CN116031943A - Self-healing recovery method for distributed power distribution network with static information and dynamic topology - Google Patents
Self-healing recovery method for distributed power distribution network with static information and dynamic topology Download PDFInfo
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Abstract
The invention relates to the field of self-healing recovery of a distributed distribution network, in particular to a self-healing recovery method of a distributed distribution network, which takes static information and dynamic topology into consideration, comprising the following steps: constructing a corresponding topology change analysis calculation model based on a topology description matrix of the power distribution network; carrying out topology analysis based on the topology change analysis calculation model, and generating a static reconstruction model of the power distribution network based on an improved power moment algorithm; based on the static reconstruction model of the power distribution network, the dynamic change of the topology is considered, and a dynamic reconstruction model of the power distribution network based on dynamic topology analysis is generated; and the self-healing recovery of the distributed distribution network is realized by solving a dynamic reconstruction model of the distribution network. According to the method and the system, the dynamic reconfiguration model of the power distribution network can be established by analyzing the static information and the dynamic topology problem of the power distribution network, so that the topology state of the power distribution network under dynamic change is rapidly analyzed, the real-time topology parameters are updated, and the flexibility and the adaptability of self-healing recovery of the distributed power distribution network can be improved.
Description
Technical Field
The invention relates to the field of self-healing recovery of distributed distribution networks, in particular to a self-healing recovery method of a distributed distribution network, which takes static information and dynamic topology into account.
Background
The distributed power supply (Distributed Generator, DG) is connected into the power distribution network, so that power can be rapidly supplied to users, and the power supply reliability and the power supply quality of the power distribution network are improved. Along with the aggravation of world energy crisis and environmental problems, the DG power supply mode mainly comprising renewable energy sources has the characteristics of flexibility, safety, cleanliness and the like, and the incorporation of the renewable energy sources DG into a power distribution network becomes a research hotspot of various countries.
However, with the access of a large amount of renewable energy sources DG, the operation structure and operation mode of the power distribution network are changed. Meanwhile, the output power of DG is directly affected by external factors such as seasons, climates and the like, and has volatility. The fluctuation of DG output causes frequent changes in the direction and magnitude of system power flow, and affects the network loss and voltage on the distribution line. The optimal operation scheme obtained under the DG output condition at a certain moment is not necessarily the optimal scheme under the output state at other moments, and sometimes is possibly an infeasible scheme for threatening the safe operation of the system. Therefore, the real-time dynamic property of the initial topology directly influences the network reconstruction result, and is closely related to the system economy, the power supply reliability, the power system investment and the like.
At present, most of distributed power distribution networks have complete self-healing functions, and the influence of power grid faults on users can be reduced to the greatest extent. However, many methods for self-healing recovery of the distributed power distribution network have many disadvantages, such as poor real-time dynamic performance of the initial topology, which results in poor self-healing recovery effect of the distributed power distribution network. Therefore, how to design a method for effectively realizing self-healing recovery of a distributed power distribution network is a technical problem to be solved.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to solve the technical problems that: how to provide a distributed power distribution network self-healing recovery method taking static information and dynamic topology into account, a power distribution network dynamic reconstruction model can be established by analyzing the static information and the dynamic topology problem of a power distribution network, so that the power distribution network topology state under dynamic change is rapidly analyzed, real-time topology parameters are updated, the flexibility and the adaptability of the distributed power distribution network self-healing recovery can be improved, and a new thought is provided for the power distribution network self-healing recovery.
In order to solve the technical problems, the invention adopts the following technical scheme:
a self-healing recovery method of a distributed power distribution network considering static information and dynamic topology comprises the following steps:
s1: constructing a corresponding topology change analysis calculation model based on a topology description matrix of the power distribution network;
s2: carrying out topology analysis based on the topology change analysis calculation model, and generating a static reconstruction model of the power distribution network based on an improved power moment algorithm;
s3: based on the static reconstruction model of the power distribution network, the dynamic change of the topology is considered, and a dynamic reconstruction model of the power distribution network based on dynamic topology analysis is generated;
s4: and the self-healing recovery of the distributed distribution network is realized by solving a dynamic reconstruction model of the distribution network.
Preferably, in step S1, the topology change analysis calculation model performs topology analysis by:
s101: simplifying the original topology of the power distribution network into an m-segment n-connection model;
s102: reading initial topological structure data of a power distribution network, wherein the initial topological structure data comprises a node switch incidence matrix C (i) Initial switch State vector S (i) With switch action feasible region
S103: calculating a power supply path matrix D (t) Matrix R of relation with node position (t) ；
S104: updating topological structure data of the power distribution network, and inputting a switch state vector S after the action (t+1) ；
S105: analyzing the affected type of the node: calculating a disconnection switch positioning vector and an action node positioning vector, matching with a node upstream and downstream position relation matrix, and calculating a-f type node marking positioning vectors;
s106: the topology change analysis and calculation process comprises the following steps: firstly, processing a class a node row and an upstream adjacent switch column, then processing an action switch column, and finally, processing b-f class node rows to obtain a node switch association matrix C in a new topology state (t+1) ；
S107: according to the node switch incidence matrix C (t+1) Calculating the adjacency matrix B at this time (t+1) Power supply path matrix D (t+1) Matrix R of relation with node position (t+1) And output C (t+1) 、B (t+1) 、D (t+1) and R(t+1) ；
S108: judging whether the search of the switch action feasible region is finished or not: if not, returning to the step S104; otherwise, ending the search.
Preferably, the node switch association matrix c= (C ij ) m×(m+n) Wherein the row index node, the column index segment and the tie switch;
element C in node switch association matrix C ij The definition is as follows:
in the formula ：cij Representing elements in the node switch association matrix C; the operator Λ represents a logical AND, i.e. the condition needs to be satisfied simultaneously; operator
The representation is included, i.e. the relationship between the two types of electrical units; />
Indicating switch S i Ps is ps j Is defined by a boundary of (2); operator→representation direction for reflecting the direction of the vector; i S i →ps j Indicating the current flowing through S i Inflow ps j The method comprises the steps of carrying out a first treatment on the surface of the Operator->
The indication is not included, i.e. the indication switch is not a power supply segment boundary; omega represents a virtual joint symbol;
the node positional relationship matrix is expressed as r= (R ij ) m×m Wherein both rows and columns index nodes;
r ij representing the position of node i relative to node j, r when node i is the upstream node of node j ij -1; when node i is a downstream node of node j, r ij ＝1；
Taking absolute values of all elements of the power supply path matrix D, and calculating r according to the following formula ij ：
in the formula ：rij Representing the position of node i relative to node j; d, d kj 、d ki Representing factors in the power supply path matrix; if () represents a logical formula.
Preferably, the power moment algorithm is improved, and a candidate switch set can be formed by analyzing the influence degree of the relationship between the rings on the power moment algorithm and generating a corresponding strategy by utilizing the topology and the electrical property of the rings.
Preferably, in step S2, the objective function of the static reconstruction model of the power distribution network is as follows:
wherein: Δp represents the sum of line losses; m is m j Representing the number of branches in the segmented contact model; k (k) ei Representing branch e i Is a switching state of (a); r is R ei Representing branch e i Resistance of (2); i ei Representing the flow through branch e i Is set to be a current of (a);
wherein ,
in the formula ：Pei and Qei Respectively, flow through branch e i Active power and reactive power of (a); u (U) epi Representing branch e i End node ep of (2) i A voltage.
Preferably, the constraint conditions of the static reconstruction model of the power distribution network are as follows:
1) And (3) load flow constraint:
in the formula ：Pepi,1 、Q epi,I Respectively represent node ep i Active and reactive injection power of (a); m is m j Representing the number of branches in the segmented contact model; u (U) epi Representing node ep i Is set to the voltage amplitude of (1); θ ij ＝θ i -θ j Representing node ep i And ep j Is a phase angle difference of (2); g ij 、b ij Respectively represent node ep i And ep j Conductance and susceptance of the inter-line; p (P) epi 、Q epi Representation sectionPoint ep i Load active and reactive power of (a);
2) Branch current constraint, node voltage constraint:
U epi,min ≤U epi ≤U epi,max ；
0≤I ei ≤I ei,max ；
in the formula ：Uepi,min 、U epi,max Representing node ep i Minimum and maximum values of voltage; i ei,max Representing branch e i Maximum value of the current amount.
Preferably, the objective function of the dynamic reconfiguration model of the power distribution network is as follows:
in the formula ：md Representing a real-time topology G r (n, m) number of branches; l represents the real-time topology G r A branch connecting node i and node j; n is n Gr Representing a real-time topology G r The number of nodes included; z l Representing a switching state quantity; p (P) i 、
Representing the active and reactive power injected into the inode, respectively; v (V) i 、V ref Respectively representing the actual operating voltage and the rated voltage of the i node; n is n Gr Representing a real-time topology G r The number of nodes included; r is (r) l And represents the branch impedance of l.
Preferably, the constraint conditions of the dynamic reconfiguration model of the power distribution network are as follows:
S l ≤S lmax ；
V imin ≤V i ≤V max ；
g r ∈T Gr ；
in the formula ：Psi 、Q si Respectively represent real-time topology G r Injecting active power and reactive power into the middle node i;
Q Li representing the active and reactive load of node i; />
Representing a real-time topology G r The actual capacity of the medium flow branch l, max and min representing the upper and lower limits; θ i Representing the voltage phase angle difference of nodes i and j; t (T) Gr Representing a real-time topology G r Is a radial topology set of (1); g r Representing a candidate topology solution; v (V) i Representing the actual operating voltage of the inode, max and min representing upper and lower limits; u (U) i Representing the voltage at node i; u (U) j Representing the voltage at node j; g i Representing a real-time topology G r ；b l Representing branch l susceptance; g l Representing the branch l conductance; z l Is the branch impedance.
Preferably, in step S4, the dynamic reconstruction model of the power distribution network is solved by a dynamic adaptive particle swarm algorithm, so as to obtain an optimal solution of the real-time topology, and further, the self-healing recovery of the distributed power distribution network is realized based on the optimal solution of the real-time topology.
Preferably, the dynamic self-adaptive particle swarm algorithm solves the dynamic reconstruction model of the power distribution network by the following steps:
s401: inputting standard topology parameters of the power distribution network topology, including a segmentation branch parameter, a connection branch parameter, load node power and the like, and obtaining network initial topology parameters according to a path search method;
initializing parameters of a self-adaptive particle swarm algorithm, including a solution form X, population particles P, upper and lower particle limits and the like;
s402: if the change of the initial grid of the power distribution network is detected, executing step S403, and updating the initial topological structure and parameters; otherwise, step S404 is performed;
s403: the dynamic topology analysis method updates the real-time topology: if the branch needs to be removed due to faults, overhauls and the like, layering nodes in the network, identifying a power-losing area, then recovering connectivity between the power-losing area and the main network, and updating G-G r The method comprises the steps of carrying out a first treatment on the surface of the If new branches or branch sets are integrated into the initial net rack, dynamically adding the branches, performing topology analysis, and updating G-G r The method comprises the steps of carrying out a first treatment on the surface of the Then, acquiring a real-time topology G according to a path searching method r Is S 'of the connected tree' G Electric island S' D And a basic loop matrix L' op ；
S404: according to the real-time topology G or G r Is a basic loop matrix L of (1) op Or L' op Automatic adjustment of solution form X-X of particle swarm algorithm r Population particle form P-P r An upper limit position of the particles, etc.;
s405: establishing a real-time topology G r And calculates P r The adaptability value of each feasible particle in the system is updated to the position of the new generation particle;
s406: steps S404 to S405 are repeated until an optimal solution of the real-time topology is obtained.
Compared with the prior art, the self-healing recovery method for the distributed power distribution network, which takes static information and dynamic topology into consideration, has the following beneficial effects:
according to the invention, the dynamic reconstruction model of the power distribution network is established on the basis of the static reconstruction model of the power distribution network by analyzing the static information and the dynamic topology problem of the power distribution network, so that the self-healing recovery of the distributed power distribution network is realized. On one hand, the method analyzes the defects of the power moment algorithm through the topology change analysis calculation model, generates the static reconstruction model of the power distribution network, and further realizes the static reconstruction of the power distribution network through the static reconstruction model of the power distribution network, so that the network loss of the power distribution network can be reduced, and meanwhile, the topology structure of the power distribution network can be optimized in a more efficient method, so that the effectiveness of self-healing recovery of the distributed power distribution network can be improved. On the other hand, the method considers the topological dynamic change on the basis of the distribution network static reconstruction model, and generates the distribution network dynamic reconstruction model, and the distribution network dynamic reconstruction model can rapidly analyze the distribution network topological state under the dynamic change and update the real-time topological parameters, so that the network connectivity is recovered to realize the self-healing recovery of the distributed distribution network, thereby improving the flexibility and the adaptability of the self-healing recovery of the distributed distribution network, and providing a new thought for the self-healing recovery of the distribution network.
According to the method, the topology change analysis calculation model is constructed based on the topology description matrix of the power distribution network, the effectiveness of the model in topology identification and topology change operation can be verified, meanwhile, the defects of a power moment algorithm are improved through the analysis of the topology change analysis calculation model, and therefore the effectiveness of self-healing recovery of the distributed power distribution network can be further improved.
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For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings, in which:
FIG. 1 is a logic block diagram of a distributed power distribution network self-healing recovery method;
FIG. 2 is a flow chart of performing topology analysis computation by a topology change analysis computation model;
fig. 3 is a flowchart of a dynamic self-adaptive particle swarm algorithm for solving a dynamic reconstruction model of a power distribution network.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein can be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. In the description of the present invention, it should be noted that, directions or positional relationships indicated by terms such as "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., are directions or positional relationships based on those shown in the drawings, or are directions or positional relationships conventionally put in use of the inventive product, are merely for convenience of describing the present invention and simplifying the description, and are not indicative or implying that the apparatus or element to be referred to must have a specific direction, be constructed and operated in a specific direction, and thus should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used merely to distinguish between descriptions and should not be construed as indicating or implying relative importance. Furthermore, the terms "horizontal," "vertical," and the like do not denote a requirement that the component be absolutely horizontal or overhang, but rather may be slightly inclined. For example, "horizontal" merely means that its direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly tilted. In the description of the present invention, it should also be noted that, unless explicitly specified and limited otherwise, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
The following is a further detailed description of the embodiments:
examples:
the embodiment discloses a self-healing recovery method of a distributed power distribution network, which takes static information and dynamic topology into consideration.
As shown in fig. 1, a self-healing recovery method of a distributed power distribution network, which takes static information and dynamic topology into account, includes:
s1: constructing a corresponding topology change analysis calculation model based on a topology description matrix of the power distribution network;
in this embodiment, the constructed topology change analysis calculation model is used to ensure the effectiveness of topology identification and topology change operation, and is the research foundation of static reconstruction model and dynamic reconstruction model in terms of topology change.
S2: carrying out topology analysis based on the topology change analysis calculation model, and generating a static reconstruction model of the power distribution network based on an improved power moment algorithm;
s3: based on the static reconstruction model of the power distribution network, the dynamic change of the topology is considered, and a dynamic reconstruction model of the power distribution network based on dynamic topology analysis is generated;
s4: and the self-healing recovery of the distributed distribution network is realized by solving a dynamic reconstruction model of the distribution network.
According to the invention, the dynamic reconstruction model of the power distribution network is established on the basis of the static reconstruction model of the power distribution network by analyzing the static information and the dynamic topology problem of the power distribution network, so that the self-healing recovery of the distributed power distribution network is realized. On one hand, the method analyzes the defects of the power moment algorithm through the topology change analysis calculation model, generates the static reconstruction model of the power distribution network, and further realizes the static reconstruction of the power distribution network through the static reconstruction model of the power distribution network, so that the network loss of the power distribution network can be reduced, and meanwhile, the topology structure of the power distribution network can be optimized in a more efficient method, so that the effectiveness of self-healing recovery of the distributed power distribution network can be improved. On the other hand, the method considers the topological dynamic change on the basis of the distribution network static reconstruction model, and generates the distribution network dynamic reconstruction model, and the distribution network dynamic reconstruction model can rapidly analyze the distribution network topological state under the dynamic change and update the real-time topological parameters, so that the network connectivity is recovered to realize the self-healing recovery of the distributed distribution network, thereby improving the flexibility and the adaptability of the self-healing recovery of the distributed distribution network, and providing a new thought for the self-healing recovery of the distribution network.
According to the method, the topology change analysis calculation model is constructed based on the topology description matrix of the power distribution network, the effectiveness of the model in topology identification and topology change operation can be verified, meanwhile, the defects of a power moment algorithm are improved through the analysis of the topology change analysis calculation model, and therefore the effectiveness of self-healing recovery of the distributed power distribution network can be further improved.
In the specific implementation process, a topology change analysis calculation model is constructed, and topology description matrixes such as a node switch association matrix, a power supply path matrix and the like are introduced on the basis of a common topology matrix. The topology change analysis and calculation model is used for realizing the transition process of the geometric and electrical topology states of the distributed power distribution network by utilizing the operation of the node switch incidence matrix.
With reference to fig. 2, the topology change analysis calculation model performs topology analysis by:
s101: simplifying the original topology of the power distribution network into an m-segment n-connection model;
in this embodiment, the purpose of simplifying the connection model of the original topology of the power distribution network is to be able to more intuitively express the topology structure of the power distribution network, and simplify the topology description and identification process.
S102: reading initial topological structure data of a power distribution network, wherein the initial topological structure data comprises a node switch incidence matrix C (i) Initial switch State vector S (i) With switch action feasible region
Wherein, all switch state combinations of the distribution network electric system form an initial switch action feasible domain (initial domain for short) δs, and in theory, δs comprises
If all possible switch state combinations are analyzed, the analytic calculation process will face dimension disaster problems. The initial topological state of the system is t, and the corresponding switch state vector S (t) ＝[s 1 (t) ,s 2 (t) ,...,s l (t) ,...,s l (t) ,...,s m+n (t) ]The node switch incidence matrix is C (t) . Action switch combination (S) i ,S j ) Order of
At this time, the system is in state, and the switch state vector is S (t+1) ＝[s 1 (t+1) ,s 2 (t +1) ,...,s i (t+1) ,...,s j (t+1) ,...,s m+n (t+1) ]。
S103: calculating a power supply path matrix D (t) Matrix R of relation with node position (t) ；
S104: updating topological structure data of the power distribution network, and inputting a switch state vector S after the action (t+1) ；
S105: analyzing the affected type of the node: calculating a disconnection switch positioning vector and an action node positioning vector, matching with a node upstream and downstream position relation matrix, and calculating a-f type node marking positioning vectors;
in this embodiment, the specific calculation process is as follows:
first, it is analyzed whether the open switch and the closed switch are node boundary switches. Calculating a switch-off positioning vector in a state transition process using a switch state vector
And closing switch flag vector->
The following are provided:
P s-dis ＝S (t) ∨S (t+1) -S (t+1) ；
p s-clo ＝S (t) vS (t+1) -S (t)
in the formula, "V" means that OR operation is performed according to the bit, and if 1 participates in the operation, the OR operation result is 1.
To the beginning of the systemStart C (t) All non-zero elements in the node switch are taken to be 1, and a decoupled node switch incidence matrix C is obtained (0) . By means of
Positioning the position of the disconnection node to obtain a disconnection node positioning vector +.>
Vectors indicating status information and position information are called positioning vectors
Similarly, a closed node positioning vector can be obtained
Motion switch positioning vector
The method comprises the following steps: />
The function is as follows: the off-switch positioning vector reflects the off-switch mounting position p s-dis ＝(p k ) (m+n)×1 P when the switch is opened k =1, otherwise
p k ＝0。
The action node positioning vector reflects the action switch mounting position p ep-act ＝(p k ) m×1 P when the action switch is node boundary k =1; otherwise p k ＝0
The closed switch positioning vector reflects the closed switch mounting position p ep-clo ＝(p k ) m×1 P when the closed switch is node boundary k =1; otherwise p k ＝0
The matching with the node upstream and downstream position relation matrix refers to positioning vectors reflecting the relation between the switch on-off position and the boundary of the node and the relation between the power supply path and the upstream and downstream nodes, and the nodes can be classified according to influence types by using the vectors. a-f are impact type markers and the node is affected correspondingly if it contains the corresponding symbol.
S106: the topology change analysis and calculation process comprises the following steps: firstly, processing a class a node row and an upstream adjacent switch column, then processing an action switch column, and finally, processing b-f class node rows to obtain a node switch association matrix C in a new topology state (t+1) ；
S107: outputting a topology description matrix in the new topology state: according to the node switch incidence matrix C (t+1) Calculating the adjacency matrix B at this time (t+1) Power supply path matrix D (t+1) Matrix R of relation with node position (t+1) Outputting all matrixes;
in the present embodiment, for the adjacency matrix B (t+1) Is calculated by (1):
definition of |v|×|v| order matrix b= (B) ij ) For the adjacency matrix, provision is made for:
s108: judging whether the search of the switch action feasible region is finished or not: if not, returning to the step S104; otherwise, ending the search.
In this embodiment, the topology analysis obtained by analyzing the calculation model through topology change provides a theoretical basis for subsequent modeling, and the subsequent reconstruction model needs to adapt to the topology analysis of the radial distribution network structure.
Considering that the reconstruction essence of the distribution network is to change the power supply path of the load point by changing the state of the switch, the redistribution of the distribution network power is realized, and the node branch association matrix and the adjacent matrix cannot intuitively express the influence of the switch action on the topology change, so that a matrix capable of quickly and explicitly expressing the reconstruction state change is required to be searched on the basis of the original topology description matrix. The concept of a node switch association matrix (Node Switching Association Matrix, NSAM) is introduced.
The node switch incidence matrix is utilized to identify which equivalent point boundary switches the action switch is in the reconstruction process. The method is to extract the open/close switch columns to find the equivalent point of the non-zero element row index. The node admittance matrix and the node impedance matrix are not repeated, and a power supply path matrix describing the electrical topology is defined.
Both the node relative position matrix and the power supply path matrix are targets of topology identification in the reconstruction process of the distribution network. Identifying the position relation of the closed node relative to other nodes through a Node Relative Position Matrix (NRPM); the power supply and power supply paths of the nodes are identified by a Power Supply Path Matrix (PSPM).
Specifically, the node switch association matrix c= (C ij ) m×(m+n) Wherein the row index node, the column index segment and the tie switch;
element C in node switch association matrix C ij The definition is as follows:
in the formula ：cij Representing elements in the node switch association matrix C; the operator Λ represents a logical AND, i.e. the condition needs to be satisfied simultaneously; operator
The representation is included, i.e. the relationship between the two types of electrical units; />
Indicating switch S i Ps is ps j Is defined by a boundary of (2); operator→representation direction for reflecting the direction of the vector; i S i →ps j Indicating the current flowing through S i Inflow ps j The method comprises the steps of carrying out a first treatment on the surface of the Operator->
The indication is not included, i.e. the indication switch is not a power supply segment boundary; omega represents a virtual joint symbol;
node position relation momentThe matrix is expressed as r= (R) ij ) m×m Wherein both rows and columns index nodes;
r ij representing the position of node i relative to node j, r when node i is the upstream node of node j ij -1; when node i is a downstream node of node j, r ij ＝1；
Taking absolute values of all elements of the power supply path matrix D, and calculating r according to the following formula ij ：
in the formula ：rij Representing the position of node i relative to node j; d, d kj 、d ki Representing factors in the power supply path matrix; if () represents a logical formula.
In the specific implementation process, an improved power moment algorithm is calculated based on topology change analysis, the degree of influence of the relationship among rings on the power moment algorithm is analyzed, and strategies for improving efficiency and improving load transfer are generated by utilizing the topology and electrical properties of the rings, so that a candidate switch set is formed.
The power moment algorithm is a power flow calculation process capable of avoiding complexity, and can obtain an optimized distribution network structure faster.
The following defects of a power moment algorithm are mainly improved through a topology change analysis calculation model:
1) Ignoring inter-loop effects: the precondition of iterative operation by using the power moment algorithm is that the rings are not affected with each other, namely when the opening and closing states of the switches of the other rings are unchanged, the network loss can be reduced only by opening and closing the contact switch and the sectionalizing switch of one ring by using the power moment algorithm. In practice, however, the degree of inter-loop influence is not effectively measured by the power moment algorithm, which results in that the reconstruction result deviation may be large and may easily fall into local optimum.
2) The operation efficiency is low: the countercurrent paths of most nodes have overlapping parts, and the repeated operation wastes calculation resources when the power moment of the nodes is calculated; when the power moment algorithm is used for searching for the disconnection switch, the search objects are too many, and the search speed is reduced.
3) Ignoring the transmission capability limit of the leg: if only the unbalance degree of the power moment at the two sides of the switch is considered in the reconstruction process, the risk of rapid voltage drop and line out-of-limit exists after the heavy load is transferred to the high voltage side, and a great amount of degradation exists, so that the transmission capacity limitation of the line needs to be considered, and the load is transferred to a thick branch as much as possible.
The improved power moment algorithm overcomes the defects that the power moment algorithm in the section ignores the influence among loops, the operation efficiency is lower, and the transmission capacity limitation of a branch is ignored, so that the static reconstruction algorithm only needs to carry out few tide calculations, the topology transformation can be conveniently realized by using a method of analytic calculation, the generation of infeasible solutions is reduced by setting a series of thresholds, the higher optimizing efficiency is ensured, and theoretical support is provided for considering the topology dynamic change for the follow-up dynamic reconstruction.
In the specific implementation process, the objective function of the static reconstruction model of the power distribution network is as follows:
wherein: Δp represents the sum of line losses; m is m j Representing the number of branches in the segmented contact model; k (k) ei Representing branch e i Is a switching state of (a); r is R ei Representing branch e i Resistance of (2); i ei Representing the flow through branch e i Is set to be a current of (a);
wherein ,
in the formula ：Pei and Qei Respectively, flow through branch e i Active power and reactive power of (a); u (U) epi Representing branch e i End node ep of (2) i A voltage.
Constraint conditions of the static reconstruction model of the power distribution network are as follows:
1) And (3) load flow constraint:
in the formula ：Pepi,1 、Q epi,I Respectively represent node ep i Active and reactive injection power of (a); m is m j Representing the number of branches in the segmented contact model; u (U) epi Representing node ep i Is set to the voltage amplitude of (1); θ ij ＝θ i -θ j Representing node ep i And ep j Is a phase angle difference of (2); g ij 、b ij Respectively represent node ep i And ep j Conductance and susceptance of the inter-line; p (P) epi 、Q epi Representing node ep i Load active and reactive power of (a);
2) Branch current constraint, node voltage constraint:
U epi,min ≤U epi ≤U epi,max ；
0≤I ei ≤I ei,max ；
in the formula ：Uepi,min 、U epi,max Representing node ep i Minimum and maximum values of voltage; i ei,max Representing branch e i Maximum value of the current amount.
In the static reconstruction analysis model of the active power distribution network, the minimum network loss of the power distribution network is achieved, namely the minimum unbalance of the power moment is achieved.
According to the invention, through the objective function and the constraint condition of the static reconstruction model of the power distribution network, the static reconstruction of the power distribution network can be realized, the network loss of the power distribution network can be reduced, and meanwhile, the topology structure of the power distribution network can be optimized by a more efficient method, so that the effectiveness of self-healing recovery of the distributed power distribution network can be further improved.
In the specific implementation process, the objective function of the dynamic reconstruction model of the power distribution network is as follows:
in the formula ：md Representing a real-time topology G r (n, m) number of branches; l represents the real-time topology G r A branch connecting node i and node j; n is n Gr Representing a real-time topology G r The number of nodes included; z l Representing the amount of switching state (0-open, 1-closed); p (P) i 、
Representing the active and reactive power injected into the inode, respectively; v (V) i 、V ref Respectively representing the actual operating voltage and the rated voltage of the i node; r is (r) l A branch impedance representing l; n is n Gr Representing a real-time topology G r The number of nodes involved. There is a positive correlation between the two objective functions, and the improvement of the voltage amplitude in the system is related to the power loss. The reduction of the power loss reduces the voltage drop across the branch, thereby increasing the node voltage value of the network.
In the specific implementation process, the constraint conditions of the dynamic reconfiguration model of the power distribution network are as follows:
S l ≤S lmax ；
V imin ≤V i ≤V max ；
g r ∈T Gr ；
in the formula ：Psi 、Q si Respectively represent real-time topology G r Injecting active power and reactive power into the middle node i; p (P) Li 、Q Li Representing the active and reactive load of node i;
representing a real-time topology G r The actual capacity of the medium flow branch l, max and min representing the upper and lower limits; θ i Representing the voltage phase angle difference of nodes i and j; t (T) Gr Representing a real-time topology G r Is a radial topology set of (1); g r Representing a candidate topology solution; v (V) i Representing the actual operating voltage of the inode, max and min representing upper and lower limits; u (U) i Representing the voltage at node i; u (U) j Representing the voltage at node j; g i Representing a real-time topology G r ；b l Representing branch l susceptance; g l Representing the branch l conductance; z l Is the branch impedance.
In the specific implementation process, a dynamic reconstruction model of the power distribution network is solved through a dynamic self-adaptive particle swarm optimization (DAPSO) to obtain an optimal solution of the real-time topology, and then self-healing recovery of the distributed power distribution network is achieved based on the optimal solution of the real-time topology.
The dynamic adaptive particle swarm algorithm (DAPSO) is an existing mature algorithm.
Referring to fig. 3, the dynamic adaptive particle swarm algorithm solves a dynamic reconstruction model of the power distribution network by the following steps:
s401: inputting standard topology parameters (including a subsection branch parameter, a connection branch parameter, load node power and the like) of a power distribution network topology (G network), and obtaining network initial topology parameters according to a path search method;
in this embodiment, the G network is assigned a power grid topology, and the initial topology parameter is a network initial topology parameter, that is, SD, SG, lop, etc.
Initializing parameters of a self-adaptive particle swarm algorithm, including a solution form X, population particles P, upper and lower particle limits and the like;
s402: if the change of the initial grid of the power distribution network is detected, executing step S403, and updating the initial topological structure and parameters; otherwise, step S404 is performed;
s403: the dynamic topology analysis method updates the real-time topology: if due to failure, maintenance, etcWhen the reasons need to remove the branches, layering nodes in the network, identifying a power-losing area, then recovering connectivity between the power-losing area and the main network, and updating G-G r The method comprises the steps of carrying out a first treatment on the surface of the If new branches or branch sets are integrated into the initial net rack, dynamically adding the branches, performing topology analysis, and updating G-G r The method comprises the steps of carrying out a first treatment on the surface of the Then, acquiring a real-time topology G according to a path searching method r Is S 'of the connected tree' G Electric island S' D And a basic loop matrix L' op ；
S404: according to the real-time topology G or G r Is a basic loop matrix L of (1) op Or L' op Automatic adjustment of solution form X-X of particle swarm algorithm r Population particle form P-P r An upper limit position of the particles, etc.;
s405: establishing a real-time topology G r And calculates P r The adaptability value of each feasible particle in the system is updated to the position of the new generation particle;
in this embodiment, the topology G is real-time r The optimization objective function of (2) is an objective function of an assigned power grid dynamic reconstruction model, wherein the objective function of network optimization reconstruction is set to reduce system network loss and improve node voltage value.
Specifically, P r For the population particle form, the following is defined by the parameters of a particle swarm algorithm, and the position is updated each time of iteration:
assuming that d-dimensional particles i select an individual w from the population at a time, calculating an objective function value f of the individual wd (t) and comparing this value with the fitness value f of the current particle i id (t) comparing. If f wd (t)＞f id (t) the current best position p of particle i id (t) from p id(t) and pwd And (t) determining. Otherwise, p id (t) from p id(t) and pgd (t) [ Global best position ]]The common decision is that,
to weight, update P→P according to the following formula r 。
S406: steps S404 to S405 are repeated until an optimal solution of the real-time topology is obtained.
In this embodiment, according to the optimal solution of the real-time topology, the switching of the switching topology realizes the self-healing recovery of the distributed distribution network.
According to the invention, through the objective function and the constraint of the dynamic reconfiguration model of the power distribution network, the topology state of the power distribution network under dynamic change can be rapidly analyzed, the real-time topology parameters are updated, the network connectivity is recovered, and further the self-healing recovery of the distributed power distribution network is realized, so that the flexibility and the adaptability of the self-healing recovery of the distributed power distribution network can be further improved.
According to the method, the dynamic reconstruction model of the power distribution network is solved through the dynamic self-adaptive particle swarm algorithm, after the initial topology of the power distribution network changes dynamically, a real-time topology structure can be obtained rapidly, and the dynamic reconstruction model is organically combined with the dynamic self-adaptive particle swarm algorithm, so that the problem of dynamic reconstruction of the power distribution network considering the dynamic change of the real-time topology is solved; meanwhile, after the initial topology change of the network is detected, the method can quickly respond, restore the connectivity of the network, update the reconstructed initial topology structure, adaptively adjust the basic parameters of the particle swarm algorithm, and obtain the optimal configuration of the network meeting the requirements.
The dynamic reconfiguration method of the invention can quickly update the real-time topology parameters according to different schemes, the update time is not longer than 0.2s at the longest, and the parameters of the DAPSO optimization algorithm are automatically adjusted to obtain the optimal configuration scheme of the real-time topology structure, the method separates the connectivity restoration and the optimization process of the network, only analyzes the branches and nodes in the network after the connectivity restoration during the reconfiguration, is beneficial to improving the efficiency of the reconfiguration method, and achieves better self-healing restoration effect of the distributed distribution network
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the technical solution, and those skilled in the art should understand that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the present invention, and all such modifications and equivalents are included in the scope of the claims.
Claims (10)
1. A self-healing recovery method of a distributed power distribution network taking both static information and dynamic topology into consideration is characterized by comprising the following steps:
s1: constructing a corresponding topology change analysis calculation model based on a topology description matrix of the power distribution network;
s2: carrying out topology analysis based on the topology change analysis calculation model, and generating a static reconstruction model of the power distribution network based on an improved power moment algorithm;
s3: based on the static reconstruction model of the power distribution network, the dynamic change of the topology is considered, and a dynamic reconstruction model of the power distribution network based on dynamic topology analysis is generated;
s4: and the self-healing recovery of the distributed distribution network is realized by solving a dynamic reconstruction model of the distribution network.
2. The self-healing recovery method of a distributed power distribution network taking both static information and dynamic topology into account as claimed in claim 1, wherein in step S1, a topology change analysis calculation model performs topology analysis by:
s101: simplifying the original topology of the power distribution network into an m-segment n-connection model;
s102: reading initial topological structure data of a power distribution network, wherein the initial topological structure data comprises a node switch incidence matrix C (i) Initial switch State vector S (i) With switch action feasible region
S103: calculating a power supply path matrix D (t) Matrix R of relation with node position (t) ；
S104: updating topological structure data of the power distribution network, and inputting a switch state vector S after the action (t+1) ；
S105: analyzing the affected type of the node: calculating a disconnection switch positioning vector and an action node positioning vector, matching with a node upstream and downstream position relation matrix, and calculating a-f type node marking positioning vectors;
s106: the topology change analysis and calculation process comprises the following steps: firstly, processing a class a node row and an upstream adjacent switch column, then processing an action switch column, and finally, processing b-f class node rows to obtain a node switch association matrix C in a new topology state (t+1) ；
S107: according to the node switch incidence matrix C (t+1) Calculating the adjacency matrix B at this time (t+1) Power supply path matrix D (t+1) Matrix R of relation with node position (t+1) And output C (t+1) 、B (t+1) 、D (t+1) and R(t+1) ；
S108: judging whether the search of the switch action feasible region is finished or not: if not, returning to the step S104; otherwise, ending the search.
3. The self-healing recovery method for the distributed power distribution network, which takes static information and dynamic topology into account, according to claim 2, is characterized in that: node switch association matrix c= (C) ij ) m×(m+n) Wherein the row index node, the column index segment and the tie switch;
element C in node switch association matrix C ij The definition is as follows:
in the formula ：cij Representing elements in the node switch association matrix C; the operator Λ represents a logical AND, i.e. the condition needs to be satisfied simultaneously; operator
The representation is included, i.e. the relationship between the two types of electrical units; />
Representation switchSwitch S i Ps is ps j Is defined by a boundary of (2); operator→representation direction for reflecting the direction of the vector; i S i →ps j Indicating the current flowing through S i Inflow ps j The method comprises the steps of carrying out a first treatment on the surface of the Operator->
The indication is not included, i.e. the indication switch is not a power supply segment boundary; omega represents a virtual joint symbol;
the node positional relationship matrix is expressed as r= (R ij ) m×m Wherein both rows and columns index nodes;
r ij representing the position of node i relative to node j, r when node i is the upstream node of node j ij -1; when node i is a downstream node of node j, r ij ＝1；
Taking absolute values of all elements of the power supply path matrix D, and calculating r according to the following formula ij ：
in the formula ：rij Representing the position of node i relative to node j; d, d kj 、d ki Representing factors in the power supply path matrix; if () represents a logical formula.
4. The self-healing recovery method of the distributed power distribution network taking both static information and dynamic topology into consideration according to claim 1, wherein the power moment algorithm is improved, the degree of influence of the inter-loop relationship on the power moment algorithm can be analyzed, and a corresponding strategy is generated by utilizing the topology and the electrical property of the loop, so that a candidate switch set is formed.
5. The self-healing recovery method of a distributed power distribution network taking both static information and dynamic topology into account as claimed in claim 1, wherein in step S2, an objective function of a static reconstruction model of the power distribution network is as follows:
wherein: Δp represents the sum of line losses; m is m j Representing the number of branches in the segmented contact model; k (k) ei Representing branch e i Is a switching state of (a); r is R ei Representing branch e i Resistance of (2); i ei Representing the flow through branch e i Is set to be a current of (a);
wherein ,
in the formula ：Pei and Qei Respectively, flow through branch e i Active power and reactive power of (a); u (U) epi Representing branch e i End node ep of (2) i A voltage.
6. The self-healing recovery method for a distributed power distribution network taking both static information and dynamic topology into account according to claim 5, wherein constraint conditions of a static reconstruction model of the power distribution network are as follows:
1) And (3) load flow constraint:
in the formula ：Pepi,1 、Q epi,I Respectively represent node ep i Active and reactive injection power of (a); m is m j Representing the number of branches in the segmented contact model; u (U) epi Representing node ep i Is set to the voltage amplitude of (1); θ ij ＝θ i -θ j Representing node ep i And ep j Is a phase angle difference of (2); g ij 、b ij Respectively represent node ep i And ep j Conductance and susceptance of the inter-line; p (P) epi 、Q epi Representation sectionPoint ep i Load active and reactive power of (a);
2) Branch current constraint, node voltage constraint:
U epi,min ≤U epi ≤U epi,max ；
0≤I ei ≤I ei,max ；
in the formula ：Uepi,min 、U epi,max Representing node ep i Minimum and maximum values of voltage; i ei,max Representing branch e i Maximum value of the current amount.
7. The self-healing recovery method of a distributed power distribution network taking both static information and dynamic topology into account as claimed in claim 6, wherein the objective function of the dynamic reconstruction model of the power distribution network is as follows:
in the formula ：md Representing a real-time topology G r (n, m) number of branches; l represents the real-time topology G r A branch connecting node i and node j; n is n Gr Representing a real-time topology G r The number of nodes included; z l Representing a switching state quantity; p (P) i 、
Representing the active and reactive power injected into the inode, respectively; v (V) i 、V ref Respectively representing the actual operating voltage and the rated voltage of the i node; n is n Gr Representing a real-time topology G r The number of nodes included; r is (r) l And represents the branch impedance of l.
8. The self-healing recovery method of a distributed power distribution network taking both static information and dynamic topology into account as claimed in claim 7, wherein constraint conditions of a dynamic reconstruction model of the power distribution network are as follows:
S l ≤S lmax ；
V imin ≤V i ≤V max ；
g r ∈T Gr ；
in the formula ：Psi 、Q si Respectively represent real-time topology G r Injecting active power and reactive power into the middle node i; p (P) Li 、Q Li Representing the active and reactive load of node i; s is S I Representing a real-time topology G r The actual capacity of the medium flow branch l, max and min representing the upper and lower limits; θ i Representing the voltage phase angle difference of nodes i and j; t (T) Gr Representing a real-time topology G r Is a radial topology set of (1); g r Representing a candidate topology solution; v (V) i Representing the actual operating voltage of the inode, max and min representing upper and lower limits; u (U) i Representing the voltage at node i; u (U) j Representing the voltage at node j; g i Representing a real-time topology G r ；b l Representing branch l susceptance; g l Representing the branch l conductance; z l Is the branch impedance.
9. The self-healing recovery method for the distributed power distribution network, which takes static information and dynamic topology into account, according to claim 8, is characterized in that: in step S4, a dynamic reconstruction model of the power distribution network is solved through a dynamic self-adaptive particle swarm algorithm, an optimal solution of the real-time topology is obtained, and then self-healing recovery of the distributed power distribution network is achieved based on the optimal solution of the real-time topology.
10. The self-healing recovery method of a distributed power distribution network taking both static information and dynamic topology into account as claimed in claim 9, wherein the dynamic adaptive particle swarm algorithm solves the dynamic reconstruction model of the power distribution network by:
s401: inputting standard topology parameters of the power distribution network topology, wherein the standard topology parameters comprise a segmentation branch parameter, a connection branch parameter and load node power, and obtaining network initial topology parameters according to a path search method;
initializing parameters of a self-adaptive particle swarm algorithm, wherein the parameters comprise a solution form X, population particles P and upper and lower particle limits;
s402: if the change of the initial grid of the power distribution network is detected, executing step S403, and updating the initial topological structure and parameters; otherwise, step S404 is performed;
s403: the dynamic topology analysis method updates the real-time topology: if the branch needs to be removed, layering nodes in the network, identifying a power-losing area, then recovering connectivity between the power-losing area and the main network, and updating G-G r The method comprises the steps of carrying out a first treatment on the surface of the If new branches or branch sets are integrated into the initial net rack, dynamically adding the branches, performing topology analysis, and updating G-G r The method comprises the steps of carrying out a first treatment on the surface of the Then, acquiring a real-time topology G according to a path searching method r Is a connected tree of (2)
Electric island S D And basic Loop matrix->
S404: according to the real-time topology G or G r Is a basic loop matrix L of (1) op Or L' op Automatic adjustment of solution form X-X of particle swarm algorithm r Population particle form P-P r An upper limit position of the particles, etc.;
s405: establishing a real-time topology G r And calculates P r The adaptability value of each feasible particle in the system is updated to the position of the new generation particle;
s406: steps S404 to S405 are repeated until an optimal solution of the real-time topology is obtained.
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