CN112615373A - Flexible power distribution system distributed control strategy optimization method considering information failure - Google Patents
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- H—ELECTRICITY
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- H04Q11/0062—Network aspects
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Abstract
The invention provides a flexible power distribution system decentralized control strategy optimization method considering information failure, which comprises the steps of establishing a flexible power distribution information physical system, and analyzing the structure and the control mode of the flexible power distribution information physical system; considering the randomness of the output of the distributed power supply, and describing the multi-scene operation characteristics of the distributed power supply by adopting a self-adaptive fuzzy clustering method; considering uncertainty of a communication system fault scene, and calculating validity of an information link by adopting a fault tree analysis method; taking the output upper limit of the off-line distributed power supply and the transmission power of each port of the off-line FMSS as decision variables, aiming at reducing the load loss risk and the voltage deviation, considering the uncertainty of the output and fault scenes of the distributed power supply, and establishing a distributed resource distributed control strategy optimization model based on the combination of the distributed control and the centralized optimization of the probability scene; and (4) solving the control strategy optimization model established in the step (4) by combining an intelligent optimization algorithm and a second-order cone optimization method.
Description
Technical Field
The invention belongs to the field of flexible power distribution systems, and particularly relates to a distributed control strategy optimization method for a flexible power distribution system considering information failure.
Background
A Flexible alternating current-direct current power distribution system formed by interconnecting a plurality of medium-voltage feeders through Flexible multi-state switches (FMSS) can achieve the purposes of balancing feeder tide, stabilizing voltage fluctuation and improving the acceptance capacity of a distributed power supply through Flexible and accurate power regulation, and can realize uninterrupted power supply of sensitive and important loads by utilizing the coordination of the fault ride-through technology of a port converter and power distribution network protection, so that the power supply reliability is improved. The method overcomes the system complexity and the technical limitation caused by the fact that the traditional power distribution network is divided and treated in solving the problems of electric energy quality, state optimization, fault treatment, economic operation and the like, and provides a comprehensive and intelligent solution. The multi-terminal interconnected AC/DC power distribution network realizes multi-time scale control according to the multi-level information of FMSS ports, equipment and the network, and once a communication fault brings great risk to system operation, therefore, the research of an emergency operation control strategy under the random fault of the communication equipment has important significance for ensuring the safe operation of the system.
The current research on the emergency control strategy after the information failure of the flexible power distribution system is less, but the initial research on the emergency control strategy of the traditional power distribution network after the information failure is already carried out. When a communication fault causes a DG to lose contact, document 1 adopts an emergency control strategy of losing contact of the DG. Based on the guiding rule, when the communication equipment fails, the information of a plurality of distributed power supplies can be interrupted, and the operation is equivalently quitted by the plurality of power supplies at the same time. However, this "one-off" strategy cannot guarantee the normal operation of the grid that depends on the generation and regulation capability of the DG, and may even cause a greater risk of state safety. In order to solve this problem, some researchers have proposed that the DG should be switched from centralized control to distributed local control after the communication is interrupted, as in document 2, and a uniform injection upper limit is set for each DG, and when a communication failure occurs, the power is applied according to a preset limit. Simulation results show that the distributed local control can effectively improve the system reliability. Document 3 researches a differentiated decentralized control strategy of a distributed power supply under a severe communication fault, aims at minimizing vulnerability of a system state, considers uncertainty of an operation scene, establishes a distributed resource differentiated decentralized control strategy optimization model, and verifies through simulation that the vulnerability of the system can be effectively reduced by an upper injection limit of differentiation of the distributed power supply under breakdown of a communication network. But only the single fault scene of communication network paralysis is considered, and the optimization result has no universality. On this basis, document 4 researches the local control strategy optimization problem of the distributed power supply under the coexistence state of partial distributed power supply disconnection and system centralized-decentralized control aiming at random faults of the communication equipment, but only considers the local control strategy under a single operation scene, the distributed power supply output has randomness, is greatly influenced by random factors such as illumination, wind speed and the like, and when the strategy obtained by optimizing only considering the single operation scene is used in other operation scenes, the error is large, and the safe operation of the system cannot be ensured. If the offline control strategy is set for each operation scene, the distributed resource with communication failure cannot acquire the global operation state of the current system, that is, cannot judge which operation scene the distributed resource is in so as to execute the offline control strategy of the corresponding scene, therefore, when the distributed resource detects the communication failure of the distributed resource, only the same control strategy can be adopted.
The flexible power distribution network is similar to a communication system of an active power distribution network, so the same method can be adopted for the processing mode of the information failure characteristics. However, the two systems have certain differences in physical system device characteristics (e.g., document 5), control objects (e.g., document 6), topology, and the like, and therefore, the problem of optimal control after a failure of a communication device also has a difference.
Document 1: IEEE Standard for Interconnecting Distributed Resources with Electric Power Systems [ C ]// IEEE Std 1547-2003 IEEE,2003.
Document 2: schacht D, Lehmann D, Kalisch L, et al. effects of regulations on reliability in smart grids [ J ] 2017,2017(1): 2250-.
Document 3: liuwenxia, Yanmeng Yao, horseiron, and the like, optimization of an unconnected distributed power supply differentiation local control strategy in an active power distribution system [ J ] power system automation, 44(11):32.
Document 4: liuwenxia, horseiron, Yangmunyao, and the like, under serious communication faults, the emergency operation strategy of the distributed power supply of the active power distribution system is optimized [ J ]. the Chinese Motor engineering newspaper 2020 and 40(3).
Document 5: dongxahu pillar, liu shiji, li peng, song guan yu, wu dispute, cheng li ming intelligent distribution network regulation and control technology based on multi-terminal flexible multi-state switch [ J ]. proceedings of electrical engineering in china, 2018,38(S1):86-92.
Document 6: wangshan, suphengbo, linpeng, wujia, chenfeng, shuyue, SNOP-based optimization and analysis of power distribution network operations [ J ] power system automation 2015,39(09):82-87.
Disclosure of Invention
Aiming at the technical problems, the invention provides a distributed control strategy optimization method of a flexible power distribution system considering information failure by considering the influence of distributed resources on states and the difference of fault scenes aiming at random faults of communication equipment of the flexible power distribution information physical system, which comprises the following steps:
step 1: establishing a flexible power distribution information physical system, and analyzing the structure and the control mode of the flexible power distribution information physical system;
step 2: considering the randomness of the output of the distributed power supply, and describing the multi-scene operation characteristics of the distributed power supply by adopting a self-adaptive fuzzy clustering method;
and step 3: considering uncertainty of a communication system fault scene, and calculating validity of an information link by adopting a fault tree analysis method;
and 4, step 4: taking the output upper limit of the off-line distributed power supply and the transmission power of each port of the off-line FMSS as decision variables, aiming at reducing the load loss risk and the voltage deviation, considering the uncertainty of the output and fault scenes of the distributed power supply, and establishing a distributed resource distributed control strategy optimization model based on the combination of the distributed control and the centralized optimization of the probability scene;
and 5: and (4) solving the control strategy optimization model established in the step (4) by combining an intelligent optimization algorithm and a second-order cone optimization method.
Preferably, the adaptive Fuzzy clustering method is an adaptive Fuzzy C-means (FCM) clustering method, the method clusters the photovoltaic and fan output into a plurality of typical scenes, generates a distributed power output representative value and probability thereof under each operation scene, converts the power of each sampling point of the wind power and the photovoltaic into sample points on a two-dimensional coordinate system, allocates each sample point to the class with the maximum membership degree, and obtains the optimal clustering by iteratively updating a clustering center and a membership degree matrix.
Further, the cost function and the constraint condition of the FCM are formula (1) and formula (2):
wherein u isijFor each sample xiFor a certain class ciDegree of membership of uij∈[0,1]M is a weighted index, generally 2 is taken, and c represents the number of the class clusters; continuously updating the clustering center and the membership degree matrix through the iterative formulas of the formula (3) and the formula (4) to minimize the cost function,
wherein, cjRepresents the center of the jth cluster class, N represents the number of samples, ckIndicating the center of the kth class cluster.
Furthermore, in the FCM clustering method, when the number of elements in a certain class is smaller than a specified value or the distance between the two classes is smaller than the specified value, the elements are combined into the same class; when the variance of the distance from each sample point to the cluster center in a certain class is larger than a specified value, the class is divided into two classes, and the two classes are shown by an equation 5:
wherein the content of the first and second substances,
is the maximum standard deviation within class j, K is the total number of clusters expected, θsIndicating the specified standard deviation.
Preferably, in the fault tree model, failure of the distributed resource i information link is used as a top event, and causes of failure of the distributed resource i information link are application layer failure, communication layer failure and interface layer failure; the reason causing the communication layer failure is that all communication channels of the information link fail as an intermediate event; information element corruption is a bottom event.
Preferably, the distributed control strategy optimization model in the step 4 is divided into an upper layer and a lower layer, the upper layer is DG or FMSS distributed control strategy optimization considering communication faults of fault scenes, an objective function is that the sum of voltage deviation weighted by the probability of the fault scenes and load loss amount is minimum, and an in-situ control strategy generated by the upper layer is transmitted to the lower layer; and the lower layer carries out simulation operation optimization of online distributed resources considering an operation scene on the flexible interconnection system, the objective function is that the voltage deviation weighted by the probability of the operation scene is minimum and the load loss amount is minimum, the lower layer online optimization result is transmitted to the upper layer to calculate a risk value, and the upper layer and the lower layer form centralized and decentralized cooperative optimization to obtain a decentralized control strategy of communication interruption of each terminal device.
Further, the objective function of the distributed control strategy optimization model is as follows:
F＝ω1g1+ω2g2 (6)
in the formula, g1Indicates the amount of loss of load, g1Representing a voltage deviation, PGnFor distributed power supply output, P, during normal operation of the communication systemGimaxFor distributed power off-line upper limit of output, NDGThe weighted summation of the voltages of all nodes is taken for the quantity and voltage deviation of distributed power supplies in the system, UmnAnd wmnRespectively representing the voltage and weight of the nth node in the mth feeder line, NnIs the number of nodes; z0iIs the impedance between node i and the beginning of the feed line, Zl,maxRepresents the maximum value of the impedance of the feed line on which the node is located, St,kIs the apparent power of the kth DG in the tth operation scene, StIs the sum of the load power of the feeder line where the node is located, Z0kTo the DG access point to balanced node impedance, ZikNode i to DG access point impedance; (ii) a Omega1And omega2Weight coefficient representing the deviation between the loss of load and the voltage, target weight vector [ omega ]1,ω2]＝[0.75,0.25]；
When DG communication fails, the total risk is expressed as
Upon failure of FMSS communications, the total risk is expressed as
Where N _ f is the number of operational scenarios, PskIs the kth operation scenario probability, N _ s is the number of failure scenarios, PfiIs the ith failure scenario probability, FkRepresenting the risk value in the k-th operational scenario.
Further, when each distributed resource distributed control strategy is obtained after information failure, the following constraint conditions should be satisfied:
(1) system power flow constraint
In the formula, PiAnd QiRespectively, the sum of the active power and the reactive power injected at node i, PDG,iAnd QDG,iThe active and reactive power injected respectively for DG on node i,
and
respectively representing active power and reactive power, P, output by the i port of the flexible multi-state switchload,iAnd Qload,iActive and reactive power, respectively, consumed by the load on node I, IijFor the current flowing from node i to node j, UiIs the voltage of node i, UjRepresenting the voltage of node j, RijAnd XijResistance and reactance, P, of branch ij, respectivelyijAnd QijRespectively, having a common power and a reactive power, B, flowing from node i to node jijAnd GijRespectively representing the real part and the imaginary part, delta, of the ith row and jth column element of the nodal admittance matrixijRepresents the voltage phase angle difference of nodes i and j;
(2) node voltage constraint
Wherein, Uimin，UimaxRespectively representing the allowable upper limit and the allowable lower limit of the voltage of the node i;
(3) branch current constraint
Wherein, IijmaxIs the upper limit of the branch current;
(4) FMSS port power constraint
In the formula, PFMSS,lossIn order to provide the active loss of the flexible multi-state switch,
the access capacity of the flexible multi-state switch i, j and h port converter,
respectively representing the active power transmitted by the FMSS ports i, j and h,
respectively representing reactive power transmitted by the FMSS ports i, j and h, wherein the capacity constraint of each FMSS port meets the 'circular' constraint;
(5) DG constraints
PDG,i≤PGimax (18)
PGimaxRepresents the upper limit of the output of the ith DG.
Preferably, in step 5, the upper layer of the model adopts an intelligent optimization algorithm (immune particle swarm optimization) to generate a distributed control strategy of the communication failure device, and the lower layer of the model adopts a second-order cone optimization method to perform rapid centralized optimization.
The invention has the beneficial effects that: 1) the emergency control strategy optimization model established by considering the output randomness of the distributed power supply and the uncertainty of the fault scene can enable the control strategy obtained by optimization to have universality on different scenes; 2) the effectiveness of the information link is calculated by adopting a fault tree method, only the 0-1 state of each communication device is needed, the effectiveness of the information link can be obtained through simple logic operation, the topology search based on graph theory is avoided, and the calculation efficiency is improved 3) the characteristic of a centralized optimization model containing an FMSS flexible power distribution system is considered, and the DG and FMSS coordinated planning belongs to the large-scale nonlinear optimization problem of multilayer nesting, multivariable and multi-constraint, so that an upper-layer distributed control model is solved by adopting an intelligent optimization algorithm, a lower-layer centralized optimization model is solved by adopting a second-order cone optimization method, and the solving speed and the calculation efficiency are improved; 4) the method can provide theoretical support for the operation strategy under the communication fault of the flexibly interconnected alternating current and direct current power distribution network, and improves the safety and the economical efficiency of system operation.
Drawings
FIG. 1 is a schematic diagram of a physical system for AC/DC distribution information;
FIG. 2 is a transition diagram of three states of operation of an cyber-physical system;
fig. 3(a) - (c) are topology structure diagrams of an EPON technology networking mode, where fig. 3(a) is a hand-in-hand type, fig. 3(b) is a double-T type, and fig. 3(c) is a ring type;
FIG. 4 is a schematic diagram of an information link failure tree model structure;
FIGS. 5(a) and (b) are flowcharts of a decentralized control strategy optimization model solution according to the present invention;
FIG. 6 is a schematic diagram of an improved IEEE-33 node power distribution system architecture;
fig. 7 is a diagram of a clustering result in a verification embodiment of the present invention.
Detailed Description
The invention will be described in detail with reference to the accompanying drawings and examples.
Step 1: and establishing a flexible power distribution information physical system, and analyzing the structure and the control mode of the flexible power distribution information physical system.
The structure of the flexible power distribution information physical system is shown in fig. 1, and the flexible power distribution information physical system comprises a physical domain and an information domain. The physical domain consists of multiple ac feeders, a distributed power supply, a flexible multi-state switch (FMSS), and a distribution grid conventional switch. The typical structure is that the direct current side of an FMSS three-terminal converter is connected through a direct current bus, and the alternating current side of the FMSS three-terminal converter is connected with three alternating current feeders. The information domain comprises an interface layer, a communication layer and an application layer. The interface layer comprises intelligent equipment for terminal acquisition and control and an FMSS equipment control part, and is a coupling part of an information and physical system; the communication layer comprises communication nodes, line equipment and a communication protocol, and the communication mode comprises an Ethernet Passive Optical Network (EPON), an Ethernet and a GPRS wireless Network and is responsible for information transmission; the application layer is the center of system control and is responsible for processing the received state information and generating control instructions. In a conventional power distribution network information system, all power distribution terminals connected with a power distribution main station send information to the main station and receive instructions of the main station, communication does not exist among the power distribution terminals, an FMSS (frequency modulation system) alternating current and direct current power distribution network is introduced, and due to the fact that the FMSS is provided with a coordination control device for alternating current and direct current information interaction, GOOSE communication among the power distribution terminals (mainly protection devices) is added besides communication between the power distribution terminals and a scheduling main station.
The coordination control of the flexible power distribution system based on the FMSS mainly comprises system optimization operation control in normal operation, self-healing control under the condition of physical system fault and centralized and decentralized cooperative control under the condition of information equipment fault. The transition diagram of three operation states of the flexible power distribution cyber-physical system is shown in fig. 2.
1) And (3) system optimization operation control in normal operation: monitoring devices installed at various positions in a power distribution network detect voltage and current of various important nodes in real time, monitoring information is sent to a power distribution main station, the power distribution main station tracks dynamic changes of distributed power supplies and loads, state constraints are considered, regulation and control instructions of three ports of a flexible multi-state switch and the distributed power supplies are optimized, the regulation and control instructions are transmitted to an FMSS and a distributed power supply control unit, and voltage, current and tide control of a flexible power distribution system is achieved.
2) Fault self-healing control under the condition of physical system fault: after a physical system of the power distribution network breaks down, a measuring device on an FMSS port detects line voltage drop, the fact that the distribution network system breaks down is determined, FMSS enters low voltage ride through, after fault isolation, FMSS low voltage ride through is finished, a load transfer mode is entered, the control mode of a fault side port is converted into passive side control, load shedding amount and transmission power values of all ports are calculated through an optimization algorithm, and load transfer after the fault is completed.
3) Centralized and decentralized coordination control under the condition of information equipment failure. When the information equipment fails, the distributed resources which lose contact with the master station are switched to distributed control, and the distributed control is implemented according to an offline control strategy preset in the equipment; the distributed resources in normal communication still adopt centralized control. And the distributed control and the centralized control are coordinated and matched, so that the controllable resources are optimized on line, and the operation safety of the power grid is ensured. The invention aims to solve the problem of how to formulate a reasonable distributed control strategy and reduce the running risk of a system by matching with the online optimization of a power grid.
Step 2: considering the randomness of the output of the distributed power supply, and describing the multi-scene operation characteristics of the distributed power supply by adopting a self-adaptive fuzzy clustering method;
the fuzzy clustering algorithm is improved, so that the clustering number can be automatically adjusted according to the clustering center position, and a clustering result is more representative. According to the output condition of the distributed power supply in one day, clustering the output of the photovoltaic and the fan into a plurality of typical scenes by adopting a self-adaptive Fuzzy C-means (FCM) clustering method, generating a representative value and probability of the output of the distributed power supply in each operation scene, and laying a foundation for the optimization of a subsequent control strategy. The cost function and constraints of FCM are shown in equations 1 and 2.
Wherein u isijFor each sample xiFor a certain class ciDegree of membership of uij∈[0,1]M is a weighted index, generally taken as 2, and c represents the number of clusters. And obtaining iterative formulas shown in formulas 3 and 4 by adopting a Lagrange multiplier method, and continuously updating a clustering center and a membership matrix through iteration to minimize the cost function.
cjRepresents the center of the jth cluster class, N represents the number of samples, ckAnd expressing the center of the kth cluster, converting the power of each sampling point of wind power and photovoltaic into sample points on a two-dimensional coordinate system, distributing each sample point to the class with the maximum membership degree, and updating the cluster center and the membership degree matrix through iteration to obtain the optimal cluster. The self-adaptive fuzzy C-means clustering method provided by the invention can automatically adjust the clustering number and search the optimal clustering number in the clustering process. When the number of elements in a certain class is smaller than a specified value or the distance between the two classes is smaller than the specified value, the elements are combined into the same class; when the variance of the distances from each sample point to the cluster center in a certain class is larger than a specified value, the two classes are split into two classes, as shown in formula 5.
Wherein the content of the first and second substances,
is the maximum standard deviation within class j, K is the total number of clusters expected, θsIndicating a specified standard deviation, and when the actual standard is greater than this specified standard deviation, classified into two categories. And generating a photovoltaic-wind power source scene set and a scene probability through a self-adaptive FCM clustering process.
And step 3: and (4) calculating the validity of the information link by adopting a fault tree analysis method in consideration of the uncertainty of the fault scene of the communication system.
Communication mode and information link distribution
At present, a power distribution system widely adopts an Ethernet Passive Optical Network (EPON) technology to construct a communication network, and the EPON is a passive optical fiber access technology, and uses an Ethernet protocol to conduct information by using an optical fiber signal in an optical fiber network. The EPON technology has many advantages such as low cost, high performance, flexible wiring method, etc. The main equipment of the system comprises optical line terminal equipment (OLT), a fiber splitter (POS), an Optical Network Unit (ONU), an optical fiber line and the like. The EPON technology has a plurality of networking modes, and the topological structure thereof is shown in fig. 3 and includes a hand-in-hand type (a), a double-T type (b), a ring type (c), and the like.
Information link failure fault tree modeling
The failure of the distributed resource i information link is taken as a top event, when three levels of the communication system work normally, the information links are communicated, and when any layer fails, the information link corresponding to the terminal equipment fails. The reliability of the application layer is usually higher, and the application layer is provided with standby equipment to operate simultaneously, and the fault condition of the application layer is not considered; the communication layer is generally provided with redundancy configuration, the influence of elements after random fault can cause that a single or a plurality of communication channels lose connectivity according to the types and positions of fault elements, and an information link fails when all the channels are interrupted; the interface layer has no redundant configuration, and the failure of the element can directly cause the failure of the information link corresponding to the terminal equipment. Accordingly, an information link failure tree model as shown in fig. 4 is established. The top layer of the fault tree is a top event, all possible reasons causing failure of the distributed resource i information link are analyzed and are respectively an application layer fault, a communication layer fault and an interface layer fault, wherein the reason causing the communication layer fault is all communication channel faults of the information link, and the reason causing the communication layer fault is an intermediate event, and the information element damage is a bottom event.
Information link validity calculation
As can be known from the fault tree model of the terminal device i, the application layer fault, the communication layer fault, and the interface layer fault are three cut sets of the information link failure of the terminal device i, so that the structural function of the information link failure of the terminal device i can be represented as:
S(i)＝S(I)∩S(T)∩S(Y) (6)
in the formula, s (i), s (t), s (y) respectively represent the states of an interface layer, a communication layer and an application layer, 0 represents failure, and 1 represents effectiveness, wherein, because the application layer generally has high reliability and is provided with standby equipment to operate simultaneously, the failure of the application layer is not considered, so that s (y) is 1.
For the communication layer, { Lane 1 failure, Lane 2 failure, … …, Lane n failure } is a cut-set of communication layer failures, the structural function of which failures are expressed as
S(T)＝S(l1)∪S(l2)∪…∪S(ln) (7)
In the formula, S (l)i) (i ═ 1,2, …, n) represents the communication channel state.
The failure structure function of the interface layer element can be expressed as
S(I)＝S(E1)∩S(E2)∩…∩S(En) (8)
In the formula, S (E)i) (i ═ 1,2, …, n) represents the interface layer element state.
In actual operation, because the communication system is in different environments, the redundancy configuration of each layer is different, and thus the fault tree model is different. Taking the ring-type communication network shown in fig. 3(c) as an example, the communication layer of the network shown in the figure only configures a redundant element for the OLT, and none of the OLT lower-layer devices has a redundant configuration, and once a fault occurs, the communication of the direct-connection terminal device fails, so that the OLT lower-layer devices POS, ONU, and IED are combined and equivalent to an interface layer device based on the consistency of fault consequences, and a structural function of the terminal device a is obtained according to the fault tree model established above, as shown in equation (9).
S(A)＝S(IA)∩S(TA) (9)
In the formula, S (I)A) Interface layer corresponding to representative terminal equipment AThe state, as shown in equation (10):
S(IA)＝S(POS1)∩S(ONU1)∩S(IED1) (10)
S(TA) The communication layer state corresponding to the representative terminal device a is expressed by equation (11):
S(TA)＝S(OLT1)∪S(OLT2) (11)
the structural function of the failure of terminal device a is expressed as:
S(A)＝[S(POS1)∩S(ONU1)∩S(IED1)∩[S(OLT1)∪S(OLT2)] (12)
the same applies to the structural function of the terminal B, C information failure.
And 4, step 4: the method comprises the steps of taking the output upper limit of an offline distributed power supply and the transmission power of each port of an offline FMSS as decision variables, aiming at reducing the load loss risk and the voltage deviation, considering the uncertainty of the output and fault scenes of the distributed power supply, and establishing a distributed resource distributed control strategy optimization model based on the combination of the distributed control and the centralized optimization of the probability scene.
The model is divided into two layers, the upper layer is DG or FMSS distributed control strategy optimization considering the communication faults of the fault scene, the target function is that the sum of voltage deviation weighted by the probability of the fault scene and loss load is minimum, the local control strategy generated by the upper layer is transmitted to the lower layer, the lower layer carries out simulation operation optimization considering the online distributed resources of the operation scene on the flexible interconnection system, the target function is that the voltage deviation weighted by the probability of the operation scene and the loss load are minimum, the online optimization result of the lower layer is transmitted to the upper layer to calculate a risk value, and the upper layer and the lower layer form centralized and distributed cooperative optimization to obtain the distributed control strategy of communication interruption of each terminal device. The objective function of the distributed control strategy optimization model comprises two parts, namely voltage deviation and load loss, as shown in formula (13):
in the formula, g1Indicates the amount of loss of load, g1Representing a voltage deviation, PGnFor normal operation of the communication systemTime-of-flight distributed power supply output, PGimaxFor distributed power off-line upper limit of output, NDGThe weighted summation of the voltages of all nodes is taken for the quantity and voltage deviation of distributed power supplies in the system, UmnAnd wmnRespectively representing the voltage and weight of the nth node in the mth feeder line, NnIs the number of nodes.
The weight coefficient of the node in the voltage deviation is related to the DG access position, and the weight coefficient w of the nth node in the mth feeder linemnCan be expressed as
Wherein Z is0iIs the impedance between node i and the beginning of the feed line, Zl,maxRepresents the maximum value of the impedance of the feed line on which the node is located, St,kIs the apparent power of the kth DG in the tth operation scene, StIs the sum of the load power of the feeder line where the node is located, Z0kTo the DG access point to balanced node impedance, ZikNode i to DG access point impedance.
Considering the operation scenario and the probability of the fault scenario, the objective function of the optimization model of the invention can be expressed as:
F＝ω1g1+ω2g2 (15)
when DG communication fails, the total risk is expressed as
Upon failure of FMSS communications, the total risk is expressed as
Where N _ f is the number of operational scenarios, PskIs the kth operation scenario probability, N _ s is the number of failure scenarios, PfiIs the ith failure scenario probability, FkRepresents the k < th >Risk values under the operating scenario. Omega1And omega2The invention adopts a judgment matrix method to determine the weight coefficient of each sub-target.
The two sub-targets of the invention are respectively the load loss and the voltage deviation, and the grades are divided according to the importance: the load loss directly influences the power supply reliability of a user and serves as a level 1 target; the voltage deviation influences the power supply quality of a user and serves as a grade 2 target, and judgment numbers are obtained according to the grade 2 target to form a judgment matrix
Through matrix processing, each target weight vector [ omega ] is obtained1,ω2]＝[0.75,0.25]。
When each distributed resource distributed control strategy is obtained after information failure, the following constraint conditions are satisfied:
(1) system power flow constraint
In the formula, PiAnd QiRespectively, the sum of the active power and the reactive power injected at node i, PDG,iAnd QDG,iThe active and reactive power injected respectively for DG on node i,
and
respectively representing active power and reactive power, P, output by the i port of the flexible multi-state switchload,iAnd Qload,iActive and reactive power, respectively, consumed by the load on node I, IijFor the current flowing from node i to node j, UiIs the voltage of node i, UjRepresenting the voltage of node j, RijAnd XijResistance and reactance, P, of branch ij, respectivelyijAnd QijRespectively, having a common power and a reactive power, B, flowing from node i to node jijAnd GijRespectively representing the real part and the imaginary part, delta, of the ith row and jth column element of the nodal admittance matrixijRepresenting the voltage phase angle difference of nodes i and j.
(2) Node voltage constraint
Wherein, Uimin，UimaxRespectively, the allowable upper and lower limits of the voltage of the node i.
(3) Branch current constraint
Wherein, IijmaxIs the upper limit of the branch current.
(4) FMSS port power constraint
In the formula, PFMSS,lossIn order to provide the active loss of the flexible multi-state switch,
the access capacity of the flexible multi-state switch i, j and h port converter,
respectively representing the active power transmitted by the FMSS ports i, j and h,
representing the reactive power transmitted by FMSS ports i, j, h, respectively. The FMSS per-port capacity constraint satisfies the "circular" constraint.
(5) DG constraints
PDG,i≤PGimax (26)
PGimaxRepresents the upper limit of the output of the ith DG.
And 5: and (4) solving the control strategy optimization model established in the step (4) by combining an intelligent optimization algorithm and a second-order cone optimization method.
In the double-layer multi-target optimization model established by the invention, the control variables comprise transmission power of each port of FMSS and DG output upper limit, the number of the control variables is large, the scene scale is large, the problem of nonlinear optimization is solved directly, the calculated amount is large, and the solving speed is low. In order to improve the solving speed, the upper layer model adopts an intelligent optimization algorithm (immune particle swarm optimization) to generate a distributed control strategy of communication failure equipment, and the lower layer model adopts a second-order cone optimization method to carry out rapid centralized optimization. Cone optimization is mathematical programming on a convex cone in a linear space, and can realize the solution of a problem in an effective time and simultaneously ensure the global optimality of the solution. In the invention, the power flow optimization model is linearized and embossed through variable replacement and second-order cone optimization.
The nonlinear constraints in the constraints are linearized by variable substitution as shown in equation (27).
In the formula, Xi，Yij，ZijTo change intoThe magnitude, satisfying equation (28) after convex relaxation, is a rotational second order cone constraint that does not cause a change in the original problem solution.
XjTo represent
The replacement variable of (2). Based on the above variable substitutions, constraints (19), (21) can be converted to linear constraints, as shown in equation (29).
Through the steps, the model is changed into a convex plan, modeling is carried out under an MATLAB environment by means of a YALMIP algorithm package, and an IPOPT tool box is called to solve. The model solution flow is shown in fig. 5.
Fig. 5(a) shows a process of solving the optimal control strategy after a DG communication failure. The distributed control strategy of the DG is generated on the upper layer, the distributed control strategy of the loss connection elements corresponding to the ith fault scene is transmitted to the lower layer, the centralized control strategy of the loss connection distributed resources is simulated and optimized under each operation scene, the risk value of each operation scene is weighted and summed according to the probability of the operation scene to obtain the risk value of the current distributed control strategy adopted by the ith fault scene, the risk of the current distributed control strategy adopted by each fault scene is calculated in a circulating mode, the risk value is transmitted to the upper layer to calculate the comprehensive risk value of the current distributed control strategy adopted by the DG, then the distributed control strategy of the DG is updated according to the basic idea of particle swarm optimization, and the DG distributed control strategy is output after the iteration termination condition is met.
FIG. 5(b) is a flow of solving the optimization control strategy of FMSS loss of contact. And the upper layer generates an FMSS distributed control strategy, transmits the FMSS distributed control strategy to the lower layer to simulate and optimize the non-loss-of-connection distributed resource centralized control strategy in each operation scene, transmits the risk value of each operation scene to the upper layer, and performs weighted summation according to the probability of the operation scenes to obtain the comprehensive risk value of the FMSS adopting the current distributed control strategy. And then updating the FMSS distributed control strategy according to the basic idea of particle swarm optimization, and outputting the FMSS distributed control strategy after the iteration termination condition is met.
The method of the present invention is verified by a specific simulation example. The simulation is based on an MATLAB R2018a platform, a model is tested by using 3 improved IEEE-33 node power distribution systems, the system structure is shown in figure 6, and the tail ends of three IEEE33 node systems are connected through a three-terminal FMSS to realize inter-region interconnection. The photovoltaic power generation system comprises two photovoltaic set access nodes 1-14 and 1-17, rated capacity is 900kV & A, power factors are 0.9, 4 wind turbine set access nodes 1-15 and 2-14,2-16 and 2-17, the rated capacity is 800kV & A, 900kV & A and 800kV & A, the power factors are 0.9, and the end 3 FMSS connection nodes 1-18, 2-18 and 3-18. The communication device failure parameters are shown in table 1:
TABLE 1 information System element reliability parameters
The information system information elements shown in the example are 21 in number, the communication lines are 23 in number, 44 scenes are total in number when the information elements and the communication lines are traversed for single fault, and scene reduction is carried out according to fault consequence consistency, so that a communication fault scene probability table shown in the following table is obtained.
Table 2 probability table of communication system fault scenario
According to historical statistical data, a fan and a photovoltaic of the power distribution network are accessed, one sampling point is arranged every 15min, based on 96 sampling points in one day, 6 typical scenes and the probability thereof are obtained through a self-adaptive FCM clustering algorithm, and a clustering result is shown in FIG. 7 and Table 3.
Table 3 operational scenario clustering results
And (3) performing optimization model solution by adopting an immunity-based particle swarm algorithm, wherein through multiple simulation tests, the optimization result of each DG local control strategy is shown in Table 4, and the comprehensive risk value is 3.0753.
TABLE 4 in-situ control strategy optimization results for distributed power supplies
As can be seen from the above table, the offline DG contribution difference is large, and can reach 96.75% at the highest, and can reach 45.79% at the lowest, and mainly relates to the position of the DG in the feeder, the DG near the head end of the feeder has a high offline contribution level, such as DG1 and DG4, and since the voltage at the tail end of the feeder is larger than the fluctuation range at the head end, the offline contribution of the DG near the tail end of the feeder should be limited, such as DG2 and DG 6.
The FMSS offline operation strategy adopts a power transmission instruction generated online at the moment before information failure, and active power transmitted by each port in different operation scenes after the FMSS information failure is shown in table 5 because the FMSS online operation strategies in different operation scenes are different.
TABLE 5 FMSS in-Place control strategy optimization results
As can be seen from the above table, the operation risks after FMSS communication failure in different operation scenarios are not very different. The output of the distributed power supply has certain influence on the transmission active power of an FMSS port, the photovoltaic output in scenes 1 to 4 is high, so that the power of a feeder line 1 is sufficient, the photovoltaic output is close to 0 in scenes 5 and 6, the load requirement of the feeder line 1 cannot be met, and the power needs to be transferred from other feeder lines. Since there is no distributed power access on the feeder 3, the feeder 3 requires power support from the other two feeders in each of the 6 scenarios. In addition, the optimization results in table 5 show that the system running risk after the FMSS communication failure is higher, because the FMSS plays the roles of supporting load power and regulating voltage in the system, and the system has stronger dependence on the FMSS, so the risk after the communication failure is higher.
The above embodiments are only preferred embodiments of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are also within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A flexible power distribution system distributed control strategy optimization method considering information failure comprises the following steps:
step 1: establishing a flexible power distribution information physical system, and analyzing the structure and the control mode of the flexible power distribution information physical system;
step 2: considering the randomness of the output of the distributed power supply, and describing the multi-scene operation characteristics of the distributed power supply by adopting a self-adaptive fuzzy clustering method;
and step 3: considering uncertainty of a communication system fault scene, and calculating validity of an information link by adopting a fault tree analysis method;
and 4, step 4: taking the output upper limit of the off-line distributed power supply and the transmission power of each port of the off-line FMSS as decision variables, aiming at reducing the load loss risk and the voltage deviation, considering the uncertainty of the output and fault scenes of the distributed power supply, and establishing a distributed resource distributed control strategy optimization model based on the combination of the distributed control and the centralized optimization of the probability scene;
and 5: and (4) solving the control strategy optimization model established in the step (4) by combining an intelligent optimization algorithm and a second-order cone optimization method.
2. The method of claim 1, wherein the method comprises the steps of: the self-adaptive Fuzzy clustering method is a self-adaptive Fuzzy C-means (FCM) clustering method, the method clusters photovoltaic and fan output into a plurality of typical scenes, generates a distributed power supply output representative value and probability thereof under each operation scene, converts power of each sampling point of wind power and photovoltaic into sample points on a two-dimensional coordinate system, distributes each sample point to a category with the maximum membership degree, and obtains optimal clustering by iteratively updating a clustering center and a membership degree matrix.
3. The method of claim 2, wherein the method comprises the steps of: the value function and the constraint condition of the FCM are expressed by an expression (1) and an expression (2):
wherein u isijFor each sample xiFor a certain class ciDegree of membership of uij∈[0,1]M is a weighted index, generally 2 is taken, and c represents the number of the class clusters; continuously updating the clustering center and the membership degree matrix through the iterative formulas of the formula (3) and the formula (4) to minimize the cost function,
wherein, cjDenotes the jthCenter of cluster, N represents number of samples, ckIndicating the center of the kth class cluster.
4. The method of claim 3, wherein the method comprises the steps of: in the FCM clustering method, when the number of elements in a certain class is smaller than a specified value or the distance between the two classes is smaller than the specified value, the elements are combined into the same class; when the variance of the distance from each sample point to the cluster center in a certain class is larger than a specified value, the class is divided into two classes, and the two classes are shown by an equation 5:
wherein the content of the first and second substances,
is the maximum standard deviation within class j, K is the total number of clusters expected, θsIndicating the specified standard deviation.
5. The method of claim 1, wherein the method comprises the steps of: in the fault tree model, failure of the distributed resource i information link is taken as a top event, and the reasons for failure of the distributed resource i information link are application layer failure, communication layer failure and interface layer failure; the reason causing the communication layer failure is that all communication channels of the information link fail as an intermediate event; information element corruption is a bottom event.
6. The method of claim 1, wherein the method comprises the steps of: the distributed control strategy optimization model in the step 4 is divided into an upper layer and a lower layer, the upper layer is DG or FMSS distributed control strategy optimization considering the communication fault of the fault scene, the target function is that the sum of voltage deviation weighted by the probability of the fault scene and load loss is minimum, and an in-situ control strategy generated by the upper layer is transmitted to the lower layer; and the lower layer carries out simulation operation optimization of online distributed resources considering an operation scene on the flexible interconnection system, the objective function is that the voltage deviation weighted by the probability of the operation scene is minimum and the load loss amount is minimum, the lower layer online optimization result is transmitted to the upper layer to calculate a risk value, and the upper layer and the lower layer form centralized and decentralized cooperative optimization to obtain a decentralized control strategy of communication interruption of each terminal device.
7. The method of claim 6, wherein the method comprises: the objective function of the distributed control strategy optimization model is as follows:
F＝ω1g1+ω2g2 (6)
in the formula, g1Indicates the amount of loss of load, g1Representing a voltage deviation, PGnFor distributed power supply output, P, during normal operation of the communication systemGimaxFor distributed power off-line upper limit of output, NDGThe weighted summation of the voltages of all nodes is taken for the quantity and voltage deviation of distributed power supplies in the system, UmnAnd wmnRespectively representing the voltage and weight of the nth node in the mth feeder line, NnIs the number of nodes; z0iIs the impedance between node i and the beginning of the feed line, Zl,maxRepresents the maximum value of the impedance of the feed line on which the node is located, St,kIs the apparent power of the kth DG in the tth operation scene, StIs the sum of the load power of the feeder line where the node is located, Z0kTo the DG access point to balanced node impedance, ZikNode i to DG access point impedance; (ii) a Omega1And omega2Weight coefficient, target, representing the amount of off-load versus voltage deviationWeight vector [ omega [ ]1,ω2]＝[0.75,0.25]；
When DG communication fails, the total risk is expressed as
Upon failure of FMSS communications, the total risk is expressed as
Where N _ f is the number of operational scenarios, PskIs the kth operation scenario probability, N _ s is the number of failure scenarios, PfiIs the ith failure scenario probability, FkRepresenting the risk value in the k-th operational scenario.
8. The method of claim 7, wherein the method comprises: when the distributed resources should be distributed with the control strategy after the information is invalid, the following constraint conditions should be satisfied:
(1) system power flow constraint
In the formula, PiAnd QiRespectively, the sum of the active power and the reactive power injected at node i, PDG,iAnd QDG,iAre respectively nodesi the active and reactive power injected by DG,
and
respectively representing active power and reactive power, P, output by the i port of the flexible multi-state switchload,iAnd Qload,iActive and reactive power, respectively, consumed by the load on node I, IijFor the current flowing from node i to node j, UiIs the voltage of node i, UjRepresenting the voltage of node j, RijAnd XijResistance and reactance, P, of branch ij, respectivelyijAnd QijRespectively, having a common power and a reactive power, B, flowing from node i to node jijAnd GijRespectively representing the real part and the imaginary part, delta, of the ith row and jth column element of the nodal admittance matrixijRepresenting the voltage phase angle difference, G, of nodes i and jijRepresents the conductance between nodes i and j;
(2) node voltage constraint
Wherein, Uimin，UimaxRespectively representing the allowable upper limit and the allowable lower limit of the voltage of the node i;
(3) branch current constraint
Wherein, IijmaxIs the upper limit of the branch current;
(4) FMSS port power constraint
In the formula, PFMSSlossIn order to provide the active loss of the flexible multi-state switch,
the access capacity of the flexible multi-state switch i, j and h port converter,
respectively representing the active power transmitted by the FMSS ports i, j and h,
respectively representing reactive power transmitted by the FMSS ports i, j and h, wherein the capacity constraint of each FMSS port meets the 'circular' constraint;
(5) DG constraints
PDG,i≤PGimax (18)
PGimaxRepresents the upper limit of the output of the ith DG.
9. The method of claim 6, wherein the method comprises: in the step 5, the upper layer of the model adopts an intelligent optimization algorithm (immune particle swarm optimization) to generate a distributed control strategy of the communication failure equipment, and the lower layer of the model adopts a second-order cone optimization method to perform rapid centralized optimization.
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