CN110752956B - Distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes - Google Patents

Distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes Download PDF

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CN110752956B
CN110752956B CN201911046448.6A CN201911046448A CN110752956B CN 110752956 B CN110752956 B CN 110752956B CN 201911046448 A CN201911046448 A CN 201911046448A CN 110752956 B CN110752956 B CN 110752956B
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陆建宇
叶海
侯勇
何星
刘拥军
王利锋
方兴其
胡蓉
胡朝阳
李珂
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Sgcc East China Branch
Shanghai Jiaotong University
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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    • H—ELECTRICITY
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention provides a distributed photovoltaic collection stationing and estimation optimization method based on trusted nodes, which comprises the following steps of calculating the similarity among photovoltaic nodes according to the static data of the distributed photovoltaic nodes in a region; selecting known credible photovoltaic nodes as measurement photovoltaic nodes, and grouping non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as a center according to the similarity; and estimating the output of the non-measured photovoltaic node according to the actual output, the similarity and the installed capacity of the measured photovoltaic node. The invention provides a distributed photovoltaic collection and point placement and estimation optimization method and system based on trusted nodes, provides an optimized layout of measured photovoltaic nodes of distributed photovoltaic nodes based on the trusted photovoltaic nodes, considers the actual output of the measured photovoltaic nodes and real-time estimation algorithms of other non-measured photovoltaic nodes, and can effectively estimate the real-time output of the non-measured photovoltaic nodes in an area in real time.

Description

Distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes
Technical Field
The invention belongs to the technical field of photovoltaic power generation, and particularly relates to a distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes.
Background
With the increasing limitation of conventional energy sources such as coal, oil and natural gas and the increasing prominence of environmental problems, new energy sources which are clean and environment-friendly and have renewable performance are increasingly gaining attention. The development of the new energy industry is not only an effective supplementary means of the whole energy supply system, but also an important measure for environmental management and ecological protection, and is a final energy selection meeting the sustainable development of the human society. Among them, solar energy, which has many advantages of cleanness, environmental protection, no pollution, high utilization value, no resource shortage, etc., has an irreplaceable status in the field of new energy. Specifically, compared with a centralized wind power and photovoltaic power station, the distributed photovoltaic node power generation has the advantages of flexible construction, less land resource occupation, convenience in absorption close to a load center and the like, and is rapidly developed in recent years.
However, with the rapid increase of the amount of distributed photovoltaic, the influence of the distributed photovoltaic on the power grid becomes more and more significant, and the part of the "unknown" low-voltage distributed photovoltaic output becomes increasingly non-negligible. However, because the number of distributed photovoltaic nodes is large, the single photovoltaic node is small in size and the distributed photovoltaic nodes are distributed dispersedly, real-time output collection is difficult to carry out, and great difficulty exists in management; in addition, due to the fluctuation and randomness of the output of the distributed photovoltaic nodes, the real-time output change affects the safety of the power grid, and the regulation pressure of the power grid is further increased. Therefore, there is a need to develop a method and system for effectively collecting and estimating output of corresponding data of distributed photovoltaic nodes to enhance the controllability of distributed photovoltaic characteristics.
In the prior art, one of the methods is distributed photovoltaic output prediction based on regional equivalence, and the main idea is to cluster distributed photovoltaic nodes in a region according to geographical positions and solar irradiation characteristics, to be equivalent to a plurality of virtual centralized photovoltaics, and to calculate the power generation power of the distributed photovoltaic nodes. Although the method can estimate the output condition of the distributed photovoltaic nodes, the method not only needs to rely on meteorological monitoring equipment, but also needs to acquire the panel temperature and the solar radiation value of the virtual centralized photovoltaic nodes and also needs to predict the solar irradiance and the temperature value, so that the problems that additional meteorological monitoring equipment is needed and the calculation method is complex exist. The method can reduce redundant measuring points, but the compressive sensing technology not only needs to collect a large amount of data in practical application, but also wastes a large amount of sampling resources in the compression process, and is complex and time-consuming in calculation.
It is noted that the information disclosed in this background section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.
Disclosure of Invention
In order to estimate the real-time output of the distributed photovoltaic nodes in real time, one of the purposes of the invention is to provide a distributed photovoltaic collection stationing and estimation optimization method based on the trusted nodes, and the other purpose is to provide a distributed photovoltaic collection stationing and output estimation system.
In order to achieve the purpose, the invention is realized by the following technical scheme: a distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes comprises the following steps,
s100: calculating the similarity between each photovoltaic node according to the static data of the distributed photovoltaic nodes in the region;
s200: taking M measured photovoltaic nodes in the region as credible photovoltaic nodes, setting the diagonal similarity s (i, i) of the M measured photovoltaic nodes as V, and setting the diagonal similarity s (i, i) of other N-M non-measured photovoltaic nodes as 0, wherein N is the total number of the photovoltaic nodes in the region, M is less than or equal to N, i is less than or equal to 1, and V is greater than 0;
s300: according to the similarity, grouping non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as a central point, and calculating the comprehensive deviation in the region;
s400: if the comprehensive deviation is smaller than the error threshold, the credible photovoltaic node is a measurement photovoltaic node, and grouping is finished; otherwise, executing step S500;
s500: selecting at least one of the N-M non-measurement photovoltaic nodes as a measurement photovoltaic node, and executing the step S300;
s600: and estimating the output of the non-measured photovoltaic nodes in the region according to the actual output, the similarity and the installed capacity of the measured photovoltaic nodes.
Optionally, before performing step S100, the method further includes normalizing the static data of the photovoltaic node, where the static data includes a vendor category, and the normalization method of the vendor category is as follows,
Figure BDA0002254256120000031
wherein Q represents the number of manufacturers of the distributed photovoltaic nodes in the region, and Q represents the number of manufacturers of the distributed photovoltaic nodes in the regioniAnd indicating the manufacturer category of the ith photovoltaic node.
Optionally, the static data further comprises the geographic position and the irradiation angle of the distributed photovoltaic node, wherein the geographic position and the irradiation angle are normalized by the following method,
Figure BDA0002254256120000032
Figure BDA0002254256120000033
Figure BDA0002254256120000034
wherein, (Long)i,Lati,Angi) Values representing longitude, latitude and irradiation angle of the ith photovoltaic node, (Long)max,Latmax,Angmax) (Long) represents the maximum of the longitude, latitude and irradiation angle of the distributed photovoltaic nodes in the regionmin,Latmin,Angmin) And the minimum value of the longitude, the latitude and the irradiation angle of the distributed photovoltaic nodes in the region is represented.
Alternatively, the similarity calculation method in step S100 is as follows,
Figure BDA0002254256120000035
wherein i, j represent different photovoltaic nodes within the region,
Figure BDA0002254256120000036
the static data after normalization for the photovoltaic nodes within the region.
Alternatively, the method of calculating the integrated deviation is as follows,
Figure BDA0002254256120000037
wherein L iskThe number of non-measured photovoltaic nodes included in the group of measured photovoltaic nodes k.
Optionally, at least one of the N-M non-measured photovoltaic nodes is selected as a measured photovoltaic node in step S500, including,
and selecting one non-measurement photovoltaic node as a credible photovoltaic node, taking the non-measurement photovoltaic node as a candidate measurement photovoltaic node, regrouping the non-measurement photovoltaic nodes in the area, calculating the comprehensive deviation, and taking the non-measurement photovoltaic node with the minimum comprehensive deviation as a newly added measurement photovoltaic node.
Optionally, the step S600 of estimating the output of the non-measured photovoltaic node in the area according to the actual output, the similarity and the installed capacity of the measured photovoltaic node includes the following steps,
s610: normalizing the output of each measured photovoltaic node,
Figure BDA0002254256120000041
wherein p isjTo measure the actual output of the photovoltaic node j, PjMeasuring the installed capacity of the photovoltaic node j;
s620: estimating a non-measured photovoltaic node XiThe output of (a) the (b) is,
Figure BDA0002254256120000042
wherein the content of the first and second substances,
Figure BDA0002254256120000043
is a normalized value of the actual output of the associated measured photovoltaic center node j,
Figure BDA0002254256120000044
the normalized value of the actual output of the other non-affiliated measured photovoltaic center nodes is obtained; piIs XiS (i, k) is a non-measured photovoltaic node XiAnd (4) similarity with other measured photovoltaic center nodes k, wherein lambda is the valuation weight of the center point.
Optionally, V ═ 10.
The invention also provides a distributed photovoltaic collection stationing and output estimation system, which comprises,
a similarity calculation unit: the method comprises the steps of calculating the similarity between photovoltaic nodes according to static data of distributed photovoltaic nodes in a region;
a measurement node selection unit: the method comprises the steps that firstly, trusted nodes in an area are used as measuring nodes, and other photovoltaic nodes are set as non-measuring photovoltaic nodes;
a node grouping unit: the system is used for grouping the non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as central points according to the similarity and calculating the comprehensive deviation in the region;
a grouping decision unit: the device is used for judging whether the comprehensive deviation is smaller than an error threshold value or not, and if not, the device is used for triggering a newly added measurement photovoltaic node of a measurement node amplification unit;
a measurement node amplification unit: the system comprises a plurality of non-measurement photovoltaic nodes and a plurality of measurement photovoltaic nodes, wherein the non-measurement photovoltaic nodes are used for selecting at least one from the non-measurement photovoltaic nodes as a measurement photovoltaic node;
an output estimation unit: and the method is used for estimating the output of the non-measured photovoltaic node according to the actual output, the similarity and the installed capacity of the measured photovoltaic node.
Compared with the prior art, the distributed photovoltaic collection stationing and estimation optimization method and system based on the trusted nodes have the following beneficial effects:
according to the static data of the distributed photovoltaic nodes in the region, calculating the similarity between the photovoltaic nodes, wherein the output estimation is more in line with the actual output;
according to the method, the centralized photovoltaic and some distributed photovoltaics with high technical level are used as credible photovoltaic nodes, the optimal layout of the measured photovoltaic nodes of the distributed photovoltaic nodes based on the credible photovoltaic nodes is provided, the actual output of the measured photovoltaic nodes and the real-time estimation algorithm of other non-measured photovoltaic nodes are considered, and the real-time output of the non-measured photovoltaic nodes in the region can be effectively estimated in real time.
Furthermore, the normalization algorithm of each step greatly reduces the operation complexity and improves the operation efficiency while ensuring the calculation accuracy.
Furthermore, additional meteorological monitoring equipment is not required, so that the labor cost and the resource cost are greatly saved; meanwhile, the relied static data are known data, so that the problem of low data utilization rate caused by additionally acquiring the temperature of a battery plate of the photovoltaic node, the solar radiation value and the like in real time is solved.
The invention integrates the distribution situation of actual photovoltaic nodes (stations), provides a complete solution for real-time output measurement and estimation of distributed photovoltaic by adopting a virtual-real combination mode, is convenient for comprehensively mastering the output situation of the distributed photovoltaic nodes, and provides important guarantee for safe operation of a power grid.
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Fig. 1 is a flowchart of a distributed photovoltaic collection stationing and estimation optimization method based on trusted nodes according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of the position of a 10KV distributed photovoltaic node in a certain area;
fig. 3 is a schematic grouping diagram of the first error threshold values of the 3 initial trusted photovoltaic nodes planned in fig. 2;
fig. 4 is a schematic grouping diagram of the second error threshold values of the 3 initial trusted photovoltaic nodes planned in fig. 2;
fig. 5 is a schematic structural diagram of a distributed photovoltaic collection stationing and output estimation system according to a second embodiment of the present invention;
wherein the reference numerals are as follows:
100-similarity calculation unit, 200-measurement node selection unit, 300-node grouping unit, 400-grouping decision unit, 500-measurement node amplification unit and 600-output estimation unit.
Detailed Description
The core idea of the invention is to solve the problems that the output of distributed photovoltaic nodes is difficult to acquire in real time and estimate in the prior art, and based on the credible photovoltaic nodes such as centralized photovoltaic nodes in the region and distributed photovoltaic nodes with good technical conditions according to actual working conditions, the nodes are used as measuring photovoltaic nodes, and then the distribution optimization of the measuring photovoltaic nodes is carried out according to actual construction conditions, so that the estimation of other distributed photovoltaic nodes is facilitated, and the output condition of the distributed photovoltaic is most possibly and comprehensively mastered.
In order to realize the idea, the invention provides a distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes, which comprises the following steps,
s100: calculating the similarity between each photovoltaic node according to the static data of the distributed photovoltaic nodes in the region;
s200: taking M measured photovoltaic nodes in the region as credible photovoltaic nodes, setting the diagonal similarity s (i, i) of the M measured photovoltaic nodes as V, taking other N-M photovoltaic nodes as non-measured photovoltaic nodes, and setting the diagonal similarity s (i, i) of the N-M measured photovoltaic nodes as 0, wherein N is the total number of the photovoltaic nodes in the region, M is less than or equal to N, i is less than or equal to 1, and V is greater than 0;
s300: according to the similarity, grouping non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as a central point, and calculating the comprehensive deviation in the region;
s400: if the comprehensive deviation is smaller than the error threshold, the credible photovoltaic node is a measurement photovoltaic node, and grouping is finished; otherwise, executing step S500;
s500: selecting at least one of the N-M non-measurement photovoltaic nodes as a measurement photovoltaic node, and executing the step S300;
s600: and estimating the output of the non-measured photovoltaic nodes in the region according to the actual output, the similarity and the installed capacity of the measured photovoltaic nodes.
In order to make the objects, advantages and features of the present invention clearer, a distributed photovoltaic collection and point placement and estimation optimization method and system based on trusted nodes according to the present invention are further described in detail below with reference to the accompanying drawings. It is to be noted that the drawings are in a very simplified form and are not to precise scale, which is merely for the purpose of facilitating and distinctly claiming the embodiments of the present invention. It should be understood that the drawings are not necessarily to scale, showing the particular construction of the invention, and that illustrative features in the drawings, which are used to illustrate certain principles of the invention, may also be somewhat simplified. Specific design features of the invention disclosed herein, including, for example, specific dimensions, orientations, locations, and configurations, will be determined in part by the particular intended application and use environment.
Example one
The following specifically explains a distributed photovoltaic collection stationing and estimation optimization method based on trusted nodes provided by the invention by planning M measurement photovoltaic nodes as implementation working conditions in an area with the total number of N distributed photovoltaic nodes. The method as shown in figure 1 comprises the following steps,
s100: calculating the similarity between each photovoltaic node according to the static data of the distributed photovoltaic nodes in the region;
s200: taking M measured photovoltaic nodes in the region as credible photovoltaic nodes, setting the diagonal similarity s (i, i) of the M measured photovoltaic nodes as V, taking other N-M photovoltaic nodes as non-measured photovoltaic nodes, and setting the diagonal similarity s (i, i) of the N-M measured photovoltaic nodes as 0, wherein N is the total number of the photovoltaic nodes in the region, M is less than or equal to N, i is less than or equal to 1, and V is greater than 0;
s300: according to the similarity, grouping non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as a central point, and calculating the comprehensive deviation in the region;
s400: if the comprehensive deviation is smaller than the error threshold, the credible photovoltaic node is a measurement photovoltaic node, and grouping is finished; otherwise, executing step S500;
s500: selecting at least one of the N-M non-measurement photovoltaic nodes as a measurement photovoltaic node, and executing the step S300;
s600: and estimating the output of the non-measured photovoltaic nodes in the region according to the actual output, the similarity and the installed capacity of the measured photovoltaic nodes.
Specifically, before step S100 is performed, the method further includes normalizing the static data of the photovoltaic node.
In one embodiment, the static data includes vendor categories, which are normalized as follows,
Figure BDA0002254256120000081
wherein Q represents a total number of vendor categories of the distributed photovoltaic nodes within the region, QiIndicates the vendor class of the ith photovoltaic node, qiThe value of (c) is any value of (0,1,2, …, Q). Preferably, the static data further comprises the geographic position and the irradiation angle of the distributed photovoltaic node, wherein the geographic position and the irradiation angle are normalized by the following method,
Figure BDA0002254256120000082
Figure BDA0002254256120000083
Figure BDA0002254256120000084
wherein, (Long)i,Lati,Angi) Values representing longitude, latitude and irradiation angle of the ith photovoltaic node, (Long)max,Latmax,Angmax) (Long) represents the maximum of the longitude, latitude and irradiation angle of the distributed photovoltaic nodes in the regionmin,Latmin,Angmin) And the minimum value of the longitude, the latitude and the irradiation angle of the distributed photovoltaic nodes in the region is represented.
In one embodiment of the present invention, the similarity calculation method in step S100 is as follows,
Figure BDA0002254256120000091
i, j represent different photovoltaic nodes within the region,
Figure BDA0002254256120000092
normalizing the photovoltaic nodes within the regionThe static data are described, wherein i is more than or equal to 1, and j is more than or equal to N. In this embodiment, k is an attribute sequence number of the static data, for example, in one embodiment, when k is 1,
Figure BDA0002254256120000093
representing a value after the manufacturer category normalization; when the number k is 2, the number k is,
Figure BDA0002254256120000094
a longitude normalized value representative of the geographic location; when the number k is 3, the number k,
Figure BDA0002254256120000095
a latitude normalized value representing the geographic location; when the number k is 4, the number k is,
Figure BDA0002254256120000096
representing the normalized value of the irradiation angle.
Where i is (1,2,3, …, N), V is preferably any number greater than 0. In this embodiment, it is preferable that the diagonal similarity V of the photovoltaic node is measured to have a value of 10. In one embodiment, concentrated photovoltaic and other well-conditioned distributed photovoltaic nodes within the area are used as measured photovoltaic nodes whose output is known, i.e., can be obtained by actual measurement. Namely, the initial credible photovoltaic node is a measurement photovoltaic node. And the other nodes except the credible photovoltaic node in the region are non-measurement photovoltaic nodes. Obviously, the trusted photovoltaic node and the non-measured photovoltaic node may be defined according to actual working conditions, and the present invention is not limited in this respect.
Therefore, the similarity between any two photovoltaic nodes in the region can be obtained.
In particular, in the present embodiment, the method of calculating the integrated deviation is as follows,
Figure BDA0002254256120000097
wherein L iskThe number of non-measured photovoltaic nodes included in the group of measured photovoltaic nodes k.
In one embodiment, the selecting at least one of the N-M non-measured photovoltaic nodes as a measured photovoltaic node in step S500 includes selecting one non-measured photovoltaic node as a trusted photovoltaic node, and using the trusted photovoltaic node as a candidate measured photovoltaic node, regrouping the non-measured photovoltaic nodes in the area, calculating the comprehensive deviation, and using the non-measured photovoltaic node with the minimum comprehensive deviation as a newly added measured photovoltaic node.
Specifically, referring to fig. 2, fig. 3, and fig. 4, fig. 2 is a schematic diagram of the location of a 10KV distributed photovoltaic node in a certain area, fig. 3 is a schematic diagram of grouping first error thresholds of 3 initial trusted photovoltaic nodes planned in fig. 2, and fig. 4 is a schematic diagram of grouping second error thresholds of 3 initial trusted photovoltaic nodes planned in fig. 2, where the first error threshold is greater than the second error threshold, that is: the accuracy of the force estimation in fig. 4 is higher than that in fig. 3. Namely: the more photovoltaic nodes are measured, the more accurate the output estimation of the non-measured photovoltaic nodes. As can be seen from fig. 3 and 4, each group includes one measured photovoltaic node and a plurality of non-measured photovoltaic nodes, which are photovoltaic nodes whose output is to be estimated. Further, it can be seen that although the initial plans are all 3 credible photovoltaic nodes, respectively, photovoltaic node 1, photovoltaic node 2 and photovoltaic node 3, since the error thresholds are different: when the first error threshold value is reached, two non-measurement photovoltaic nodes are expanded to serve as measurement photovoltaic nodes, namely the photovoltaic nodes 18 and the photovoltaic nodes 103, namely the number of the measurement photovoltaic nodes is finally 5, and the photovoltaic nodes in the region are divided into 5 groups by taking the 5 measurement photovoltaic nodes as centers, as shown in the attached figure 3; and when the error is the second error threshold, 5 measurement photovoltaic nodes are expanded, namely, the number of the measurement photovoltaic nodes is 8 finally, and the photovoltaic nodes in the area are divided into 8 groups by taking the 8 measurement photovoltaic nodes as the center, as shown in fig. 4. The non-measured photovoltaic nodes in fig. 3 and 4 that are wired to the same measured photovoltaic node belong to the same group.
The method for newly adding the measurement photovoltaic node by means of the comprehensive deviation is only the description of the preferred embodiment of the collection and distribution point of the present invention, and is not the limitation of the present invention. Further, as a preferred embodiment, since the manner of adding the new measurement photovoltaic node in the above embodiment is obtained by comprehensive deviation, after the new measurement photovoltaic node is determined, the step S400 may be directly executed to determine whether the comprehensive deviation is smaller than the error threshold, and the operation from the step S300 is not needed, so that the calculation efficiency may be improved. Obviously, persons skilled in the art may add new measurement photovoltaic nodes to complete the collection and distribution by other means, for example, select more measurement points where the distribution density of the photovoltaic nodes is high, and select fewer measurement points where the distribution density is low according to the distribution density of the photovoltaic nodes.
Preferably, in this embodiment, the step S600 of estimating the contribution of the non-measured photovoltaic nodes in the area according to the actual contribution, the similarity and the installed capacity of the measured photovoltaic nodes includes the following steps,
s610: normalizing the output of each measured photovoltaic node,
Figure BDA0002254256120000111
wherein p isjTo measure the actual output of the photovoltaic node j, PjMeasuring the installed capacity of the photovoltaic node j;
s620: estimating said intra-group non-measured photovoltaic node XiThe output of (a) the (b) is,
Figure BDA0002254256120000112
wherein, the non-measurement photovoltaic node XiAnd the photovoltaic node j belongs to the measurement photovoltaic node j.
Figure BDA0002254256120000113
Is a normalized value of the actual output of the associated measured photovoltaic center node j,
Figure BDA0002254256120000114
the normalized value of the actual output force of the photovoltaic center node k is measured for other non-affiliated points; piIs XiS (i, k) is a non-measured photovoltaic node XiAnd (4) similarity with other measured photovoltaic nodes k, wherein lambda is the valuation weight of the central point.
Example two
This embodiment provides a distributed photovoltaic collection, stationing and output estimation system, as shown in fig. 5, comprising,
the similarity calculation unit 100: the method comprises the steps of calculating the similarity between photovoltaic nodes according to static data of distributed photovoltaic nodes in a region;
measurement node selection unit 200: initially, taking a trusted node in an area as a measurement node, and setting other photovoltaic nodes as non-measurement photovoltaic nodes;
node grouping unit 300: the system is used for grouping the non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as central points according to the similarity and calculating the comprehensive deviation in the region;
grouping decision unit 400: the device is used for judging whether the comprehensive deviation is smaller than an error threshold value or not, and if not, the device is used for triggering a newly added measurement photovoltaic node of a measurement node amplification unit;
measurement node amplification unit 500: the system comprises a plurality of non-measurement photovoltaic nodes and a plurality of measurement photovoltaic nodes, wherein the non-measurement photovoltaic nodes are used for selecting at least one from the non-measurement photovoltaic nodes as a measurement photovoltaic node;
output estimation unit 600: and the method is used for estimating the output of the non-measured photovoltaic nodes in the group according to the actual output, the similarity and the installed capacity of the measured photovoltaic nodes.
In this embodiment, when the method for distributed photovoltaic collection, point placement and output estimation in the first embodiment is in operation, please refer to the first embodiment, which is not described herein again.
In summary, the invention mainly considers the characteristics of weak technical level and difficult management of the distributed photovoltaic power station, and provides a method for optimizing distribution of the measuring points and estimating output according to the actual construction condition by taking the nodes as determined credible measuring points aiming at the centralized photovoltaic in the area and other distributed photovoltaic points with good technical conditions in the actual condition. Namely, the initial credible nodes are all measurement nodes, then the measurement nodes are continuously added according to the similarity and error requirements, and the non-measurement nodes are grouped into a certain measurement node. And finally, estimating the output of the non-measurement node. Therefore, other distributed photovoltaic points can be estimated, and the output situation of the distributed photovoltaic can be mastered comprehensively.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
In summary, the foregoing embodiments describe in detail different configurations of the distributed photovoltaic collection and estimation optimization method and system based on trusted nodes, and certainly, the foregoing description is only a description of the preferred embodiments of the present invention, and does not limit the scope of the present invention in any way.

Claims (9)

1. A distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes is characterized by comprising the following steps,
s100: calculating the similarity between each photovoltaic node according to the static data of the distributed photovoltaic nodes in the region;
s200: taking M measured photovoltaic nodes in the region as credible photovoltaic nodes, setting the diagonal similarity s (i, i) of the M measured photovoltaic nodes as V, and setting the diagonal similarity s (i, i) of other N-M non-measured photovoltaic nodes as 0, wherein N is the total number of the photovoltaic nodes in the region, M is less than or equal to N, i is less than or equal to 1, and V is greater than 0;
s300: according to the similarity, grouping other non-measured photovoltaic nodes by taking the measured photovoltaic node as a central point, and calculating the comprehensive deviation in the region;
s400: if the comprehensive deviation is smaller than the error threshold, the credible photovoltaic node is a measurement photovoltaic node, and grouping is finished; otherwise, executing step S500;
s500: selecting at least one of the N-M non-measurement photovoltaic nodes as a measurement photovoltaic node, and executing the step S300;
s600: and estimating the output of the non-measured photovoltaic nodes in the region according to the actual output, the similarity and the installed capacity of the measured photovoltaic nodes.
2. The distributed photovoltaic collection, stationing and estimation optimization method based on trusted nodes according to claim 1, further comprising, before performing step S100, normalizing the static data of the photovoltaic nodes, wherein the static data includes a vendor category, and the normalization method of the vendor category is as follows,
Figure FDA0003422929980000011
wherein Q represents the number of manufacturers of the distributed photovoltaic nodes in the region, and Q represents the number of manufacturers of the distributed photovoltaic nodes in the regioniAnd indicating the manufacturer category of the ith photovoltaic node.
3. The distributed photovoltaic collection and estimation optimization method based on the trusted nodes as claimed in claim 2, wherein the static data further comprises geographic positions and irradiation angles of the distributed photovoltaic nodes, wherein the normalization method of the geographic positions and the irradiation angles is as follows,
Figure FDA0003422929980000021
Figure FDA0003422929980000022
Figure FDA0003422929980000023
wherein, (Long)i,Lati,Angi) Values representing longitude, latitude and irradiation angle of the ith photovoltaic node, (Long)max,Latmax,Angmax) (Long) represents the maximum of the longitude, latitude and irradiation angle of the distributed photovoltaic nodes in the regionmin,Latmin,Angmin) And the minimum value of the longitude, the latitude and the irradiation angle of the distributed photovoltaic nodes in the region is represented.
4. The distributed photovoltaic collection and estimation optimization method based on trusted nodes according to claim 3, wherein the similarity calculation method in step S100 is as follows,
Figure FDA0003422929980000024
wherein i, j represent different photovoltaic nodes within the region,
Figure FDA0003422929980000025
and
Figure FDA0003422929980000026
the normalized static data vectors of the ith and j photovoltaic nodes in the region are respectively, k is the attribute serial number of the static data, and the value ranges are 1,2,3 and 4; when the number k is 1, the number k is,
Figure FDA0003422929980000027
respectively representing the normalized values of the manufacturer categories of the ith and j photovoltaic nodes in the region; when the number k is 2, the number k is,
Figure FDA0003422929980000028
respectively representing longitude-normalized values of the geographic positions of ith and j photovoltaic nodes in the area; when the number k is 3, the number k,
Figure FDA0003422929980000029
a latitude-normalized value representing the geographic location of an ith, jth photovoltaic node within the region; when the number k is 4, the number k is,
Figure FDA00034229299800000210
and representing the value of the irradiation angle normalization of the ith and j photovoltaic nodes in the region.
5. The distributed photovoltaic collection and estimation optimization method based on the credible nodes is characterized in that the method for calculating the comprehensive deviation is as follows,
Figure FDA00034229299800000211
wherein L iskThe number of non-measured photovoltaic nodes included in the group of measured photovoltaic nodes k.
6. The distributed photovoltaic collection stationing and estimation optimization method based on trusted nodes as claimed in claim 1, wherein the step S500 is performed by selecting at least one of N-M non-measured photovoltaic nodes as a measured photovoltaic node, including,
and selecting one non-measurement photovoltaic node as a candidate measurement photovoltaic node, regrouping the non-measurement photovoltaic nodes in the area, calculating the comprehensive deviation, and taking the non-measurement photovoltaic node with the minimum comprehensive deviation as a newly added measurement photovoltaic node.
7. The distributed photovoltaic collection and distribution and estimation optimization method based on trusted nodes as claimed in claim 1, wherein said step S600 of estimating the output of non-measured photovoltaic nodes in the area according to the actual output, similarity and installed capacity of said measured photovoltaic nodes comprises the following steps,
s610: normalizing the output of each measured photovoltaic node,
Figure FDA0003422929980000031
wherein p isjTo measure the actual output of the photovoltaic node j, PjMeasuring the installed capacity of the photovoltaic node j;
s620: estimating a non-measured photovoltaic node XiThe output of (a) the (b) is,
Figure FDA0003422929980000032
wherein the content of the first and second substances,
Figure FDA0003422929980000033
is a normalized value of the actual output of the associated measured photovoltaic center node j,
Figure FDA0003422929980000034
the normalized value of the actual output of the other non-affiliated measured photovoltaic center nodes is obtained; piIs XiS (i, k) is a non-measured photovoltaic node XiAnd (4) similarity with other measured photovoltaic center nodes k, wherein lambda is the valuation weight of the center point.
8. The distributed photovoltaic collection and estimation optimization method based on trusted nodes according to any one of claims 1 to 7, wherein V is 10.
9. A distributed photovoltaic collection, stationing and output estimation system, comprising,
a similarity calculation unit: the method comprises the steps of calculating the similarity between photovoltaic nodes according to static data of distributed photovoltaic nodes in a region;
a measurement node selection unit: the method comprises the steps that firstly, trusted nodes in an area are used as measuring nodes, and other photovoltaic nodes are set as non-measuring photovoltaic nodes;
a node grouping unit: the system is used for grouping the non-measurement photovoltaic nodes by taking the measurement photovoltaic nodes as central points according to the similarity and calculating the comprehensive deviation in the region;
a grouping decision unit: the device is used for judging whether the comprehensive deviation is smaller than an error threshold value or not, and if not, the device is used for triggering a newly added measurement photovoltaic node of a measurement node amplification unit;
a measurement node amplification unit: the system comprises a plurality of non-measurement photovoltaic nodes and a plurality of measurement photovoltaic nodes, wherein the non-measurement photovoltaic nodes are used for selecting at least one from the non-measurement photovoltaic nodes as a measurement photovoltaic node;
an output estimation unit: and the method is used for estimating the output of the non-measured photovoltaic nodes in the region according to the actual output, the similarity and the installed capacity of the measured photovoltaic nodes.
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