A distributed photovoltaic grid planning method and site selection method

Abstract

The present invention discloses a distributed photovoltaic grid planning method and site selection method, wherein the planning method includes: obtaining network parameters of a distribution network; dividing nodes that meet a preset node electrical distance into the same preliminary cluster based on a cluster division index; traversing and calculating the electrical module function of all nodes of the distribution network based on the initial cluster center in the preliminary cluster, and dividing all nodes of the distribution network into at least one cluster to be updated based on the relationship between the node and the electrical module function; updating the cluster center of the cluster to be updated based on the node electrical distance; judging whether the local consumption capacity index of the updated cluster meets the preset requirements according to the distributed photovoltaic data and the load data of the distribution network nodes; when it is judged that the local consumption capacity index of the updated cluster meets the preset requirements and it is judged that the electrical module function of the updated cluster converges, outputting the grid planning result of the distribution network. The present invention can effectively improve the efficiency of distributed photovoltaic grid planning.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a distributed photovoltaic grid planning method and a site selection method. Background Art

[0002] Due to the rapid development of distributed photovoltaics, a large number of distributed photovoltaics will be connected to the distribution network, which will bring convenience to users, but will also have many adverse effects on the distribution network, such as making the power flow operation of the power grid more complicated; changing the fault short-circuit current; making the relay protection device prone to malfunction, etc. Therefore, in order to ensure the safe and reliable operation of the distribution network, it is imperative to scientifically configure the location and capacity of distributed photovoltaics.

[0003] With the large-scale access of distributed photovoltaic power generation to the distribution network, the proportion of distributed photovoltaic power generation in the power grid has gradually increased. When considering the research on distributed photovoltaic multi-point grid connection, if the fixed point iteration method of distributed photovoltaic single-point grid connection is selected to simulate the planning of high-proportion distributed photovoltaic power generation, the amount of simulation planning calculation will increase exponentially. As the proportion of distributed photovoltaic power generation increases, the convergence characteristic will become worse, and the planning results may have problems such as non-convergence. Therefore, it is urgent to propose a scientific and reliable method for site selection and capacity planning of high-proportion distributed photovoltaic power generation.

[0004] Existing distributed photovoltaic grid planning methods usually use fixed point iteration methods to traverse all nodes of the calculation planning model. However, when the number of distributed grid-connected networks increases and the grid-connected capacity changes, the amount of calculation increases exponentially, resulting in low efficiency of distributed photovoltaic grid simulation planning. Summary of the invention

[0005] The present invention provides a distributed photovoltaic grid planning method and a site selection method to solve the technical problem that the existing distributed photovoltaic grid planning method has low efficiency due to the exponential increase in calculation amount when the number of distributed grid-connected networks increases and the grid-connected capacity changes.

[0006] The present invention provides a distributed photovoltaic grid planning method, comprising:

[0007] Acquire network parameters of the distribution network, wherein the network parameters of the distribution network include distributed photovoltaic data and distribution network node load data;

[0008] Selecting a node electrical distance as a cluster division index, and dividing nodes that meet a preset node electrical distance into the same preliminary cluster based on the cluster division index;

[0009] Based on the initial cluster centers in the preliminary clusters, traversing and calculating the electrical modularity functions of all nodes in the distribution network, and dividing all nodes in the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity functions;

[0010] Calculate the average electrical distance of all nodes in the cluster to be updated, compare the average electrical distance with each node in the cluster to be updated to determine the closest node, and update the cluster center of the cluster to be updated with the closest node;

[0011] According to the distributed photovoltaic data and the distribution network node load data, it is determined whether the local consumption capacity index of the updated cluster meets the preset requirements;

[0012] When it is determined that the local consumption capacity index of the updated cluster meets the preset requirements and when it is determined that the electrical module function of the updated cluster converges, the grid planning result of the distribution network is output.

[0013] Furthermore, the calculation formula of the node electrical distance is as follows:

[0014]

[0015] Where ΔV is the voltage amplitude change of the node, ΔS is the injected power change of the node; Y is the power-voltage sensitivity matrix, and the element L in the i-th row and j-th column of the matrix is ij represents the voltage amplitude change of node i corresponding to the unit power injected into node j; d ij Indicates the power change of node j, the ratio of the voltage change of node j to that of node i, d ij The larger the value, the smaller the influence of node j on node i, which means the electrical distance between the two nodes is greater.

[0016] Furthermore, comparing the average value with the electrical distance of each node in the cluster to determine the closest node includes:

[0017] The average value is subtracted from the electrical distance of each node to obtain an absolute value, and the node with the smallest absolute value is determined as the closest node.

[0018] Furthermore, judging whether the local consumption capacity index of the cluster meets the preset requirements based on the distributed photovoltaic data and the distribution network node load data includes:

[0019] Inputting the distributed photovoltaic data and the distribution network node load data into the cluster after the cluster center is updated, and obtaining the distributed photovoltaic data expected to be connected in the cluster and the load data of all nodes in the cluster;

[0020] The distributed photovoltaic data expected to be connected in the cluster is subtracted from the load data of all nodes in the cluster. If the calculation result is a negative number, it is determined that the local absorption capacity index meets the preset requirements; if the calculation result is a positive number, it is determined that the absorption capacity index does not meet the preset requirements.

[0021] Furthermore, the dividing all nodes of the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity function includes:

[0022] If the current node is added to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will increase, then the current node is added to the current preliminary cluster;

[0023] If the current node is connected to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will not increase, then the current node will not be added to the current preliminary cluster;

[0024] If the current node is added to each preliminary cluster and the electrical modularity function of the preliminary cluster does not increase or decrease, the current node is used as a new preliminary cluster.

[0025] The present invention also provides a distributed photovoltaic site selection method, comprising:

[0026] Based on the distributed photovoltaic grid planning method as described above, a grid planning result of the distribution network is obtained;

[0027] A distributed photovoltaic planning model is established according to the grid planning results of the distribution network, wherein the objective function of the distributed photovoltaic planning model includes maximizing the distributed photovoltaic grid-connected active capacity, minimizing the distributed photovoltaic grid-connected loss, and measuring the voltage change of each node based on voltage sensitivity; the constraints of the distributed photovoltaic planning model include power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints, and short-circuit current constraints;

[0028] Solve the distributed photovoltaic planning model to obtain a distributed photovoltaic site selection result.

[0029] Furthermore, solving the distributed photovoltaic planning model to obtain a distributed photovoltaic site selection result includes:

[0030] The distributed photovoltaic planning model is solved according to the non-dominated sorting genetic algorithm and fuzzy decision making to obtain the distributed photovoltaic site selection result.

[0031] The present invention also provides a distributed photovoltaic grid planning device, comprising:

[0032] A network parameter acquisition module is used to acquire network parameters of the distribution network, wherein the network parameters of the distribution network include distributed photovoltaic data and distribution network node load data;

[0033] A preliminary cluster division module, used for selecting a node electrical distance as a cluster division index, and dividing nodes meeting a preset node electrical distance into the same preliminary cluster based on the cluster division index;

[0034] A cluster division module to be updated, used for traversing and calculating the electrical modularity function of all nodes in the distribution network based on the initial cluster centers in the preliminary clusters, and dividing all nodes in the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity function;

[0035] A cluster center updating module is used to obtain the average electrical distance of all nodes in the cluster to be updated, compare the average electrical distance with the electrical distance of each node in the cluster to be updated to determine the closest node, and update the cluster center of the cluster to be updated with the closest node;

[0036] A local consumption capacity index judgment module is used to judge whether the local consumption capacity index of the updated cluster meets the preset requirements according to the distributed photovoltaic data and the distribution network node load data;

[0037] The grid planning result output module is used to output the grid planning result of the distribution network when it is determined that the local consumption capacity index of the updated cluster meets the preset requirements and the electrical module function of the updated cluster converges.

[0038] The present invention also provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and the processor implements the above-mentioned distributed photovoltaic grid planning method when executing the computer program.

[0039] The present invention also provides a computer-readable storage medium, which includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distributed photovoltaic grid planning method as described above.

[0040] The embodiment of the present invention can effectively divide the nodes of the distribution network into different preliminary clusters by using the node electrical distance as an indicator of cluster division. The electrical distance is used to represent the measure of the electrical connection strength between nodes, which can truly reflect the actual operating status of the power grid, avoid unified and large-scale calculations for the entire power grid, and instead concentrate the calculations in local clusters, thereby effectively reducing the amount of calculations for distributed photovoltaic grid planning. In addition, the embodiment of the present invention can more accurately determine the clustering relationship of the nodes through the calculation of the electrical modularity function, avoid global search of all nodes, further reduce unnecessary calculations, and effectively improve the efficiency of distributed photovoltaic grid planning.

[0041] Furthermore, the embodiment of the present invention adopts an iterative updating method of the cluster center, by calculating the average electrical distance of all nodes in the cluster to be updated and comparing it with the electrical distance of the node, the closest node is quickly located as the new cluster center. The embodiment of the present invention also judges the local absorption capacity index, which can identify possible problem clusters in the early stage of planning, avoiding large-scale adjustments in the later stage, thereby improving the efficiency of the entire distributed photovoltaic grid planning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 is a schematic diagram of a distributed photovoltaic grid planning method provided by an embodiment of the present invention;

[0043] Figure 2 Another schematic diagram of the distributed photovoltaic grid planning method provided by the embodiment of the present invention is

[0044] Figure 3 is a schematic diagram of a process of a distributed photovoltaic site selection method provided by an embodiment of the present invention;

[0045] Figure 4 is a distributed photovoltaic multi-point grid-connected site selection and capacity determination flow chart provided by an embodiment of the present invention;

[0046] Figure 5 It is a structural schematic diagram of a distributed photovoltaic grid planning device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0048] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the feature. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0049] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0050] See also Figure 1 The present invention provides a distributed photovoltaic grid planning method, comprising:

[0051] S1. Obtain network parameters of the distribution network, which include distributed photovoltaic data and distribution network node load data;

[0052] S2, selecting the node electrical distance as the cluster division index, and dividing the nodes that meet the preset node electrical distance into the same preliminary cluster based on the cluster division index;

[0053] S3, based on the initial cluster center in the preliminary cluster, traverse and calculate the electrical modularity function of all nodes in the distribution network, and divide all nodes in the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity function;

[0054] S4, obtaining the average electrical distance of all nodes in the cluster to be updated, comparing the average electrical distance with the electrical distance of each node in the cluster to be updated to determine the closest node, and updating the cluster center of the cluster to be updated with the closest node;

[0055] In the embodiment of the present invention, if there is at least one closest node with an equal value, one of the closest nodes is selected to update the cluster center of the cluster to be updated.

[0056] S5. Based on the distributed photovoltaic data and the distribution network node load data, determine whether the local consumption capacity index of the updated cluster meets the preset requirements;

[0057] S6. When it is determined that the local consumption capacity index of the updated cluster meets the preset requirements and when it is determined that the electrical module function of the updated cluster converges, the grid planning result of the distribution network is output.

[0058] In the embodiment of the present invention, the K-means algorithm may be used to divide the clusters, that is, to divide the distribution network into grids.

[0059] The embodiment of the present invention can set the number of distributed photovoltaic grid-connected nodes as the number of distribution network cluster clusters, the initial cluster center is the initial virtual access node of distributed photovoltaic, and the selection of the initial cluster center is randomly selected in a relatively dispersed state; the electrical modularity index of each node to the initial cluster center node is traversed and calculated, and if the electrical module function increases, the calculation node is included in the cluster, and the nodes in each cluster can be repeatedly selected, and the cluster range can overlap; the electrical distance of each node in the cluster is summed and averaged, and the cluster center is updated. The new cluster center is the node with the electrical distance closest to the average value; the local consumption capacity index of each cluster is calculated; repeated iterations are performed to determine whether the electrical module function of each cluster tends to converge, whether each cluster center tends to be stable, and whether the local consumption of distributed photovoltaic grid-connected nodes in each cluster can meet the requirements. If so, the cluster division result is output, otherwise, the iteration is continued until each convergence condition is met, the grid processing of the distribution network is completed, and the grid planning result of the distribution network is output.

[0060] In the embodiment of the present invention, cluster division is to divide each node in the distribution network into several areas, and each area is regarded as a cluster. The embodiment of the present invention can utilize the characteristics of close connection within the cluster and loose connection between clusters to achieve internal autonomy of the cluster without interfering with other clusters, while the clusters can also cooperate with each other. The cluster division principle can ensure the rationality of the cluster, and the cluster division principle includes:

[0061] (1) Structural principle: close connections within the group facilitate collaboration, and loose connections between groups facilitate division of labor;

[0062] (2) Functional principle: the nodes within the cluster should have certain similarities or complementarities.

[0063] The basis for the division of power system clusters is the theoretical basis for the division of clusters, and the basis for the division can be as follows:

[0064] (1) Voltage level: Determine the system cluster division requirements according to different voltage levels. The high-voltage distribution network is a ring network, and the main problems are voltage over-limit and power reverse transmission; while the low-voltage distribution network is a radial network, and the main problem is voltage over-limit. Clusters are divided from top to bottom according to voltage levels and from large to small ranges.

[0065] (2) Spatial characteristics: Nodes are divided into clusters based on their actual geographical locations. The closer the geographical locations of the nodes in a cluster are, the tighter the spatial characteristics of the cluster are.

[0066] (3) Electrical characteristics: The electrical connection between nodes should be as weak as possible between clusters and as strong as possible within a cluster, so that when electrical quantity control is performed within a cluster, the impact on adjacent clusters is minimized.

[0067] The embodiment of the present invention can effectively divide the nodes of the distribution network into different preliminary clusters by using the node electrical distance as an indicator of cluster division. The electrical distance is used to represent the measure of the electrical connection strength between nodes, which can truly reflect the actual operating status of the power grid, avoid unified and large-scale calculations for the entire power grid, and instead concentrate the calculations in local clusters, thereby effectively reducing the amount of calculations for distributed photovoltaic grid planning. In addition, the embodiment of the present invention can more accurately determine the clustering relationship of the nodes through the calculation of the electrical modularity function, avoid global search of all nodes, further reduce unnecessary calculations, and effectively improve the efficiency of distributed photovoltaic grid planning.

[0068] Furthermore, the embodiment of the present invention adopts an iterative updating method of the cluster center, by calculating the average electrical distance of all nodes in the cluster to be updated and comparing it with the electrical distance of the node, the closest node is quickly located as the new cluster center. The embodiment of the present invention also judges the local absorption capacity index, which can identify possible problem clusters in the early stage of planning, avoiding large-scale adjustments in the later stage, thereby improving the efficiency of the entire distributed photovoltaic grid planning process.

[0069] In one embodiment, the modularity index based on electrical distance can be used to measure the structural strength of the cluster. The larger the value of the electrical modularity function Q, the more reasonable the partition result, the higher the coupling tightness within the cluster, the closer the connection within the cluster, and the modularity function Q does not exceed 1; the smaller Q, the lower the coupling tightness within the cluster. When all nodes are a cluster, Q = 0; when each node is a cluster, Q < 0, this result indicates that the partition result has no obvious cluster structure. The electrical modularity function formula is as follows:

[0070]

[0071] In the formula, e ij is the weight of the edge between node i and node j; k i is the sum of all edge weights of node i; m is the sum of edge weights of all nodes in the network.

[0072] In one embodiment, the calculation formula of the node electrical distance in step S2 is as follows:

[0073]

[0074] Where ΔV is the voltage amplitude change of the node, ΔS is the injected power change of the node; Y is the power-voltage sensitivity matrix, and the element L in the i-th row and j-th column of the matrix is ij represents the voltage amplitude change of node i corresponding to the unit power injected into node j; d ij Indicates the power change of node j, the ratio of the voltage change of node j to that of node i, dij The larger the value, the smaller the influence of node j on node i, which means the electrical distance between the two nodes is greater.

[0075] In the embodiment of the present invention, the sensitivity matrix can be established by inputting the node power and making the relationship of the changes of other nodes, so as to calculate the electrical distance.

[0076] In one embodiment, considering that the electrical distance between two nodes is also related to other nodes in the distribution network, the electrical distance between node i and node j after improvement is:

[0077]

[0078] Where, L ij is the electrical distance between node i and node j. The larger the value, the greater the electrical distance between nodes, and the smaller the edge weight in the corresponding modularity function.

[0079] The relationship between node edge weight and electrical distance is expressed as:

[0080] e ij =1-L ij / max(L)

[0081] Where L is the electrical distance matrix.

[0082] In one embodiment, step S3, based on the relationship between the nodes and the electrical modularity function, all nodes of the distribution network are divided into at least one cluster to be updated, including:

[0083] S31, if adding the current node to the current preliminary cluster will increase the electrical modularity function of the current preliminary cluster, then adding the current node to the current preliminary cluster;

[0084] S32: if the current node is connected to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will not increase, then the current node is not added to the current preliminary cluster;

[0085] S33: If the current node is added to each preliminary cluster and the electrical modularity function of the preliminary cluster does not increase or decrease, the current node is used as a new preliminary cluster.

[0086] In one embodiment, step S4, comparing the average value with the electrical distance of each node in the cluster to determine the closest node, includes:

[0087] The average value is subtracted from the electrical distance of each node to obtain the absolute value, and the node with the smallest absolute value is determined as the closest node.

[0088] In one embodiment, step S5, judging whether the local consumption capacity index of the cluster meets the preset requirements based on the distributed photovoltaic data and the distribution network node load data, includes:

[0089] S51, inputting the distributed photovoltaic data and the distribution network node load data into the cluster after the cluster center is updated, and obtaining the distributed photovoltaic data expected to be connected in the cluster and the load data of all nodes in the cluster;

[0090] In the embodiment of the present invention, in the scenario of high-proportion distributed photovoltaic multi-point grid connection, one of the important indicators for cluster division is the local absorption capacity indicator. The local absorption capacity indicator represents the difference between the distributed photovoltaic output and the load of each node in the cluster, which can describe the relationship between distributed photovoltaic and load, and is used to evaluate the local absorption level of distributed photovoltaic in the cluster, thereby improving the stability of the power grid and the utilization rate of light energy in the cluster. The calculation formula of the local absorption capacity indicator is as follows:

[0091] P NET_i =P pv_i -P L_i

[0092] Where PNET_i is the net load in the i-th cluster, i.e., the local consumption capacity indicator; Ppv_i is the distributed photovoltaic grid-connected capacity in cluster i; and PL_i is the total load of each node in cluster i.

[0093] S52. Subtract the distributed photovoltaic data expected to be connected in the cluster from the load data of all nodes in the cluster. If the calculation result is a negative number, it is determined that the local absorption capacity index meets the preset requirements; if the calculation result is a positive number, it is determined that the absorption capacity index does not meet the preset requirements.

[0094] In the embodiment of the present invention, when the calculation result is a negative number, the distributed photovoltaic will not have power reverse transmission; when the calculation result is a positive number, the distributed photovoltaic packaging capacity is adjusted to recalculate the local absorption capacity index.

[0095] See also Figure 2 , is another flow chart of a distributed photovoltaic grid planning method provided by an embodiment of the present invention.

[0096] The implementation of the embodiments of the present invention has the following beneficial effects:

[0097] The embodiment of the present invention can effectively divide the nodes of the distribution network into different preliminary clusters by using the node electrical distance as an indicator of cluster division. The electrical distance is used to represent the measure of the electrical connection strength between nodes, which can truly reflect the actual operating status of the power grid, avoid unified and large-scale calculations for the entire power grid, and instead concentrate the calculations in local clusters, thereby effectively reducing the amount of calculations for distributed photovoltaic grid planning. In addition, the embodiment of the present invention can more accurately determine the clustering relationship of the nodes through the calculation of the electrical modularity function, avoid global search of all nodes, further reduce unnecessary calculations, and effectively improve the efficiency of distributed photovoltaic grid planning.

[0098] Furthermore, the embodiment of the present invention adopts an iterative updating method of the cluster center, by calculating the average electrical distance of all nodes in the cluster to be updated and comparing it with the electrical distance of the node, the closest node is quickly located as the new cluster center. The embodiment of the present invention also judges the local absorption capacity index, which can identify possible problem clusters in the early stage of planning, avoiding large-scale adjustments in the later stage, thereby improving the efficiency of the entire distributed photovoltaic grid planning process.

[0099] See also Figure 3 The present invention also provides a distributed photovoltaic site selection method, comprising:

[0100] S101, based on the above-mentioned distributed photovoltaic grid planning method, obtaining a grid planning result of the distribution network;

[0101] S201. Establish a distributed photovoltaic planning model based on the grid planning results of the distribution network. The objective functions of the distributed photovoltaic planning model include maximizing the distributed photovoltaic grid-connected active capacity, minimizing the loss after the distributed photovoltaic grid-connected, and measuring the voltage change of each node based on voltage sensitivity. The constraints of the distributed photovoltaic planning model include power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints, and short-circuit current constraints.

[0102] In the embodiment of the present invention, the expressions of the various objective functions are as follows:

[0103] The objective function is to maximize the active capacity of distributed photovoltaic grid-connected:

[0104] In order to meet the power demand of users from the user's perspective, the embodiment of the present invention takes the user's installed capacity as the maximum access capacity, selects the site and determines the capacity of distributed photovoltaics under various constraints, considers that distributed photovoltaics are connected to the grid with a unity power factor, ignores the influence of distributed photovoltaic reactive power, and takes the maximum distributed photovoltaic grid-connected active capacity as the objective function:

[0105] f 1 =maxf(Ppv )=P pv P pv ≤P pv.s

[0106] Where: Ppv.s is the installed capacity of distributed photovoltaic users.

[0107] The objective function is to minimize the loss of distributed photovoltaic power generation after grid connection:

[0108] It should be noted that the distribution network is usually a ring network structure, and its power flow is unidirectional. With the access of distributed photovoltaics, the distribution system changes from a radial structure to a multi-power structure, which changes the power flow and causes changes in the loss in the distribution network. In order to reduce the network loss of the power system, the embodiment of the present invention takes the minimum loss after the distributed photovoltaic grid is connected as the objective function:

[0109]

[0110] Where: N is the number of power sources in the distribution network; PGi is the active power generated by the i-th power source; M is the number of loads in the distribution network; PLi is the active power consumed by the j-th load.

[0111] The objective function for measuring the voltage change of each node based on voltage sensitivity is:

[0112] In order to evaluate the impact of distributed photovoltaic access capacity and access location on the voltage of each node, the voltage sensitivity method is used, that is, the active power injected by distributed photovoltaics will cause the voltage of each node of the distribution network to change. The objective function of the embodiment of the present invention to measure the voltage change of each node of the system based on voltage sensitivity is:

[0113]

[0114] Where: ΔP i is the active power change of distributed photovoltaic access node i; ΔV j is the voltage change of node j; n is the distribution network node except the balancing node.

[0115] In the embodiment of the present invention, the power flow constraint condition is:

[0116]

[0117] Among them, P is , Q is are the active and reactive injection amounts of node i respectively; U i is the voltage amplitude of node i; j∈i represents all nodes directly connected to node i; G ij , B ij are the corresponding real and imaginary parts of the node admittance matrix respectively; θ ij is the phase difference between the two end nodes of branch ij.

[0118] By constructing power flow constraint conditions, the embodiment of the present invention can maintain a power balance relationship between nodes and lines when distributed photovoltaics inject power into the distribution network, thereby ensuring stable operation of the system.

[0119] In the embodiment of the present invention, the node voltage constraint condition is:

[0120]

[0121] Among them, P D , Q D are respectively the active and reactive power injected into the node by distributed photovoltaic; U is the distribution network voltage.

[0122] In the embodiment of the present invention, the distributed photovoltaic is connected to the grid near the load, and the surrounding load can be compensated according to its access capacity, the transmission capacity of the power supply line is reduced, the line voltage drop is reduced, and the voltage of the nodes around the distributed grid connection point is raised. When the distributed photovoltaic output is too large or the line is lightly loaded, the node voltage is raised too much, resulting in a high voltage phenomenon; conversely, when the distributed photovoltaic is off-grid or the line is overloaded, the line voltage drop increases, causing the node voltage to decrease, and the voltage deviation exceeds the allowable value, resulting in a low voltage phenomenon. Therefore, in order to ensure the safe and stable operation of the distribution network, the node voltage limit also becomes a factor that constrains the site selection and capacity setting of distributed photovoltaics. According to the prescribed limit of voltage deviation in GB / T 12325-2008, and the duration of high and low voltage exceeds 60s, it is indicated that the node voltage exceeds the limit.

[0123] In the embodiment of the present invention, the broadband oscillation constraint condition is:

[0124] Δf=ff N =m(P D -P ref )-0.5≤Δf≤0.5

[0125] Among them, ΔU is the node frequency deviation; f is the actual frequency of the node; f N is the system rated frequency; P D P is the distributed photovoltaic output active power; ref It is the reference output active power for distributed photovoltaic control.

[0126] In the embodiment of the present invention, since the inertia of the distributed photovoltaic power generator is smaller and the distributed photovoltaic inverter responds quickly during the grid-connection and off-grid process, the damping effect of the photovoltaic power generation system is insufficient and broadband oscillation is prone to occur. In order to suppress oscillation, a suitable distributed photovoltaic inverter can be used, and the control strategy can be optimized to ensure that it works within a normal frequency range and improve the frequency stability of the system. It can be seen from the above inverter control strategy that the droop control strategy can be used to set the active droop control coefficient m to control the grid-connected oscillation frequency.

[0127] In the embodiment of the present invention, the thermal stability constraint condition is:

[0128]

[0129] Where S is the cross-sectional area of ​​the conductor; C is the thermal stability coefficient of the conductor; T is the short-circuit time; I ij is the line short-circuit current; I L is the thermal stability current limit of the conductor, that is, the short-circuit current after the distributed photovoltaic access is less than the thermal stability current limit of the line; S N is the rated capacity of the transformer; t is the thermal stability time constant of the transformer; U N is the rated voltage of the transformer; K θ is the ambient temperature correction coefficient; p is the transformer load rate; I T It is the thermal stability current limit of the transformer, that is, the short-circuit current after the distributed photovoltaic is connected is less than the thermal stability current limit of the transformer.

[0130] In the embodiment of the present invention, referring to the technical specification of the SC model dry-type transformer manufacturer, the thermal stability limit of the transformer is shown in Table 1:

[0131] Table 1 SC type dry-type transformer thermal stability limit table

[0132] Transformer model Transformer capacity Thermal stability limit SC14-400 / 10 / 0.4 400kVA 25A SC14-630 / 10 / 0.4 630kVA 35A SC14-5000 / 35 / 10 5000kVA 300A

[0133] Refer to the "High Voltage Power Cable Ampacity Reference Manual" to obtain the cable thermal stability limits shown in Table 2:

[0134] Table 2 Thermal stability limit values ​​of polyvinyl chloride cables

[0135]

[0136] In the embodiment of the present invention, the distributed photovoltaic access to the distribution network reduces the transmission capacity of the line and the load rate of the upper transformer under normal working conditions, and the problem of thermal stability exceeding the limit will not occur. However, in the case of short circuit, the influence of distributed photovoltaic on short circuit current is analyzed as auxiliary increase and reverse. The auxiliary increase short circuit current is easy to cause the downstream line to overload, and the reverse short circuit current is easy to cause the transformer to overload operation. Therefore, the distributed photovoltaic access has a certain impact on the thermal stability conditions of the line and transformer. The embodiment of the present invention can determine the thermal stability constraint conditions for the line and transformer according to DL / T 5222-2005 and GB 1094-2008.

[0137] In the embodiment of the present invention, the short-circuit current constraint condition is:

[0138]

[0139] Among them, I m The interrupting current of a switch or circuit breaker.

[0140] It should be noted that when a fault occurs in a distribution network containing distributed photovoltaics, the current at the fault point will increase, that is, when the fault occurs upstream of the distributed photovoltaic line, the distributed photovoltaic outputs short-circuit current to the short-circuit point; when the fault occurs downstream, the system output short-circuit current decreases, the distributed photovoltaic output short-circuit current increases, and the total short-circuit current increases. According to the provisions of "Q / GDW 617-2011", after the distributed photovoltaic is connected to the distribution network, the system short-circuit current does not exceed the short-circuit current limit, that is, the maximum photovoltaic short-circuit current is less than the breaking current of the switch or circuit breaker, ensuring the reliable operation of the circuit breaker, thereby determining the short-circuit current constraint condition.

[0141] S301, solving a distributed photovoltaic planning model to obtain a distributed photovoltaic site selection result.

[0142] The embodiment of the present invention simplifies the site selection and capacity determination problem of high-proportion distributed photovoltaic multi-point grid-connected to a planning problem within each cluster, increases the convergence of planning results, and thus can effectively improve the accuracy of distributed photovoltaic site selection results.

[0143] In one embodiment, S301, solving a distributed photovoltaic planning model to obtain a distributed photovoltaic site selection result includes:

[0144] The distributed photovoltaic planning model is solved based on the non-dominated sorting genetic algorithm (NSGA-Ⅱ algorithm) and fuzzy decision making, and the distributed photovoltaic site selection results are obtained.

[0145] In an embodiment of the present invention, the process of solving the distributed photovoltaic planning model according to the non-dominated sorting genetic algorithm is as follows:

[0146] S100, using the grid planning result of the distribution network as the cluster division result, inputting the parameters of the distribution network and the cluster division result into the distributed photovoltaic planning model, encoding and setting multiple distributed photovoltaic access capacities and access nodes to form a parent population;

[0147] S200, generating offspring populations with different planning schemes by crossover and mutation of the parent population;

[0148] S300, merging the parent and child populations, sorting the large population using fast non-dominated sorting, calculating the congestion degree, and selecting a new population based on the sorting and calculation results;

[0149] S400, determining the iteration termination condition, and outputting the optimal solution set of each cluster if the iteration is stopped; otherwise, repeating steps S200 to S400;

[0150] S500, perform fuzzy decision on the optimal solution set of each cluster, and output the planning results of each cluster.

[0151] The embodiment of the present invention solves the multi-objective functions of the distributed photovoltaic planning model according to the non-dominated sorting genetic algorithm. The optimization result will form a Pareto optimal solution set. Each solution in the solution set is the optimal solution (non-inferior solution) of the planning problem. The decision maker can weigh between multiple objective functions according to the actual situation and select the most satisfactory optimization result.

[0152] In the embodiment of the present invention, the optimal planning solution decision process based on fuzzy decision is as follows:

[0153] S110, determining evaluation indicators, the number of which is m;

[0154] S210, determine the level of evaluation indicators, the number is o;

[0155] S310, determine the evaluation index weight A = (a 1 , a 2 , ..., a m ), where Σa m =1,a m >0;

[0156] S410, single factor fuzzy evaluation, for each optimal solution, calculate the membership of each indicator level, and obtain the fuzzy relationship matrix R, which is an m-row and o-column matrix. The membership function is as follows:

[0157]

[0158] Where: fij is the membership degree of the jth optimal solution to the i-th evaluation index function value; fimin is the lower limit of the i-th evaluation index function value; fimax is the upper limit of the i-th evaluation index function value;

[0159] S510, multi-index comprehensive evaluation. The weight vector A and the fuzzy relationship matrix R are used to synthesize the fuzzy comprehensive evaluation result vector B, that is, B = A·R, where the solution with the largest B value is the optimal solution.

[0160] See also Figure 4 , which is a distributed photovoltaic multi-point grid-connected site selection and capacity determination flow chart provided in an embodiment of the present invention.

[0161] See also Figure 5 Based on the same inventive concept as the above embodiment, the present invention also provides a distributed photovoltaic grid planning device, including:

[0162] A network parameter acquisition module 10 is used to acquire network parameters of the distribution network, where the network parameters of the distribution network include distributed photovoltaic data and distribution network node load data;

[0163] A preliminary cluster division module 20, configured to select a node electrical distance as a cluster division index, and divide nodes meeting a preset node electrical distance into the same preliminary cluster based on the cluster division index;

[0164] The to-be-updated cluster division module 30 is used to traverse and calculate the electrical modularity functions of all nodes in the distribution network based on the initial cluster centers in the preliminary clusters, and to divide all nodes in the distribution network into at least one to-be-updated cluster based on the relationship between the nodes and the electrical modularity functions;

[0165] The cluster center updating module 40 is used to obtain the average electrical distance of all nodes in the cluster to be updated, compare the average electrical distance with each node in the cluster to be updated to determine the closest node, and update the cluster center of the cluster to be updated with the closest node;

[0166] The local consumption capacity index judgment module 50 is used to judge whether the local consumption capacity index of the updated cluster meets the preset requirements according to the distributed photovoltaic data and the distribution network node load data;

[0167] The grid planning result output module 60 is used to output the grid planning result of the distribution network when it is determined that the local consumption capacity index of the updated cluster meets the preset requirements and the electrical module function of the updated cluster converges.

[0168] In one embodiment, the calculation formula of the node electrical distance is as follows:

[0169]

[0170] Among them, ΔV is the change in voltage amplitude of the node, ΔS is the change in injected power of the node; Y is the power-voltage sensitivity matrix, and the element Lij in the i-th row and j-th column in the matrix represents the change in voltage amplitude of node i corresponding to the unit power injected into node j; dij represents the ratio of the voltage change of node j to that of node i when the power of node j is changed. The larger the dij value, the smaller the influence of node j on node i, which means that the electrical distance between the two nodes is farther.

[0171] In one embodiment, the cluster center updating module 40 is further used for:

[0172] The average value is subtracted from the electrical distance of each node to obtain the absolute value, and the node with the smallest absolute value is determined as the closest node.

[0173] In one embodiment, the local consumption capacity index determination module 50 is further used to:

[0174] Input the distributed photovoltaic data and the distribution network node load data into the cluster after updating the cluster center, and obtain the distributed photovoltaic data expected to be connected in the cluster and the load data of all nodes in the cluster;

[0175] Subtract the distributed photovoltaic data expected to be connected in the cluster from the load data of all nodes in the cluster. If the calculation result is a negative number, it is determined that the local absorption capacity index meets the preset requirements; if the calculation result is a positive number, it is determined that the absorption capacity index does not meet the preset requirements.

[0176] In one embodiment, the to-be-updated cluster partitioning module 30 is further used for:

[0177] If the current node is added to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will increase, then the current node is added to the current preliminary cluster;

[0178] If the current node is connected to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will not increase, then the current node will not be added to the current preliminary cluster;

[0179] If the current node is added to each preliminary cluster and the electrical modularity function of the preliminary cluster does not increase or decrease, the current node is used as a new preliminary cluster.

[0180] Accordingly, an embodiment of the present invention further provides a terminal device, comprising: a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, the distributed photovoltaic grid planning method of any one of the above embodiments is implemented.

[0181] The terminal device of this embodiment includes: a processor, a memory, and a computer program and computer instructions stored in the memory and executable on the processor. When the processor executes the computer program, each step in the above embodiment 1 is implemented, for example Figure 1 Alternatively, when the processor executes the computer program, the functions of each module / unit in the above device embodiment, such as the cluster center updating module 40, are implemented.

[0182] Exemplarily, the computer program can be divided into one or more modules / units, one or more modules / units are stored in a memory and executed by a processor to complete the present invention. One or more modules / units can be a series of computer program instruction segments that can perform specific functions, and the instruction segments are used to describe the execution process of the computer program in a terminal device. For example, the cluster center update module 40 is used to obtain the average electrical distance of all nodes in the cluster to be updated, compare the average with the electrical distance of each node in the cluster to be updated to determine the closest node, and update the cluster center of the cluster to be updated with the closest node.

[0183] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, and a cloud server. The terminal device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that the schematic diagram is merely an example of a terminal device and does not constitute a limitation on the terminal device. The terminal device may include more or fewer components than shown in the diagram, or may combine certain components, or different components. For example, the terminal device may also include an input / output device, a network access device, a bus, etc.

[0184] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0185] The memory can be used to store computer programs and / or modules. The processor realizes various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function, etc.; the data storage area can store data created according to the use of the mobile terminal, etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0186] Among them, if the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electric carrier signals and telecommunication signals.

[0187] Accordingly, an embodiment of the present invention further provides a computer-readable storage medium, the computer-readable storage medium including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distributed photovoltaic grid planning method of any one of the above embodiments.

[0188] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. It is particularly pointed out that for those skilled in the art, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of protection of the present invention.

Claims

1. A distributed photovoltaic grid planning method, characterized in that: include: Acquire network parameters of the distribution network, wherein the network parameters of the distribution network include distributed photovoltaic data and distribution network node load data; Selecting a node electrical distance as a cluster division index, and dividing nodes that meet a preset node electrical distance into the same preliminary cluster based on the cluster division index; Based on the initial cluster centers in the preliminary clusters, traversing and calculating the electrical modularity functions of all nodes in the distribution network, and dividing all nodes in the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity functions; Calculate the average electrical distance of all nodes in the cluster to be updated, compare the average electrical distance with each node in the cluster to be updated to determine the closest node, and update the cluster center of the cluster to be updated with the closest node; According to the distributed photovoltaic data and the distribution network node load data, it is determined whether the local consumption capacity index of the updated cluster meets the preset requirements; When it is determined that the local consumption capacity index of the updated cluster meets the preset requirements and when it is determined that the electrical module function of the updated cluster converges, the grid planning result of the distribution network is output.

2. The distributed photovoltaic grid planning method according to claim 1, characterized in that: The calculation formula of the node electrical distance is as follows: Where ΔV is the voltage amplitude change of the node, ΔS is the injected power change of the node; Y is the power-voltage sensitivity matrix, and the element L in the i-th row and j-th column of the matrix is ij represents the voltage amplitude change of node i corresponding to the unit power injected into node j; d ij Indicates the power change of node j, the ratio of the voltage change of node j to that of node i, d ij The larger the value, the smaller the influence of node j on node i, which means the electrical distance between the two nodes is greater.

3. The distributed photovoltaic grid planning method according to claim 1, characterized in that: The step of comparing the average value with the electrical distance of each node in the cluster to determine the closest node includes: The average value is subtracted from the electrical distance of each node to obtain an absolute value, and the node with the smallest absolute value is determined as the closest node.

4. The distributed photovoltaic grid planning method according to claim 1, characterized in that: The step of judging whether the local consumption capacity index of the cluster meets the preset requirements according to the distributed photovoltaic data and the distribution network node load data includes: Inputting the distributed photovoltaic data and the distribution network node load data into the cluster after the cluster center is updated, and obtaining the distributed photovoltaic data expected to be connected in the cluster and the load data of all nodes in the cluster; The distributed photovoltaic data expected to be connected in the cluster is subtracted from the load data of all nodes in the cluster. If the calculation result is a negative number, it is determined that the local absorption capacity index meets the preset requirements; if the calculation result is a positive number, it is determined that the absorption capacity index does not meet the preset requirements.

5. The distributed photovoltaic grid planning method according to claim 1, characterized in that: The dividing all nodes of the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity function includes: If the current node is added to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will increase, then the current node is added to the current preliminary cluster; If the current node is connected to the current preliminary cluster, the electrical modularity function of the current preliminary cluster will not increase, then the current node will not be added to the current preliminary cluster; If the current node is added to each preliminary cluster and the electrical modularity function of the preliminary cluster does not increase or decrease, the current node is used as a new preliminary cluster.

6. A distributed photovoltaic site selection method, characterized in that: include: Based on the distributed photovoltaic grid planning method according to any one of claims 1 to 5, a grid planning result of a distribution network is obtained; A distributed photovoltaic planning model is established according to the grid planning results of the distribution network, wherein the objective function of the distributed photovoltaic planning model includes maximizing the distributed photovoltaic grid-connected active capacity, minimizing the distributed photovoltaic grid-connected loss, and measuring the voltage change of each node based on voltage sensitivity; the constraints of the distributed photovoltaic planning model include power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints, and short-circuit current constraints; Solve the distributed photovoltaic planning model to obtain a distributed photovoltaic site selection result.

7. The distributed photovoltaic site selection method according to claim 6, characterized in that: The step of solving the distributed photovoltaic planning model to obtain a distributed photovoltaic site selection result includes: The distributed photovoltaic planning model is solved according to the non-dominated sorting genetic algorithm and fuzzy decision making to obtain the distributed photovoltaic site selection result.

8. A distributed photovoltaic grid planning device, characterized in that: include: A network parameter acquisition module is used to acquire network parameters of the distribution network, wherein the network parameters of the distribution network include distributed photovoltaic data and distribution network node load data; A preliminary cluster division module, used for selecting a node electrical distance as a cluster division index, and dividing nodes meeting a preset node electrical distance into the same preliminary cluster based on the cluster division index; A cluster division module to be updated, used for traversing and calculating the electrical modularity function of all nodes in the distribution network based on the initial cluster centers in the preliminary clusters, and dividing all nodes in the distribution network into at least one cluster to be updated based on the relationship between the nodes and the electrical modularity function; A cluster center updating module is used to obtain the average electrical distance of all nodes in the cluster to be updated, compare the average electrical distance with the electrical distance of each node in the cluster to be updated to determine the closest node, and update the cluster center of the cluster to be updated with the closest node; A local consumption capacity index judgment module is used to judge whether the local consumption capacity index of the updated cluster meets the preset requirements according to the distributed photovoltaic data and the distribution network node load data; The grid planning result output module is used to output the grid planning result of the distribution network when it is determined that the local consumption capacity index of the updated cluster meets the preset requirements and the electrical module function of the updated cluster converges.

9. A terminal device, characterized in that: include: A processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, the distributed photovoltaic grid planning method according to any one of claims 1 to 5 is implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program; wherein, when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the distributed photovoltaic grid planning method according to any one of claims 1 to 5.

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