CN110676871A - Group division method for accessing distributed photovoltaic into rural power distribution network in large scale - Google Patents
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Abstract
The invention discloses a group division method for accessing distributed photovoltaic into a rural power distribution network in a large scale, belongs to the field of novel energy application, and particularly relates to a group division method for distributed photovoltaic, which comprises the following steps: acquiring relevant network parameters, photovoltaic power sources and load data of the power distribution network; selecting and calculating a photovoltaic group division index; calculating a similarity matrix, and establishing a photovoltaic power supply network; clustering and clustering the photovoltaic power generation units based on the improved FCM algorithm. The group division method provided by the invention has stronger source-load coupling.
Description
Technical Field
The invention belongs to the technical field of planning and control of a power distribution network of a renewable energy power source, and particularly relates to a group division method for realizing large-scale access of distributed photovoltaic to a rural power distribution network.
Background
With continuous and deep photovoltaic poverty relief and attack war in China, the distributed photovoltaic has been developed in vast rural areas, and the production and living level of farmers is greatly improved. However, the diversified lean-relieving power supply is connected to a relatively weak rural power grid, so that the transformation pressure of the rural power grid is increased sharply, and the electric energy quality of the rural power grid is deteriorated due to the characteristics of intermittence of the distributed power supply and the like. The renewable power supply is connected with the single machines in a small capacity, a large quantity and scattered geographic positions, so that the micro-grid is difficult to operate in a centralized control mode, and the group-based regulation and control mode can fully utilize the autonomous characteristics of the groups to ensure that the large-scale distributed photovoltaic power generation is connected to the grid orderly and reliably, so that the renewable power supply is an important solution for large-scale photovoltaic power supply grid connection.
In recent years, research and application of a photovoltaic group division method in the field of power systems have attracted attention. The simplest, intuitive photovoltaic group partitioning can be done according to geographical location or administrative area, but such partitioning is too coarse. For this purpose, the following classes of grouping occur: the method has the advantages that the operation characteristics of the photovoltaic power supplies are divided into groups based on the similarity of the operation characteristics of the photovoltaic power supplies, so that the problem of overhigh node voltage caused by the fact that the distributed photovoltaic power supplies are connected to a power distribution network is solved, and the influence of electrical distance on the similarity is not taken into account; the improved fuzzy C-means group division method is applied to the regional centralized photovoltaic power generation system, so that the problem that the number of groups is still required to be determined in advance and the problem of large-scale distributed photovoltaic group division is not easy to process due to the fact that the groups are in local optimization is avoided. The K-means clustering algorithm and the genetic algorithm are combined, the problem of K value selection of the K-means clustering algorithm is solved, the number of groups is reduced, and the accuracy of the equivalent model is improved.
In summary, the existing photovoltaic group division method has certain limitations in the selection of indexes and the use of algorithms, so that a group division method for accessing distributed photovoltaic into a rural power distribution network in a large scale is needed to better describe the actual operation condition of a large-scale distributed photovoltaic power generation system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a group division method for realizing large-scale access of distributed photovoltaic to a rural power distribution network, which is used for solving the problem of limitation of the current photovoltaic group division method.
A group division method for accessing distributed photovoltaic into a rural power distribution network in a large scale comprises the following steps:
s1: acquiring relevant network parameters, photovoltaic power sources and load data of the power distribution network;
s2: selecting and calculating a photovoltaic group division index;
s3: calculating a similarity matrix, and establishing a photovoltaic power supply network;
s4: clustering and clustering the photovoltaic power generation units based on the improved FCM algorithm.
Preferably, the S2 includes the steps of:
s201: compute node payload indicator PiThe modularity index PiIs represented as follows:
Pi＝Ppv,i-PL,i(15)
in the formula Pi，Ppv,i，PL,iThe net load power, the photovoltaic power output and the load power of the node i of the power distribution network are respectively. Most of photovoltaic power generation in the power distribution network is in a mode of 'spontaneous self-use and residual electricity on line', so that the node net load is taken as one of the standards for group division, and the relation of energy exchange between the nodes accessed to the photovoltaic power supply and the power distribution network can be reflected.
S202: calculating reactive power balance degree QiAnd active power balance degree PiIndex, said degree of reactive balance QiThe indices are expressed as follows:
in the formula QsupMaximum value of reactive power supply for node i, including reactive power supplied by node reactive power compensation device and supplied by part of inverterReactive power; qneedThe demand value of the reactive power of the node i not only indicates the normal reactive power demand of the node, but also includes the minimum reactive power required by regulating the voltage of the node when the photovoltaic output in the network is overhigh.
The active balance degree PiThe indices are expressed as follows:
in the formula, Pclu(t)iIs the net power value of node i in a typical time scenario; t denotes the number of time points T in a typical time scenario.
S203: calculating control mode weight psi (C) of photovoltaic power supplyi,Cj) Index, control mode weight psi (C) of the photovoltaic power supplyi,Cj) The indices are expressed as follows:
in the formula Ci，CjRespectively controlling the photovoltaic power supply of the nodes i and j of the power distribution network; pa,i，Pa,jThe photovoltaic power supply active power regulation capacity of each power distribution network node i, j is respectively.
S204: index normalization processing, wherein a normalization formula is as follows:
in the formula: x'i,mThe index value is the mth index value of the ith node after normalization; x is the number ofi,mThe mth index value of the ith node; max (x)i,m) And min (x)i,m) The maximum and minimum values in the mth index of the node i, respectively.
Preferably, the S3 includes the steps of:
s301: calculating an inter-node impact factor dijIndex, said inter-node influence factor dijThe indices are expressed as follows:
in the formula: s is a sensitivity matrix; the ith row and the jth column of elements S in the matrixijAnd injecting the voltage change value of the node i corresponding to the unit reactive power into the node j.
Calculating a sensitivity matrix S, said sensitivity matrix S being represented as follows:
in the formula: Δ V is a voltage amplitude variation; delta Q is the reactive variable quantity; Δ P is the active change.
S302: calculating the similarity between every two nodes in the power distribution network to form an initial similarity matrix, wherein the calculation formula is as follows:
in the formula: a is an initial similarity matrix; alpha is alphamIs the weight of the mth index.
S303, calculating a similarity matrix A ', wherein the similarity matrix A' is expressed as follows:
in the formula: e is an identity matrix; d is a group consisting ofijA constituent electrical distance matrix; lambda [ alpha ]1，λ2Is a weight; the more similar the two nodes, the closer the corresponding element in the similarity matrix is to 1.
Preferably, the S4 includes the steps of:
s401: and selecting an initial clustering center. And (3) grouping the 2 photovoltaic power generation units with the closest similarity into a group, and taking the middle point of the group as a first initial clustering center. And setting a minimum distance threshold value alpha between the clusters, finding out the photovoltaic power generation units with the similarity greater than alpha with the 2 photovoltaic power generation units in the first cluster through the similarity matrix A', classifying the 2 closest photovoltaic power generation units into one cluster, and taking the midpoint of the cluster as a 2 nd initial clustering center. The above steps are repeated until c initial cluster centers are determined.
S402: updating membership degree matrix mu and clustering center viSaid expression is as follows:
in the formula: sijIs the similarity between nodes i and j; c is the number of clusters; i sj-viThe I is the Euclidean distance between the similarity of the ith clustering center point and the jth photovoltaic power generation unit; m belongs to [1, ∞) as a fuzzy coefficient, and is generally 2; mu.sijAnd (3) the j-th photovoltaic power generation unit belongs to the membership value of the group corresponding to the i-th clustering center point, and the normalization regulation is satisfied:
s403, calculating a division coefficient (K) under the clustering number cpc) And class entropy (K)CE)2 evaluation indexes used for evaluating the clustering effect of the photovoltaic power generation unit and determining the optimal clustering number are expressed as follows:
Kpcthe method is used for evaluating the separation degree among different photovoltaic power generation unit clustering groups, and the larger the value is, the better the value is; kCEThe method is used for evaluating the fuzzy degree among different photovoltaic power generation unit clustering groups, and the smaller the value is, the better the value is.
S404: compare the differencesK under cluster cpc、KCEAnd determining the optimal clustering number, and determining the clustering result of the photovoltaic power generation units according to the membership matrix under the optimal clustering number.
The technical scheme of the invention has the following beneficial effects:
1. the method comprehensively considers relevant indexes of node electrical connection and power balance in the group, and the electrical connection of the nodes in the group is tight, the connection between the groups is loose, so that the operation management of the group is convenient; in power balance, the group has certain reactive power supply capacity, so that the group has certain self-regulation capacity when the node voltage exceeds the limit, and in the characteristic outside the group, the division takes the characteristic complementation between nodes as the principle, so that the group regulation and control capacity of the group can be fully exerted.
2. Considering that the FCM algorithm is sensitive to the initial clustering center, if the membership matrix is initialized unreasonably, the algorithm is easy to fall into a local optimal solution. The principle of selecting the initial clustering centers is that the distance between each initial clustering center is larger than the distance threshold value alpha, so that the initial clustering centers are selected in a plurality of feasible intervals, and the defect that the algorithm is trapped in local convergence due to too short distance of the initial clustering centers is avoided.
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The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
FIG. 1 is a flow chart of a group division method for a distributed photovoltaic large-scale access to a rural power distribution network;
FIG. 2 is a diagram of a photovoltaic power supply access distribution network;
FIG. 3 is a graph of photovoltaic output active power;
fig. 4 is a photovoltaic output reactive power curve.
Detailed Description
In order to clearly understand the technical solution of the present invention, a detailed structure thereof will be set forth in the following description. It is apparent that the specific implementation of the embodiments of the present invention is not limited to the specific details familiar to those skilled in the art. The preferred embodiments of the present invention are described in detail below, and other embodiments are possible in addition to the embodiments described in detail.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
Example 1:
the invention provides a group division method for accessing distributed photovoltaic into a rural power distribution network in a large scale, which comprises the following steps:
s1: and acquiring related network parameters, photovoltaic power sources and load data of the power distribution network. Taking a certain village in the south of the Gansu province as an example, the structure diagram of the photovoltaic power supply access distribution network of the village is shown in fig. 2.
S2: selecting and calculating a photovoltaic group division index; compute node payload indicator PiThe modularity index PiIs represented as follows:
Pi＝Ppv,i-PL,i(29)
in the formula Pi，Ppv,i，PL,iThe net load power, the photovoltaic power output and the load power of the node i of the power distribution network are respectively. Most of photovoltaic power generation in the power distribution network is in a mode of 'spontaneous self-use and residual electricity on line', so that the node net load is taken as one of the standards for group division, and the relation of energy exchange between the nodes accessed to the photovoltaic power supply and the power distribution network can be reflected.
Calculating reactive power balance degree QiAnd active power balance degree PiIndex, said degree of reactive balance QiThe indices are expressed as follows:
in the formula QsupThe maximum value of the reactive power supply for the node i comprises the reactive power provided by the node reactive power compensation device and the reactive power provided by part of inverters; qneedThe demand value of the reactive power of the node i not only indicates the normal reactive power demand of the node, but also includes the minimum reactive power required by adjusting the overvoltage of the node when the permeability of the renewable energy source in the network is too high.
The active balance degree PiIndex representationThe following were used:
in the formula, Pclu(t)iIs the net power value of node i in a typical time scenario, denoted as Pclu(1)i,Pclu(1)i,...,Pclu(1)i,...,Pclu(T)i]The power value is obtained by adding power values of all nodes under a typical time scene; t denotes the number of time points T in a typical time scenario.
Calculating control mode weight psi (C) of photovoltaic power supplyi,Cj) Index, control mode weight psi (C) of the photovoltaic power supplyi,Cj) The indices are expressed as follows:
in the formula Ci，CjRespectively controlling the photovoltaic power supply of the nodes i and j of the power distribution network; pa,i，Pa,jThe photovoltaic power supply active power regulation capacity of each power distribution network node i, j is respectively. Index normalization processing, wherein a normalization formula is as follows:
in the formula: x'i,mThe index value is the mth index value of the ith node after normalization; x is the number ofi,mThe mth index value of the ith node; max (x)i,m) And min (x)i,m) The maximum and minimum values in the mth index of the node i, respectively.
S3: and calculating a similarity matrix and establishing a photovoltaic power supply network. Calculating inter-node impact factor dijIndex, said inter-node influence factor dijThe indices are expressed as follows:
in the formula: s is a sensitivity matrix; the ith row and the jth column of elements S in the matrixijAnd injecting the voltage change value of the node i corresponding to the unit reactive power into the node j.
Calculating a sensitivity matrix S, said sensitivity matrix S being represented as follows:
in the formula: Δ V is a voltage amplitude variation; delta Q is the reactive variable quantity; Δ P is the active change.
Calculating the similarity between every two nodes in the power distribution network to form an initial similarity matrix, wherein the calculation formula is as follows:
in the formula: a is an initial similarity matrix; alpha is alphamIs the weight of the mth index.
Calculating a similarity matrix A ', the similarity matrix A' being represented as follows:
in the formula: e is an identity matrix; d is a group consisting ofijA constituent electrical distance matrix; lambda [ alpha ]1，λ2Are weights. The more similar the two nodes, the closer the corresponding element in the similarity matrix is to 1.
S4: clustering and clustering the photovoltaic power generation units based on the improved FCM algorithm. The improved FCM algorithm clusters the photovoltaic power generation units, takes different clustering numbers c, and calculates the obtained PC and CE values as shown in Table 2.
TABLE 1 clustering Effect evaluation index
When the clustering number is 8, the PC value of the clustering evaluation index is large and the CE value is small, and it is preferable to divide the photovoltaic power generation units into 8 clusters. The photovoltaic cluster division results obtained by the improved FCM clustering algorithm and the K-means clustering algorithm are shown in Table 2
Table 2 results of different methods grouping
By analyzing the results of the clusters, it can be seen that: the number of photovoltaic nodes in the network is 33, the invention is divided into 8 groups in total, and no single node is taken as one group.
Under the same power distribution network and index, photovoltaic power source nodes in two scenes are respectively grouped and divided, and the selected photovoltaic power source nodes are compared with a K-means method to respectively obtain active and reactive output curves of distributed photovoltaic power sources, as shown in figures 3 and 4.
As can be seen from the graphs in FIGS. 3 and 4, the group equivalent model established by the method is closer to the detailed model and obviously superior to the K-means equivalent model, and can reflect the operating characteristics of the photovoltaic power supply more accurately. This shows that the accuracy of the group division method is better than that of the K-means algorithm, the method has stronger adaptability, and the final obtained result is more reasonable.
The method comprehensively considers complementarity and relevance among nodes in the group, and ensures coupling relation and pressure regulating capability among the nodes on the basis of ensuring reasonable matching of loads in the group.
Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can make modifications and equivalents to the specific embodiments of the invention without departing from the spirit and scope of the invention, which is set forth in the claims appended hereto.
Claims (4)
1. A group division method for accessing distributed photovoltaic into a rural power distribution network in a large scale is characterized by comprising the following steps:
s1: acquiring relevant network parameters, photovoltaic power sources and load data of the power distribution network;
s2: selecting and calculating a photovoltaic group division index;
s3: calculating a similarity matrix, and establishing a photovoltaic power supply network;
s4: clustering and clustering the photovoltaic power generation units based on the improved FCM algorithm.
2. The group division method for the distributed photovoltaic large-scale access to the rural power distribution network according to claim 1, wherein the S2 comprises the following steps:
s201: compute node payload indicator PiThe modularity index PiIs represented as follows:
Pi＝Ppv,i-PL,i(1)
in the formula Pi，Ppv,i，PL,iThe net load power, the photovoltaic power output and the load power of the node i of the power distribution network are respectively;
s202: calculating reactive power balance degree QiAnd active power balance degree PiIndex, said degree of reactive balance QiThe indices are expressed as follows:
in the formula: qsupThe maximum value of the reactive power supply for the node i comprises the reactive power provided by the node reactive power compensation device and the reactive power provided by part of inverters; qneedFor the value of the reactive power requirement of the node i, not only indicates the normal reactive power requirement of the nodeSolving, including the minimum reactive power required by adjusting the node voltage when the photovoltaic output in the network is too high;
the active balance degree PiThe indices are expressed as follows:
in the formula, Pclu(t)iIs the net power value of node i in a typical time scenario; t represents the number of time points T in a typical time scene;
s203: calculating control mode weight psi (C) of photovoltaic power supplyi,Cj) Index, control mode weight psi (C) of the photovoltaic power supplyi,Cj) The indices are expressed as follows:
in the formula Ci，CjRespectively controlling the photovoltaic power supply of the nodes i and j of the power distribution network; pa,i，Pa,jThe photovoltaic power supply active power regulation capacity of each power distribution network node i, j is respectively;
s204: index normalization processing, wherein a normalization formula is as follows:
in the formula: x'i,mThe index value is the mth index value of the ith node after normalization; x is the number ofi,mThe mth index value of the ith node; max (x)i,m) And min (x)i,m) The maximum and minimum values in the mth index of the node i, respectively.
3. The group division method for the distributed photovoltaic large-scale access to the rural power distribution network according to claim 1, wherein the S3 comprises the following steps:
s301: calculating an inter-node impact factor dijIndex, said inter-node influence factor dijThe indices are expressed as follows:
in the formula: s is a sensitivity matrix; the ith row and the jth column of elements S in the matrixijInjecting a voltage change value of a node i corresponding to unit reactive power into a node j;
calculating a sensitivity matrix S, said sensitivity matrix S being represented as follows:
in the formula: Δ V is a voltage amplitude variation; delta Q is the reactive variable quantity; delta P is the active variation;
s302: calculating the similarity between every two nodes in the power distribution network to form an initial similarity matrix, wherein the calculation formula is as follows:
in the formula: a is an initial similarity matrix; alpha is alphamIs the weight of the mth index;
s303, calculating a similarity matrix A ', wherein the similarity matrix A' is expressed as follows:
in the formula: e is an identity matrix; d is a group consisting ofijA constituent electrical distance matrix; lambda [ alpha ]1，λ2Is a weight; the more similar the two nodes, the closer the corresponding element in the similarity matrix is to 1.
4. The group division method for the distributed photovoltaic large-scale access to the rural power distribution network according to claim 1, wherein the S4 comprises the following steps:
s401: selecting an initial clustering center; grouping 2 photovoltaic power generation units with the closest similarity into a group, and taking the midpoint of the group as a first initial clustering center; setting a minimum distance threshold value alpha between groups, finding out photovoltaic power generation units with similarity greater than alpha with 2 photovoltaic power generation units in a first group through a similarity matrix A', classifying the 2 closest photovoltaic power generation units into one group, and taking the midpoint of the group as a 2 nd initial clustering center; repeating the steps until c initial clustering centers are determined;
s402: updating membership degree matrix mu and clustering center viSaid expression is as follows:
in the formula: sijIs the similarity between nodes i and j; c is the number of clusters; i sj-viThe I is the Euclidean distance between the similarity of the ith clustering center point and the jth photovoltaic power generation unit; m belongs to [1, ∞) as a fuzzy coefficient, and is generally 2; mu.sijAnd (3) the j-th photovoltaic power generation unit belongs to the membership value of the group corresponding to the i-th clustering center point, and the normalization regulation is satisfied:
s403, calculating a division coefficient (K) under the clustering number cpc) And class entropy (K)CE)2 evaluation indexes used for evaluating the clustering effect of the photovoltaic power generation unit and determining the optimal clustering number are expressed as follows:
Kpcthe method is used for evaluating the separation degree among different photovoltaic power generation unit clustering groups, and the larger the value is, the better the value is; kCEThe fuzzy degree evaluation method is used for evaluating the fuzzy degree among different photovoltaic power generation unit clustering groups, and the smaller the value is, the better the value is;
s404: comparing K under different clusters cpc、KCEAnd determining the optimal clustering number, and determining the clustering result of the photovoltaic power generation units according to the membership matrix under the optimal clustering number.
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| CN114611842A (en) * | 2022-05-10 | 2022-06-10 | 国网山西省电力公司晋城供电公司 | Whole county roof distributed photovoltaic power prediction method |
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| CN112184052A (en) * | 2020-10-14 | 2021-01-05 | 国网河北省电力有限公司雄安新区供电公司 | Method for dividing power grid planning area |
| CN114611842A (en) * | 2022-05-10 | 2022-06-10 | 国网山西省电力公司晋城供电公司 | Whole county roof distributed photovoltaic power prediction method |
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