CN113626963B - Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system - Google Patents

Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system Download PDF

Info

Publication number
CN113626963B
CN113626963B CN202110923790.0A CN202110923790A CN113626963B CN 113626963 B CN113626963 B CN 113626963B CN 202110923790 A CN202110923790 A CN 202110923790A CN 113626963 B CN113626963 B CN 113626963B
Authority
CN
China
Prior art keywords
energy storage
rate
fault
grid
bess
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.)
Active
Application number
CN202110923790.0A
Other languages
Chinese (zh)
Other versions
CN113626963A (en
Inventor
肖先勇
陈智凡
汪颖
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Sichuan University
Original Assignee
Sichuan University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Sichuan University filed Critical Sichuan University
Priority to CN202110923790.0A priority Critical patent/CN113626963B/en
Publication of CN113626963A publication Critical patent/CN113626963A/en
Application granted granted Critical
Publication of CN113626963B publication Critical patent/CN113626963B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/18—Network design, e.g. design based on topological or interconnect aspects of utility systems, piping, heating ventilation air conditioning [HVAC] or cabling
    • 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Geometry (AREA)
  • Economics (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computational Mathematics (AREA)
  • Strategic Management (AREA)
  • Tourism & Hospitality (AREA)
  • Water Supply & Treatment (AREA)
  • General Business, Economics & Management (AREA)
  • Public Health (AREA)
  • Marketing (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Human Resources & Organizations (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a multi-level comprehensive evaluation method for reliability of an energy storage grid-connected system, which constructs a hierarchical state space of a BESS grid-connected model from top to bottom, comprehensively considers various operation strategies and equipment operation characteristics, simulates a system cooperative work mechanism by using a reliability parameter coupling calculation method, provides a multi-level analysis method for the state space of the BESS grid-connected system, calculates to obtain an evaluation result which truly reflects the reliability of the BESS grid-connected system, and meets the engineering application requirement for developing the reliability evaluation of the BESS grid-connected system under the actual working condition.

Description

Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system
Technical Field
The invention relates to the technical field of performance evaluation of a battery energy storage system, in particular to a multi-level comprehensive evaluation method for reliability of an energy storage grid-connected system.
Background
In recent years, with the widespread popularization of renewable energy sources, the reliability of a power system is influenced by the intermittence and fluctuation characteristics of distributed power supplies (DG for short) such as wind power and photovoltaic power. In order to smooth out fluctuations in the processing of renewable energy sources and to suppress their impact on the grid, BESS (battery energy storage system) is increasingly incorporated into the grid. BESS has high energy density, high cycle response speed and low construction environment requirement, and is ideal equipment for improving the stability and the reliability of a system. However, the safety characteristics and the operation strategy of the large-capacity BESS have great influence on the reliable operation of the power system, when the large-capacity BESS is connected to the grid, if the battery capacity and the access position do not meet the common operation conditions, the problems of overvoltage, short-circuit current increase, power grid harmonic wave, voltage sag, power grid reliability reduction and the like of a power transmission line can be caused, the research on the BESS grid connection reliability at home and abroad at present is not deep enough, the BESS grid connection system has complicated levels and numerous elements, once a local fault occurs, the fault source is difficult to be screened orderly, and a systematic and numerical evaluation method aiming at the BESS grid connection system reliability is lacked.
Aiming at various problems possibly encountered during high-capacity BESS grid connection, the invention establishes a BESS general grid connection framework and establishes a top-down BESS grid connection system reliability model based on a state space analysis algorithm. Aiming at a distributed power supply with strong volatility, a universal method for constructing a DG state space by using parameter correction is provided; aiming at typical load distribution and wiring modes of a BESS grid-connected system, a method for constructing a power distribution network state space according to a historical database is provided; and establishing a BESS state space by considering the energy storage safety characteristic and the operation strategy. The invention provides a BESS grid-connected system reliability evaluation method considering engineering practice, different systems are gradually coupled from a base state space in a layered and graded manner, and finally BESS grid-connected comprehensive reliability parameters which accord with practical application are obtained through calculation.
The existing BESS grid-connected system reliability evaluation method is described in the following documents:
[1] the influence of the Huang Jing, Xie Xiao, Wang Ruozi, et al distributed power supply on the reliability of the power distribution network [ J ] electrician materials, 2021,01):48-50.
[2] Distribution network reliability evaluation with distributed power supplies [ J ] power capacitors and reactive compensation, 2021,42(02):139-45.
[3] Bolt, flare flame, power distribution network power supply reliability of the distributed power supply, 2007,22 and 46-9.
The reliability parameters are calculated by using a variable correction method or a historical database, and the technical scheme is as follows:
1) for DG, firstly constructing a distributed power supply model under a rated working condition in a laboratory, carrying out a cycle test, measuring element reliability parameters corresponding to various fault types of each element under the rated working condition, determining the operation variables of the distributed power supply, determining a variable distribution function, and obtaining the element reliability parameters corresponding to various fault types under the actual working condition by a variable correction method.
2) For the power distribution network and the BESS, a historical database and experience estimation combined method is generally adopted, a typical wiring mode and an operation strategy of the power distribution network and the BESS are preset, then reliability parameters of all elements are obtained by searching based on the historical database, and the reliability parameters of a system level are simply estimated by combining an expert evaluation method.
The disadvantages are that:
1) in the prior art, a complete BESS grid-connected system model is established, occurrence mechanisms of various faults of the BESS grid-connected system are explained, reliability of a single element is studied relatively deeply, the BESS grid-connected system is a complex system formed by multi-level coupling of a large number of elements essentially, reliability evaluation is carried out on the complex system, cooperative work reliability among the complex systems is considered, the existing reliability evaluation method usually focuses on reliability of the single element under a preset condition of a laboratory, parameters of the elements are simply accumulated, cooperative work characteristics of the elements are ignored, and the working reliability of the BESS grid-connected system under an actual working condition is difficult to accurately reflect.
2) In the prior art, reliability evaluation of a BESS grid-connected system usually depends on online monitoring data of each part of the system, an empirical estimation method is adopted to set a reliability threshold, reliability evaluation precision of the BESS grid-connected system is related to data monitoring precision, the BESS grid-connected system is complex in environmental conditions and diverse in operation strategies in actual operation, reliability parameter estimation of each element has deviation of different degrees, if actual working conditions are not considered, the operation state and self safety characteristics of the complex system are directly preset, and the reliability threshold is set by experience, so that the monitoring data of the BESS grid-connected system in normal operation easily exceed the preset threshold, fault misjudgment is caused, and the reliability evaluation result cannot reflect the actual operation state of the BESS grid-connected system.
Interpretation of terms:
battery Energy Storage System (BESS): the battery is used as an energy storage carrier and is formed by combining hundreds of battery cell monomers, and the energy storage system for storing electric energy and supplying the electric energy generally comprises a battery cabinet for storing the energy and a control cabinet for monitoring, regulating and controlling the energy.
Distributed Generator (DG): the distributed power supply generally has strong volatility and discontinuity, so the distributed power supply is often matched with a large-capacity energy storage system to assist in scheduling and is merged into a power grid.
State space analysis method: the state space analysis method is a problem representation and solving method based on a matrix, and a hierarchical state space of a complex system is gradually established by setting an initial safe operation state and a fault state of the complex system until the complex system is completely described.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a multi-level comprehensive evaluation method for reliability of an energy storage grid-connected system, which is used for calculating to obtain an evaluation result truly reflecting reliability of the BESS grid-connected system and meeting the engineering application requirement for developing reliability evaluation of the BESS grid-connected system under actual working conditions. The technical scheme is as follows:
a multi-level comprehensive evaluation method for reliability of an energy storage grid-connected system is characterized by comprising the following steps:
step 1: state space analysis method-based grid-connected reliability model for battery energy storage system
Step 1.1: establishing a grid-connected model state space of the battery energy storage system: assuming that the grid-connected model faults of the battery energy storage system are independent from each other and the composite fault is not considered, the grid-connected model of the battery energy storage system has 4 states: normal operating conditions, distributed power supply faults, power distribution network faults and battery energy storage system faults;
constructing a state transition matrix by adopting a state space analysis method, and determining a calculation formula of the probability of each state;
constructing a state transition matrix A:
Figure GDA0003591693890000031
wherein λ isDG、λDASAnd λBESSThe failure rates of the distributed power supply failure, the power distribution network failure and the battery energy storage system failure are respectively; mu.sDG、μDASAnd muBESSThe repair rates of the three fault states are respectively;
computing the probability P ═ P that the system is in 4 states0,pDG,pDAS,pBESS]：
Figure GDA0003591693890000032
Figure GDA0003591693890000033
Wherein λ isi、μiThe failure rate and the repair rate of each failure state are obtained;
step 1.2: reliability parameter coupling calculation
Extending the state space downwards, performing accident cause analysis on the system aiming at each part of a distributed power supply, a power distribution network and a battery energy storage system in a grid-connected model of the battery energy storage system, determining the fault rate and the repair rate of each element, coupling each element, and calculating to obtain the comprehensive fault rate/repair rate of the part:
regarding the components or the component groups which may have a certain type of faults in the working process, the components or the component groups are regarded as parallel systems in terms of reliability, and the parallel fault rate lambda of the kth type of faultsGP-kComprises the following steps:
Figure GDA0003591693890000034
in the formula, NPkNumber of elements in which a k-th type failure is likely to occur, λPkThe single failure rate of each element corresponding to the kth type failure; n is a radical ofSIs the total number of types of possible failures;
from the reliability point of view, the different types of faults belong to the series system, the fault rates can be linearly superposed, the series fault rate of the different types of faults can be calculated, and the comprehensive fault rate lambda of the complex system is alsoG：
Figure GDA0003591693890000041
In the formula, λGP-kIs the individual/parallel failure rate of each element/element group;
according to the comprehensive formulas (4) and (5), aiming at various different faults and fault elements which may occur in the complex system, firstly, the parallel fault rate in one type of fault is calculated, then the series fault rate of the different types of faults is calculated, and finally, the comprehensive fault rate lambda of the complex system is obtainedGExpression (c):
Figure GDA0003591693890000042
the repairing process is different from the element fault process, the element or element group to be repaired after the k-th fault occurs belongs to a series system in terms of reliability, and the series repairing rate mu of the element or element groupGS-kIs composed of
Figure GDA0003591693890000043
In the formula, NSkFor the number of components, mu, to be repaired after the occurrence of a kth-type faultSkThe repair rate of the element after the k-th fault occurs;
the comprehensive repair rate mu of the complex system is obtained by combining the derivation of the formula (2)GComprises the following steps:
Figure GDA0003591693890000044
step 2: establishing state spaces of all elements of a grid-connected model of the battery energy storage system: the method comprises the following steps of extending a grid-connected model state space of the battery energy storage system downwards to a distributed power supply state space, a power distribution network state space and a battery energy storage system state space, and calculating the fault rate and the repair rate of the distributed power supply, the power distribution network and the battery energy storage system in the energy storage grid-connected system:
1) distributed power state space: the distributed power supply belongs to a parallel system in terms of reliability, and a reliability model construction method comprises the following steps: determining the rated fault rate and the repair rate of elements under various fault types in the bottom layer state space, obtaining the fault rate and the repair rate of the actual working condition through variable correction, and forming the comprehensive fault rate lambda of the distributed power supply through the coupling calculation method in the step 1.2DGAnd the repair rate muDG；
2) Distribution network state space: the load of the power distribution network is approximately described based on normal distribution, the number of sampling points is N, and the value probability of each normal interval sampling point is pn(ii) a The number of the load samples of the power distribution network in each interval is close to pnX is N; the mean μ and variance σ are:
Figure GDA0003591693890000045
in the formula, aiver、amaxAnd aminRespectively the annual average, maximum and minimum load rates of the distribution network,
Figure GDA0003591693890000051
the load peak value of a certain node is obtained;
determining load distribution according to annual average, maximum and minimum load rates of the power distribution network, determining a wiring mode of the power distribution network, acquiring element fault rate and repair rate of each load interval according to a historical database, and calculating the comprehensive fault rate lambda of the complex system by adopting a coupling calculation method in the step 1.2DASAnd the repair rate muDAS；
3) Battery energy storage system state space: the battery energy storage system has different operation strategies at different time intervals, and the reliability of each element is directly related to the operation strategy and the charge and discharge amount of the element;
establishing a BESS electric quantity model based on the generated energy-load balance:
ΔP(t)＝PDG(t)-Pload(t) (10)
in the formula, PDG(t)The actual power generation amount of the distributed power supply in the cycle time t; pload(t)Predicting the load of the power distribution network; Δ p (t) is the charge and discharge capacity of the battery energy storage system in the time period t; calculating according to delta P (t) to obtain the comprehensive failure rate lambda of the battery energy storage systemBESSAnd the comprehensive repair rate muBESS：
Figure GDA0003591693890000052
In the formula:
Figure GDA0003591693890000053
respectively representing the charging and discharging time ratios of the battery energy storage system, H1Is Δ P (t)>Period of 0, H2Is Δ P (t)<A period of 0; lambda [ alpha ]BESS1And muBESS1Respectively charging the fault rate and the repair rate of the battery energy storage system; lambda [ alpha ]BESS2And muBESS2Respectively calculating the fault rate and the repair rate of the battery energy storage system during discharging by using the coupling calculation method in the step 1.2;
and 3, step 3: reliability evaluation of energy storage grid-connected system
Calculating the step 2 to obtain the fault rates and the repair rates lambda of DG, BESS and the power distribution network in the energy storage grid-connected systemDG、μDG、λDAS、μDAS、λBESS、μBESSAnd substituting the calculated values into the formula (2) and the formula (3) in the step 1, and finally calculating the probability that the grid-connected system of the battery energy storage system is in a normal working state, the distributed power supply fails, the distribution network fails and the battery energy storage system fails.
Further, when the distributed power source in step 2 is a wind power generator, the determination process of the fault rate and the repair rate of the actual working condition is as follows:
the fault rate of a single element in the wind driven generator is obtained by the fan parameter and the wind speed, and the repair rate is a determined constant; the distribution function f (v) of the wind speed v is approximately described based on a two-parameter Weibull distribution:
Figure GDA0003591693890000054
in the formula, c is a Weibull fan size coefficient, d is a fan shape coefficient, and c and d are obtained by fitting fan laboratory prototype experiments or historical data;
fault probability lambda of each element of fanwrThe functional relationship with the wind speed v is as follows:
Figure GDA0003591693890000061
in the formula, vci、vcoAnd vrCut-in wind speed, cut-out wind speed and rated wind speed, lambda, of the wind turbinerThe failure rate of the elements under the rated wind speed is obtained for laboratory prototype experiments.
The invention has the beneficial effects that:
1) according to the invention, a state space analysis method is used, a BESS grid-connected system operation strategy and safety characteristics are combined, a hierarchical state space from top to bottom of a BESS grid-connected model is constructed, and the probability that the BESS grid-connected system is in a normal working state, namely the reliability of the BESS grid-connected system, is calculated and obtained based on the state space analysis method. Compared with the traditional method which considers the reliability of a single element under the preset condition, the method simply accumulates the parameters of each element and neglects the cooperative working characteristics of each element, and meets the engineering application requirement of developing reliability evaluation of the BESS grid-connected system under the actual working condition.
2) In the invention, various operation strategies and equipment operation characteristics are comprehensively considered in the face of different components of the BESS grid-connected system, a system cooperative work mechanism is simulated by using a reliability parameter coupling calculation method, a state space multilevel analysis method of the BESS grid-connected system is provided, and the defects of low reliability evaluation precision, high misjudgment rate and separation from actual working conditions of the traditional method are jointly overcome by three technical progresses.
3) Aiming at the volatility and discontinuity of the distributed power supply, the invention provides a feasible reliability parameter correction method, which reasonably corrects the reliability parameters of the distributed power supply measured in a small-power prototype experiment based on a variable correction method, gives consideration to the economy and the parameter estimation accuracy, and has strong universality on various devices.
Drawings
Fig. 1 is a flow chart of a multi-level comprehensive evaluation method for reliability of an energy storage grid-connected system.
FIG. 2 is a state transition diagram of the BESS grid-connected model.
Fig. 3 is a schematic diagram of normal distribution of load of the distribution network.
Detailed Description
The invention is described in further detail below with reference to the figures and specific embodiments. The technical scheme of the invention can be divided into three major steps, namely establishing a BESS grid-connected reliability model based on a state space analysis method, establishing state spaces of elements of the BESS grid-connected model and evaluating the reliability of an energy storage grid-connected system, wherein a flow chart is shown in figure 1, and the specific process comprises the following steps:
step 1: BESS grid-connected reliability model established based on state space analysis method
The state space refers to the whole set of the system running state, the state space analysis method is a method for describing the running reliability of the system through the system state transition relation based on the Markov process, and the comprehensive fault rate and the repair rate of the complex module are solved through respectively and independently calculating each element of the system.
Step 1.1: establishing BESS grid-connected model state space
The reliability of the BESS grid-connected model formed by DG, BESS and power distribution network loads is determined by the running, fault or recovery state of each element, 4 states of the BESS grid-connected model are provided that the BESS grid-connected model faults are independent and the composite fault is not considered, a state space analysis method can be used for establishing a state transition diagram, as shown in figure 2, wherein '0' represents that the BESS grid-connected model is in a normal working state, and '1', 2 'and 3' respectively represent 3 states of the BESS grid-connected model faults caused by DG, power distribution network faults and BESS faults, and lambdaDG/DAS/BESSAnd muDG/DAS/BESSThe failure rate and the repair rate of each part are respectively.
To calculate the probability P ═ P of the system in each state0,p1,p2,p3]Constructing a state transition matrix A
Figure GDA0003591693890000071
Further, there are
Figure GDA0003591693890000072
Figure GDA0003591693890000073
The solution of the state probability of the simplified BESS grid-connected model can be popularized to a more complex N-element general model, and the number of the elements in the steps (1), (2) and (3) is only required to be modified into N.
Step 1.2: reliability parameter coupling calculation method
After the top-level design of the BESS grid-connected model is completed, the state space is extended downwards, DG, the power distribution network and each part of the BESS in the BESS grid-connected model are used as complex systems, a state transition diagram as shown in figure 2 can be made, accident cause analysis is carried out on the systems aiming at each part, the fault rate and the repair rate of each element are determined, then each element can be coupled, and the comprehensive fault rate/repair rate of the part can be obtained through calculation.
Regarding the elements or element groups which may have a certain type of fault in the working process, regarding them as parallel systems in the aspect of reliability, the parallel fault rate lambda of the k-th type faultGP-kIs composed of
Figure GDA0003591693890000074
In the formula, NPkNumber of elements in which a k-th type failure is likely to occur, λPkThe single failure rate of each element corresponding to the kth type failure; n is a radical ofSIs the total number of possible failure types.
From the reliability point of view, the different types of faults belong to a series system, the fault rates can be linearly superposed, the series fault rate of the different types of faults can be calculated, and the comprehensive fault rate lambda of the complex system is alsoG
Figure GDA0003591693890000081
In the formula, λGP-kIs the individual/parallel failure rate of each element/element group.
According to the comprehensive formulas (4) and (5), aiming at various different faults and fault elements which may occur in the complex system, firstly, the parallel fault rate in one type of fault is calculated, then the series fault rate of the different types of faults is calculated, and finally, the comprehensive fault rate lambda of the complex system is obtainedG
Figure GDA0003591693890000082
The repairing process is different from the element fault process, the element or element group to be repaired after the k-th fault occurs belongs to a series system in terms of reliability, and the series repairing rate mu of the element or element groupGS-kIs composed of
Figure GDA0003591693890000083
In the formula, NSkFor the number of components, mu, to be repaired after the occurrence of a kth-type faultSkThe repair rate after a class k failure for the element.
After the heterogeneous faults occur, the system can be ensured to operate reliably only by completely repairing the heterogeneous faults, and the comprehensive repair rate mu of the complex system is obtained by deduction in a combined mode (2)GIs composed of
Figure GDA0003591693890000084
Step 2: establishing state space of each element of BESS grid-connected model
In order to accurately describe BESS grid-connected reliability, a BESS grid-connected model state space is extended downwards to three typical parts, namely a DG part, a distribution network and a BESS part.
2.1DG State space
In the aspect of the distributed power supply, a wind driven generator is selected as an example, as shown in table 1, the common fault type and the corresponding element of a fan of the wind driven generator are shown, the fault rate and the repair rate of a single element can be obtained according to the fan parameter and the wind speed, and the fault rate and the repair rate of a type of fault can be obtained according to the formula of step 1.2.
The single element fault probability is related to the fan parameters and the wind speed, and the repair rate is a determined constant. In order to solve the problem, the invention aims to construct a wind turbine model under a rated working condition in a laboratory and perform a cyclic test to measure the fault rate of each element of the fan under the rated working condition, and the fault rate under the non-rated working condition can be obtained through a wind speed correction formula. The distribution function f (v) of the wind speed v can be approximately described based on a two-parameter Weibull distribution:
Figure GDA0003591693890000091
in the formula, c is a Weibull fan size coefficient, d is a fan shape coefficient, and c and d can be obtained through fan laboratory prototype experiments or fitting of historical data.
Fault probability lambda of each element of fanwrThe functional relationship with the wind speed v is as follows:
Figure GDA0003591693890000092
in the formula, vci/vcoAnd vrCut-in/cut-out wind speed and rated wind speed, lambda, of a wind turbine, respectivelyrThe method is a correction formula of the rated fault rate for the element fault rate under the rated wind speed obtained by a laboratory prototype experiment.
TABLE 1 common fault types of fans and corresponding elements
Figure GDA0003591693890000093
Without loss of generality, distributed power supplies such as photovoltaic generators and diesel generators belong to parallel systems in terms of reliability, and reliability models of the distributed power supplies have completely consistent construction methods: namely, laboratory test operation is carried out, the rated fault rate and the repair rate of elements under various fault types in the bottom layer state space are determined, the fault rate and the repair rate of the actual working condition are obtained through variable correction, and the step 1.2 method is adopted to couple lambdaWTi/μWTi(i 1, …,8), and the total failure rate λ of the wind turbine generator is determinedWTAnd the repair rate muWTThe same method is adopted to form the comprehensive failure rate lambda of the distributed power supplyDGAnd the repair rate muDG。
2.2 distribution network State space
Fault in distribution networkThe power is directly related to a typical wiring mode and the load of a power distribution network, and the typical wiring mode generally adopted by BESS grid connection is of a ring network power supply type and a multi-power supply type; the load of the power distribution network can be approximately described based on normal distribution, as shown in fig. 3, the number of sampling points is set to be N, and the value probability of each sampling point in a normal interval is set to be pi(i is 1,2 … 7), and the number of distribution network load samples in each interval is close to pi × N. The mean μ and variance σ are:
Figure GDA0003591693890000101
in the formula, aiver、amaxAnd aminRespectively the annual average, maximum and minimum load rates of the distribution network,
Figure GDA0003591693890000103
then it is a certain node load peak.
As shown in Table 2, for common fault types and corresponding elements of the power distribution network, firstly, load distribution is determined according to the average, maximum and minimum load rates of the power distribution network, then, the wiring mode of the power distribution network is determined, namely, the fault rate and the repair rate of the elements in each load interval can be obtained according to a historical database, and the method of the step 1.2 is adopted to obtain the comprehensive fault rate lambda of the complex systemDASAnd the repair rate muDAS。
TABLE 2 common fault types and corresponding elements of distribution network
Figure GDA0003591693890000102
2.3 BESS State space
According to the invention, an energy storage system of the lithium iron phosphate battery is selected, and a power-time curve of the energy storage system approximately presents a linear relation. The battery energy storage system is different from a distributed power supply and a power distribution network load and may have different operation strategies in different time periods, and the reliability of each element of the BESS is directly related to the BESS operation strategy and the BESS charging and discharging amount.
When the DG power generation amount is larger than the load, the BESS is used as an energy storage device, and is reliableA sexual parameter lambdaBESS1、μBESS1Can be obtained from the literature [ fifth institute of electronics, Industrial & Informatization department ] reliability prediction model of electronic device and data manual [ M ]]The national market supervision and administration headquarters; the national Committee for standardization of China 2019:92.]Directly obtained, and part of parameters are shown in table 3.
TABLE 3 BESS common Fault types, corresponding Components and reliability parameters
Figure GDA0003591693890000111
When the DG power generation amount is smaller than the load, BESS is equivalent to a power supply to discharge outwards, at the moment, the construction method of the BESS state space is completely consistent with the step 2.1, the description is omitted, and the reliability parameter lambda of the discharge process can be obtained through calculationBESS2、μBESS2。
In order to synthesize BESS reliability parameters of the charging and discharging process, a BESS electric quantity model based on electric quantity generation-load balance is established:
ΔP(t)＝PDG(t)-Pload(t) (12)
in the formula, PDG(t)Is DG actual power generation in the cycle time t; pload(t)Predicting the load of the power distribution network; Δ P (t) is the BESS charge-discharge capacity in t; the BESS comprehensive fault rate lambda can be obtained by calculation according to delta P (t)BESSAnd the comprehensive repair rate muBESS：
Figure GDA0003591693890000112
In the formula:
Figure GDA0003591693890000113
respectively represents the time ratio of BESS charging and discharging, H1Is Δ P (t)>Period of 0, H1Is Δ P (t)<A period of 0.
Step three: reliability evaluation of energy storage grid-connected system
Calculating to obtain the fault rates of DG, BESS and power distribution network in the energy storage grid-connected systemAnd the repair rate lambdaDG、μDG、λDAS、μDAS、λBESS、μBESSAnd then, returning to the BESS grid-connected model state space in the step 1, and calculating the probability that the BESS grid-connected system is in a normal working state according to the formula (2).
p0The reliability of the BESS grid-connected system can be expressed, and the reliability of each part in the system can be calculated through the formula (3) by the same state space analysis method.

Claims (2)

1. A multi-level comprehensive evaluation method for reliability of an energy storage grid-connected system is characterized by comprising the following steps:
step 1: state space analysis method-based grid-connected reliability model for battery energy storage system
Step 1.1: establishing a grid-connected model state space of the battery energy storage system: assuming that the grid-connected model faults of the battery energy storage system are independent from each other and the composite fault is not considered, the grid-connected model of the battery energy storage system has 4 states: normal operating conditions, distributed power supply faults, power distribution network faults and battery energy storage system faults;
constructing a state transition matrix by adopting a state space analysis method, and determining a calculation formula of the probability of each state;
constructing a state transition matrix A:
Figure FDA0003591693880000011
wherein λ isDG、λDASAnd λBESSThe failure rates of the distributed power supply failure, the power distribution network failure and the battery energy storage system failure are respectively; mu.sDG、μDASAnd muBESSThe repair rates of the three fault states are respectively;
computing the probability P ═ P that the system is in 4 states0,pDG,pDAS,pBESS]：
Figure FDA0003591693880000012
Figure FDA0003591693880000013
Wherein λ isi、μiThe failure rate and the repair rate of each failure state are obtained;
step 1.2: reliability parameter coupling calculation
Extending the state space downwards, performing accident cause analysis on the system aiming at each part of a distributed power supply, a power distribution network and a battery energy storage system in a grid-connected model of the battery energy storage system, determining the fault rate and the repair rate of each element, coupling each element, and calculating to obtain the comprehensive fault rate/repair rate of the part:
regarding the components or the component groups which may have a certain type of faults in the working process, regarding the components or the component groups as parallel systems in terms of reliability, the parallel fault rate lambda of the kth type fault of each component/component groupGP-kComprises the following steps:
Figure FDA0003591693880000014
in the formula, NPkNumber of elements in which a k-th type failure is likely to occur, λPkThe single failure rate of each element corresponding to the kth type failure; n is a radical ofSIs the total number of types of possible failures;
from the reliability point of view, the different types of faults belong to the series system, the fault rates can be linearly superposed, the series fault rate of the different types of faults can be calculated, and the comprehensive fault rate lambda of the complex system is alsoG：
Figure FDA0003591693880000024
In the formula of lambdaGP-kThe parallel failure rate is the kth type failure of each element/element group;
the comprehensive formula (4) and (5),aiming at various different faults and fault elements which may occur in a complex system, firstly, the parallel fault rate in one type of fault is calculated, then the series fault rate of the different types of faults is calculated, and finally, the comprehensive fault rate lambda of the complex system is obtainedGExpression (c):
Figure FDA0003591693880000021
the repairing process is different from the element fault process, the element or element group to be repaired after the k-th fault occurs belongs to a series system in terms of reliability, and the series repairing rate mu of the element or element groupGS-kIs composed of
Figure FDA0003591693880000022
In the formula, NSkFor the number of components, mu, to be repaired after the occurrence of a kth-type faultSkThe repair rate of the element after the k-th fault occurs; the comprehensive repair rate mu of the complex system is obtained by combining the derivation of the formula (2)GComprises the following steps:
Figure FDA0003591693880000023
step 2: establishing state spaces of all elements of a grid-connected model of the battery energy storage system: the method comprises the following steps of extending a grid-connected model state space of the battery energy storage system downwards to a distributed power supply state space, a power distribution network state space and a battery energy storage system state space, and calculating the fault rate and the repair rate of the distributed power supply, the power distribution network and the battery energy storage system in the energy storage grid-connected system:
1) distributed power state space: the distributed power supply belongs to a parallel system in terms of reliability, and a reliability model construction method comprises the following steps: determining the rated fault rate and the repair rate of elements under various fault types in the bottom layer state space, obtaining the fault rate and the repair rate of the actual working condition through variable correction, and forming the comprehensive fault rate lambda of the distributed power supply through the coupling calculation method in the step 1.2DGAnd the repair rate muDG；
2) Distribution network state space: the load of the power distribution network is approximately described based on normal distribution, the number of sampling points is N, and the value probability of each normal interval sampling point is pn(ii) a The number of the load samples of the power distribution network in each interval is close to pnX is N; the mean μ and variance σ are:
Figure FDA0003591693880000031
in the formula, aiver、amaxAnd aminRespectively the annual average, maximum and minimum load rates of the distribution network,
Figure FDA0003591693880000032
the load peak value of a certain node is obtained;
determining load distribution according to annual average, maximum and minimum load rates of the power distribution network, determining a wiring mode of the power distribution network, acquiring element fault rate and repair rate of each load interval according to a historical database, and calculating the comprehensive fault rate lambda of the complex system by adopting a coupling calculation method in the step 1.2DASAnd the repair rate muDAS；
3) Battery energy storage system state space: the battery energy storage system has different operation strategies at different time intervals, and the reliability of each element is directly related to the operation strategy and the charge and discharge amount of the element;
establishing a battery energy storage system electric quantity model based on generated energy-load balance:
ΔP(t)＝PDG(t)-Pload(t) (10)
in the formula, PDG(t)The actual power generation amount of the distributed power supply in the cycle time t; pload(t)Predicting the load of the power distribution network; Δ p (t) is the charge and discharge capacity of the battery energy storage system in the time period t; calculating according to delta P (t) to obtain the comprehensive failure rate lambda of the battery energy storage systemBESSAnd the comprehensive repair rate muBESS：
λBESS＝λBESS1×ω1+λBESS2×ω2 (11)
μBESS＝μBESS1×ω1+μBESS2×ω2
In the formula:
Figure FDA0003591693880000033
respectively representing the charging and discharging time ratios of the battery energy storage system, H1Is Δ P (t)>Period of 0, H2Is Δ P (t)<A period of 0; lambda [ alpha ]BESS1And muBESS1Respectively charging the fault rate and the repair rate of the battery energy storage system; lambda [ alpha ]BESS2And muBESS2Respectively calculating the fault rate and the repair rate of the battery energy storage system during discharging by using the coupling calculation method in the step 1.2;
and step 3: reliability evaluation of energy storage grid-connected system
Calculating the step 2 to obtain the fault rate and the repair rate lambda of the distributed power supply, the battery energy storage system and the power distribution network in the energy storage grid-connected systemDG、μDG、λDAS、μDAS、λBESS、μBESSAnd substituting the calculated values into the formula (2) and the formula (3) in the step 1, and finally calculating the probability that the grid-connected system of the battery energy storage system is in a normal working state, the distributed power supply fails, the distribution network fails and the battery energy storage system fails.
2. The multi-level comprehensive evaluation method for the reliability of the energy storage grid-connected system according to claim 1, wherein when the distributed power source in the step 2 is a wind driven generator, the determination process of the fault rate and the repair rate of the actual working condition is as follows:
the fault rate of a single element in the wind driven generator is obtained by the fan parameter and the wind speed, and the repair rate is a determined constant; the distribution function f (v) of the wind speed v is approximately described based on a two-parameter Weibull distribution:
Figure FDA0003591693880000041
in the formula, c is a Weibull fan size coefficient, d is a fan shape coefficient, and c and d are obtained by fitting fan laboratory prototype experiments or historical data;
fault probability lambda of each element of fanwrThe functional relationship with the wind speed v is as follows:
Figure FDA0003591693880000042
in the formula, vci、vcoAnd vrCut-in wind speed, cut-out wind speed and rated wind speed, lambda, of the wind turbinerThe failure rate of the elements under the rated wind speed is obtained for the experiment of a laboratory prototype.
CN202110923790.0A 2021-08-12 2021-08-12 Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system Active CN113626963B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110923790.0A CN113626963B (en) 2021-08-12 2021-08-12 Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110923790.0A CN113626963B (en) 2021-08-12 2021-08-12 Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system

Publications (2)

Publication Number Publication Date
CN113626963A CN113626963A (en) 2021-11-09
CN113626963B true CN113626963B (en) 2022-05-27

Family

ID=78384985

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110923790.0A Active CN113626963B (en) 2021-08-12 2021-08-12 Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system

Country Status (1)

Country Link
CN (1) CN113626963B (en)

Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107591833A (en) * 2016-07-08 2018-01-16 华北电力大学（保定） A kind of microgrid reliability estimation method of meter and different operation reserves

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7272516B2 (en) * 2002-12-23 2007-09-18 Abb Research Failure rate adjustment for electric power network reliability analysis
US9843189B2 (en) * 2014-05-19 2017-12-12 The University Of North Carolina At Charlotte Grid tied system controller including logic coupled to a photovoltaic station and an energy storage system
CN105552945B (en) * 2016-02-29 2019-03-01 桂林融通科技有限公司 Battery energy storage system and energy storage method
CN110610303B (en) * 2019-08-23 2022-05-31 太原理工大学 Direct-current power distribution network reliability assessment method considering source-load uncertainty
CN112946522A (en) * 2021-02-05 2021-06-11 四川大学 On-line monitoring method for short-circuit fault in battery energy storage system caused by low-temperature working condition

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107591833A (en) * 2016-07-08 2018-01-16 华北电力大学（保定） A kind of microgrid reliability estimation method of meter and different operation reserves

Also Published As

Publication number Publication date
CN113626963A (en) 2021-11-09

Similar Documents

Publication Publication Date Title
CN104485681B (en) A kind of monitoring method of wind energy turbine set energy-storage system
CN106327006A (en) Comprehensive benefit analysis-based micro-power-grid optimal configuration method
CN105205232A (en) RTDS (real time digital simulator) based stability simulation testing platform for micro grid system
Xia et al. Modeling and simulation of Battery Energy Storage System (BESS) used in power system
CN109066746B (en) Method for obtaining inertia time constant of power system with energy storage system
Cui et al. Equivalent Circuit Model of Lead-acid Battery in Energy Storage Power Station and Its State-of-Charge Estimation Based on Extended Kalman Filtering Method.
Li et al. Research on clustering equivalent modeling of large-scale photovoltaic power plants
CN104505907A (en) Monitoring device of battery energy storage system with reactive adjusting function
CN114033617B (en) Controllable wind power generation method and system with control parameters adjusted in self-adaptive mode
CN104538981B (en) A kind of monitoring method of the battery energy storage system with Reactive-power control function
Chao et al. A comprehensive review on dynamic equivalent modeling of large photovoltaic power plants
CN104734192A (en) Wholly-balanced micro-grid system
CN110661250A (en) Reliability evaluation method and system for wind-solar energy storage and power generation power transmission system
CN113626963B (en) Multi-level comprehensive evaluation method for reliability of energy storage grid-connected system
CN111581856A (en) Black-start process modeling and simulation method based on isolated power grid
CN115204048A (en) Photovoltaic power generation system model identification system and method
CN113492727B (en) Fuel cell hybrid power system control method based on EMPC
CN104505850A (en) Wind power plant energy storage system
CN104485682A (en) Monitoring device for wind power plant energy storage system
Qiu et al. Frequency Characteristics Analysis of Isolated Power Grid Based on VSC Droop Controller
Yee et al. Optimal sizing supercapacitor-battery hybrid energy storage system in solar application using the genetic algorithms
CN117369307A (en) Transient modeling simulation system of photovoltaic power station
CN112217204B (en) Micro-grid simulation method containing wave energy power generation device
Lagunas et al. Model design of wind/PV/fossil-fuel hybrid systems
Wang et al. A Two-stage Optimization Method for Storage Coordinated with Thermal Power Unit in AGC

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant