CN109636027A - A kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method - Google Patents
A kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method Download PDFInfo
- Publication number
- CN109636027A CN109636027A CN201811495538.9A CN201811495538A CN109636027A CN 109636027 A CN109636027 A CN 109636027A CN 201811495538 A CN201811495538 A CN 201811495538A CN 109636027 A CN109636027 A CN 109636027A
- Authority
- CN
- China
- Prior art keywords
- energy
- complement
- multiple forms
- providing multiple
- reliability
- 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
- 230000000295 complement effect Effects 0.000 title claims abstract description 97
- 238000000034 method Methods 0.000 title claims abstract description 52
- 238000000342 Monte Carlo simulation Methods 0.000 title claims abstract description 30
- 238000005070 sampling Methods 0.000 claims abstract description 20
- 230000005540 biological transmission Effects 0.000 claims abstract description 10
- 238000006243 chemical reaction Methods 0.000 claims abstract description 7
- 238000002474 experimental method Methods 0.000 claims abstract description 5
- 238000002948 stochastic simulation Methods 0.000 claims abstract description 5
- 230000008439 repair process Effects 0.000 claims description 15
- 230000015556 catabolic process Effects 0.000 claims description 11
- 238000004088 simulation Methods 0.000 claims description 10
- 230000007812 deficiency Effects 0.000 claims description 9
- 230000008569 process Effects 0.000 claims description 6
- 238000004458 analytical method Methods 0.000 claims description 4
- 238000000605 extraction Methods 0.000 claims description 3
- 241000208340 Araliaceae Species 0.000 claims 1
- 241000229175 Calotes Species 0.000 claims 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 claims 1
- 235000003140 Panax quinquefolius Nutrition 0.000 claims 1
- 235000008434 ginseng Nutrition 0.000 claims 1
- 238000005457 optimization Methods 0.000 abstract description 3
- 239000011159 matrix material Substances 0.000 description 19
- 230000005611 electricity Effects 0.000 description 6
- 238000004146 energy storage Methods 0.000 description 4
- 239000002028 Biomass Substances 0.000 description 3
- 239000000567 combustion gas Substances 0.000 description 3
- 239000002737 fuel gas Substances 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 238000012614 Monte-Carlo sampling Methods 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000002485 combustion reaction Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- -1 geothermal energy Substances 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- 238000013459 approach Methods 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000005094 computer simulation Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000009472 formulation Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 150000002500 ions Chemical class 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/80—Management or planning
- Y02P90/82—Energy audits or management systems therefor
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- General Business, Economics & Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Development Economics (AREA)
- General Physics & Mathematics (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Health & Medical Sciences (AREA)
- Educational Administration (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
What the present invention relates to a kind of based on Monte Carlo Method provide multiple forms of energy to complement each other system energy supply reliability estimation method, it is therefore intended that it solves different zones and provides multiple forms of energy to complement each other the energy supply reliability of system, the grid structure and device layout of system so that optimization is provided multiple forms of energy to complement each other.The present invention calculates the energy supply reliability for system of providing multiple forms of energy to complement each other using Monte Carlo method, the system of providing multiple forms of energy to complement each other includes energy source, energy transmission equipment, energy transmission mode conversion equipment and energy acceptance equipment, Monte Carlo method is to carry out random sampling according to current state of the probability theory in statistics to element each in the system of providing multiple forms of energy to complement each other, and the sample mode of all elements is reconfigured, whether the system that judges to provide multiple forms of energy to complement each other under each state again meets energy supply service condition, finally obtains reliability index；For appraisal procedure based on the original dependability parameter of element each in network of providing multiple forms of energy to complement each other, the various states and a large amount of simulated experiment result that are likely to occur according to stochastic simulation calculate the reliability index for the system of providing multiple forms of energy to complement each other.
Description
Technical field
The present invention relates to system energy supply reliability assessment fields of providing multiple forms of energy to complement each other, and are based on Monte Carlo Method more particularly, to one kind
Provide multiple forms of energy to complement each other system energy supply reliability estimation method, can effectively assess the energy supply reliability for the system of providing multiple forms of energy to complement each other.
Background technique
Since 21 century, developed country researches and develops New-generation distributed energy resource system one after another and provides multiple forms of energy to complement each other comprehensive energy
Source system (system of hereinafter referred to as providing multiple forms of energy to complement each other) tries hard to for using energy source level and environmental protection standard to be increased to a new level,
Important support technology as the third time industrial revolution.The core for system of providing multiple forms of energy to complement each other is distributed energy and carries out around it
Regional Energy supply, is a kind of form for combining solar energy, wind energy, geothermal energy, combustion gas or even biomass energy.Multipotency
On the one hand complementary system passes through realizes multiple-energy-source collaboration optimization and the complementary utilization rate for improving renewable energy；On the other hand pass through
It realizes energy cascade utilization, improves the level of comprehensive utilization of the energy.The system of providing multiple forms of energy to complement each other is opening up for traditional distributed energy source use
Exhibition, is integration theory being embodied in energy systems engineering field, so that the application of distributed energy is expanded to by point
Face, by locally moving towards system.Specifically, distributed energy resource system of providing multiple forms of energy to complement each other, which refers to, can contain the input of various energy resources resource,
And has the function of a variety of outputs and transport " Regional Energy internet " system of form.It is not the simple superposition of various energy resources,
And will in system altitude according to different energy sources taste height carry out integrated complementary utilization, and the good various energy of overall arrangement it
Between matching relationship and conversion use, to obtain most reasonable utilization results of energy and benefit.However, since the system of providing multiple forms of energy to complement each other is
One kind has compared with multivariable, and characteristic is complicated, randomness is strong, the nonlinear system of Multiple Time Scales, the reliability assessment of energy supply compared with
For complexity.
Monte Carlo simulation approach (Monte Carlo Simulation-MCS) is to obtain reliability using statistical method
A kind of computer simulation method of index is to be in current state to each element for basis with the Probability Principles in statistics
Duration carries out random sampling, and the sample mode of all elements is reconfigured, the system in the case where judging each state
Whether meet energy supply service condition, finally obtains reliability index, such as application No. is 201710816212.0 Chinese patents.It covers
Special Monte Carlo Simulation of Ions Inside method is likely to occur based on the original dependability parameter of element each in network of providing multiple forms of energy to complement each other according to stochastic simulation
Various states and a large amount of simulated experiment result calculate Reliability Index.Such method disregards system scale and model
Dimension, therefore algorithm and program structure are generally fairly simple, convergence rate is relatively fast, can also obtain the general of reliability index
Rate distribution form.But the shortcomings that simulation is to need to carry out large-scale stochastic simulation sampling, calculates the time with respect to analytic method
For it is long very much.
In conclusion the system of providing multiple forms of energy to complement each other is the nonlinear system complicated, that randomness is strong an of multiple-input and multiple-output,
And the reliability index that Monte Carlo Method is applicable to the strong complication system of multidimensional number, randomness calculates, therefore by Monte Carlo
Method is applied to Reliability Index calculating of providing multiple forms of energy to complement each other, and is practicable.
Summary of the invention
It is an object of the invention to solve different zones to provide multiple forms of energy to complement each other the energy supply reliability of system, so that optimization is provided multiple forms of energy to complement each other
The grid structure and device layout of system provide for new system design and construction of providing multiple forms of energy to complement each other with the built system of providing multiple forms of energy to complement each other is transformed
Reference frame.System of providing multiple forms of energy to complement each other, which energizes reliability assessment, will become a routine of the multi-energy complementation network planning and operation
Sex work.
Technical solution used by the present invention solves the above problems is: a kind of system of providing multiple forms of energy to complement each other based on Monte Carlo Method
Energize reliability estimation method, which is characterized in that the energy supply reliability for system of providing multiple forms of energy to complement each other, multipotency are calculated using Monte Carlo method
Complementary system includes that energy source (such as electric network source, wind-power electricity generation, photovoltaic power generation heat supply, fuel gas generation heat supply), energy transmission are set
Standby (transmission line of electricity, heat supply pipeline etc.), energy transmission mode conversion equipment (such as breaker, valve, inverter, transformer) and
Energy acceptance equipment (alternating current-direct current sensitive load, electric automobile charging station, thermic load etc.), Monte Carlo method is according in statistics
Probability theory random sampling carried out to the current state of element each in the system of providing multiple forms of energy to complement each other, and by the sample mode of all elements
It is reconfigured, then whether the system that judges to provide multiple forms of energy to complement each other under each state meets service condition, finally obtains whole system
Operational reliability index；Appraisal procedure is based on the original dependability parameter of element each in network of providing multiple forms of energy to complement each other, according to random
It simulates the various states being likely to occur and a large amount of simulated experiment result calculates the reliability index for the system of providing multiple forms of energy to complement each other.
System of providing multiple forms of energy to complement each other includes the renewable energy such as wind energy, solar energy, combustion gas, geothermal energy, biomass energy, passes through these
The capacity reasonable disposition of the energy is greatly improved the utilization rate and conversion ratio of these the energy.
The reliability of system of providing multiple forms of energy to complement each other energy supply depends primarily on the reliability index of load point, the reliability index of load point
It is that dependability parameter based on equipment acquires, the dependability parameter of equipment is the history based on each element operation of the system of providing multiple forms of energy to complement each other
What data statistics obtained.
The reliability index of load point reflects the reliability standard continuously energized to certain load point, mainly there is mean failure rate
Rate, average time for repair of breakdowns and average idle time, they are all probability levels, react the expectation under certain probability distribution
Value；Since all elements between load point and energy supply point are in series relationship, the condition that load point can be energized normally is it
Between all elements all operate normally.
Failure rate that load point reliability index includes, average idle time and average time for repair of breakdowns are specifically such as
Under:
(1) failure rate: refer to load point i in given time section (usually 1 year) because of network element of providing multiple forms of energy to complement each other
Stop energy supply number caused by failure, uses λIIt indicates, unit times/year；
Wherein, λjIndicate the failure rate of element j；
(2) average idle time: refer to user within given time (usually 1 year) stopping energy supply time, use UiIt indicates,
Unit is hour/year；
Wherein, γjIndicate the average time for repair of breakdowns of element j；
(3) average time for repair of breakdowns: referring to that load point stops supply from energy and occurs to the time average for restoring energy supply,
Use γiIndicate, unit be hour/time；
Reliability index based on load point can establish the reliability index for system energy supply of entirely providing multiple forms of energy to complement each other, and be used to anti-
Reflect the energy supply degree of reliability for system of entirely providing multiple forms of energy to complement each other.
According to the difference of reliability assessment content, it includes when stopping energy supply frequency that system of providing multiple forms of energy to complement each other, which energizes reliability index,
Between class index and stop energy supply load and energy class index, be specifically divided into following several:
(1) system averagely stops energy frequency index (system average interruption frequency
Index, SAIFI), the average stopping for referring to that each user by system energy supply of providing multiple forms of energy to complement each other is subjected in 1 year energizes number,
Unit is secondary/(user year)；
When statistics, when the reliability index caused by all stoppings are energized all is included in, it is denoted as SAIFI-1；It is outer when disregarding
When portion stops reliability index caused by energizing, it is denoted as SAIFI-2；Stop supplying when disregarding plan caused by energy source deficiency limit energy
When energy, limitation energy supply etc. cause the reliability index for stopping energizing, it is denoted as SAIFI-3；
(2) system averagely stops energizing duration index (system average interruption duration
Index, SAIDI), when the average stopping energy supply for referring to that each user by system energy supply of providing multiple forms of energy to complement each other is subjected in 1 year continues
Between, unit is hour/(user year)；
Equally, when statistics, when the reliability index caused by all stoppings are energized all is included in, it is denoted as SAIDI-1；When
When disregarding reliability index caused by external stopping energizes, it is denoted as SAIDI-2；Plan caused by energy is limited when disregarding energy source deficiency
When stopping the reliability index that energy supply, limitation energy supply etc. cause stopping to energize, it is denoted as SAIDI-3；
(3) user averagely stops energizing duration index (customer average interruption
Duration index, CAIDI), refer to the average stopping energy supply duration that the user that energy supply is stopped in 1 year is subjected to, it is single
Position is hour/(stopping energy supply user year)；
(4) Availability Index (average service availability index, ASAI) averagely is energized, refers to one
What user was subjected in year does not stop energizing the ratio between total energy supply hourage of hour sum and user's requirement；
(5) energy deficiency expectation (expect of energy not supplied, EENS), refers in 1 year because of element
The vacancy of the energy provided a user caused by stoppage in transit, unit are megawatt hour/year；
EENS=∑ La(i)Ui
Monte Carlo Method is to be taken out premised on the initial data of each component reliability of the system of providing multiple forms of energy to complement each other with computer
Sample simulates the operating status that may occur at random, and calculates the reliability index of requirement by the method for probability statistics.Root
Usually it is divided into two methods of sequential and non-sequential Monte Carlo again according to the difference of the methods of sampling.The present invention is mainly using sequential
Monte Carlo EGS4 method assesses the reliability for system energy supply of providing multiple forms of energy to complement each other.
Monte Carlo sampling method is also known as arbitrary sampling method, is substantially a kind of probability simulation method.Using Meng Teka
Sieve method assessment provide multiple forms of energy to complement each other system energy supply reliability process be broadly divided into system element state sampling, system mode analysis with
System index counts three steps.Its basic thought is: being selected using the method for sampling equipment component state；Then right
Whether the state of extraction carries out state estimation, judge each parameter of this state within the scope of requiring；Finally using statistics
Method obtain the reliability index of system.
In Monte Carlo Method, the state sampling to element each in system is first had to, wherein system element includes various
System equipment, route, pipeline and different load levels.Carrying out the essence of random sampling to each element of system is exactly to be
The transfer process of system state will determine two parameters in the process, and one is the duration of state, the other is determining
It is which element generating state variation.Assuming that system has been in state i, sequence of events causes system mode
Variation, causing POWER SYSTEM STATE changed in these events is actually most first occurred event.Therefore it is required that being
System will find out most first occurred event in the time of state i.
Theorem 1 is given below:
Theorem 1 sets T1..., TnIt is respectively a to obey parameter1..., anExponential distribution, independently of each other, then T=MinTi,
And T is also the stochastic variable for obeying exponential distribution, parameter is
It can be obtained by theorem 1, it is assumed that element state changes the probability P for making system enter state i in systemi, and
System is T in state i durationi, through time T after system enters state iiSystem element state changes, and is system
Into next state (i+1).Known system is in state i duration TiExponential distribution is obeyed, i.e.,Thus
Can anti-solution find out time Ti。
Enter state i in system, by TiAfter time, since element state changes so that system mode in system
It changes, so that system enters state (i+1) from state i.Due to element state each in system change be it is random,
Also to determine it is which occurs at first at random.
Theorem 2 is given below:
Theorem 2 sets system and is in state i, if the time that at this moment all events reach is exponential distribution, event eiIt arrives
1/a is desired for up to the timeei, then event eiMost first occurred probability are as follows:
It can produce the random number between one [0,1] using theorem 2, the variation of simulation system element that can be random.
For any element i in the system of providing multiple forms of energy to complement each other, if its failure rate is fi, it is an operating status, then XiProbability
Function P (Xi) are as follows:
For a system comprising n element, Xi=(Xi1, Xi2..., Xin) be system running state sample,
According to the forced outage rate and cross correlation of each element, its joint distribution function P (X can be determinedi)。
Compared with prior art, the present invention having the following advantages that and effect: appraisal procedure of the present invention can be effective
The energy supply reliability of system of providing multiple forms of energy to complement each other complicated and changeable is calculated, system of providing multiple forms of energy to complement each other energy supply reliability assessment result can effectively refer to
Lead the Optimizing Reconstruction of new system Construction planning and built system of providing multiple forms of energy to complement each other of providing multiple forms of energy to complement each other.System can be directly defeated in the present invention
The various energy resources form such as electric energy, thermal energy (including cold energy) out, reduces energy conversion, to increase economic efficiency.
Detailed description of the invention
Fig. 1 is the flow chart of Monte Carlo Method in the embodiment of the present invention.
Fig. 2 is the structural schematic diagram of system of providing multiple forms of energy to complement each other in the embodiment of the present invention.
Specific embodiment
The present invention is described in further detail with reference to the accompanying drawing and by embodiment, and following embodiment is to this hair
Bright explanation and the invention is not limited to following embodiments.
Embodiment.
The system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method in the present embodiment, utilizes Meng Teka
Lip river method calculates the energy supply reliability of system of providing multiple forms of energy to complement each other, the system of providing multiple forms of energy to complement each other include energy source (such as electric network source, wind-power electricity generation,
Photovoltaic power generation heat supply, fuel gas generation heat supply), energy transmission equipment (transmission line of electricity, heat supply pipeline etc.), energy transmission mode convert
Equipment (such as breaker, valve, inverter, transformer) and energy acceptance equipment (alternating current-direct current sensitive load, electric car charging
Stand, thermic load etc.), Monte Carlo method is the current shape according to the probability theory in statistics to element each in the system of providing multiple forms of energy to complement each other
State carries out random sampling, and the sample mode of all elements is reconfigured, then judge to provide multiple forms of energy to complement each other under each state and be
Whether system meets service condition, finally obtains the operational reliability index of whole system；Appraisal procedure is in network of providing multiple forms of energy to complement each other
The various states being likely to occur based on the original dependability parameter of each element according to stochastic simulation and a large amount of simulated experiment knot
Fruit calculates the reliability index for the system of providing multiple forms of energy to complement each other.
System of providing multiple forms of energy to complement each other includes the renewable energy such as wind energy, solar energy, combustion gas, geothermal energy, biomass energy, passes through these
The capacity reasonable disposition of the energy is greatly improved the utilization rate and conversion ratio of these the energy.
The reliability of system of providing multiple forms of energy to complement each other energy supply depends primarily on the reliability index of load point, the reliability index of load point
It is that dependability parameter based on equipment acquires, the dependability parameter of equipment is the history based on each element operation of the system of providing multiple forms of energy to complement each other
What data statistics obtained.
The reliability index of load point reflects the reliability standard continuously energized to certain load point, mainly there is mean failure rate
Rate, average time for repair of breakdowns and average idle time, they are all probability levels, react the expectation under certain probability distribution
Value；Since all elements between load point and energy supply point are in series relationship, the condition that load point can be energized normally is it
Between all elements all operate normally.
Failure rate that load point reliability index includes, average idle time and average time for repair of breakdowns are specifically such as
Under:
(1) failure rate: refer to load point i in given time section (usually 1 year) because of network element of providing multiple forms of energy to complement each other
Stop energy supply number caused by failure, uses λIIt indicates, unit times/year；
Wherein, λjIndicate the failure rate of element j；
(2) average idle time: refer to user within given time (usually 1 year) stopping energy supply time, use UiIt indicates,
Unit is hour/year；
Wherein, γjIndicate the average time for repair of breakdowns of element j；
(3) average time for repair of breakdowns: referring to that load point stops supply from energy and occurs to the time average for restoring energy supply,
Use γiIndicate, unit be hour/time；
Reliability index based on load point can establish the reliability index for system energy supply of entirely providing multiple forms of energy to complement each other, and be used to anti-
Reflect the energy supply degree of reliability for system of entirely providing multiple forms of energy to complement each other.
According to the difference of reliability assessment content, it includes when stopping energy supply frequency that system of providing multiple forms of energy to complement each other, which energizes reliability index,
Between class index and stop energy supply load and energy class index, be specifically divided into following several:
(1) system averagely stops energy frequency index (system average interruption frequency
Index, SAIFI), the average stopping for referring to that each user by system energy supply of providing multiple forms of energy to complement each other is subjected in 1 year energizes number,
Unit is secondary/(user year)；
When statistics, when the reliability index caused by all stoppings are energized all is included in, it is denoted as SAIFI-1；It is outer when disregarding
When portion stops reliability index caused by energizing, it is denoted as SAIFI-2；Stop supplying when disregarding plan caused by energy source deficiency limit energy
When energy, limitation energy supply etc. cause the reliability index for stopping energizing, it is denoted as SAIFI-3；
(2) system averagely stops energizing duration index (system average interruption duration
Index, SAIDI), when the average stopping energy supply for referring to that each user by system energy supply of providing multiple forms of energy to complement each other is subjected in 1 year continues
Between, unit is hour/(user year)；
Equally, when statistics, when the reliability index caused by all stoppings are energized all is included in, it is denoted as SAIDI-1；When
When disregarding reliability index caused by external stopping energizes, it is denoted as SAIDI-2；Plan caused by energy is limited when disregarding energy source deficiency
When stopping the reliability index that energy supply, limitation energy supply etc. cause stopping to energize, it is denoted as SAIDI-3；
(3) user averagely stops energizing duration index (customer average interruption
Duration index, CAIDI), refer to the average stopping energy supply duration that the user that energy supply is stopped in 1 year is subjected to, it is single
Position is hour/(stopping energy supply user year)；
(4) Availability Index (average service availability index, ASAI) averagely is energized, refers to one
What user was subjected in year does not stop energizing the ratio between total energy supply hourage of hour sum and user's requirement；
(5) energy deficiency expectation (expect of energy not supplied, EENS), refers in 1 year because of element
The vacancy of the energy provided a user caused by stoppage in transit, unit are megawatt hour/year；
EENS=∑ La(i)Ui
Monte Carlo Method is to be taken out premised on the initial data of each component reliability of the system of providing multiple forms of energy to complement each other with computer
Sample simulates the operating status that may occur at random, and calculates the reliability index of requirement by the method for probability statistics.Root
Usually it is divided into two methods of sequential and non-sequential Monte Carlo again according to the difference of the methods of sampling.The present invention is mainly using sequential
Monte Carlo EGS4 method assesses the reliability for system energy supply of providing multiple forms of energy to complement each other.
Monte Carlo sampling method is also known as arbitrary sampling method, is substantially a kind of probability simulation method.Using Meng Teka
Sieve method assessment provide multiple forms of energy to complement each other system energy supply reliability process be broadly divided into system element state sampling, system mode analysis with
System index counts three steps.Its basic thought is: being selected using the method for sampling equipment component state；Then right
Whether the state of extraction carries out state estimation, judge each parameter of this state within the scope of requiring；Finally using statistics
Method obtain the reliability index of system.
In Monte Carlo Method, the state sampling to element each in system is first had to, wherein system element includes various
System equipment and different load levels.It is exactly turning for system mode to the essence that each element of system carries out random sampling
Move past journey, to determine two parameters in the process, one be state duration, the other is which member determination is
The variation of part generating state.Assuming that system has been in state i, sequence of events causes system mode to change, at this
Causing POWER SYSTEM STATE changed in a little events is actually most first occurred event.Therefore it is required that system is in state
The time of i will find out most first occurred event.
Referring to Fig. 1 to Fig. 2, include grid transmission, fuel gas generation, wind-power electricity generation, photovoltaic power generation, energy storage etc. in the present embodiment
The power supply of diversified forms, borne forms are also diversified, including DC load, electric automobile charging station in sensitive AC load, factory
Deng.Power grid electric energy is pooled to the bus of middle straightening stream power distribution network by transformer and converter station together with the electric energy of distributed generation resource
On, supply low-voltage direct power distribution network, DC load and AC load, bidirectionally with energy storage device and electric car electric charging station etc.
Positive energy exchange can improve electric energy by electric energy reverse transfer to power distribution network when power grid has short of electricity or these power supplys have remaining capacity
Utilization rate.The present embodiment key equipment mainly have converter station, AC transformer, commutator transformer, two-way DC/DC converter,
Inverter, blower, photovoltaic, internal combustion engine, energy storage, breaker, alternating current-direct current bus, ac and dc circuit etc..All kinds of power supplys and load hold
Amount is as shown in table 1.
All kinds of power supplys of table 1 and load capacity
| Serial number | Title | Capacity (MW) | Serial number | Title | Capacity (MW) |
| 1 | Converter station 1 (AC network) | 10 | 6 | Electric automobile charging station | 5 |
| 2 | Blower | 1 | 7 | LA1 (sensitive load 1) | 2 |
| 3 | Photovoltaic | 2 | 8 | LA2 (sensitive load 2) | 4 |
| 4 | Energy storage | 2 | 9 | LD1 (DC load in factory) | 0.5 |
| 5 | Internal combustion engine | 0.63 | 10 | LD2 (DC load in factory) | 1 |
The present embodiment step:
1) parameter list writes formulation
All elements in attached drawing 2 are numbered first, write parameter list later.The content of parameter list includes: all members
Part list, element number, the initial connectivity of element, the first and last node serial number of each element, failure rate, the parameters such as repair rate.
According to component reliability parameter and real topology, excel table is formed, is stored in MATLAB and is denoted as LA matrix.Later
Other matrixes such as adjacency matrix L are mainly formed by the matrix.
2) parameter matrixs such as failure rate are formed
It is respectively formed the failure rate of element according to number order according to the parameter list matrix L A of formation, repair time, repairs
Multiple rate matrix is the matrix (number of elements in M expression system) of 1 row M column.
3) adjacency matrix is formed
According to the first node of element each in LA matrix and end-node and every time simulation when each element connectivity (1 or
0) adjacency matrix for, forming topological structure of handing in hand, i.e., show the connection relationship of element each in topological structure with matrix.
On the basis of adjacency matrix, according to power principle, judgment matrix P (N × N) (section in N expression system of adjacency matrix is formed
Point quantity), for judging the connectivity of two nodes in topological structure.P (i, j)=1 indicates that node i is connected to node j；P
(i, j)=0 indicates that node i is not connected to node j.It should be noted that connection does not refer to direct connection singly, as long as two nodes
Between can form access, can be connected to (connection while to consider the capacity matching problem of all kinds of power supplys and load).
4) computing system state duration
Due to element j system mode time TjObedience parameter is QjExponential distribution, wherein QjIt is element j normal condition
Repair rate under failure rate or malfunction.According to Monte-Carlo step principle, with the random number m between the 0~1 of generationi
Calculate the system mode duration T in i-th simulationi。
5) state parameter of each load point when i-th simulation is calculated
The judgment matrix P formed when being simulated according to step 3 i-th determines the node and power supply of each load point in topology
Connectivity between node judges that can it normal power supply.If connection, state are denoted as 1, idle time is denoted as 0；If not being connected to,
State is denoted as 0, and idle time is denoted as Ti.The state parameter of each point is listed one by one, is put into LD matrix.
6) determination of state transfer element
The principle of random number and state transition rate (failure rate or the reparation of all elements are generated based on Monte Carlo Method
Rate) the total state transition rate of Zhan specific gravity, with the random number n between 0~1iIt can simulate and determine what i-th state shifted
Element is labeled as p, before simulation next time, changes the connected state of element p.
7) load point index matrix is formed
According to the LD matrix eventually formed and simulation total time T is repeatedly simulated, each load point unit time is calculated
It (1 year) mean failure rate number and average idle time, is put into load matrix.
8) calculating of Reliability Index
On the basis of the load matrix of step 7, in conjunction with the number of users of each load point, all load point users are calculated
Power failure total degree ACI and customer outage hours summation CID.According to the two indexs and in reliability index calculation formula,
Calculate separately out five big indexs of system reliability: system System average interruption frequency index S AIFI, system System average interruption duration
Index S AIDI, user System average interruption duration index CAIDI, averagely power supply Availability Index ASAI, expected energy not supplied
EENS。
It is any to be familiar with although the present invention is disclosed as above with embodiment, its protection scope being not intended to limit the invention
The technical staff of this technology changes and retouches made without departing from the spirit and scope of the invention, should belong to this hair
Bright protection scope.
Claims (6)
1. a kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method, which is characterized in that special using covering
Calot's method calculates the energy supply reliability for system of providing multiple forms of energy to complement each other, and the system of providing multiple forms of energy to complement each other includes energy source, energy transmission equipment, energy biography
Defeated mode conversion equipment and energy acceptance equipment, Monte Carlo method are according to the probability theory in statistics in the system of providing multiple forms of energy to complement each other
The current state of each element carries out random sampling, and the sample mode of all elements is reconfigured, then judges each
Whether system of providing multiple forms of energy to complement each other under state meets service condition, finally obtains the operational reliability index of whole system；Appraisal procedure
Based on the original dependability parameter of element each in network of providing multiple forms of energy to complement each other, the various states that are likely to occur according to stochastic simulation and
A large amount of simulated experiment result calculates the reliability index for the system of providing multiple forms of energy to complement each other.
2. the system energy supply reliability estimation method of providing multiple forms of energy to complement each other according to claim 1 based on Monte Carlo Method, special
Sign is that the reliability of system of providing multiple forms of energy to complement each other energy supply depends primarily on the reliability index of load point, and the reliability of load point refers to
Mark is that dependability parameter based on equipment acquires, and the dependability parameter of equipment is going through based on each element operation of the system of providing multiple forms of energy to complement each other
History data statistics obtains.
3. the system energy supply reliability estimation method of providing multiple forms of energy to complement each other according to claim 2 based on Monte Carlo Method, special
Sign is, the reliability index of load point reflects the degree of reliability continuously energized to certain load point, including failure rate, flat
Equal fault correction time and average idle time, they are all probability levels, react the desired value under certain probability distribution；By
All elements between load point and energy supply point are in series relationship, therefore the condition that can normally energize of load point is between them
All elements all operate normally.
4. the system energy supply reliability estimation method of providing multiple forms of energy to complement each other according to claim 3 based on Monte Carlo Method, special
Sign is, failure rate, average idle time and the average time for repair of breakdowns that load point reliability index includes are specifically such as
Under:
(1) failure rate: refer to load point i in given time section because stopping caused by network element fails of providing multiple forms of energy to complement each other
Number is energized, λ is usediIt indicates, unit times/year；
Wherein, λjIndicate the failure rate of element j；
(2) average idle time: refer to stopping energy supply time of the user i within given time, use UiIt indicates, unit is hour/year；
Wherein, γjIndicate the average time for repair of breakdowns of element j；
(3) average time for repair of breakdowns: refer to that load point stops supply from energy and occurs to the time average for restoring energy supply, use γi
Indicate, unit be hour/time；
Reliability index based on load point can establish the reliability index for system energy supply of entirely providing multiple forms of energy to complement each other, entire for reflecting
The energy supply degree of reliability for system of providing multiple forms of energy to complement each other.
5. the system energy supply reliability estimation method of providing multiple forms of energy to complement each other according to claim 4 based on Monte Carlo Method, special
Sign is, according to the difference of reliability assessment content, it includes when stopping energy supply frequency that system of providing multiple forms of energy to complement each other, which energizes reliability index,
Between class index and stop energy supply load and energy class index, be specifically divided into following several:
(1) system averagely stops energy frequency index, refers to that each user by system energy supply of providing multiple forms of energy to complement each other is subjected in 1 year
Average stopping energize number, unit is time/(user year)；
When statistics, when the reliability index caused by all stoppings are energized all is included in, it is denoted as SAIFI-1；Stop when disregarding outside
When reliability index caused by only energizing, it is denoted as SAIFI-2；When disregard energy source deficiency limit energy caused by plan stop energy supply,
When limitation energy supply causes the reliability index for stopping energizing, it is denoted as SAIFI-3；
(2) system averagely stops energizing duration index, refers to each user by system energy supply of providing multiple forms of energy to complement each other in 1 year
The average stopping energy supply duration being subjected to, unit are hour/(user year)；
Equally, when statistics, when the reliability index caused by all stoppings are energized all is included in, it is denoted as SAIDI-1；When disregarding
When outside stops reliability index caused by energizing, it is denoted as SAIDI-2；Plan to stop caused by energy source deficiency limit energy when disregarding
When energy supply, limitation energy supply cause the reliability index for stopping energizing, it is denoted as SAIDI-3；
(3) user averagely stops energizing duration index, refers to the average stopping that the user that energy supply is stopped in 1 year is subjected to
The duration is energized, unit is hour/(stopping energy supply user year)；
(4) averagely energize Availability Index, refer to that user in 1 year is subjected to do not stop energizing hour sum and user requires
The ratio between total energy supply hourage；
(5) energy deficiency it is expected, refers to that the vacancy of the energy provided a user because caused by stopping transport element in 1 year, unit are
Megawatt hour/year；
EENS=∑ La(i)Ui
6. the system energy supply reliability estimation method of providing multiple forms of energy to complement each other according to claim 1 based on Monte Carlo Method, special
Sign is that Monte Carlo Method is substantially a kind of probability simulation method, and system energy supply of providing multiple forms of energy to complement each other is assessed using Monte Carlo Method
The process of reliability is divided into system element state sampling, system mode analysis and system index and counts three steps；First with
The method of sampling selects element state；Then state estimation is carried out to the state of extraction, judges each ginseng of this state
Whether amount is within the scope of requiring；The reliability index for the system of providing multiple forms of energy to complement each other finally is obtained using the method for statistics.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811495538.9A CN109636027A (en) | 2018-12-07 | 2018-12-07 | A kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811495538.9A CN109636027A (en) | 2018-12-07 | 2018-12-07 | A kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109636027A true CN109636027A (en) | 2019-04-16 |
Family
ID=66072012
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811495538.9A Pending CN109636027A (en) | 2018-12-07 | 2018-12-07 | A kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109636027A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111475953A (en) * | 2020-04-10 | 2020-07-31 | 广东电网有限责任公司 | Energy supply reliability influence analysis method, device and equipment and storage medium |
| CN111798111A (en) * | 2020-06-27 | 2020-10-20 | 上海交通大学 | Comprehensive energy system energy supply reliability assessment method and computer system |
| CN111898239A (en) * | 2020-06-10 | 2020-11-06 | 华电电力科学研究院有限公司 | Distributed residual voltage power generation system energy supply reliability evaluation method based on Monte Carlo simulation method |
| CN113221500A (en) * | 2021-06-18 | 2021-08-06 | 苏州复鹄电子科技有限公司 | Chip routing layout automatic design method based on artificial intelligence algorithm |
| CN115021335A (en) * | 2022-06-16 | 2022-09-06 | 西安交通大学 | Multi-period robustness and reliability assessment method considering energy storage and new energy |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105427195A (en) * | 2015-12-23 | 2016-03-23 | 国家电网公司 | Calculation method of reliability index of power transmission and distribution integration |
| CN107358352A (en) * | 2017-07-05 | 2017-11-17 | 国网山东省电力公司电力科学研究院 | Model in Reliability Evaluation of Power Systems system and method based on Monte Carlo simulation |
| CN108830485A (en) * | 2018-06-19 | 2018-11-16 | 广州供电局有限公司 | A kind of electric-thermal integrated energy system method for evaluating reliability |
| CN108921727A (en) * | 2018-06-30 | 2018-11-30 | 天津大学 | Consider the regional complex energy resource system reliability estimation method of thermic load dynamic characteristic |
-
2018
- 2018-12-07 CN CN201811495538.9A patent/CN109636027A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105427195A (en) * | 2015-12-23 | 2016-03-23 | 国家电网公司 | Calculation method of reliability index of power transmission and distribution integration |
| CN107358352A (en) * | 2017-07-05 | 2017-11-17 | 国网山东省电力公司电力科学研究院 | Model in Reliability Evaluation of Power Systems system and method based on Monte Carlo simulation |
| CN108830485A (en) * | 2018-06-19 | 2018-11-16 | 广州供电局有限公司 | A kind of electric-thermal integrated energy system method for evaluating reliability |
| CN108921727A (en) * | 2018-06-30 | 2018-11-30 | 天津大学 | Consider the regional complex energy resource system reliability estimation method of thermic load dynamic characteristic |
Non-Patent Citations (2)
| Title |
|---|
| GENGFENG LI等: "Reliability evaluation of integrated energy systems based on smart agent communication", 《APPLIED ENERGY》 * |
| SUPRIYA M D等: "Distribution System Reliability Evaluation using Time Sequential Monte Carlo Simulation", 《ITSI TRANSACTIONS ON ELECTRICAL AND ELECTRONICS ENGINEERING》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111475953A (en) * | 2020-04-10 | 2020-07-31 | 广东电网有限责任公司 | Energy supply reliability influence analysis method, device and equipment and storage medium |
| CN111898239A (en) * | 2020-06-10 | 2020-11-06 | 华电电力科学研究院有限公司 | Distributed residual voltage power generation system energy supply reliability evaluation method based on Monte Carlo simulation method |
| CN111798111A (en) * | 2020-06-27 | 2020-10-20 | 上海交通大学 | Comprehensive energy system energy supply reliability assessment method and computer system |
| CN113221500A (en) * | 2021-06-18 | 2021-08-06 | 苏州复鹄电子科技有限公司 | Chip routing layout automatic design method based on artificial intelligence algorithm |
| CN115021335A (en) * | 2022-06-16 | 2022-09-06 | 西安交通大学 | Multi-period robustness and reliability assessment method considering energy storage and new energy |
| CN115021335B (en) * | 2022-06-16 | 2024-03-26 | 西安交通大学 | Multi-period robust reliability assessment method considering energy storage and new energy |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109636027A (en) | A kind of system energy supply reliability estimation method of providing multiple forms of energy to complement each other based on Monte Carlo Method | |
| CN108183512B (en) | Reliability assessment method for power system accessed with new energy | |
| CN111313429B (en) | Reliability assessment method and system for comprehensive energy system | |
| CN111030094B (en) | Method for evaluating reliability of energy supply of multi-energy complementary park | |
| Pan et al. | Analysis on integrated energy system cascading failures considering interaction of coupled heating and power networks | |
| CN105260952A (en) | Markov chain Monte Carlo method-based photovoltaic power station reliability evaluation method | |
| CN108549983A (en) | A kind of appraisal procedure of DC distribution net reliability index | |
| Li et al. | Researches on the reliability evaluation of integrated energy system based on Energy Hub | |
| Zhao et al. | Reliability evaluation of community integrated energy systems based on fault incidence matrix | |
| CN105305488A (en) | Evaluation method considering influence of new energy grid connection on utilization rate of transmission network | |
| CN115907281A (en) | Power system carbon emission flow analysis method considering photovoltaic uncertainty | |
| CN111798111A (en) | Comprehensive energy system energy supply reliability assessment method and computer system | |
| CN114142461B (en) | New energy differential configuration method considering grid morphology evolution and structure development | |
| Li et al. | Reliability modeling and assessment for integrated energy system: a review of the research status and future prospects | |
| CN116384825A (en) | Power system operation reliability assessment method considering load flexibility of data center | |
| Guocheng et al. | Reliability analysis method for microgrid with intermittent distributed generations | |
| Liu et al. | A Reliability assessment of an integrated energy system based on coupling energy flow and thermal inertia | |
| Xu et al. | Security analysis of integrated energy system under complex network | |
| CN111898239A (en) | Distributed residual voltage power generation system energy supply reliability evaluation method based on Monte Carlo simulation method | |
| CN113346541A (en) | Wind power prevention control optimization method under typhoon disaster | |
| CN115935711B (en) | Multi-energy complementary active distribution network system reliability assessment method based on graph theory | |
| Yang et al. | Comprehensive evaluation of distribution network reliability for power consumer based on AHP and entropy combination method | |
| Peng et al. | Stochastic Vulnerability Analysis methodology for Power Transmission Network Considering Wind Generation | |
| CN112819327B (en) | Comprehensive energy system reliability evaluation method for complementary optimization operation during fault | |
| An et al. | The IECPS Reliability Assessment Method Considering the Working State of Information Equipment |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190416 |
|
| RJ01 | Rejection of invention patent application after publication |