CN113222472A - Power distribution network grid optimization division method and system based on load clustering algorithm - Google Patents
Power distribution network grid optimization division method and system based on load clustering algorithm Download PDFInfo
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- CN113222472A CN113222472A CN202110623728.XA CN202110623728A CN113222472A CN 113222472 A CN113222472 A CN 113222472A CN 202110623728 A CN202110623728 A CN 202110623728A CN 113222472 A CN113222472 A CN 113222472A
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Abstract
The invention provides a power distribution network grid optimization division method based on a load clustering algorithm, and belongs to the technical field of power distribution network planning. The method comprises the following steps: clustering the load points according to the spatial positions, and calculating the clustered load central point A0(ii) a Selecting the distance A from the load center point0The nearest substation is taken as the load point AiThe main supply station of (1); selecting the distance A from the load center point0Nearest load center point B0The corresponding main supply station is taken as the load point AiA standby supply station; and dividing grid partitions for the load points according to the corresponding relation between the load points and the main power supply station and the standby power supply station. The invention also provides a load clustering algorithmA power distribution network grid optimization division system is disclosed.
Description
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a power distribution network grid optimization division method and system based on a load clustering algorithm.
Background
Generally, a planning area in a power distribution network is divided into a plurality of grids, and power distribution network optimization planning refers to seeking a group of optimal decision variables on the premise of meeting constraints on power supply to users and network operation, so that the sum of investment and operation cost is minimized, wherein the network operation constraints comprise radial constraints, node voltage and feeder line section current constraints.
The medium-voltage target network frame planning problem of the power distribution network has the characteristics of large scale, nonlinearity, discreteness and the like, is a large-scale mixed integer nonlinear planning problem, and mainly adopts a mathematical optimization method and an artificial intelligence search algorithm in the conventional solving method. When the mathematical optimization algorithm is used for processing the radial constraint conditions, complex parameters need to be manually set, and the developed algorithm is difficult to be practically applied due to different power distribution network parameters; the artificial intelligence search algorithm not only can generate a large number of infeasible solutions, but also is easy to fall into a local optimal solution.
The conventional power distribution network meshing has two ideas: firstly, the division randomness is high on the basis of the functional positioning, development depth or reliability requirements of a control and regulation land block, or the influence of the good and bad layout of electrical equipment (such as a transformer substation and a medium-voltage feeder) on the grid power supply reliability and economy is ignored; and secondly, the grids are divided based on the power supply capacity and range of a plurality of groups of standard wiring, but different planners can obtain completely different results because the power supply range of the standard wiring is not specifically and definitely divided, so that two grid division ideas lack mutual coordination of the power supply ranges of grids in the whole planning area, the planning concept of 'feasible technology and optimal economy' is not embodied on the basis of overall planning, the optimization scheme of grid division is difficult to obtain, and unnecessary waste is caused.
Disclosure of Invention
In view of the above, the invention provides a power distribution network grid optimal division method and system based on a load clustering algorithm, which can realize optimal division of a power supply grid in a global range, realize nearby power supply of loads, meet transfer and supply between nearby stations, reduce line loss and improve power supply reliability.
The technical scheme adopted by the embodiment of the invention for solving the technical problem is as follows:
a power distribution network grid optimization division method based on a load clustering algorithm comprises the following steps:
clustering the load points according to the spatial positions, and calculating the clustered load central point A0，
Wherein, the (x)i，yi) Is the ith load point AiCoordinates of said PiIs the ith load point AiPower, said (u, v) is said load center point A0Coordinates of (2), each type of load point AiThe corresponding load size range is [3MW,16MW ]]，
Wherein d isjFor each of said load points AiTo the load center point A0The distance of (d);
selecting the distance A from the load center point0The nearest substation is taken as the load point AiThe main supply station of (1);
selecting the distance A from the load center point0Nearest load center point B0The corresponding main supply station is taken as the load point AiA standby supply station;
and dividing grid partitions for the load points according to the corresponding relation between the load points and the main power supply station and the standby power supply station.
Preferably, the dividing the grid partition for the load point according to the corresponding relationship between the load point and the main power station and the standby power station includes:
when the load point AiAnd said load point AiWhen the standby supply stations are the same transformer substation, all the load points A are connectediAnd dividing into the same grid partition.
Preferably, the dividing the grid partition for the load point according to the corresponding relationship between the load point and the main power station and the standby power station includes:
when the load point AiAnd the load point BiThe main supply stations of (A) are the same transformer substation, and the load point AiAnd the standby station and the load point BiWhen the standby supply station is the same substation, the load point A is setiAnd the load point BiAnd dividing into the same grid partition.
The invention also provides a power distribution network grid optimization division system based on the load clustering algorithm, which comprises the following steps:
a calculation module for clustering the load points according to the spatial position and calculating the clustered load center point A0，
Wherein, the (x)i，yi) Is the ith load point AiCoordinates of said PiIs the ith load point AiPower, said (u, v) is said load center point A0Coordinates of (2), each type of load point AiThe corresponding load size range is [3MW,16MW ]]，
Wherein d isjFor each of said load points AiTo the load center point A0The distance of (d);
a selecting module for selecting the distance from the load center point A0The nearest substation is taken as the load point AiThe main supply station of (1);
the selecting module is used for selecting the distance from the load central point A0Nearest load center point B0The corresponding main supply station is taken as the load point AiA standby supply station;
and the dividing module is used for dividing grid partitions for the load points according to the corresponding relation between the load points and the main supply station and the standby supply station.
Preferably, the dividing module is configured to divide the load point A into two or more load pointsiAnd said load point AiWhen the standby supply stations are the same transformer substation, all the load points A are connectediAnd dividing into the same grid partition.
Preferably, the dividing module is configured to divide the load point A into two or more load pointsiAnd the load point BiThe main supply stations of (A) are the same transformer substation, and the load point AiAnd the standby station and the load point BiWhen the standby supply station is the same substation, the load point A is setiAnd the load point BiAnd dividing into the same grid partition.
According to the technical scheme, the power distribution network grid optimal division method and system based on the load clustering algorithm can achieve optimal division of a power supply grid in a global range, achieve nearby power supply of loads, meet switching of power supply between nearby stations, reduce line loss and improve power supply reliability.
Drawings
Fig. 1 is a flow chart of a power distribution network grid optimization division method based on a load clustering algorithm.
Fig. 2 is a structural diagram of a power distribution network grid optimization partitioning system based on a load clustering algorithm.
Detailed Description
The technical scheme and the technical effect of the invention are further elaborated in the following by combining the drawings of the invention.
The K-means algorithm is the most classical clustering method based on division and is one of ten classical data mining algorithms. The basic idea of the K-means algorithm is as follows: clustering is performed centering on k points in space, classifying the objects closest to them. And (4) gradually updating the value of each clustering center through an iterative method until the best clustering result is obtained. The algorithm has the greatest advantage of being compact and fast. The key to the algorithm is the choice of initial center and distance formula.
The invention adopts a K-means algorithm to carry out iterative calculation, calculates the load central point of the load point in the area through a clustering algorithm, and then carries out grid division in the power distribution network.
As shown in fig. 1, the method for optimally dividing the power distribution grid based on the nearby power supply and load clustering algorithm of the present invention specifically includes the following steps:
step S1, clustering the load points according to the spatial position, and calculating the clustered load center point A0As shown in equation (1):
wherein (x)i，yi) Is the ith load point AiCoordinate, PiIs the ith load point AiPower, (u, v) is the load center point A0Coordinates of (2), each type of load point AiThe corresponding load size range is [3MW,16MW ]]，
Wherein，djFor each load point AiTo the load center point A0The distance of (d);
step S2, selecting distance load center point A0The nearest substation is taken as a load point AiThe main supply station of (1);
step S3, selecting distance load center point A0Nearest load center point B0The corresponding main supply station is taken as a load point AiA standby supply station;
and step S4, dividing grid partitions for the load points according to the corresponding relation between the load points and the main supply station and the standby supply station. The specific division scheme is as follows: when the load point AiMain supply station and load point aiWhen the standby supply station is the same substation, all the load points A are connectediDividing the grid into the same grid subarea; when the load point AiMain supply station and load point BiThe main supply stations of (A) are the same transformer substation and the load point AiStandby station and load point BiWhen the standby supply station is the same substation, the load point A is connectediAnd a load point BiAnd dividing into the same grid partition.
Step S1 calculating the load center point A by K-means algorithm0The process specifically comprises the following steps:
step S11, randomly selecting k load points A from m load pointsiAs an initial cluster center;
step S12, calculating each load point AiThe distance to each cluster center, and the object is allocated to the cluster with the closest distance;
step S13, all load points AiAfter the distribution is finished, recalculating centers of the k clusters;
step S14, comparing with k clustering centers obtained by previous calculation, if the clustering centers change, jumping to step S12, and starting to converge; if no change has occurred, the process goes to step S15.
Step S15, outputting clustering result to obtain the load center point A0And (4) coordinates.
As shown in fig. 2, the present invention further provides a power distribution grid optimization partitioning system based on a load clustering algorithm, which specifically includes:
a calculating module 21, configured to cluster the load points according to the spatial positions, and calculate a clustered load center point a0The calculation method is as in the aforementioned step S1;
a selection module 22 for selecting a distance load center point A0The nearest substation is taken as a load point AiThe main supply station of (1);
a selection module 22 for selecting a distance load center point A0Nearest load center point B0The corresponding main supply station is taken as a load point AiA standby supply station;
and the dividing module 23 is configured to divide the grid partitions for the load points according to the corresponding relationship between the load points and the main supply station and the standby supply station. The specific division scheme is as follows: when the load point AiMain supply station and load point aiWhen the standby supply station is the same substation, all the load points A are connectediDividing the grid into the same grid subarea; when the load point AiMain supply station and load point BiThe main supply stations of (A) are the same transformer substation and the load point AiStandby station and load point BiWhen the standby supply station is the same substation, the load point A is connectediAnd a load point BiAnd dividing into the same grid partition.
The power distribution network grid optimal division method based on the load clustering algorithm can realize optimal division of a power supply grid in a global range, realize nearby power supply of loads, meet switching supply between nearby stations, reduce line loss and improve power supply reliability.
While the invention has been described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention.
Claims (6)
1. A power distribution network grid optimization division method based on a nearby power supply and load clustering algorithm is characterized by comprising the following steps:
press the load pointClustering spatial positions, and calculating the clustered load central point A0，
Wherein, the (x)i,yi) Is the ith load point AiCoordinates of said PiIs the ith load point AiPower, said (u, v) is said load center point A0Coordinates of (2), each type of load point AiThe corresponding load size range is [3MW,16MW ]]，
Wherein d isjFor each of said load points AiTo the load center point A0The distance of (d);
selecting the distance A from the load center point0The nearest substation is taken as the load point AiThe main supply station of (1);
selecting the distance A from the load center point0Nearest load center point B0The corresponding main supply station is taken as the load point AiA standby supply station;
and dividing grid partitions for the load points according to the corresponding relation between the load points and the main power supply station and the standby power supply station.
2. The method for optimally dividing the power distribution network grid based on the nearby power supply and load clustering algorithm according to claim 1, wherein the dividing the grid partition for the load points according to the corresponding relationship between the load points and the main supply station and the standby supply station comprises:
when the load point AiAnd said load point AiWhen the standby supply stations are the same transformer substation, all the load points A are connectediAnd dividing into the same grid partition.
3. The method for optimally dividing the power distribution network grid based on the nearby power supply and load clustering algorithm according to claim 1, wherein the dividing the grid partition for the load points according to the corresponding relationship between the load points and the main supply station and the standby supply station comprises:
when the load point AiAnd the load point BiThe main supply stations of (A) are the same transformer substation, and the load point AiAnd the standby station and the load point BiWhen the standby supply station is the same substation, the load point A is setiAnd the load point BiAnd dividing into the same grid partition.
4. A power distribution network grid optimization division system based on a nearby power supply and load clustering algorithm is characterized by comprising the following steps:
a calculation module for clustering the load points according to the spatial position and calculating the clustered load center point A0，
Wherein, the (x)i,yi) Is the ith load point AiCoordinates of said PiIs the ith load point AiPower, said (u, v) is said load center point A0Coordinates of (2), each type of load point AiThe corresponding load size range is [3MW,16MW ]]，
Wherein d isjFor each of said load points AiTo the load center point A0The distance of (d);
a selecting module for selecting the distance from the load center point A0The nearest substation is taken as the load point AiThe main supply station of (1);
the selecting module is used for selecting the distance from the load central point A0Nearest load center point B0The corresponding main supply station is taken as the load point AiA standby supply station;
and the dividing module is used for dividing grid partitions for the load points according to the corresponding relation between the load points and the main supply station and the standby supply station.
5. The system for grid-optimized partitioning of a power distribution network based on a near-sourcing and load-clustering algorithm according to claim 4,
the dividing module is used for dividing the load point AiAnd said load point AiWhen the standby supply stations are the same transformer substation, all the load points A are connectediAnd dividing into the same grid partition.
6. The system for grid-optimized partitioning of a power distribution network based on a near-sourcing and load-clustering algorithm according to claim 4,
the dividing module is used for dividing the load point AiAnd the load point BiThe main supply stations of (A) are the same transformer substation, and the load point AiAnd the standby station and the load point BiWhen the standby supply station is the same substation, the load point A is setiAnd the load point BiAnd dividing into the same grid partition.
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