CN111461506A - Multi-microgrid system reliability modeling and evaluating method based on Bayesian network - Google Patents
Multi-microgrid system reliability modeling and evaluating method based on Bayesian network Download PDFInfo
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Abstract
The invention discloses a Bayesian network-based multi-microgrid system reliability modeling and evaluating method aiming at power resource supply and demand balance. The method considers the fault and maintenance of equipment in the modeling process, constructs a unified model aiming at the system performance and reliability, and is suitable for the reliability evaluation of a multi-microgrid system consisting of a plurality of microgrids participating in grid connection. The method can guide the construction of the multi-microgrid system in the early stage and can monitor the operation of the multi-microgrid system in the later stage. The invention belongs to the field of reliability system engineering, and mainly comprises the following steps: (1) constructing a unified model of the performance and reliability of the multi-microgrid system equipment; (2) constructing an energy scheduling and reliability model of the multi-microgrid system; (3) and evaluating the reliability of the multi-microgrid system based on the Bayesian network.
Description
Technical Field
The invention provides a Bayesian network-based multi-micro power grid (micro grid for short) system reliability evaluation modeling and evaluation method aiming at power resource supply and demand balance. The method considers the fault and maintenance of equipment in the modeling process, constructs a unified model aiming at the system performance and reliability, and is suitable for the reliability evaluation of a multi-microgrid system consisting of a plurality of microgrids participating in grid connection. According to the method, a reliability model is established for equipment in the multi-microgrid system at an equipment level, then a unified model is established according to the operation modes of the multi-microgrid system under different fault conditions, and the proposed multi-microgrid reliability index is calculated conveniently and rapidly. The method can guide the construction of the multi-microgrid system in the early stage and can monitor the operation of the multi-microgrid system in the later stage. The invention belongs to the field of reliability system engineering.
Background
In the past decades, the traditional power system mainly characterized by large units and large systems plays an important role and exposes a lot of problems: large investment, long construction period, and large-scale power outage due to abnormal operation state spreading along the power grid. The proposed concept of a microgrid consisting of a combination of distributed generation equipment (DG) and loads provides a solution to the above-mentioned problems. In 2006, a multi-microgrid plan is proposed in the european union, a plurality of microgrids and distributed power supplies of the multi-microgrid plan are connected to form a multi-microgrid system, and the multi-microgrid plan rapidly becomes a research hotspot, and a typical multi-microgrid is shown in fig. 1. In developed countries, multi-microgrid systems are regarded as a transitional mode of smart grids, and reliable operation thereof is extremely important.
In recent years, many researches are devoted to proposing reliability evaluation methods of a multi-microgrid system, and the reliability evaluation methods all use the balance of supply and demand of power resources as the standard of system reliability evaluation, but the factors of different methods are different. Some approaches take into account the latest techniques applied to multi-microgrid systems, such as "electric vehicle grid connection" during reliability assessment. Some approaches take into account faults in multi-microgrid systems, typically giving them the entire microgrid, or just faults in the system without taking into account maintenance conditions. In a word, the existing multi-microgrid system reliability evaluation method focuses on the balance of supply and demand of power resources and the fusion of new technologies, and has less attention to equipment-level faults and maintenance activities.
The present invention also uses the balance of supply and demand of power resources as the standard for system reliability evaluation, but is different from the existing method in the following aspects. Firstly, the study brings the failure and maintenance activities of all the devices in the system into an evaluation range, and the failure and maintenance of the devices affect the supply, demand and transmission of power resources, so as to affect the balance of the supply and demand of the power resources. Secondly, the constraint of the power distribution network on power resource transmission is brought into an evaluation range in the research, the transmission capacity of the power distribution network is usually limited, and the transmission capacity of the power distribution network can affect the scheduling of the power resources and the reliability of the multi-microgrid system. Moreover, the method uses a Markov random process and a Bayesian network to model the system, compared with the traditional analysis method, the real-time reliability of the system can be obtained, and the method has a reasoning function and can assist the design and operation of the multi-microgrid system.
Disclosure of Invention
The invention provides a Bayesian network-based multi-microgrid system reliability evaluation modeling and evaluation method aiming at power resource supply and demand balance. Aims and solves the problems that: a unified model is established for the performance and the reliability of the multi-microgrid system, a Bayesian network is adopted to carry out calculation reasoning on the unified model, the reliability of the multi-microgrid system is evaluated, and the influence of equipment faults on the reliability of the multi-microgrid is analyzed. In order to make the model more intuitive, the invention abstracts and simplifies the multi-microgrid system, as shown in fig. 2. In the simplified model, the devices in the multiple micro-grids can be divided into two types, namely a distributed power generation unit and a load unit, and the distributed power generation device is responsible for supplying power to the load unit. The method provided by the invention firstly adopts a Markov random process to carry out performance and reliability unified modeling on the equipment in the multi-microgrid system, and accurately describes the fault and maintenance activities of the system at the equipment level. And then, according to the system operation principle, an operation model is constructed for the power resource supply relation among the devices, and the operation process of the multiple micro-grids is truly reflected. After the multi-microgrid system performance and reliability model is built, a reliability evaluation model of the multi-microgrid system based on the Bayesian network is built through building a structural model and a parameter model of the Bayesian network, and the model can simply, quickly and accurately calculate the instantaneous availability index of the multi-microgrid system.
The invention relates to a Bayesian network-based multi-microgrid system reliability modeling and evaluating method considering equipment failure and maintenance, wherein the flow of the method is shown in FIG. 3 and mainly comprises the following three parts:
a first part: and uniformly modeling the performance and reliability of the multi-microgrid system equipment.
The multi-microgrid system equipment performance model and the reliability model lay a foundation for the construction of a later multi-microgrid system reliability model, and the construction of the model mainly comprises two steps, which are detailed as follows:
step 1: and constructing a performance model of the equipment in the multi-microgrid system.
In the invention, core equipment of a multi-microgrid system is mainly considered, and the core equipment mainly comprises distributed power generation equipment, a load unit and a power distribution network.
(1) The distributed power generation equipment is equipment for generating power resources in multiple micro-grids, and has K due to the influence of factors such as faults, maintenance or environment and the likenA performance level of which is set as
In order to facilitate the step 2 modeling, the performance levels of the distributed power generation devices in the set are arranged from small to large. The set of probabilities that the distributed power generation device is in this state is denoted as
At any time t, distributed power plant performance level Gn(t) is taken from the set gnAnd the probability in this state is:
(2) the load is a power resource consumption unit of the multi-microgrid system, and the demand of the load on the power resource is changed under the normal condition, and the load has HnA set of demand levels of
Similarly, to facilitate step 2 modeling, the demand levels of the loads in the set are ranked from small to large. The corresponding load demand level probability set is recorded as
At any time t, the performance level W of the loadn(t) is taken from the set wnAnd the probability in this state is:
(3) the power distribution network has L transmission capacity levels, and the transmission capacity level set is c ═ c1,c2,...,cLAnd similarly, in order to facilitate the reliability modeling of the step 2, the transmission capacity levels of the power distribution networks in the set are arranged from small to large, and similarly, the probability set of the power distribution networks at the corresponding transmission levels is β ═ β1(t),β2(t),...,βL(t) }. At any time t, the transmission level of the distribution network c (t) is taken from the set c and the probability of being in this state is:
P(C(t)＝ci)＝βi(t)(1≤i≤L)
step 2: and constructing a unified model of the performance and reliability of the multi-microgrid system equipment.
The performance level of devices in multiple piconets may be degraded due to failure and upgraded due to maintenance activities, using discrete states-when continuousThe inter-markov random process builds an availability model for the failure and maintenance activities of the equipment. A discrete state-continuous time markov random process model is shown in figure 4. Taking a distributed power generation facility as an example, a fault causes the performance level of the distributed power generation facility to change from a high state
Down to a low performance level state
(corresponding to a failure rate of
) Maintenance to bring the performance level of the distributed power generation facility from the performance level
Increase to high performance level
(corresponding to a repair rate of
). The performance level of the distributed power generation equipment is a function of time, and the probability of the real-time performance level of the distributed power generation equipment can be solved by a Chapman-Kolmogorov formula.
A second part: and (4) energy scheduling and reliability modeling of the multi-microgrid system.
The multi-microgrid system reliability modeling is a basis for multi-microgrid system reliability evaluation, and the multi-microgrid system reliability evaluation part mainly comprises a single-microgrid operation model, a multi-microgrid grid-connected operation model and a multi-microgrid system operation process model based on an equipment reliability model. The energy scheduling and reliability model of the multi-microgrid system is constructed according to the following two steps:
step 1: and constructing a unified model of the operation process and the reliability of the single microgrid.
One of the important aspects of the microgrid power supply rule is that the power resources generated by its internal distributed power generation equipment will preferentially meet the demands of its internal load. Redundant resources in the microgrid can be shared by the power distribution network to the microgrid with an unsatisfied load demand. Assuming that the microgrid is in an island mode, after power resources generated by the microgrid are supplied to a load at a certain moment, if the power is surplus, the surplus performance is recorded as Sn(t) if the demand of the load is not met, the scarce power resource is denoted as Dn(t) array of status of piconets with performance surplus and performance deficit (S)n(t),Dn(t)) is shown. Wherein:
step 2: and (4) energy scheduling among the micro-grids and multi-micro-grid reliability model construction.
Each grid-connected microgrid can transmit surplus energy to the microgrid with insufficient power resources through the power distribution network, so that the power requirements of all the microgrids are met. Assuming that power resources between the micro-grids are not scheduled, and the redundancy performance of all the micro-grids is recorded as s (t), then:
similarly, the performance that all piconets lack is denoted as d (t), then:
energy scheduling between the micro-grids is achieved through the power distribution network, and transmission capacity of the power distribution network can restrict energy transfer between the micro-grids. The number of power resources actually transmitted is denoted as t (t), then:
T(t)＝min(C(t),S(t))
if the transmitted power resource amount is larger than the power resource amount D (t) lacked in the whole multi-microgrid system, the requirements of all the microgrids are met, and the microgrid is available; if the transmitted power resource amount is smaller than the power resource amount d (t) lacking in the entire multi-microgrid system, there is a microgrid whose demand is not satisfied, and the multi-microgrid system is unreliable. The availability of the system, a (t), can be expressed as:
A(t)＝P(T(t)≥D(t))
and a third part: and evaluating the reliability of the multi-microgrid system based on the Bayesian network.
After the multi-microgrid system is subjected to reliability modeling, the reliability index of the multi-microgrid system can be rapidly and accurately calculated through the Bayesian network. The Bayesian network is a reliability modeling analysis method with a solid theoretical basis and strong computational reasoning capability, and is widely applied to reliability analysis of complex polymorphic dynamic systems at present. According to the Bayesian network reliability evaluation method, a Bayesian network reliability evaluation model of the multi-microgrid system is constructed according to the construction principle of the Bayesian network, and reliability indexes of the multi-microgrid system are calculated. The Bayesian network reliability evaluation model of the multi-microgrid system can be constructed according to the following three steps:
step 1: and constructing a reliability evaluation structure model of the multi-microgrid system.
And establishing a Bayesian network structure model by adopting a causal relationship method based on the multi-microgrid system behavior and reliability modeling. According to the physical structure of a multi-microgrid system, the system is divided into three levels: device level, piconet level, system level. It should be noted that there are some conversion nodes in the bayesian network structure, such as "failure scheduling system", which can make the reliability evaluation model of the bayesian network in the multi-micro-network system easier to construct. A structural model of the bayesian network reliability evaluation model of the multi-microgrid system is shown in fig. 5, wherein a node "n-numbered distributed power generation equipment" and a node "n-numbered load" point to a node "n-numbered microgrid"; all the nodes of the micro-grid point to the transition nodes of the 'failure scheduling system', energy in the micro-grid is redistributed through the 'distribution network', and therefore the nodes of the 'failure scheduling system' and the 'distribution network' point to the 'multi-micro-grid system'.
Step 2: and constructing a reliability evaluation parameter model of the multi-microgrid system.
The core of the multi-microgrid system reliability evaluation parameter model is to clarify the states of all nodes and the functional relation among the states. The device level nodes are root nodes of a Bayesian network model, a unified model of performance and reliability of multi-microgrid devices is constructed in the first part of the invention, and in the model, the nodes 'n number distributed power generation devices' have K in totalnA probability for each state at time t is:
similarly, the node "n load" has HnThe node "distribution network" has L states, and the probabilities corresponding to the time t are respectively:
P(C(t)＝ci)＝βi(t)(1≤i≤L)
the node 'n-number microgrid' is an intermediate node of the Bayesian network model, and the microgrid reliability model and the array(s) constructed according to the step 1 in the second part of the patent aren,dn) For representing the state of the node "n microgrid", at a known performance level G of the distributed generation equipmentn(t) and the demand level W of the loadn(t), then the state of the piconet is unique, which can be expressed as a bayesian formula:
the transition node 'failure scheduling system' is used for counting the energy redundancy and the lack condition of the microgrid in the system. According to the second part of step 2 of this patent, when the piconet state is uniquely determined, the state of the transition node is also uniquely determined, which can be expressed as a bayesian formula:
p{(S(t),D(t)|(S1(t),D1(t)),(S2(t),D2(t)),...,(SN(t),DN(t))}＝1
the node multi-microgrid system is a leaf node of a Bayesian network model, the state of the node multi-microgrid system depends on a transition node 'system which cannot be scheduled' and a node 'power distribution network', and when all load requirements in the microgrid are met, the microgrid is available; if the load performance in the multi-microgrid system is not met, the multi-microgrid system is unavailable. The state of the node 'multi-microgrid system' is represented by A, and if all load requirements in the multi-microgrid system are met, A is 1; otherwise, a is 0. It can be expressed as bayesian formula:
p{A＝1|min(C(t),S(t))≥D(t)}＝1
and step 3: and calculating and reasoning the reliability indexes of the multi-microgrid system.
The reliability index calculation of the multi-microgrid system needs to be obtained through computational reasoning of the model. The reliability evaluation index of the multi-microgrid system is the instantaneous availability of the multi-microgrid system, and in the Bayesian network model, when a leaf node is in an 'A ═ 1' state, the multi-microgrid system is available, namely
A(t)＝p(A＝1)
Drawings
Fig. 1 multi-piconet system architecture
Fig. 2 multi-microgrid system abstraction model
FIG. 3 is a flow chart of a method
Figure 4 polymorphic markov random process model
Fig. 5 is a model of a multi-microgrid system reliability evaluation structure based on a bayesian network
Fig. 6 is a Bayesian network structure model of three microgrid systems
Fig. 7 is a graph of instantaneous availability of a multi-microgrid system as a function of time
Detailed Description
Description of the embodiments: the method can guide the design and operation of the multi-microgrid system. In this section, a multi-microgrid system including 3 microgrids is taken as a case (N is 3 in fig. 1), and reliability modeling and evaluation are performed on the multi-microgrid system.
The specific embodiments are described as follows:
a first part: and uniformly modeling the performance and reliability of the multi-microgrid system equipment.
Since the performance of the devices in the multi-microgrid system is similar to the reliability unified model calculation method, only one of the devices is taken as an example in this section to explain the performance and reliability unified model calculation method.
Step 1: and constructing a performance model of the equipment in the multi-microgrid system.
The description will be given by taking a distributed power generation device in a number 1 microgrid as an example. Number 1 distributed power plant has 3 performance levels, denoted g1Given as {0MW,2MV,3MV }, the set of probabilities that a property is in the corresponding state at any time t is denoted as
Step 2: and constructing a unified model of the performance and reliability of the multi-microgrid system equipment.
The distributed power generation equipment in the No. 1 microgrid is still taken as an example for explanation. Failure and maintenance activities can cause the performance levels of distributed power generation equipment to shift from one another, and for this equipment, the equipment failure rates are:
the maintenance rates of the equipment are respectively as follows:
by means of a markov model, the following Chapman-Kolmogorov formula is obtained:
the initial state conditions were:
obtaining by solution:
please refer to tables 1-3 for the relevant parameters of other devices in the multi-piconet system.
TABLE 1 distributed Generation facility parameter Table
TABLE 2 load device parameter Table
TABLE 3 distribution network parameter table
A second part: and (4) energy scheduling and reliability modeling of the multi-microgrid system.
Step 1: and constructing a unified model of the operation process and the reliability of the single microgrid.
Taking the No. 1 microgrid as an example, the operation process and reliability unified model of a single microgrid is constructed.
The performance level and the demand level of the No. 1 microgrid can be known through the first part, based on a random process theory, the microgrid is combined in pairs, and when G is used as the power source at any time t1(t)＝gMW,W1(t) wMW, then:
the state of the microgrid may be represented as (S)1(t)，D1(t))＝[max(g-w,0),max(w-g,0)]And the probability in this state is:
the possible states of piconet number 1 are as follows:
[S1(t)，D1(t)]＝[(0,0)；(3,0)；(2,0)；(1,0)；(0,1)；(0,2)；(0,3)]
the possible states of the # 2 piconet are as follows:
[S2(t)，D2(t)]＝[(0,0)；(1,0)；(2,0)；(3,0)；(4,0)；(0,2)]
the possible states of the # 3 ss are the following:
[S3(t)，D3(t)]＝[(0,0)；(3,0)；(0,1)；(0,3)；(0,4)]
step 2: and (4) constructing an energy scheduling and multi-microgrid reliability model among the microgrids.
According to the unified model of performance and reliability of the single microgrid constructed in the second part of the step 1, at a certain time t, the states and corresponding probabilities of 3 microgrids in the case are respectively as follows:
P[(S1(t)，D1(t))＝(s1MW,d1MW)]＝p1
P[(S2(t)，D2(t))＝(s2MW,d2MW)]＝p2
P[(S3(t)，D3(t))＝(s3MW,d3MW)]＝p3
therefore, when the whole microgrid is not performance-shared, all surplus performance, performance shortage and corresponding probabilities in the multi-microgrid system are as follows:
P[S(t)＝s,D(t)＝d]＝p1·p2·p3＝p
for the system, before the energy of the multi-microgrid system is not allocated, the state combinations of the multi-microgrid system are 60 types:
[S(t)，D(t)]＝[(0,0)；(0,1)；···(0,9)；(1,0)；(1,1)；···(1,7)；(2,0)；(2,1)；···(2,7)； (3,0)；(3,1)；···(3,7)；(4,0)；(4,1)；···(4,7)；(5,0)；(1,1)；···(5,4)； (6,0)；(6,1)；···(6,4)；(7,0)；(7,1)；···(7,4)；(8,0)；(9,0)；(10,0)]
at the same time t, the transmission capacity of the distribution network and the corresponding probability:
Cn(t)＝cMW
P[Cn(t)＝cMW]＝β
for this embodiment, at time t, the power resources that the system can allocate can be represented as:
Tn(t)＝min(C(t),S(t))＝min(c,s)
P[Tn(t)＝min(c,s)]＝β·p
according to the definition of system availability, if the demands of all loads in the system are met, the multi-microgrid system is available, and in this embodiment, the instantaneous availability of the system may be specifically expressed as:
A(t)＝P(Tn(t)≥D(t))
and a third part: and evaluating the reliability of the multi-microgrid system based on the Bayesian network.
Step 1: and constructing a reliability evaluation structure model of the multi-microgrid system.
According to the method of step 1 in the third part of the disclosure, a structural model of the multi-piconet system in this case is shown in fig. 6.
Step 2: and constructing a reliability evaluation parameter model of the multi-microgrid system.
The bayesian formula established in the third part of the inventive contents is used to fill the conditional probability tables between the nodes, and the conditional probability tables are more, so that the part only shows typical ones.
Table 4 is a conditional probability table for node 1 piconet, table 5 is a partial conditional probability table for nodes of the multi-piconet system, and table 6 is a conditional probability table for the node multi-piconet system.
Table 4 node "No. 1 microgrid" conditional probability table
TABLE 5 node conditional probability tables (parts)
Table 6 conditional probability table of node "multi-microgrid system
And step 3: and calculating and reasoning the reliability indexes of the multi-microgrid system.
At present, more software can be used for Bayesian network computational inference, and the Matlab Bayesian network toolbox is adopted for computational inference in the embodiment, and the obtained result is shown in FIG. 7.
Claims (1)
1. A reliability modeling and evaluation method for a multi-microgrid system based on a Bayesian network mainly comprises the following three parts:
a first part: and uniformly modeling the performance and reliability of the multi-microgrid system equipment.
The construction of the unified model of the performance and reliability of the multi-microgrid system mainly comprises two steps, which are detailed as follows:
step 1: and constructing a multi-micro network system equipment performance model.
The core equipment of the multi-microgrid system mainly comprises distributed power generation equipment, a load unit and a power distribution network. Thus, the performance model mainly comprises the distributed power generation equipment KnIndividual performance level
Load HnLevel of need
And L transmission capacity levels c ═ c in the distribution network1,c2,...,cL}。
Step 2: and constructing a unified model of the performance and reliability of the multi-microgrid system equipment.
The method comprises the steps of constructing a fault and maintenance model of the equipment with multiple states by adopting a multi-state Markov random process model, and resolving the real-time state of the equipment by using a Chapman-Kolmogorov formula.
A second part: and (4) energy scheduling and reliability modeling of the multi-microgrid system.
The part mainly comprises an operation model of a single microgrid, a multi-microgrid grid-connected operation model and an operation process model of a multi-microgrid system, wherein the operation model is based on an equipment reliability model. The energy scheduling and reliability model of the multi-microgrid system is constructed according to the following two steps:
step 1: and constructing a unified model of the operation process and the reliability of the single microgrid.
The step mainly refers to an array (S) of power resource supply and demand balance relation in the microgrid, and performance surplus and performance loss can be passedn(t),Dn(t)) is shown.
Step 2: and (4) energy scheduling among the micro-grids and multi-micro-grid reliability model construction.
The method mainly builds a model for the energy scheduling of the microgrid through the power distribution network. Firstly, the surplus performance of all units of the whole system is counted
Performance with all microgrid defects
Under the constraint of limited transmission capacity level C (t) of the power distribution network, if the requirements of all micro-grids in the system can be met, the micro-grid is considered to be available; otherwise, the piconet is considered to be in an unavailable state.
And a third part: and evaluating the reliability of the multi-microgrid system based on the Bayesian network.
The method is characterized in that a Bayesian network reliability evaluation model of the multi-micro network system is constructed according to a Bayesian network construction principle, and reliability indexes of the multi-micro network system are calculated. The Bayesian network reliability evaluation model of the multi-microgrid system can be constructed according to the following three steps:
step 1: and constructing a reliability evaluation structure model of the multi-microgrid system.
According to the physical structure of the multi-microgrid system, the multi-microgrid system is divided into three levels: device level, piconet level, system level. And then, according to the topological structure of the multi-micro network, a structural model of the Bayesian network reliability evaluation model of the multi-micro network system is constructed by adopting a causal relationship method, and finally, a directed acyclic graph model is obtained.
Step 2: and constructing a reliability evaluation parameter model of the multi-microgrid system.
According to the operation process and the energy scheduling principle of the multi-micro network system, an expert reasoning method is adopted, the relation among all nodes is expressed by a Bayesian formula, and a conditional probability table of the nodes is constructed to form a parameter model.
And step 3: reliability index calculation and reasoning of multi-microgrid system
And (3) obtaining the instantaneous availability index A (t) of the multi-micro network system by inference calculation by using a programming or software means.
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