Distributed photovoltaic locating and sizing method and device and terminal equipment

Abstract

The invention discloses a distributed photovoltaic locating and sizing method and device and terminal equipment. The method comprises the following steps: acquiring power data and photovoltaic access quantity of a power distribution network; wherein the power data comprises a power distribution network voltage grade and power distribution network load information; performing multi-point grid-connected calculation according to the power data and the photovoltaic access quantity to obtain a plurality of initial cluster division results of the power distribution network; wherein the initial cluster division result comprises an initial photovoltaic access capacity and an initial photovoltaic access node; processing each initial cluster division result according to a non-dominated genetic sorting algorithm to obtain a plurality of to-be-decided cluster division results corresponding to each initial cluster division result; performing fuzzy decision on each to-be-decided cluster division result to obtain an optimized cluster division result corresponding to each initial cluster division result; and adjusting the photovoltaic addressing and sizing of the power distribution network according to each optimization cluster division result. By implementing the method, the cluster division result can be optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid optimization and control, and in particular to a distributed photovoltaic site selection and capacity determination method, device and terminal equipment. Background Art

[0002] Due to the rapid development of distributed photovoltaics, a large number of distributed photovoltaics will be connected to the distribution network. With the large-scale distributed photovoltaics connected to the distribution network, the proportion of distributed photovoltaics in the power grid has gradually increased. In the study of multi-point grid connection of distributed photovoltaics, the fixed point iteration method of single-point grid connection of distributed photovoltaics is mainly used to select the site and capacity of distributed photovoltaics. This method of distributed photovoltaic access to the distribution network simulation will inevitably use numerical iteration methods for calculation, which will lead to nonlinearity of the short-circuit current, power flow and other calculation network equations of the power grid. As the penetration rate of distributed photovoltaics increases, the convergence characteristics of this fixed point iteration method will deteriorate, and the planning results will not converge in the site selection and capacity of distributed photovoltaics. As a result, the control effect is poor when the distribution network is optimized and controlled based on the planning results. Summary of the invention

[0003] The embodiments of the present invention provide a distributed photovoltaic site selection and capacity determination method, apparatus and terminal equipment, which can obtain a better optimized cluster division result and improve the effect of adjusting the distribution network based on the optimized cluster division result.

[0004] An embodiment of the present invention provides a distributed photovoltaic site selection and capacity determination method, comprising:

[0005] Obtaining power data and the number of photovoltaic connections of the distribution network; wherein the power data includes: distribution network voltage level and distribution network load information;

[0006] Perform multi-point grid connection calculation according to the power data and the number of photovoltaic access points to obtain several initial cluster division results of the distribution network; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes;

[0007] Processing each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result;

[0008] Make fuzzy decisions on the cluster division results to be decided, and obtain the optimized cluster division results corresponding to the initial cluster division results;

[0009] The photovoltaic site selection and sizing of the distribution network are adjusted based on the results of the optimized cluster division.

[0010] Furthermore, the initial cluster division results are processed according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to the initial cluster division results, including:

[0011] For each initial clustering result, a non-dominated genetic algorithm sorting operation is performed until the iteration termination condition is reached, and the clustering result corresponding to the waiting-for-decision population is used as the waiting-for-decision clustering result corresponding to the current initial clustering result;

[0012] Wherein, the non-dominated genetic algorithm sorting operation includes:

[0013] Encoding each initial photovoltaic access capacity and each initial photovoltaic access node according to the power data to generate a parent population of the current initial cluster division result;

[0014] Perform crossover mutation processing on the parent population of the current initial clustering result to generate the child population of the current initial clustering result;

[0015] Construct a population set to be processed based on the parent population and child population of the current initial cluster division result;

[0016] Perform fast non-dominated sorting on the population set to be processed, and generate a sorting result of the population set to be processed;

[0017] Calculate the crowding degree of each population in the population set to be processed;

[0018] The population to be decided is determined according to the crowding degree of each population in the population set to be processed and the sorting result of the population set to be processed.

[0019] Furthermore, after obtaining the power data of the distribution network and the number of photovoltaic connections, it also includes:

[0020] Obtain basic data of the distribution network; wherein the basic data includes: node topology relationship, voltage amplitude of each node, node admittance matrix, active power injected into the distributed photovoltaic node, reactive power injected into the distributed photovoltaic node, distribution network voltage, active droop control coefficient, distributed photovoltaic output active power, reference output active power of distributed photovoltaic, conductor cross-sectional area, conductor thermal stability coefficient, transformer rated capacity, transformer thermal stability time constant, transformer rated voltage and transformer load rate;

[0021] According to the basic data, power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints and short-circuit current constraints are constructed when distributed photovoltaics are connected to the distribution network;

[0022] When adjusting the photovoltaic site selection and sizing of the distribution network based on the results of each optimized cluster division, each optimized cluster division result must meet the power flow constraint, node voltage constraint, broadband oscillation constraint, thermal stability constraint and short-circuit current constraint.

[0023] Furthermore, the power flow constraint is specifically:

[0024]

[0025] Among them, P iS is the active power injection amount of node i; Q iS is the reactive power injection amount of node i; U i is the voltage amplitude of node i; j∈i represents all nodes directly connected to node i; G ij is the real part of the node admittance matrix; B ij is the imaginary part of the node admittance matrix; θ ij is the phase difference between the two end nodes of branch ij.

[0026] Furthermore, the node voltage constraint is specifically:

[0027]

[0028] 10kV / 380V: -7%≤ΔU≤7%

[0029] 220V: -10%≤ΔU≤7%

[0030] Where ΔU is the node voltage deviation; P D1 Active power injected into the node for distributed photovoltaic; Q D is the reactive power injected into the node by distributed photovoltaic; U is the distribution network voltage.

[0031] Furthermore, the broadband oscillation constraint is specifically:

[0032] Δf=ff N =m(P D2 -P ref )

[0033] -0.5≤Δf≤0.5

[0034] Among them, Δf is the node frequency deviation; f is the actual frequency of the node; f N is the system rated frequency; m is the active power droop control coefficient; P D2 P is the distributed photovoltaic output active power; ref It is the reference output active power of distributed photovoltaic.

[0035] Furthermore, the thermal stability constraint is specifically:

[0036]

[0037] Among them, I L is the thermal stability current limit of the conductor; I T is the thermal stability current limit of the transformer; 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 short-circuit current of line ij; 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.

[0038] Furthermore, the short-circuit current constraint is specifically:

[0039]

[0040] Among them, I m To cut off the current; U N is the rated voltage of the transformer.

[0041] Based on the above method embodiment, the present invention provides a corresponding device embodiment;

[0042] An embodiment of the present invention provides a distributed photovoltaic site selection and capacity determination device, including: a data acquisition module, a multi-point grid-connected calculation module, a cluster optimization module, a decision module and a control module;

[0043] The data acquisition module is used to acquire power data of the distribution network and the number of photovoltaic accesses; wherein the power data includes: distribution network voltage level and distribution network load information;

[0044] The multi-point grid-connected calculation module is used to perform multi-point grid-connected calculation according to the power data and the number of photovoltaic accesses to obtain several initial cluster division results of the distribution network; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes;

[0045] The cluster optimization module is used to process each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result;

[0046] The decision module is used to make fuzzy decisions on each cluster division result to be decided, and obtain optimized cluster division results corresponding to each initial cluster division result;

[0047] The control module is used to adjust the photovoltaic site selection and capacity determination of the distribution network based on the optimization cluster division results.

[0048] Another embodiment of the present invention provides a terminal device, including 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, a distributed photovoltaic site selection and capacity determination method described in the above-mentioned embodiment of the invention is implemented.

[0049] The following beneficial effects are achieved by implementing the present invention:

[0050] The present invention provides a distributed photovoltaic site selection and capacity determination method, device and terminal equipment. The method obtains power data and the number of photovoltaic accesses of the distribution network, and then performs multi-point grid-connected calculation according to the power data and the number of photovoltaic accesses to obtain a number of initial cluster division results of the distribution network; then processes each initial cluster division result according to a non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result, performs fuzzy decision on each cluster division result to be decided, and obtains an optimized cluster division result corresponding to each initial cluster division result; and adjusts the photovoltaic site selection and capacity determination of the distribution network with each optimized cluster division result. Through multi-point grid-connected calculation, the distributed photovoltaic site selection and capacity determination problem of multi-point grid-connected is simplified to the optimal planning problem within each initial cluster division result. The initial cluster division results of the multi-point grid-connected calculation are further processed in combination with the non-dominated genetic sorting algorithm, and the fuzzy decision solution is performed based on the cluster division results to be decided to obtain the optimal optimized cluster division result. This method of solving the optimal problem after multi-point parallel processing is less prone to error accumulation than the existing fixed point iterative solution method, and the solved optimized cluster division result has better convergence, that is, a better optimized cluster division result can be obtained, thereby improving the effect of adjusting the distribution network based on the optimized cluster division result. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a first flow chart of a distributed photovoltaic site selection and capacity determination method provided by an embodiment of the present invention.

[0052] Figure 2 It is a second flow chart of a distributed photovoltaic site selection and capacity determination method provided by an embodiment of the present invention.

[0053] Figure 3 It is a flowchart of grid division based on K-means clustering algorithm provided by one embodiment of the present invention.

[0054] Figure 4 It is a structural schematic diagram of a distributed photovoltaic site selection and capacity determination device provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] like Figure 1 and Figure 2 As shown, a distributed photovoltaic site selection and capacity determination method provided by an embodiment of the present invention includes:

[0057] Step S1: Obtaining power data of the distribution network and the number of photovoltaic connections; wherein the power data includes: distribution network voltage level and distribution network load information;

[0058] Step S2: performing multi-point grid connection calculation according to the power data and the number of photovoltaic access points to obtain several initial cluster division results of the distribution network; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes;

[0059] Step S3: processing each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result;

[0060] Step S4: perform fuzzy decision making on each cluster division result to be decided, and obtain optimized cluster division results corresponding to each initial cluster division result;

[0061] Step S5: adjusting the photovoltaic site selection and capacity determination of the distribution network based on the results of the optimization cluster division.

[0062] For step S1, obtain the power data of the distribution network and the number of photovoltaic accesses; wherein the power data includes the voltage level of the distribution network and the load information of the distribution network, and the load information of the distribution network includes active power, reactive power, power factor, current, voltage, load curve, peak load and load rate.

[0063] For step S2, multi-point grid-connected calculation is performed based on the power data and the number of photovoltaic access points obtained above, and the grid-connected information is gridded using the K-means clustering algorithm. After cluster division, several initial cluster division results of the distribution network are obtained; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes.

[0064] When performing grid-connected calculations, you first need to clarify the voltage requirements of the photovoltaic power generation system; then calculate the total power required by the photovoltaic power generation system based on the actual voltage requirements of the photovoltaic power generation system; obtain the rated power of a single photovoltaic panel by referring to the specifications of the photovoltaic panels, and divide the total power required by the photovoltaic power generation system by the power of a single photovoltaic panel to obtain the number of photovoltaics required in series when connected to the grid.

[0065] like Figure 3 As shown, it is a flowchart of grid division based on K-means clustering algorithm provided by an embodiment of the present invention. When grid division is performed based on K-means clustering algorithm, first obtain the relevant network parameters of the distribution network, distributed photovoltaic and distribution network load information; then select the cluster division index, set the number of cluster clusters and randomly select each initial cluster center; traverse all nodes of the distribution network to calculate the electrical modularity function, if the function increases, divide the node into the cluster; sum and average the electrical distances within the cluster, find the node with the closest value to update the cluster center, and evaluate the local consumption index of each cluster; when the preset conditions are met, output the cluster division result, that is, obtain the initial cluster division result; when the preset conditions are not met, return to the step of traversing all nodes of the distribution network to calculate the electrical modularity function and execute again. Among them, the preset conditions include: each comprehensive module function tends to converge, the cluster center is stable and the local consumption index meets the requirements.

[0066] It should be noted that the K-means clustering algorithm is suitable for clustering distributed photovoltaic multi-point access distribution networks, so that each cluster can be divided and calculated under the consideration of distributed photovoltaic grid-connected parameters. The grid-based processing of the distribution network can convert the distributed photovoltaic multi-point grid-connected planning problem into the distributed photovoltaic site selection and capacity determination problem within each grid.

[0067] For step S3, each initial cluster division result is processed according to the NSGA-II algorithm, that is, the non-dominated genetic sorting algorithm 2, to obtain a number of cluster division results to be decided corresponding to each initial cluster division result.

[0068] In a preferred embodiment, the processing of each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result includes: for each initial cluster division result, performing a non-dominated genetic algorithm sorting operation until the iteration termination condition is reached, and taking the cluster division result corresponding to the population to be decided as the cluster division result to be decided corresponding to the current initial cluster division result; wherein the non-dominated genetic algorithm sorting operation includes: encoding each initial photovoltaic access capacity and each initial photovoltaic access node according to the power data to generate a parent population of the current initial cluster division result; performing crossover mutation processing on the parent population of the current initial cluster division result to generate a child population of the current initial cluster division result; constructing a population set to be processed according to the parent population and the child population of the current initial cluster division result; performing fast non-dominated sorting on the population set to be processed to generate a sorting result of the population set to be processed; calculating the crowding degree of each population in the population set to be processed; and determining the population to be decided according to the crowding degree of each population in the population set to be processed and the sorting result of the population set to be processed.

[0069] Specifically, Figure 2 As shown in the figure, for each initial clustering result, the NSGA-II algorithm is executed to perform cluster replanning. For each initial clustering result, firstly, according to the power data and the current initial clustering result, each initial photovoltaic access capacity and each initial photovoltaic access node in the current initial clustering result are encoded to generate the parent population of the current initial clustering result.

[0070] Preferably, when encoding and setting multiple distributed photovoltaic access capacities and access nodes of the current initial cluster division result, it is necessary to evaluate the impact of distributed photovoltaic access capacity and access location on the voltage of each node. The voltage sensitivity method is used in the evaluation, that is, the active power injected by distributed photovoltaics will cause the voltage of each node of the distribution network to change. The voltage change of each node of the system is measured based on the voltage sensitivity, and the objective function is established with the minimum power factor of each branch as the goal to limit the voltage change of each power grid during cluster division. The objective function is as follows:

[0071]

[0072] in, is the power factor of branch ij; ΔV j is the voltage change at node j; ΔP i is the active power change of distributed photovoltaic access node i; n is the distribution network node except the balancing node.

[0073] After generating the parent population of the current initial clustering result, generate the child populations of different planning schemes corresponding to the current initial clustering result by crossover and mutation of the parent population of the current initial clustering result; merge the parent population of the current initial clustering result and the child population of the current initial clustering result to construct a set of populations to be processed. Use fast non-dominated sorting to sort the set of populations to be processed and generate the sorting result of the set of populations to be processed; calculate the crowding degree of each population (including the parent population and the child population) in the set of populations to be processed, and determine a new population to be decided based on the sorting and calculation results. Determine whether the current population to be decided meets the iteration termination condition, that is, whether the population to be decided is the optimal solution of the current initial clustering result. If so, the clustering result corresponding to the population to be decided is used as the clustering result to be decided corresponding to the current initial clustering result; if not, return to the step of performing crossover mutation on the parent population of the current initial clustering result to regenerate a new child population of the current initial clustering result, and continue to execute subsequent sorting and population to be decided selection steps until the output population to be decided is the optimal solution.

[0074] For step S4, fuzzy decision is made on each cluster division result to be decided, and the optimized cluster division result corresponding to each initial cluster division result is obtained; wherein, the optimized cluster division result includes optimizing photovoltaic access capacity and optimizing photovoltaic access nodes. Since the solutions in the Pareto front obtained by the NSGA-II algorithm in step S3 are all optimal solutions, it is necessary to use fuzzy decision to evaluate and sort the solutions in the Pareto front. Fuzzy decision algorithm is a decision-making method based on fuzzy logic theory, which can effectively compare the comprehensive evaluation vector index to sort the optimal solutions, so that the decision maker can choose the top ranked solution, that is, it can extract the most suitable cluster planning scheme after further comprehensive evaluation from the optimal solutions output by multiple Pareto fronts.

[0075] Fuzzy decision making mainly includes the following steps:

[0076] Step S101: Determine fuzzy evaluation indicators, the number of which is m; wherein the fuzzy evaluation indicators are selected according to the demand of the distribution network, and the fuzzy evaluation indicators may include power supply reliability, distributed energy utilization rate, distribution network operation cost, etc.

[0077] Step S201: Determine the level of each fuzzy evaluation index, the number is o;

[0078] Step S301: Determine the weight of each fuzzy evaluation index A = (a1, a2, ..., a m );

[0079] where ∑a m =1,a m >0;

[0080] Step S401: Calculate the membership of each fuzzy evaluation index level for each cluster division result to be decided, and obtain the fuzzy relationship matrix R, which is an m-row and o-column matrix, wherein the calculation formula of the membership function is as follows:

[0081]

[0082] Among them, f ij is the membership degree of the jth cluster division result to be decided in the ith fuzzy evaluation index function value; f imin is the lower limit of the value of the i-th fuzzy evaluation index function; f imax is the upper limit of the value of the i-th fuzzy evaluation index function.

[0083] Step S501: using the weight A and the fuzzy relationship matrix R to synthesize the fuzzy comprehensive evaluation result vector B; wherein B=A·R.

[0084] Step S601: selecting the cluster division result to be decided with the largest B value as the optimized cluster division result corresponding to the current initial cluster division result; wherein the optimized cluster division result includes: optimizing photovoltaic access capacity and optimizing photovoltaic access nodes.

[0085] For step S5, the photovoltaic site selection and capacity determination of the distribution network are adjusted according to the optimized cluster division results, that is, the optimized photovoltaic access capacity and optimized photovoltaic access nodes in the optimized cluster division results are used to replace the corresponding initial cluster division results.

[0086] In a preferred embodiment, after obtaining the power data and the number of photovoltaic accesses of the distribution network, it also includes: obtaining basic data of the distribution network; wherein the basic data includes: node topology relationship, voltage amplitude of each node, node admittance matrix, active power of distributed photovoltaic injection node, reactive power of distributed photovoltaic injection node, distribution network voltage, active droop control coefficient, distributed photovoltaic output active power, reference output active power of distributed photovoltaic, conductor cross-sectional area, conductor thermal stability coefficient, transformer rated capacity, transformer thermal stability time constant, transformer rated voltage and transformer load rate; constructing power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints and short-circuit current constraints when distributed photovoltaic is connected to the distribution network according to the basic data; when adjusting the photovoltaic site selection and sizing of the distribution network based on the optimization cluster division results, the optimization cluster division results must meet the power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints and short-circuit current constraints.

[0087] When distributed photovoltaics inject power into the distribution network, it is necessary to ensure that the power balance relationship is maintained between each node and line to ensure the stable operation of the system. Therefore, the access of distributed photovoltaics must meet the power flow constraints.

[0088] In a preferred embodiment, the power flow constraint is specifically:

[0089]

[0090] Among them, P iS is the active power injection amount of node i; Q iS is the reactive power injection amount of node i; U i is the voltage amplitude of node i; j∈i represents all nodes directly connected to node i; G ij is the real part of the node admittance matrix; B ij is the imaginary part of the node admittance matrix; θ ij is the phase difference between the two end nodes of branch ij.

[0091] When the distributed photovoltaic output is too large or the line is lightly loaded, the node voltage is raised too much, resulting in high voltage. On the contrary, when the distributed photovoltaic is off-grid or the line is overloaded, the line voltage drop increases, causing the node voltage to decrease, exceeding the allowable value of voltage deviation and resulting in low voltage. Therefore, in order to ensure the safe and stable operation of the distribution network, node voltage exceeding the limit also becomes a factor constraining the site selection and capacity determination of distributed photovoltaics. According to the prescribed limit values ​​of voltage deviation in GB / T 12325-2008, if the duration of high and low voltage exceeds 60s, it means that the node voltage is over-limited, and the voltage over-limit corresponds to the node voltage constraint.

[0092] In a preferred embodiment, the node voltage constraint is specifically:

[0093]

[0094] 10kV / 380V: -7%≤ΔU≤7%

[0095] 220V: -10%≤ΔU≤7%

[0096] Where ΔU is the node voltage deviation; P D1 Active power injected into the node for distributed photovoltaic; Q D is the reactive power injected into the node by distributed photovoltaic; U is the distribution network voltage.

[0097] In order to suppress oscillation, it is necessary to use suitable distributed photovoltaic inverters and optimize the control strategy to ensure that they work within the normal frequency range and improve the frequency stability of the system. The droop control strategy is often used to set the active droop control coefficient m to control the grid-connected oscillation frequency. According to the frequency deviation regulations of GB / T 12325-2008, a wide-band oscillation constraint is constructed.

[0098] In a preferred embodiment, the broadband oscillation constraint is specifically:

[0099] Δf=ffN =m(P D2 -P ref )

[0100] -0.5≤Δf≤0.5

[0101] Among them, Δf is the node frequency deviation; f is the actual frequency of the node; f N is the system rated frequency; m is the active power droop control coefficient; P D2 P is the distributed photovoltaic output active power; ref It is the reference output active power of distributed photovoltaic.

[0102] Under normal working conditions, the access of distributed photovoltaic power to the distribution network reduces the transmission capacity of the line and the load rate of the upper transformer, and the problem of thermal stability exceeding the limit will not occur. However, under short-circuit conditions, the impact of distributed photovoltaic power on short-circuit current is analyzed as auxiliary increase and reverse. The auxiliary increase of short-circuit current is likely to cause overload of downstream lines, while the reverse short-circuit current is likely to cause overload operation of transformers. Therefore, the access of distributed photovoltaic power has a certain impact on the thermal stability conditions of lines and transformers. According to the provisions of DL / T 5222-2005 and GB 1094-2008 on the thermal stability of lines and transformers, thermal stability constraints are constructed.

[0103] In a preferred embodiment, the thermal stability constraint is specifically:

[0104]

[0105] Among them, 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; I T is the thermal stability current limit of the transformer, that is, the short-circuit current after the distributed photovoltaic access is less than the thermal stability current limit of the transformer; 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 short-circuit current of line ij; 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.

[0106] 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 photovoltaics output 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 "Q / GDW 617-2011", after the distributed photovoltaics are 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, to ensure the reliable operation of the circuit breaker; short-circuit current constraints are constructed based on the above regulations.

[0107] In a preferred embodiment, the short-circuit current constraint is specifically:

[0108]

[0109] Among them, I m To cut off the current; U N is the rated voltage of the transformer.

[0110] Based on the above method embodiment, the present invention provides a corresponding device embodiment.

[0111] like Figure 4 As shown, an embodiment of the present invention provides a distributed photovoltaic site selection and capacity determination device, including: a data acquisition module, a multi-point grid-connected calculation module, a cluster optimization module, a decision module and a control module;

[0112] The data acquisition module is used to acquire power data of the distribution network and the number of photovoltaic accesses; wherein the power data includes: distribution network voltage level and distribution network load information;

[0113] The multi-point grid-connected calculation module is used to perform multi-point grid-connected calculation according to the power data and the number of photovoltaic accesses to obtain several initial cluster division results of the distribution network; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes;

[0114] The cluster optimization module is used to process each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result;

[0115] The decision module is used to make fuzzy decisions on each cluster division result to be decided, and obtain optimized cluster division results corresponding to each initial cluster division result;

[0116] The control module is used to adjust the photovoltaic site selection and capacity determination of the distribution network based on the optimization cluster division results.

[0117] It should be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the accompanying drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art may understand and implement it without paying any creative effort.

[0118] Those skilled in the art can clearly understand that for the sake of convenience and brevity, the specific working process of the device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0119] Based on the above method item embodiments, the present invention provides corresponding terminal device item embodiments.

[0120] An embodiment of the present invention provides a terminal device, including 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, a distributed photovoltaic site selection and capacity determination method as described in any one of the present invention is implemented.

[0121] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0122] 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. The 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.

[0123] The memory can be used to store the computer program, and the processor realizes various functions of the terminal device by running or executing the computer program 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 phone, 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.

[0124] The storage medium is a computer-readable storage medium, and the computer program is stored in the computer-readable storage medium. When the computer program is executed by the processor, the steps of each of the above-mentioned method embodiments can be implemented. 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 the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium.

[0125] The above is a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several improvements and modifications without departing from the principle of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A distributed photovoltaic site selection and capacity determination method, characterized in that: include: Obtaining power data and the number of photovoltaic connections of the distribution network; wherein the power data includes: distribution network voltage level and distribution network load information; Perform multi-point grid connection calculation according to the power data and the number of photovoltaic access points to obtain several initial cluster division results of the distribution network; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes; Processing each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result; Make fuzzy decisions on the cluster division results to be decided, and obtain the optimized cluster division results corresponding to the initial cluster division results; The photovoltaic site selection and sizing of the distribution network are adjusted based on the results of the optimized cluster division.

2. A distributed photovoltaic site selection and capacity determination method as claimed in claim 1, characterized in that: The initial cluster division results are processed according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to the initial cluster division results, including: For each initial clustering result, a non-dominated genetic algorithm sorting operation is performed until the iteration termination condition is reached, and the clustering result corresponding to the waiting-for-decision population is used as the waiting-for-decision clustering result corresponding to the current initial clustering result; Wherein, the non-dominated genetic algorithm sorting operation includes: Encoding each initial photovoltaic access capacity and each initial photovoltaic access node according to the power data to generate a parent population of the current initial cluster division result; Perform crossover mutation processing on the parent population of the current initial clustering result to generate the child population of the current initial clustering result; Construct a population set to be processed based on the parent population and child population of the current initial cluster division result; Perform fast non-dominated sorting on the population set to be processed, and generate a sorting result of the population set to be processed; Calculate the crowding degree of each population in the population set to be processed; The population to be decided is determined according to the crowding degree of each population in the population set to be processed and the sorting result of the population set to be processed.

3. A distributed photovoltaic site selection and capacity determination method as claimed in claim 1, characterized in that: After obtaining the power data of the distribution network and the number of photovoltaic connections, it also includes: Obtain basic data of the distribution network; wherein the basic data includes: node topology relationship, voltage amplitude of each node, node admittance matrix, active power injected into the distributed photovoltaic node, reactive power injected into the distributed photovoltaic node, distribution network voltage, active droop control coefficient, distributed photovoltaic output active power, reference output active power of distributed photovoltaic, conductor cross-sectional area, conductor thermal stability coefficient, transformer rated capacity, transformer thermal stability time constant, transformer rated voltage and transformer load rate; According to the basic data, power flow constraints, node voltage constraints, broadband oscillation constraints, thermal stability constraints and short-circuit current constraints are constructed when distributed photovoltaics are connected to the distribution network; When adjusting the photovoltaic site selection and sizing of the distribution network based on the results of each optimized cluster division, each optimized cluster division result must meet the power flow constraint, node voltage constraint, broadband oscillation constraint, thermal stability constraint and short-circuit current constraint.

4. A distributed photovoltaic site selection and capacity determination method as claimed in claim 3, characterized in that: The power flow constraints are specifically: Among them, P iS is the active power injection amount of node i; Q iS is the reactive power injection amount of node i; U i is the voltage amplitude of node i; j∈i represents all nodes directly connected to node i; G ij is the real part of the node admittance matrix; B ij is the imaginary part of the node admittance matrix; θ ij is the phase difference between the two end nodes of branch ij.

5. A distributed photovoltaic site selection and capacity determination method as claimed in claim 3, characterized in that: The node voltage constraint is specifically: 10kV / 380V: -7%≤ΔU≤7% 220V: -10%≤ΔU≤7% Where ΔU is the node voltage deviation; P D1 Active power injected into the node for distributed photovoltaic; Q D is the reactive power injected into the node by distributed photovoltaic; U is the distribution network voltage.

6. A distributed photovoltaic site selection and capacity determination method as claimed in claim 3, characterized in that: The broadband oscillation constraint is specifically: Δf=f-f N =m(P D2 -P ref ) -0.5≤Δf≤0.5 Among them, Δf is the node frequency deviation; f is the actual frequency of the node; f N is the system rated frequency; m is the active power droop control coefficient; P D2 P is the distributed photovoltaic output active power; ref It is the reference output active power of distributed photovoltaic.

7. A distributed photovoltaic site selection and capacity determination method as claimed in claim 3, characterized in that: The thermal stability constraints are specifically: Among them, I L I is the thermal stability current limit of the conductor; T is the thermal stability current limit of the transformer; 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 short-circuit current of line ij; 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.

8. A distributed photovoltaic site selection and capacity determination method as claimed in claim 3, characterized in that: The short-circuit current constraint is specifically: Among them, I m To cut off the current; U N is the rated voltage of the transformer.

9. A distributed photovoltaic site selection and capacity determination device, characterized in that: include: Data acquisition module, multi-point grid-connected calculation module, cluster optimization module, decision-making module and control module; The data acquisition module is used to acquire power data of the distribution network and the number of photovoltaic accesses; wherein the power data includes: distribution network voltage level and distribution network load information; The multi-point grid-connected calculation module is used to perform multi-point grid-connected calculation according to the power data and the number of photovoltaic accesses to obtain several initial cluster division results of the distribution network; wherein the initial cluster division results include: initial photovoltaic access capacity and initial photovoltaic access nodes; The cluster optimization module is used to process each initial cluster division result according to the non-dominated genetic sorting algorithm to obtain a number of cluster division results to be decided corresponding to each initial cluster division result; The decision module is used to make fuzzy decisions on each cluster division result to be decided, and obtain optimized cluster division results corresponding to each initial cluster division result; The control module is used to adjust the photovoltaic site selection and capacity determination of the distribution network based on the optimization cluster division results.

10. A terminal device, characterized in that: It comprises 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, it implements a distributed photovoltaic site selection and capacity determination method as described in any one of claims 1 to 8.

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