric stop valve 7 is installed on the emergency stop bypass 16, one end of the emergency stop bypass 16 is connected to the steam pipeline 14 between the flow meter 4 and the first electric stop valve 5, and the other end of the emergency stop bypass 16 is connected to the turbine outlet pipeline 15.
Referring to fig. 1, the operation steps are as follows:
1) compiling and setting of parameter table
All elements in fig. 2 are first numbered and then a parameter table is written. The contents of the parameter table include: all element lists, element numbers, initial connectivity of elements, head and tail node numbers of each element, failure rate, repair rate and other parameters. And forming an excel table according to the element reliability parameters and the actual topological structure, and storing the excel table into MATLAB to be recorded as an LA matrix. Other matrices such as the adjacent matrix L are formed mainly by this matrix later.
2) Failure rate isoparametric matrix formation
According to the formed parameter table matrix LA, according to the numbering sequence, the failure rate, repair time and repair rate matrix of the elements are respectively formed, and the matrix is a matrix with 1 row and M columns (M is the number of the elements in the system).
3) Forming a contiguous matrix
And forming an adjacent matrix of the hand-in-hand topological structure according to the first node and the last node of each element in the LA matrix and the connectivity (1 or 0) of each element in each simulation, namely representing the connection relation of each element in the topological structure by using the matrix. On the basis of the adjacency matrix, a judgment matrix P (N multiplied by N) of the adjacency matrix is formed according to the power principle (N is the number of energy supply nodes in the system) and is used for judging the connectivity of two nodes in the topological structure. P (i, j) ═ 1, meaning that node i and node j are connected; p (i, j) ═ 0, indicates that node i and node j are not connected. It should be noted that the communication is not only direct communication, but also communication can be performed as long as a path can be formed between two nodes (the communication needs to consider the matching problem of various energy supplies and the energy capacity of the user side).
4) Computing system state duration
System state time T due to element jjObedience parameter is QjIn an exponential distribution of (1), whereinQjIs the failure rate of the normal state of the element j or the repair rate in the failure state. According to the Monte Carlo sampling principle, a random number m between 0 and 1 is generatediCalculating the system state duration T at the ith simulationi。
5) Calculating the state parameters of each user side in the ith simulation
And (3) determining the connectivity between each user and the energy supply side in the topology according to a judgment matrix P formed in the ith simulation in the step 3, and judging whether the energy supply can be normally carried out. If the connection is made, the state is recorded as 1, and the outage time is recorded as 0; if not connected, the state is recorded as 0, and the outage time is recorded as Ti. And listing the state parameters of each point one by one, and putting the state parameters into an LD matrix.
6) Determination of state transfer elements
Based on the principle of generating random numbers by Monte Carlo and the proportion of the state transition rate (fault rate or repair rate) of all elements to the total state transition rate, a random number n between 0 and 1 is usediThe element that determines the transition of the ith state, labeled p, can be simulated and the connected state of element p is changed before the next simulation.
7) User-side index matrix formation
And calculating the average failure times and the average outage time of each load point in unit time (one year) according to the LD matrix finally formed by multiple times of simulation and the total simulation time T, and putting the average failure times and the average outage time into a load matrix.
8) Calculation of System reliability index
And 4) calculating the total times ACI of stopping energy supply of all users and the total time CID of stopping energy supply of the users on the basis of the load matrix in the step 7) by combining the number of users at each load point. According to the two indexes and a calculation formula introduced in the chapter i, five indexes of the reliability of the distributed excess pressure power generation system are calculated respectively: the average system energy supply stopping frequency index SAIFI, the average system energy supply stopping duration index SAIDI, the average user energy supply stopping duration index CAIDI, the average energy supply availability index ASAI and the energy shortage expectation EENS.
Those not described in detail in this specification are well within the skill of the art.
Although the present invention has been described with reference to the above embodiments, it should be understood that the scope of the present invention is not limited thereto, and that various changes and modifications can be made by those skilled in the art without departing from the spirit and scope of the present invention.
Claims (3)
1. A distributed residual pressure power generation system energy supply reliability assessment method based on a Monte Carlo simulation method is characterized in that the Monte Carlo simulation method is used for calculating the energy supply reliability of the distributed residual pressure power generation system; by utilizing the characteristic that steam pressure parameters of both supply sides are not matched, the pressure difference energy is converted into mechanical energy by adopting a turbine expander, and the mechanical energy is used for driving a generator to generate electricity so as to realize energy conversion and output electric energy; the distributed residual pressure power generation system converts originally wasted differential pressure energy into green high-quality electric energy to be on-line or consumed by users on site, and greatly improves the energy efficiency of a distributed energy station/cogeneration user in the heat supply process, thereby improving the economic benefit;
randomly sampling the time of the current state of each device or component in the system according to a probability theory in statistics, recombining the sampling states of all the devices or components, and judging whether the system in each state meets the energy supply condition, thereby obtaining the energy supply reliability index of the whole distributed residual voltage power generation system;
based on the original reliability parameters of each device or component in the distributed residual pressure power generation system, the reliability index of the whole distributed residual pressure power generation system is calculated according to various states which may occur in random simulation and a large number of simulation experiment results.
2. The distributed residual voltage power generation system energy supply reliability assessment method based on the Monte Carlo simulation method as claimed in claim 1, wherein the reliability of the distributed residual voltage power generation system energy supply mainly depends on the reliability index of the user side energy supply, the reliability index of the user side energy supply is obtained based on the reliability parameters of the equipment, and the reliability parameters of the equipment are obtained based on the historical data statistics of the operation of each component of the system; the user side energy supply reliability index reflects the reliability degree of continuous energy supply for a user, mainly comprises an average fault rate, an average fault repair time and an average outage time, which are probability indexes and reflect expected values under certain probability distribution; because all the elements between the energy-using side and the energy-supplying side are in series relationship, the condition that the energy-using side can supply energy normally is that all the elements between them are normally operated;
the energy utilization side reliability indexes mainly comprise average fault rate, average outage time and average fault repair time;
(1) average failure rate: the energy utilization side refers to the number of times of stopping energy supply caused by the failure of equipment elements in the distributed residual voltage power generation system in a given time interval, and the energy utilization side uses lambdaiExpressed in units of times/year;
wherein λ isjIs the failure rate of element j;
(2) average downtime: refers to the time of stopping power supply in a given time by a user, using UiExpressed in units of hours/year;
wherein, γjThe time to repair the failure for element j;
(3) mean time to failure repair: means the time average value of energy utilization from the occurrence of energy supply stoppage to the restoration of energy supply, using gammaiExpressed in units of hours/time;
and establishing a reliability index of the energy supply of the whole distributed excess pressure power generation system based on the reliability index of the energy utilization side, and reflecting the reliability degree of the energy supply of the whole system.
3. The method for evaluating the energy supply reliability of the distributed residual voltage power generation system based on the Monte Carlo simulation method according to claim 2, wherein the energy supply reliability indexes of the distributed residual voltage power generation system comprise energy supply stopping frequency and time indexes and energy supply stopping load energy indexes according to different reliability evaluation contents, and the method is divided into the following items:
(1) the system average stop energy frequency index refers to the average stop energy supply times of each user supplied by the system in one year, and the unit is times/(user-year);
counting, and recording as SAIFI-1 when all reliability indexes caused by stopping energy supply are counted; when the reliability index caused by external power supply stopping is not counted, the reliability index is marked as SAIFI-2; when the reliability index of stopping energy supply caused by planned stopping of energy supply and limitation of energy supply due to insufficient energy source is not counted, the reliability index is marked as SAIFI-3;
(2) the system average power supply stopping duration index refers to the average power supply stopping duration which each user supplied with power by the system experiences in one year, and the unit is hour/(user-year);
similarly, during statistics, when reliability indexes caused by stopping energy supply are counted, the reliability indexes are recorded as SAIDI-1; when the reliability index caused by external energy supply stopping is not counted, the reliability index is marked as SAIDI-2; when the reliability index of stopping energy supply caused by planned stopping energy supply and limiting energy supply due to energy source shortage is not counted, the reliability index is marked as SAIDI-3;
(3) the user average power supply stopping duration index refers to the average power supply stopping duration experienced by users who stop supplying power in one year, and the unit is hour/(power supply stopping user-year);
(4) the average energy supply availability index refers to the ratio of the total number of energy supply hours which are not stopped and are experienced by the user in one year to the total energy supply hours required by the user;
(5) the energy shortage expectation refers to the shortage of energy provided for users due to component outage in one year, and the unit is megajoules per year;
EENS＝∑La(i)Ui。
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