A Method for Distribution Network Reconfiguration and Photovoltaic Configuration under Wide-Frequency-Domain Harmonic Interaction

Abstract

The present invention discloses a method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction, including: 1. Establishing an equivalent impedance network model of multiple grid-connected inverters; 2. Through the reconstruction of the distribution network and the configuration of photovoltaic, reducing the grid-side impedance and achieving harmonic interaction suppression; 3. Determining monitoring points according to the correlation between nodes in the distribution network; 4. Establishing an objective function that simultaneously considers the average harmonic distortion rate, network loss, and voltage deviation of the monitoring points in the distribution network; 5. Determining the constraint conditions for the operation of the distribution network under photovoltaic access; 6. Solving the distribution network reconstruction and photovoltaic configuration model under broadband harmonic interaction through the NSGAII algorithm, and solving the optimal switch combination, as well as the photovoltaic access position and capacity. The present invention optimizes the total impedance of the entire distributed impedance network by reconstructing the distribution network and configuring photovoltaic to change the equivalent impedance of the access point, so as to achieve harmonic interaction suppression between multiple grid-connected inverters and the distribution network.

Description

Technical Field

[0001] The present invention relates to distribution network reconstruction and photovoltaic site selection and capacity determination, and specifically to an optimization planning strategy that uses distribution network reconstruction and photovoltaic configuration as the main means to suppress harmonic amplification of grid-side harmonic voltage near the resonance frequency point. Background Art

[0002] In the past decade or so, the power demand has been continuously increasing. Compared with traditional power generation equipment, the proportion of clean energy such as PV power generation and wind power generation is also getting higher and higher. The power quality problems such as harmonics and voltage deviation brought by their connection to the grid have gradually been taken seriously. The core device of the PV equipment, the inverter, is a non-linear device. When a non-linear device is connected to the grid, it will inject broadband harmonics into the grid. This broadband harmonics will interact with the background harmonics existing in the distribution network, resulting in the problem of harmonic amplification. In order to reduce the impact of harmonic interaction on the distribution system, the existing methods mainly start from the design of the PV inverter, and establish an equivalent impedance model and corresponding control strategies to reduce the impact of harmonic interaction. Such methods compensate and correct from the source of harmonic interaction, and the adjustable range of this optimization strategy is limited; in addition, most of the PV configurations proposed by domestic and foreign scholars at present are based on the premise that the system parameters on the equipment side and the impedance parameters on the grid side in the distribution system planning are regarded as fixed values, to improve the power quality of the distribution network and reduce the network loss, without considering the impact of harmonic interaction, so that the optimization effect of the final configuration result does not reach the ideal effect in the scenario considering the background harmonics of the distribution network. Summary of the Invention

[0003] In order to avoid the deficiencies of the above-mentioned prior art, the present invention provides a method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction, aiming to suppress harmonic interaction by changing the impedance characteristics of the grid side, so as to reduce the impedance amplitude of the total impedance of the entire impedance network near the interaction point, and effectively suppress the harmonic amplification generated by the harmonic voltage from the grid side or the inverter side near this frequency point, so as to realize the suppression of harmonic interaction between multiple grid-connected inverters and the distribution network in the distribution network planning stage.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction of the present invention is characterized in that it is applied to a distribution network containing distributed generation DG, and is carried out according to the following steps:

[0006] Step 1: Establish an equivalent impedance network model of multiple grid-connected inverters;

[0007] Step 2: Under the condition of meeting the current harmonic requirements when a single inverter is connected to the grid, simulate the background harmonic interaction between multiple grid-connected inverters and the distribution network, so as to obtain the amplitude and phase information of the output impedance of the multiple grid-connected inverters and the grid impedance near the interaction point. According to the amplitude and phase information, by reconstructing the distribution network and configuring the photovoltaic power generation, reduce the grid-side impedance and achieve harmonic interaction suppression;

[0008] Step 3: Determine the monitoring points according to the correlation between nodes in the distribution network;

[0009] Step 4: Establish an objective function that simultaneously considers the average value of harmonic distortion rate, network loss, and voltage deviation of the monitoring points in the distribution network;

[0010] Step 5: Determine the constraint conditions for the operation of the distribution network under the access of photovoltaic power generation, and form a distribution network reconstruction and photovoltaic configuration model under broadband harmonic interaction with the objective function;

[0011] Step 6: Solve the distribution network reconstruction and photovoltaic configuration model through the NSGAII algorithm, so as to obtain the optimal switch combination, photovoltaic access position and capacity, in order to optimize the average value of harmonic distortion rate, network loss, and node voltage deviation of the monitoring points.

[0012] The characteristics of a distribution network reconstruction and photovoltaic configuration method under broadband harmonic interaction according to the present invention also lie in that in Step 1, an expression of the grid-connected current of a single grid-connected inverter is constructed by using Equation (1), and then an expression of the grid-connected current of multiple grid-connected inverters is constructed by using Equation (2):

[0013]

[0014]

[0015] In Equation (1) and Equation (2), i g is the grid-connected current of a single grid-connected inverter; i o (s) is the controlled source current; u g is the grid voltage; Z o (s) is the impedance of the grid-connected inverter; Z grid is the grid impedance; i grid is the grid-connected current of multiple grid-connected inverters.

[0016] The said Step 3 includes:

[0017] Step 3.1: Assume that the position of the harmonic source is random, and use Equation (3) to obtain the difference ΔV k between the k-th node and the slack node in the distribution network at adjacent moments; thus, obtain the differences between each node in the distribution network and the slack node at adjacent moments and form a Pearson correlation coefficient matrix C:

[0018]

[0019] In Equation (3), represents the voltage of the balance node at the current time t, represents the voltage of the k-th node at the current time t, represents the voltage of the balance node at the next time t+1, represents the voltage of the k-th node at the next time t+1;

[0020] Step 3.2: Obtain the binary correlation matrix C using Equation (4) bin The element c at the i-th row and j-th column in bin (i, j):

[0021]

[0022] In Equation (4), c(i, j) is the element at the i-th row and j-th column in the Pearson correlation coefficient matrix C;

[0023] Step 3.3: Construct an objective function using Equation (5) to minimize the number of monitoring points in the distribution network:

[0024]

[0025] In Equation (5), y(i) represents whether the i-th node is a monitoring point. If y(i)=0, it means the i-th node is not used as a monitoring point. If y(i)=1, it means the i-th node is a monitoring point; N b represents the number of nodes in the distribution network;

[0026] Step 3.4: Construct the constraint conditions of the objective function using Equation (6):

[0027]

[0028] In Equation (6), y represents the position vector of the monitoring points in the distribution network;

[0029] Step 3.5: Solve the monitoring point optimization model through a genetic algorithm to obtain the optimal positions of the monitoring points.

[0030] The said Step 4 includes:

[0031] Step 4.1: Construct the first objective function f1 with the average value of the harmonic voltage distortion rate of the monitoring points as the objective using Equation (7):

[0032]

[0033] In Equation (7): N is the total number of monitoring points; THD m is the harmonic voltage distortion rate of the m-th monitoring point;

[0034] Step 4.2: Use Equation (8) to construct the second objective function \(f_2\) with the minimum distribution network power loss as the objective:

[0035]

[0036] In Equation (8): \(N\) L is the total number of branches in the distribution network, \(R\) l is the resistance of the \(l\)-th branch, \(P\) l is the active power of the \(l\)-th branch, \(Q\) l is the reactive power of the \(l\)-th branch, \(U\) l is the rated voltage of the \(l\)-th branch;

[0037] Step 4.3: Use Equation (9) to construct the third objective function \(f_3\) with the voltage deviation as the objective:

[0038]

[0039] In Equation (9): \(U\) i is the voltage of the \(i\)-th node, \(U\) n is the nominal voltage.

[0040] The constraint conditions in Step 5 include:

[0041] Step 5.1: Use Equation (10) to construct the node power balance constraint:

[0042]

[0043] In Equation (10): \(P\) grid , \(Q\) grid are the active power and reactive power flowing in from the superior power grid respectively, \(P\) i,PV , \(Q\) i,PV are the active power output and reactive power output of the photovoltaic at the \(i\)-th node respectively, \(P\) load , \(Q\) load are the active power and reactive power demands of the load respectively, \(P\) loss , \(Q\) loss are the active power loss and reactive power loss of the distribution network respectively, \(U\) i , \(U\) j are the voltage amplitudes of the \(i\)-th node and the \(j\)-th node respectively, \(G\) ij , \(B\) ij , \(\theta\) ij are the conductance, susceptance and phase angle difference at both ends of the line between the \(i\)-th node and the \(j\)-th node respectively;

[0044] Step 5.2: Use Equation (11) to construct the node voltage constraint:

[0045] \(U\) min ≤ \(U\)i ≤U max (11)

[0046] In Equation (11): U min and U max are respectively the upper limit and the lower limit of the amplitude of the voltage of the i-th node;

[0047] Step 5.3: Use Equation (12) to construct the PV capacity constraint:

[0048]

[0049] In Equation (12): P i,PV min and P i,PV max are respectively the minimum value and the maximum value of the PV output at the i-th node; Q i,PV min and Q i,PV max are the minimum value and the maximum value of the PV reactive power output at the i-th node; ∑P DG max is the maximum PV output allowed to be connected to the distribution network;

[0050] Step 5.4: Construct the topological constraint of the distribution network as a structure without islands and without loops.

[0051] An electronic device according to the present invention includes a memory and a processor, characterized in that the memory is used to store a program for supporting the processor to execute the distribution network reconstruction and photovoltaic configuration method, and the processor is configured to execute the program stored in the memory.

[0052] A computer-readable storage medium according to the present invention has a computer program stored thereon, characterized in that the computer program, when run by a processor, executes the steps of the distribution network reconstruction and photovoltaic configuration method.

[0053] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0054] 1. On the basis of reducing the harmonic interaction effect by optimizing the parameters and control strategies on the traditional device side, the present invention provides a method for reducing the equivalent impedance value of the grid side at the point of common coupling between the photovoltaic inverter and the distribution network by reconstructing the distribution network and locating the photovoltaic from the grid side, changing the phase difference between the grid impedance and the inverter impedance near the interaction point, so as to achieve the suppression of harmonic interaction amplification, and overcome the defect of insufficient adjustment ability of the optimization range under the traditional device-side optimization method.

[0055] 2. The present invention uses the Pearson correlation coefficient for harmonic monitoring point location, proposes a new optimization model, reduces the complexity of harmonic monitoring point location, thereby reducing the number of required harmonic detection devices, and improves the overall harmonic index optimization efficiency. Description of the Drawings

[0056] Figure 1 It is the equivalent impedance model of multiple grid-connected inverters of the present invention;

[0057] Figure 2 It is the Bode plot of the output impedance of the present invention;

[0058] Figure 3a It is the grid-connected current waveform diagram under the ideal grid of the present invention;

[0059] Figure 3b It is the grid-connected current waveform diagram under the distorted grid of the present invention;

[0060] Figure 4 It is the IEEE-33 node distribution system diagram of the present invention;

[0061] Figure 5 It is the branch loss diagram of the distribution system of the present invention;

[0062] Figure 6 It is the node voltage change curve diagram of the present invention. Detailed Embodiment

[0063] In this embodiment, a method for distribution network reconstruction and photovoltaic configuration under wide-frequency harmonic interaction is applied to a distribution network containing distributed generation DG, and is carried out according to the following steps:

[0064] Step 1: Establish an equivalent impedance network model of multiple grid-connected inverters;

[0065] Use Equation (1) to construct the grid-connected current expression of a single grid-connected inverter, and thus use Equation (2) to construct the grid-connected current expression of multiple grid-connected inverters:

[0066]

[0067]

[0068] In Equation (1) and Equation (2), i g is the grid-connected current of a single grid-connected inverter; i o (s) is the controlled source current; u g is the grid voltage; Z o (s) is the grid-connected inverter impedance; Z grid is the grid impedance; i grid is the grid-connected current of multiple grid-connected inverters.

[0069] In specific implementation, according to the above formula, the equivalent impedance model of multiple grid-connected inverters can be obtained as Figure 2 shown.

[0070] Analyzing Equation (2), it can be seen that when the magnitudes of the equivalent impedance Z0(s) / n of the multiple grid-connected inverters and the grid impedance Z grid (s) are equal and the phase difference is equal to or close to 180° at a certain frequency, resonance will occur in the entire impedance network, and the total impedance Z total (s) = Z o (s) / n + Z grid (s) will also reach the minimum value at this frequency. When the grid voltage harmonics or the grid-connected inverter harmonics are equal to or close to this frequency, the grid-connected current harmonics of this frequency will be amplified, causing the grid-connected current to be distorted or even exceed the grid-connected standard. From this, it can be seen that there is an interaction between the harmonics of the grid-connected inverter and the harmonics in the grid.

[0071] In specific implementation, in order to analyze the magnitudes and phases of the output impedance of the multiple grid-connected inverters and the grid impedance near the interaction point, based on the established multiple grid-connected inverter model, a simulation of the interaction between the multiple grid-connected inverters and the background harmonics of the distribution network is carried out. This simulation is based on Simulink, and the system parameters are shown in Table 1:

[0072] Table 1

[0073]

[0074] Based on this, the magnitude and phase information of the output impedance of the multiple grid-connected inverters and the grid impedance near the interaction point are obtained, as Figure 3a and Figure 3b shown. Through analysis, it can be seen that the resonance of the equivalent output impedance of the multiple inverters and the grid impedance at a specific frequency is the fundamental reason for the harmonic interaction amplification between the multiple grid-connected inverters and the grid. The harmonic interaction is suppressed by changing the impedance characteristics on the inverter side and the grid side. Under certain conditions of the inverter, the impedance of the grid-connected inverter usually cannot be changed. Therefore, it is only possible to try to suppress the harmonic interaction between the multiple grid-connected inverters and the grid by changing the impedance characteristics on the grid side. When the harmonic interaction between the multiple grid-connected inverters and the distribution network is suppressed, the harmonic content injected into the distribution network by the PV through the grid-connected inverter is reduced, and at this time, the harmonic level of the entire distribution network will be reduced. The total impedance on the grid side of different nodes where the PV is connected to the distribution network is also different, and the change in the PV access capacity will also cause a change in the grid-connected current. The location and capacity of the PV connected to the distribution network through the grid-connected inverter will also affect the harmonic interaction. In summary, distribution network reconstruction and PV configuration can be used to suppress the interaction between the PV grid-connected inverter and the background harmonics of the distribution network.

[0075] Step 2: Under the condition of meeting the current harmonic requirements for a single inverter to be connected to the grid, simulate the background harmonic interaction between multiple grid-connected inverters and the distribution network, so as to obtain the amplitude and phase information of the output impedance of the multiple grid-connected inverters and the grid impedance near the interaction point. According to the amplitude and phase information, reconstruct the distribution network and configure the photovoltaic system to reduce the grid-side impedance and achieve harmonic interaction suppression;

[0076] Step 3: Determine the monitoring points according to the correlation between nodes in the distribution network;

[0077] Step 3.1: Assume that the location of the harmonic source is random, and use Equation (3) to obtain the difference ΔV between the k-th node and the balanced node in the distribution network at adjacent times k ; thus, obtain the differences between each node in the distribution network and the balanced node at adjacent times and form the Pearson correlation coefficient matrix C:

[0078]

[0079] In Equation (3), represents the voltage of the balanced node at the current time t, represents the voltage of the k-th node at the current time t, represents the voltage of the balanced node at the next time t + 1, represents the voltage of the k-th node at the next time t + 1;

[0080] Step 3.2: Use Equation (4) to obtain the binary correlation matrix C bin the element c bin (i, j) in the i-th row and j-th column:

[0081]

[0082] In Equation (4), c(i, j) is the element in the i-th row and j-th column of the Pearson correlation coefficient matrix C;

[0083] Step 3.3: Use Equation (5) to construct the objective function that minimizes the number of monitoring points in the distribution network:

[0084]

[0085] In Equation (5), y(i) represents whether the i-th node is a monitoring point. If y(i) = 0, it means that the i-th node is not used as a monitoring point. If y(i) = 1, it means that the i-th node is a monitoring point; N b represents the number of nodes in the distribution network;

[0086] Step 3.4: Use Equation (6) to construct the constraint conditions of the objective function:

[0087]

[0088] In Equation (6), y represents the position vector of the monitoring points in the distribution network;

[0089] Step 3.5: Solve the monitoring point optimization model through the genetic algorithm to obtain the position information of the optimal monitoring points.

[0090] In the specific implementation, the results of the above model optimization are as follows: The harmonic monitoring nodes are: 5, 6, 15, 16, 21, 22, 24, 25, 27, 30, 33.

[0091] Step 4: Establish an objective function that simultaneously considers the average harmonic distortion rate, network loss, and voltage deviation of the monitoring points in the distribution network;

[0092] Step 4.1: Use Equation (7) to construct the first objective function f1 with the average harmonic voltage distortion rate of the monitoring points as the objective:

[0093]

[0094] In Equation (7): N is the total number of monitoring points; THD m is the harmonic voltage distortion rate of the m-th monitoring point;

[0095] Step 4.2: Use Equation (8) to construct the second objective function f2 with the minimum network loss of the distribution network as the objective:

[0096]

[0097] In Equation (8): N l is the total number of branches in the distribution network, R l is the resistance of branch l, P l is the active power of branch l, Q l is the reactive power of branch l, U l is the rated voltage of branch l;

[0098] Step 4.3: Use Equation (9) to construct the third objective function f3 with the voltage deviation as the objective:

[0099]

[0100] In Equation (9): U i is the voltage of the i-th node, U n is the nominal voltage.

[0101] Step 5: Determine the constraint conditions for the operation of the distribution network under the access of photovoltaic power, and form a distribution network reconstruction and photovoltaic configuration model under broadband harmonic interaction with the objective function;

[0102] Step 5.1: Use Equation (10) to construct the node power balance constraint:

[0103]

[0104] In Equation (10): P grid , Q grid are the active power and reactive power flowing in from the superior power grid respectively, and P i,PV , Q i,PV are the active power output and reactive power output of the photovoltaic at Node i respectively, and P load , Q load are the active power and reactive power demands of the load respectively, and P loss , Q loss are the active power loss and reactive power loss of the distribution network respectively, U i , U j are the voltage amplitudes of Node i and Node j respectively, G ij , B ij , θ ij are the conductance, susceptance of the line between Node i and Node j and the phase angle difference at both ends respectively;

[0105] Step 5.2: Construct the node voltage constraint using Equation (11):

[0106] U min ≤U i ≤U max (11)

[0107] In Equation (11): U min , U max are the upper limit and lower limit of the voltage amplitude of the i-th node respectively;

[0108] Step 5.3: Construct the PV capacity constraint using Equation (12):

[0109]

[0110] In Equation (12): P i,PV min , P i,PV max are the minimum value and maximum value of the PV output at the i-th node respectively; Q i,PV min , Q i,PV max are the minimum value and maximum value of the PV reactive power output at the i-th node; ∑P DG max is the maximum PV output allowed to be connected to the distribution network;

[0111] In specific implementation, the overall planning process is shown in Figure 4 .

[0112] Step 5.4: Construct the topological constraints of the distribution network as no-island and no-loop network structure.

[0113] Step 6: Solve the distribution network reconstruction and PV configuration model through the NSGAII algorithm to obtain the optimal switch combination, as well as the PV access location and capacity, so as to optimize the average value of the harmonic distortion rate at the monitoring point, optimize the network loss, and optimize the node voltage deviation.

[0114] Based on the IEEE-33 node distribution network system, perform simulation analysis. Refer to Figure 1 , the total network load is 3715kW + j2300kvar, the system rated voltage is 12.66kV, and it includes 5 tie switches: s33, s34, s35, s36, s37. The background harmonics include the 5th, 7th, 11th, 13th, 17th, 19th, 23rd, 25th, 29th, 31st, 35th, 37th, 41st power frequency harmonics, and the harmonic content of each order is 2%, 2%, 1.5%, 1.5%, 1%, 1%, 0.5%, 0.5%, 0.5%, 0.5%, 0.5%, 0.5%, 0.5% respectively. Use the NSGAII algorithm combined with Simulink to solve the multi-objective optimization problem.

[0115] The parameters of the NSGAII algorithm are as follows: the number of iterations is 100, the population size is 500, the mutation factor is 0.8, the crossover factor is 0.2, and the number of variables is 66. The constraint condition parameters are shown in Table 2:

[0116] Table 2

[0117]

[0118] In specific implementation, compare the impact of distribution network reconstruction and PV configuration on the operation of the distribution network. The present invention conducts a comparative study on different optimization planning operations in the distribution network, and the specific example settings are shown in Table 3 below:

[0119] Table 3

[0120]

[0121] According to the NSGAII algorithm combined with the ordinal preference method of information entropy, obtain the PV access location and capacity of each objective function value and the optimal switch combination of the distribution network reconstruction. The optimization results are shown in Table 4 below:

[0122] Table 4

[0123]

[0124] Analyze the data in the table. In the objective function f1, due to the addition of background harmonics in the original network, the harmonic monitoring points exhibit harmonics. When PV is connected to the distribution network, the harmonic level increases. In Example 1, the objective function related to harmonics is not added, resulting in a relatively high average harmonic voltage at the harmonic monitoring points. Comparing Example 2 and Example 3, the position and capacity of PV in Example 3 are based on the results of Example 2. Through distribution network reconfiguration, the overall harmonic level can be improved to a certain extent. However, by comparing Example 2 and Example 4, as well as Example 3 and Example 4, and comparing the objective function f1, it can be seen that the joint optimization of distribution network reconfiguration and PV configuration can better improve the average harmonic distortion rate of the harmonic monitoring points. By comparing the objective function f2, it is found that when comparing Examples 1 - 4 with the original network, the network losses are all reduced compared to the original network. Combining Figure 5 it can be calculated that Examples 1 - 4 have decreased by 78.61%, 21.69%, 36.74%, and 76.83% respectively. By comparing the objective function f3, it is found that when comparing Examples 1 - 4 with the original network, the voltage deviation improvement effects in Examples 1 and 4 are the best. Combining Figure 6 it can be seen that the joint optimization operation in Example 4 has increased the minimum voltage of the distribution network from 0.90 pu to 0.93 pu. The above comparison shows that the joint optimization of distribution network reconfiguration and PV configuration has a better effect than only PV configuration or only distribution network reconfiguration.

[0125] The equivalent impedances of the PV access nodes under different examples are shown in Table 5 as follows:

[0126] Table 5

[0127]

[0128] According to the optimization results of the examples, it can be seen that when comparing Example 2 and Example 3 with Example 4 respectively, the equivalent impedance values of the PV access nodes under the joint optimization of distribution network reconfiguration and PV configuration are significantly lower than those under only distribution network reconfiguration and only PV configuration optimization. It shows that through the joint optimization of distribution network reconfiguration and PV configuration, the equivalent impedance of the PV access nodes can be reduced, effectively reducing the grid-side impedance value of the grid-connected inverter, thereby suppressing the harmonic amplification caused by the interaction between the PV-injected harmonics and the distribution network background harmonics.

[0129] The harmonic distortion rates of the harmonic voltages at the harmonic monitoring points under each scenario are shown in Table 6 as follows:

[0130] Table 6

[0131]

[0132] In the original network, due to the existence of background harmonics, certain harmonics can also be detected at the harmonic monitoring points. From the harmonic voltage distortion rate at the harmonic monitoring points, it can be seen that when PV is connected to the distribution network, the harmonic voltage distortion rate at most harmonic monitoring points increases due to the harmonics injected by PV. Since harmonic constraints are not considered, there is also a situation where the harmonic content exceeds the limit in Case 1. By comparing Case 2 - 4, it can be seen that the optimization method adopted in Case 4 reduces the harmonic voltage at the harmonic monitoring points to a certain extent, which also reflects from another perspective that the combined optimization of distribution network reconfiguration and PV configuration changes the grid-side impedance of the grid-connected inverter, suppresses the interaction between the harmonics injected by PV and the background harmonics of the distribution network, and thus reduces the harmonic voltage distortion rate of the overall distribution network.

[0133] The experimental results show that a method for distribution network reconfiguration and photovoltaic configuration under broadband harmonic interaction proposed by the present invention can effectively suppress the interaction between the harmonics injected by PV and the background harmonics of the distribution network; reduce the distribution network losses and the voltage deviations at each node, and improve the stability of the distribution network; at the same time, by setting a small number of harmonic monitoring points, the harmonic level of the entire distribution system can also be well reflected. The above theoretical and experimental conclusions have certain guiding significance for improving the power quality of the system in the distribution network planning stage.

[0134] In this embodiment, an electronic device includes a memory and a processor. The memory is used to store a program that supports the processor to execute the above-mentioned distribution network reconfiguration and photovoltaic configuration method, and the processor is configured to execute the program stored in the memory.

[0135] In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is run by a processor, it executes the steps of the above-mentioned distribution network reconfiguration and photovoltaic configuration method.

Claims

1. A method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction, characterized in that It is applied to a distribution network with distributed generation (DG) and is carried out according to the following steps: Step 1: Use Equation (1) to construct the grid-connected current expression of a single grid-connected inverter, and then use Equation (2) to construct the grid-connected current expression of multiple grid-connected inverters: (1) (2) In Equations (1) and (2), is the grid-connected current of a single grid-connected inverter; is the controlled source current; is the grid voltage; is the grid-connected inverter impedance; is the grid impedance; is the grid-connected current of multiple grid-connected inverters; Step 2: Under the condition of meeting the current harmonic requirements when a single inverter is grid-connected, simulate the interaction between multiple grid-connected inverters and the background harmonics of the distribution network, so as to obtain the amplitude and phase information of the output impedance of the multiple grid-connected inverters and the grid impedance near the interaction point. According to the amplitude and phase information, by reconstructing the distribution network and configuring the photovoltaic system, the grid-side impedance is reduced, and harmonic interaction suppression is achieved; Step 3: Determine the monitoring points according to the correlation between nodes in the distribution network; Step 4: Establish an objective function that simultaneously considers the average harmonic distortion rate, network loss, and voltage deviation of the monitoring points in the distribution network; Step 5: Determine the constraint conditions for the operation of the distribution network under photovoltaic access, and form a distribution network reconstruction and photovoltaic configuration model under wide-frequency harmonic interaction with the objective function; Step 6: Solve the distribution network reconstruction and photovoltaic configuration model through the NSGA-II algorithm to obtain the optimal switch combination, as well as the photovoltaic access position and capacity, so as to optimize the average harmonic distortion rate of the monitoring points, optimize the network loss, and optimize the node voltage deviation.

2. A method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction according to claim 1, characterized in that The said Step 3 includes: Step 3.

1. Assume that the position of the harmonic source is random, and use Equation (3) to obtain the difference between the k-th node and the slack node in the distribution network at adjacent moments ; thus, obtain the differences between each node and the slack node in the distribution network at adjacent moments and form a Pearson correlation coefficient matrix C: (3) In formula (3), represents the voltage of the balancing node at the current moment t, represents the voltage of the k-th node at the current moment t, represents the voltage of the balancing node at the next moment t + 1, represents the voltage of the k-th node at the next moment t + 1; Step 3.2: Obtain the binary correlation matrix using Equation (4) The element in the i-th row and j-th column of : (4) In formula (4), is the element at the i-th row and j-th column in the Pearson correlation coefficient matrix C; Step 3.3: Use Equation (5) to construct an objective function that minimizes the number of monitoring points in the distribution network: (5) In formula (5), indicates whether the i-th node is a monitoring point. If , it means that the i-th node is not used as a monitoring point. If , it means that the i-th node is a monitoring point; represents the number of nodes in the said distribution network; Step 3.4: Use Equation (6) to construct the constraint conditions of the objective function: (6) In formula (6), represents the position vector of the monitoring point in the said distribution network; Step 3.5: Solve the monitoring point optimization model through the genetic algorithm to obtain the optimal position of the monitoring points.

3. A method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction according to claim 1, characterized in that, The said Step 4 includes: Step 4.1: Construct a first objective function with the average harmonic voltage distortion rate of the monitoring points as the target by using Equation (7) : (7) In formula (7): is the total number of monitoring points; is the harmonic voltage distortion rate of the Step 4.2: Construct the second objective function aiming at the minimum distribution network power loss by using Equation (8). : (8) In Equation (8): is the total number of branches in the distribution network, is the resistance of the l-th branch, is the active power of the l-th branch, is the reactive power of the l-th branch, is the rated voltage of the l-th branch; Step 4.3: Construct a third objective function with voltage deviation as the objective using Equation (9) :[[]]END]] (9) In Equation (9): is the voltage of the i-th node, is the nominal voltage.

4. A method for distribution network reconstruction and photovoltaic configuration under broadband harmonic interaction according to claim 1, characterized in that, The constraint conditions in Step 5 include: Step 5.1: Use Equation (10) to construct the node power balance constraint: (10) In formula (10): and are the active power and reactive power flowing in from the superior power grid respectively, and are the active power output and reactive power output of the photovoltaic at the i-th node respectively, and are the active power and reactive power demands of the load respectively, and are the active power loss and reactive power loss of the distribution network respectively, and are the voltage amplitudes of the i-th node and the j-th node respectively, and and are the conductance, susceptance and phase angle difference at both ends of the line between the i-th node and the j-th node respectively; Step 5.2: Use Equation (11) to construct the node voltage constraint: (11) In Equation (11): and are the upper and lower limits of the magnitude of the voltage of the i-th node, respectively; Step 5.3: Use Equation (12) to construct the PV capacity constraint: (12) In Equation (12): and are respectively the minimum and maximum values of the PV output at the i-th node; and are the minimum and maximum values of the PV reactive power output at the i-th node; is the maximum PV output allowed to be connected to the distribution network; Step 5.4: Construct the topological constraint of the distribution network as a non-island and non-loop network structure.

5. An electronic device, comprising a memory and a processor, characterized in that, The memory is used to store a program that supports the processor to execute the distribution network reconstruction and photovoltaic configuration method described in any one of claims 1-4, and the processor is configured to execute the program stored in the memory.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it executes the steps of the distribution network reconstruction and photovoltaic configuration method described in any one of claims 1-4.

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