CN111049176B - Method for selecting black start network reconstruction optimal path - Google Patents

Abstract

The invention relates to a method for selecting a black start network reconstruction optimal path, which comprises the steps of firstly taking power grid switching stations, network hub substations and hydropower stations in all regions as nodes in a black start process, taking connection lines among the power grid switching stations, the network hub substations and the hydropower stations as edges, comprehensively considering important loads, branch parameters and the like in the black start process, weighting each edge to form a starting node vector, a terminating node vector and an edge weight vector, forming a sparse matrix of an association matrix, building a directed graph object, displaying each path weight, finally solving a shortest path from a designated starting node to a destination and marking the shortest path. The invention has the capability of selecting the optimal path of the network reconstruction in the black start process, searches the optimal path for the black start network reconstruction, reduces the black start time, lays a foundation for the subsequent comprehensive load recovery of the power grid, and has important significance for the whole black start process.

Description

Method for selecting black start network reconstruction optimal path

Technical Field

The invention belongs to the technical field of operation of power systems, and particularly relates to a method for selecting a black start network reconstruction optimal path.

Background

The power supply recovery after the power system has a power failure is a very complex and time-consuming work, and the large-area power failure of the system is a low-probability event, and no matter the dispatcher or operators of a power plant and a transformer substation have too much actual experience, so that each power grid is required to make a system recovery scheme of black start or off-area power receiving start in advance according to respective actual networks, and preparation for recovery scheduling is made. After the whole system is powered off or large-area regional power off, the recovery time is longer and more complex, main contradictions and targets of the system are different in different periods in the recovery process, the main task is to recover the power generation capacity of the system in the initial stage of recovery, the main task is to recover the line to construct a main network frame of the system in the middle stage, and finally, the large-scale load recovery is realized. Thus, it can be divided into 3 stages: a black start phase, a network reconfiguration phase and a load recovery phase. And in the network reconstruction stage, on the basis of successful implementation of initial black start, the recovery process of the main network frame of the whole power grid is realized through the rapid recovery of power plants, important loads, network hub substations and key circuits. At present, a great deal of beneficial results are achieved through research on the network reconfiguration problem. Firstly, the network reconstruction is respectively solved according to the serial and parallel power transmission stages. Secondly, the reverse search optimizes the line recovery sequence for the algorithm of the strategy aiming at the process of recovering the system from the tree network to the ring network. Thirdly, modeling the network reconstruction as a problem of solving a local minimum tree, and optimizing a power transmission path with high correlation with the network reconstruction. Most of the algorithms proposed above focus on the optimization of the power transmission path or the optimization of the operation sequence, without considering the limitation of the start-up time of the unit, the black start process is not fast enough.

Disclosure of Invention

The invention aims to solve the defects of the prior art, provides a method for selecting the optimal path reconstructed by the black start network, reduces black start time by finding out the optimal path among designated nodes, lays a foundation for the subsequent comprehensive load recovery of a power grid, and has great significance in the whole black start process.

The aim of the invention is realized by the following technical scheme:

a black start network reconstruction optimal path selection method comprises the following steps:

step one: and taking the power grid switching stations, the network hub substations and the hydropower stations in all regions as nodes in the black start process, taking connecting lines among the power grid switching stations, the network hub substations and the hydropower stations in all regions as edges, comprehensively considering important loads and branch parameter conditions in the black start process, and weighting each edge to form an initial node vector S, a termination node vector E and an edge weight vector W.

Wherein, the initial node vector S= [123 … n-1]; termination node vector e= [3 … … n ]; the edge weight vector w= [ W1W 2W 3 W4...wm ]. Wherein n represents the total number of power grid switching stations, network hub substations and hydropower station nodes in each region; w1, W2, W3, W4.. Wm are weights given to each side by comprehensively considering important loads and branch parameter conditions in the black start process, the weights given to key lines directly connected with power plants, important loads and network hub substations are larger, and the weights given to lines not directly connected with regional power grid main network frames are smaller.

Step two: and step one, analyzing the regional power grid to form an initial node vector, an end node vector and an edge weight vector, and constructing a correlation sparse matrix DG.

Where dg=spark (S, E, W). It is composed of S [ ] vector and E [ ] vector and edge weight vector W [ ], DG is a matrix of N3, the first row represents node start point, the second row represents node end point, and the third row is weight. S, E, W are the initial node vector, the final node vector, and the edge weight vector, respectively, constructed in step one.

Step three: and establishing a regional power grid directed graph object P and displaying the weight of each path of the regional power grid.

Wherein p=biograph (DG, [ ]). DG is the association sparse matrix in step two, here as the connection matrix of the graph; [] A name is identified for the node. The display of the path weights of the power grid is expressed as view (p) and is used for displaying the edge weights of the paths of the regional power grid.

Step four: and solving the shortest path from the designated starting node to the destination of the regional power grid.

Wherein the shortest path sought is denoted as [ dist, path, pred ]. dist is the shortest distance between designated nodes; path is the path node through which the shortest distance passes; pred refers to the previous node of the target node in the shortest path from the starting node to the target node.

Step five: the shortest path from the designated originating node to the target node is indicated.

And marking the shortest paths from the designated nodes to the target nodes of the regional power grid on the basis of the first step, the second step, the third step and the fourth step.

The method comprises the steps of firstly taking power grid switching stations, network hub substations and hydropower stations in all areas as nodes in a black start process, taking connection lines among the power grid switching stations, the network hub substations and the hydropower stations as edges, comprehensively considering important loads, branch parameters and the like in the black start process, weighting each edge to form a starting node vector, a terminating node vector and an edge weight vector, forming a sparse matrix of an association matrix, building a directed graph object, displaying each path weight, and finally solving and marking the shortest path from a designated starting node to a destination. The invention has the capability of selecting the optimal path of the network reconstruction in the black start process, searches the optimal path for the black start network reconstruction, reduces the black start time, lays a foundation for the subsequent comprehensive load recovery of the power grid, and has important significance for the whole black start process.

The method adopted by the invention is to select the optimal path reconstructed by the black start network. The method can find the optimal path for the black start network reconstruction, reduce the black start time and lay a foundation for the subsequent comprehensive recovery load of the power grid.

Drawings

FIG. 1 is a schematic diagram of the method of the present invention;

FIG. 2 is a main wiring diagram of a 110kV system of a Yangtze river power grid;

FIG. 3 is a schematic diagram of a network reconstruction optimal path in a water region of a Yangtze river network;

fig. 4 is a schematic diagram of an optimal path for network reconstruction in the tarmac region of the Yangtze river power grid;

Detailed Description

As shown in fig. 1, the method for selecting the optimal path for reconstructing the black start network according to the present invention specifically includes the following steps:

step one: and taking the power grid switching stations, the network hub substations and the hydropower stations in all regions as nodes in the black start process, taking connecting lines among the power grid switching stations, the network hub substations and the hydropower stations in all regions as edges, comprehensively considering important loads, branch parameters and other conditions in the black start process, and weighting each edge to form an initial node vector S, a termination node vector E and an edge weight vector W.

Wherein, the initial node vector S= [123 … n-1]; termination node vector e= [3 … … n ]; the edge weight vector w= [ W1W 2W 3 W4...wm ]. Wherein n represents the total number of power grid switching stations, network hub substations and hydropower station nodes in each region; w1, W2, W3, W4.. Wm, etc. are weights assigned to each side by comprehensively considering the important load, branch parameters, etc. in the black start process, the weights assigned to the key lines directly connected with the power plant, the important load and the network hub substation are larger, and the weights assigned to the lines not directly connected with the regional power grid backbone frame are smaller.

Step two: and step one, analyzing the regional power grid to form an initial node vector, an end node vector and an edge weight vector, and constructing a correlation sparse matrix DG.

Where dg=spark (S, E, W). It is composed of S [ ] vector and E [ ] vector and edge weight vector W [ ], DG is a matrix of N3, the first row represents node start point, the second row represents node end point, and the third row is weight. S, E, W are the initial node vector, the final node vector, and the edge weight vector, respectively, constructed in step one.

Step three: and establishing a regional power grid directed graph object P and displaying the weight of each path of the regional power grid.

Wherein p=biograph (DG, [ ]). DG is the association sparse matrix in step two, here as the connection matrix of the graph; [] A name is identified for the node. The display of the path weights of the power grid is expressed as view (p) and is used for displaying the edge weights of the paths of the regional power grid.

Step four: and solving the shortest path from the designated starting node to the destination of the regional power grid.

Wherein the shortest path sought is denoted as [ dist, path, pred ]. dist is the shortest distance between designated nodes; path is the path node through which the shortest distance passes; pred refers to the previous node of the target node in the shortest path from the starting node to the target node.

Step five: the shortest path from the designated originating node to the target node is indicated.

And marking the shortest paths from the designated nodes to the target nodes of the regional power grid on the basis of the first step, the second step, the third step and the fourth step.

The method is applied to the actual power grid structure of the Yunnan Yangjiang power grid, and a good effect is achieved.

According to the 110kV system main wiring diagram of the Yangtze river power grid, the 110kV system main wiring diagram of the Yangtze river power grid is divided into 4 areas, namely a water filtering area, a Fugong area, an Lanping area and a gong mountain area. And (3) testing the black start network reconstruction optimal path of the filter water patch area and the tarmac patch area by using the method to find out the black start network reconstruction optimal path of each patch area.

Case 1

According to the method, a grid switching station, a network hub transformer substation and a hydropower station in a lukewater region are used as nodes in a black start process, connecting lines among the grid switching station, the network hub transformer substation and the hydropower station in each region are used as edges, important loads, branch parameters and the like in the black start process are comprehensively considered, weights are assigned to each edge to form a starting node vector, a terminating node vector and an edge weight vector, a sparse matrix of an association matrix is formed, a directed graph object is built again, each path weight is displayed, and finally the shortest path from a designated starting node to a destination is obtained and marked. In the case, a Minghe power station is designated as a starting node, a Chinesemese secondary power station is taken as a termination node, and an optimal path in a black start process is searched. The results are shown below:

dist＝132.4640

path＝134910

pred＝0NaN 13443349

wherein, 1, 3, 4, 9 and 10 nodes respectively represent a Minghe power station, a six-warehouse center-to-220 kV Chongren-to-scale rod switching station and a Chiga primary power station of a lushui region. The result shows that the nodes through which the optimal recovery path passes are respectively a Minghe power station, a six-warehouse center-to-220 kV Chongren transformer, a weighing pole switching station and a Chi Gaa primary power station in a water film area, and the total length of the path is 132.4640kM.

Case 2

According to the method, a 110kV apron area system of a Yangtze river power grid is tested, a power grid switching station, a network hub transformer substation and a hydropower station in an apron area are used as nodes in a black start process, connecting lines among the power grid switching station, the network hub transformer substation and the hydropower station are used as edges, important loads, branch parameters and the like in the black start process are comprehensively considered, weights are assigned to each edge to form an initial node vector, a termination node vector and an edge weight vector, a sparse matrix of an association matrix is formed, a directed graph object is built, each path weight is displayed, and finally a shortest path from an appointed initial node to an end point is calculated and marked. In the case, an external marble power grid is designated as an initial node, a Laga Lu He power station is taken as a termination node, and an optimal path in the black start process is searched. The results are shown below:

dist＝158.7310

path＝1245910

pred＝0122446759

wherein 1, 2, 4, 5, 9 and 10 are respectively an external marble grid of a tarmac zone, 220kV tarmac change, a tarmac center change, huang Mu change, a medium-row switching station and a Laga Lu He power station. The result shows that the nodes through which the optimal recovery path passes are respectively an external marble grid of a tarmac zone, a 220kV tarmac change, a tarmac center change, a Huang Mu change, a medium-row switching station and a Laga Lu He power station, and the total length of the path is 158.7310kM.

While the present invention has been described in detail with reference to the drawings, the present invention is not limited to the above embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (1)

1. A black start network reconstruction optimal path selection method is characterized by comprising the following steps:

step one: taking power grid switching stations, network hub substations and hydropower stations in all areas as nodes in the black start process, taking connecting lines among the power grid switching stations, the network hub substations and the hydropower stations in all areas as edges, comprehensively considering important loads and branch parameter conditions in the black start process, and weighting each edge to form an initial node vector S, a termination node vector E and an edge weight vector W;

wherein, the initial node vector S= [123 … n-1]; termination node vector e= [3 … … n ]; the edge weight vector w= [ W1W 2W 3 W4...wm ]; wherein n represents the total number of power grid switching stations, network hub substations and hydropower station nodes in each region; w1, W2, W3, W4.. Wm are respectively the weight assigned to each side by comprehensively considering the important load and the branch parameter condition in the black start process, the weight assigned to a key line directly connected with a power plant, the important load and a network hub substation is larger, and the weight assigned to a line which is not directly connected with a regional power grid backbone frame is smaller;

step two: firstly, analyzing a regional power grid to form an initial node vector, a final node vector and an edge weight vector, and constructing a correlation sparse matrix DG;

where dg=spark (S, E, W); it is composed of S [ ] vector and E [ ] vector and edge weight vector W [ ], DG is a matrix of N3, the first row represents node start point, the second row represents node end point, the third row is weight; s, E, W are the initial node vector, the final node vector and the edge weight vector constructed in the first step;

step three: establishing a regional power grid directed graph object P and displaying the weight of each path of the regional power grid;

wherein p=biograph (DG, [ ]); DG is the association sparse matrix in step two, here as the connection matrix of the graph; [] Identifying a name for the node; the display of the weight of each path of the power grid is expressed as view (p) and is used for displaying the edge weight of each path of the regional power grid;

step four: solving the shortest path from a designated starting node to a designated destination of a regional power grid;

wherein the shortest path sought is denoted [ dist, path, pred ]; dist is the shortest distance between designated nodes; path is the path node through which the shortest distance passes; pred refers to the previous node of the target node in the shortest path from the starting node to the target node;

step five: marking the shortest path from the designated starting node to the target node;

and marking the shortest paths from the designated nodes to the target nodes of the regional power grid on the basis of the first step, the second step, the third step and the fourth step.