Medium and low voltage power distribution network cooperative regulation and control method and medium and low voltage power distribution network cooperative regulation and control device

Abstract

The embodiment of the invention provides a medium and low voltage power distribution network coordinated regulation and control method and a medium and low voltage power distribution network coordinated regulation and control device. The method comprises the following steps: firstly, acquiring medium-voltage bus broadband impedance spectrum data synchronously acquired by medium and low-voltage power distribution networks, and harmonic emission characteristic data of a low-voltage distributed power supply; then, based on the broadband impedance spectrum data, a multi-target impedance matching equation is constructed, and the multi-target impedance matching equation comprises an impedance matching degree target and an equipment capacity constraint target; and then, based on a multi-target impedance matching equation, determining a medium-voltage side harmonic admittance compensation matrix of the medium-low voltage distribution network, and dynamically calculating a harmonic responsibility coefficient of each distributed power supply according to the harmonic admittance compensation matrix and the harmonic emission characteristic data. And finally, adjusting inverter filtering parameters of each distributed power supply based on the harmonic wave responsibility coefficient. According to the method, the triggering accuracy of filter parameter adjustment is improved.

Description

Technical Field

[0001] This application relates to the field of smart grid technology, and in particular to a method and device for coordinated control of medium and low voltage distribution networks. Background Art

[0002] With the development of new energy technologies, a large number of distributed power sources, especially those connected to the grid via inverters, are being integrated into medium and low voltage distribution networks. Because these distributed power sources rely on renewable energy sources such as wind or solar power, their unstable supply leads to power quality issues, such as harmonic pollution, impedance mismatch, and resonance risks.

[0003] In existing technologies, the situation of each distributed power source access point is estimated based on static impedance. Combined with the impedance characteristics of the power grid, the potential problems in the power grid are predicted, and the corresponding filter parameters are adjusted to promptly alleviate harmonic pollution and potential resonance problems, thereby improving the overall power quality.

[0004] However, existing technologies suffer from low triggering accuracy when adjusting filter parameters. Summary of the Invention

[0005] This application provides a method and device for coordinated control of medium and low voltage distribution networks, which are used to improve the triggering accuracy of adjusting filter parameters.

[0006] In a first aspect, embodiments of this application provide a method for coordinated control of medium and low voltage distribution networks, including:

[0007] Acquire broadband impedance spectrum data of medium-voltage busbar synchronously collected from medium- and low-voltage distribution networks, as well as harmonic emission characteristic data of low-voltage distributed power sources. The broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band of the preset harmonic order, and the harmonic emission characteristic data includes the harmonic emission intensity of each distributed power source connected to the medium- and low-voltage distribution network.

[0008] Based on broadband impedance spectrum data, a multi-objective impedance matching equation is constructed, which includes impedance matching degree objective and equipment capacity constraint objective.

[0009] Based on the multi-objective impedance matching equation, the harmonic admittance compensation matrix of the medium-voltage side of the medium- and low-voltage distribution network is determined.

[0010] Based on the harmonic admittance compensation matrix and harmonic emission characteristic data, the harmonic responsibility coefficient of each distributed power source is dynamically calculated.

[0011] Based on the harmonic responsibility factor, the inverter filter parameters of each distributed power source are adjusted.

[0012] In one possible implementation, the formula is: min∑|Z m (f)·YA (f)-1|, construct the impedance matching degree objective of the multi-objective impedance matching equation;

[0013] In the above formula, Z m (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0014] In one possible implementation, the formula is: min∑|Y A (f)|, constructing the equipment capacity constraint objective of the multi-objective impedance matching equation;

[0015] In the above formula, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0016] In one possible implementation, a preset range of values ​​is obtained as a constraint condition for the value of the harmonic admittance compensation matrix in the multi-objective impedance matching equation; based on the value constraint condition, the impedance matching degree optimization objective and the equipment capacity constraint objective are mapped to the Ising model of the quantum annealing machine, and the medium-voltage side harmonic admittance compensation matrix is ​​obtained by solving.

[0017] In one possible implementation, through formula λ i =(H i ·Y A ) / (∑H i ·|Y A |), calculate the harmonic liability factor;

[0018] In the above formula, λ i H is the harmonic liability factor for the i-th distributed power source. i For the harmonic emission characteristics data of the i-th distributed power source, Y A This is the harmonic admittance compensation matrix.

[0019] In one possible implementation, the phase angle of the harmonic current and the phase angle of the admittance matrix of the distributed power source are obtained; the harmonic liability factor is corrected based on the phase angle of the harmonic current and the phase angle of the admittance matrix.

[0020] In one possible implementation, the distributed power sources are sorted sequentially based on the harmonic responsibility coefficients from largest to smallest to obtain a priority list; the resonance risk index is obtained; and the inverter filter parameters of the distributed power sources in the priority list are adjusted according to the resonance risk index and a preset index threshold.

[0021] In one possible implementation, the formula is: β=∑|Z new (f)·Y A (f)-1|, calculate the resonance risk index;

[0022] In the above formula, β is the resonance risk index, and Z new (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0023] In one possible implementation, the preset index threshold is updated based on the magnitude of the resonance risk index and the preset index threshold; in response to the next adjustment of the inverter filter parameters of each distributed power source, new constraints are constructed based on the preset index deviation range, the updated preset index threshold, and the resonance risk index, and added to the multi-objective impedance matching equation.

[0024] In one possible implementation, if the resonance risk index is less than the product of a preset index threshold and a first adjustment coefficient within a consecutive preset number of control cycles, then the preset index threshold is multiplied by a second adjustment coefficient to obtain an updated preset index threshold, wherein the first adjustment coefficient is less than the second adjustment coefficient, and both the first and second adjustment coefficients are greater than 0 and less than 1; if the resonance risk index is greater than the product of a preset index threshold and a third adjustment coefficient within a single control cycle, then the preset index threshold is multiplied by a fourth adjustment coefficient to obtain an updated preset index threshold, wherein both the third and fourth adjustment coefficients are greater than 1, and the fourth adjustment coefficient is less than the third adjustment coefficient.

[0025] Secondly, embodiments of this application provide a medium- and low-voltage distribution network coordinated control device, comprising:

[0026] The data acquisition module is used to acquire broadband impedance spectrum data of medium-voltage busbar synchronously collected from medium- and low-voltage distribution networks, as well as harmonic emission characteristic data of low-voltage distributed power sources. The broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band of the preset harmonic order, and the harmonic emission characteristic data includes the harmonic emission intensity of each distributed power source connected to the medium- and low-voltage distribution network.

[0027] The equation construction module is used to construct multi-objective impedance matching equations based on broadband impedance spectrum data. The multi-objective impedance matching equations include impedance matching degree objectives and equipment capacity constraint objectives.

[0028] The matrix determination module is used to determine the harmonic admittance compensation matrix on the medium-voltage side of the medium- and low-voltage distribution network based on the multi-objective impedance matching equation.

[0029] The coefficient calculation module is used to dynamically calculate the harmonic responsibility coefficient of each distributed power source based on the harmonic admittance compensation matrix and harmonic emission characteristic data.

[0030] The parameter adjustment module is used to adjust the inverter filter parameters of each distributed power source based on the harmonic responsibility coefficient.

[0031] Thirdly, embodiments of this application provide an electronic device, including: a memory and a processor;

[0032] The memory stores the instructions that the computer executes;

[0033] The processor executes computer execution instructions stored in memory, causing the processor to perform the first aspect and / or various possible implementations of the first aspect as described above.

[0034] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the first aspect and / or various possible implementations of the first aspect.

[0035] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the first aspect and / or various possible implementations of the first aspect.

[0036] The medium- and low-voltage distribution network coordinated control method and device provided in this application acquire broadband impedance spectrum data of the medium-voltage bus and harmonic emission characteristic data of low-voltage distributed power sources synchronously collected from the medium- and low-voltage distribution network. The broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band at a preset harmonic order, serving as the basis for impedance assessment. Then, based on the broadband impedance spectrum data, a multi-objective impedance matching equation is constructed, including an impedance matching degree target and an equipment capacity constraint target, thus balancing the minimum impedance deviation on the medium-voltage side and equipment capacity. Next, based on the multi-objective impedance matching equation, after determining the harmonic admittance compensation matrix of the medium- and low-voltage distribution network on the medium- and low-voltage side, the harmonic responsibility coefficient of each distributed power source is dynamically calculated according to the harmonic admittance compensation matrix and harmonic emission characteristic data, serving as the basis for the sequential adjustment of each distributed power source. Finally, based on the harmonic responsibility coefficient, the inverter filter parameters of each distributed power source are adjusted. This ensures that each distributed power source receives the adjustment command at the appropriate time, improving the triggering accuracy of the filter parameter adjustment. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] Figure 1 A flowchart illustrating the coordinated control method for medium and low voltage distribution networks provided in this application embodiment. Figure 1 ;

[0039] Figure 2 A flowchart illustrating the coordinated control method for medium and low voltage distribution networks provided in this application embodiment. Figure 2 ;

[0040] Figure 3 This is a schematic diagram of the structure of the medium and low voltage power distribution network coordinated control device provided in the embodiments of this application;

[0041] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0042] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. Detailed Implementation

[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] First, let me explain the terms used in this application:

[0045] Medium and low voltage distribution networks refer to the distribution portion of a power system, responsible for distributing electrical energy transmitted from medium-voltage substations to end users. Typically, medium voltage ranges from 10kV to 35kV, while low voltage refers to voltage levels below 1kV (such as the common 220V / 380V). Medium and low voltage distribution networks encompass all facilities and equipment from the medium-voltage substation up to the user's meter.

[0046] Medium-voltage busbar: This refers to an electrical device located in a substation or power distribution network, used to connect multiple power sources and loads to facilitate power distribution. It carries medium-voltage current and allows for flexible distribution of power from different sources.

[0047] Low-voltage distributed power sources refer to small power generation equipment installed on the user side and directly connected to the low-voltage power distribution network, such as solar photovoltaic panels, small wind turbines, and fuel cells.

[0048] Inverter: A device that converts direct current (DC) to alternating current (AC). It is commonly used in solar power systems, uninterruptible power supplies (UPS), and electric vehicles to meet the needs of household, industrial, or grid-connected electricity.

[0049] With the development of new energy power generation technologies and the large-scale integration of distributed generation (DG), medium and low voltage distribution networks are facing increasingly more power quality problems, especially complex electromagnetic compatibility issues such as harmonic pollution, impedance mismatch, and resonance risks. Distributed generation often uses inverters for grid connection, and its grid connection characteristics are significantly affected by its control parameters and filter design, which leads to a high degree of uncertainty in its harmonic emission characteristics under time-varying operating conditions.

[0050] In existing technologies, the static impedance of the distributed power source access point is used to understand the response characteristics of the power grid at a specific frequency, thereby setting up a filter with ideal performance for filtering. Furthermore, when the characteristics of the power grid change, potential problems are identified, and the filter parameters are adjusted appropriately to better match the power grid impedance and reduce reflected harmonics.

[0051] However, the power grid characteristics estimated based on static impedance cannot adapt to the dynamic changes in the frequency domain of the power grid, which may cause the filter parameters to be adjusted at inappropriate times, resulting in a decline in power quality.

[0052] Therefore, the inventors believe that impedance spectrum data in the power grid can be observed in real time and used as a trigger for adjusting filter parameters. Equipment capacity, taking cost factors into account, is also considered as a trigger. By optimizing these two types of triggers, the responsibility coefficient of each distributed power source in ensuring stable power output is calculated. Thus, when filter parameter adjustments are needed, the priority list reflected by the responsibility coefficients is used to send the adjustments sequentially to the corresponding distributed power sources. This ensures that distributed power sources with higher responsibility coefficients adjust filter parameters at the appropriate time, improving the trigger accuracy of filter parameter adjustments and guaranteeing power quality.

[0053] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0054] Figure 1 A flowchart illustrating the coordinated control method for medium and low voltage distribution networks provided in this application embodiment. Figure 1 ,like Figure 1 As shown, the method includes:

[0055] S101. Acquire broadband impedance spectrum data of medium-voltage busbars and harmonic emission characteristic data of low-voltage distributed power sources synchronously collected from medium- and low-voltage distribution networks.

[0056] Among them, broadband impedance spectrum data refers to the electrical impedance values ​​and corresponding phase angle information measured at different frequency points within a set frequency range.

[0057] Broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band at the preset harmonic order.

[0058] It should be understood that the fundamental frequency is the basic frequency in a circuit, while the Nth harmonic is N times that fundamental frequency. For example, if the fundamental frequency is 50Hz, then the second harmonic frequency is 100Hz, and the third harmonic frequency is 150Hz. Therefore, preset harmonic order refers to pre-setting the required harmonic order.

[0059] In this context, the impedance in the harmonic frequency band describes the opposition of circuit elements to alternating current. Phase angle refers to the leading or lagging angle of voltage relative to current.

[0060] Harmonic emission characteristic data refers to the magnitude and distribution of harmonic currents injected into the medium and low voltage power grid by various distributed power sources.

[0061] Harmonic emission characteristic data includes the harmonic emission intensity of each distributed power source connected to the medium and low voltage distribution network.

[0062] S102. Based on broadband impedance spectrum data, construct multi-objective impedance matching equations.

[0063] Among them, the multi-objective impedance matching equation includes the impedance matching degree objective and the equipment capacity constraint objective. The impedance matching degree objective is the objective equation that measures whether the impedance is close to the ideal value, and the equipment capacity constraint objective is the objective equation that measures whether the equipment capacity is close to the ideal value.

[0064] In one possible implementation, the formula is: min∑|Z m (f)·Y A (f)-1|, construct the impedance matching degree objective of the multi-objective impedance matching equation;

[0065] In the above formula, Z m (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0066] It should be understood that the impedance matching problem on the medium-voltage side is defined as minimizing ∑|Z m (f)·Y A (f)-1|, with minimizing the target impedance deviation function on the medium-voltage side as the optimization objective, reconstructs the frequency domain coupling relationship between medium and low voltage, effectively alleviates the impedance mismatch problem caused by distributed access, and reduces the risk of system harmonic amplification and resonance.

[0067] Based on broadband impedance spectrum data, a multi-objective impedance matching equation is constructed to define the equipment capacity constraint objectives, including:

[0068] Through the formula: min∑|Y A (f)|, constructing the equipment capacity constraint objective of the multi-objective impedance matching equation;

[0069] In the above formula, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0070] It should be understood that the equipment capacity-constrained objective optimization problem is defined as minimizing ∑|Y A (f)| takes into account the practical constraints of low-voltage distributed power sources, such as filter capacity constraints, control response capabilities, and cost functions.

[0071] S103. Based on the multi-objective impedance matching equation, determine the medium-voltage side harmonic admittance compensation matrix of the medium- and low-voltage distribution network.

[0072] Among them, the medium-voltage side harmonic admittance compensation matrix refers to the model for improving harmonic performance, which is represented by a matrix.

[0073] In one possible implementation, a preset value range is obtained as a constraint condition for the value of the harmonic admittance compensation matrix in the multi-objective impedance matching equation; based on the value constraint condition, the impedance matching degree optimization objective and the equipment capacity constraint objective are mapped to the Ising model of the quantum annealing machine, and the medium-voltage side harmonic admittance compensation matrix is ​​obtained by solving.

[0074] The preset value range refers to the reasonable range set for variables during the optimization process of impedance matching target and equipment capacity constraint target. In this case, the variable refers to the harmonic admittance compensation matrix on the medium voltage side.

[0075] Value constraints refer to the constraints that must be followed during the optimization process of impedance matching and equipment capacity objectives. In practical applications, a preset value range is used as the value constraint.

[0076] It should be understood that mapping the impedance matching optimization objective and the device capacity constraint objective to the Ising model of the quantum annealing machine, and then combining the value constraints, allows the quantum annealing machine to explore all possible states and try to find the optimal solution as the final medium-voltage side harmonic admittance compensation matrix.

[0077] S104. Based on the harmonic admittance compensation matrix and harmonic emission characteristic data, dynamically calculate the harmonic responsibility coefficient of each distributed power source.

[0078] Among them, the harmonic liability coefficient refers to the index value that quantifies the responsibility of each distributed power source for harmonic pollution in the entire network.

[0079] It should be understood that once the harmonic responsibility coefficient of each distributed power source is determined, their respective contribution ratio to the total harmonic distortion in the power grid can be clearly known. Based on this, targeted optimization or adjustment can be made to those distributed power sources that have a greater impact on harmonic pollution of the power grid, such as adjusting their inverter filter parameters to reduce harmonic injection.

[0080] In one possible implementation, via formula λ i =(H i ·Y A ) / (∑H i ·|Y A |), calculate the harmonic liability factor;

[0081] In the above formula, λ i H is the harmonic liability factor for the i-th distributed power source. i For the harmonic emission characteristics data of the i-th distributed power source, Y A This is the harmonic admittance compensation matrix.

[0082] In practical applications, the harmonic liability coefficient is combined with the phase matching degree by introducing a phase angle coupling factor, thereby correcting the harmonic liability coefficient.

[0083] Therefore, in one possible implementation, the phase angle of the harmonic current and the phase angle of the admittance matrix of the distributed power source are obtained; and the harmonic liability coefficient is corrected based on the phase angle of the harmonic current and the phase angle of the admittance matrix.

[0084] The harmonic current phase angle refers to the phase difference between the harmonic current injected into the grid by the distributed generation and a certain reference point, which is usually the fundamental voltage.

[0085] The phase angle of the admittance matrix refers to the phase angle of the elements of the admittance matrix that describe the interaction between nodes.

[0086] In practical applications, through formula λ i ′ =λ i ·cos(θ i -θ Y ), calculate the corrected harmonic liability factor.

[0087] Where, λ i ′ For the corrected harmonic liability factor, θ i For the phase angle of the harmonic current of the distributed power source, θ Y The phase angle is the admittance matrix.

[0088] S105. Based on the harmonic responsibility coefficient, adjust the inverter filter parameters of each distributed power source.

[0089] Inverter filter parameters refer to the electrical parameters that control and optimize the output waveform quality of the inverter. When distributed power sources are connected to low-voltage distribution networks, the proper setting of inverter filter parameters is crucial to the stability of the power grid and the power quality.

[0090] In one possible implementation, the distributed power sources are sorted sequentially based on the harmonic responsibility coefficients from largest to smallest to obtain a priority list; the resonance risk index is obtained; and the inverter filter parameters of the distributed power sources in the priority list are adjusted according to the resonance risk index and a preset index threshold.

[0091] The priority list is used to determine which distributed power sources should have their filter parameters adjusted first to most effectively reduce total harmonic distortion in the power grid.

[0092] The resonance risk index is a measure of the likelihood of resonance occurring in a power system. When the resonance risk index exceeds a set value, resonance may occur. The set value here is the preset index threshold.

[0093] In practical applications, when the resonance risk index is detected to exceed the set threshold at the current moment, the distributed power source inverter filter parameter adjustment command is sent to the top N distributed power sources in the list first. The value of N is positively correlated with the degree of exceeding the limit. That is, the greater the degree of exceeding the set threshold, the more distributed power source inverters in the priority list receive the filter parameter adjustment command.

[0094] In one possible implementation, the formula is: β=∑|Z new (f)·Y A (f)-1|, calculate the resonance risk index;

[0095] In the above formula, β is the resonance risk index, and Z new (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0096] In practical applications, it is determined whether β exceeds a preset exponential threshold to decide whether to make adjustments.

[0097] To make the determined harmonic admittance compensation matrix more accurate, the preset index threshold can be dynamically updated by comparing the resonant risk index with the preset index threshold, and then fed back to the construction of the multi-objective impedance matching equation to form a closed-loop control.

[0098] Therefore, in one possible implementation, the preset index threshold is updated based on the magnitude of the resonance risk index and the preset index threshold; in response to the next adjustment of the inverter filter parameters of each distributed power source, new constraints are constructed based on the preset index deviation range, the updated preset index threshold, and the resonance risk index, and added to the multi-objective impedance matching equation.

[0099] The preset index deviation range refers to the range of allowable preset index threshold deviation.

[0100] The new constraints are based on preset exponential thresholds, which make the construction of multi-objective impedance matching equations more accurate, thereby improving the accuracy of the harmonic admittance compensation matrix.

[0101] For example, when the preset exponential deviation range is represented by Δβ, a new constraint |β is added. current -β max |≤Δβ, where Δβ is the preset exponential deviation range, β current β is the resonance risk index calculated at the current moment. max This is the dynamically updated resonance risk threshold.

[0102] The preset index threshold can be updated based on changes in the resonance risk index. In one possible implementation, if the resonance risk index is less than the product of the preset index threshold and the first adjustment coefficient within a consecutive preset number of control cycles, then the preset index threshold is multiplied by the second adjustment coefficient to obtain the updated preset index threshold. The first adjustment coefficient is less than the second adjustment coefficient, and both the first and second adjustment coefficients are greater than 0 and less than 1.

[0103] If the resonance risk index is greater than the product of the preset index threshold and the third adjustment coefficient within a single control cycle, then the preset index threshold is multiplied by the fourth adjustment coefficient to obtain the updated preset index threshold. Both the third and fourth adjustment coefficients are greater than 1, and the fourth adjustment coefficient is less than the third adjustment coefficient.

[0104] The preset number of cycles is a threshold number used to determine whether the resonance risk index meets the requirements. For example, if the preset number of cycles is M, then the resonance risk index needs to be determined within the control cycle of M consecutive cycles.

[0105] The first adjustment coefficient is used to determine whether the resonance risk index meets the requirements within multiple consecutive control cycles.

[0106] The second adjustment factor is used to calculate a new threshold when it is determined that the preset index threshold needs to be adjusted over a long period of time.

[0107] The third adjustment factor is used to determine whether the resonance risk index meets the requirements in the short term.

[0108] The fourth adjustment coefficient is used to calculate a new threshold when determining the preset index threshold in the short term.

[0109] For example, if the resonance risk index β satisfies β < α·β within a number of consecutive preset control cycles. max Then update β max =β max ·γ, where α is the first adjustment coefficient, γ is the second adjustment coefficient, 0 < α < 1, 0 < γ < 1, and γ > α. If β > η·β within a single adjustment cycle max Then update β max =β max ·δ, where η is the third adjustment coefficient, δ is the fourth adjustment coefficient, η>1, δ>1, and δ>η.

[0110] The medium- and low-voltage distribution network coordinated control method provided in this application collects grid impedance characteristics and harmonic emission data of distributed power sources, establishes a multi-objective optimization model that takes into account impedance matching effect and safe equipment operation, solves the harmonic admittance compensation matrix on the medium-voltage side, and dynamically evaluates the harmonic responsibility of each distributed power source accordingly. Finally, by adjusting its inverter filter parameters, it achieves precise control and dynamic optimization of harmonic pollution, thereby improving the accuracy of medium- and low-voltage distribution network coordinated control.

[0111] Figure 2 A flowchart illustrating the coordinated control method for medium and low voltage distribution networks provided in this application embodiment. Figure 2 ,like Figure 2 As shown, in this embodiment... Figure 2 Based on the examples, a detailed description of the coordinated control method for medium and low voltage distribution networks is provided. This method includes:

[0112] S201. Obtain broadband impedance spectrum data, which includes the impedance and phase angle of the harmonic frequency band of the preset harmonic order.

[0113] S202. Based on broadband impedance spectrum data, construct a multi-objective impedance matching equation, which includes impedance matching degree objective and equipment capacity constraint objective.

[0114] S203. Based on the multi-objective impedance matching equation, determine the harmonic admittance compensation matrix on the medium-voltage side of the medium- and low-voltage distribution network.

[0115] S204. Based on the harmonic admittance compensation matrix and harmonic emission characteristic data, dynamically calculate the harmonic responsibility coefficient of each distributed power source.

[0116] S205. Correct the harmonic liability coefficient based on the phase angle in the broadband impedance spectrum data;

[0117] S206. Based on the order of each harmonic responsibility coefficient from largest to smallest, sort each distributed power source in sequence to obtain a priority list;

[0118] S207. Determine whether the resonance risk index exceeds the preset index threshold. If it does, proceed to S208; if it does not, proceed to S209.

[0119] S208. Adjust the inverter filter parameters of the distributed power sources in the priority list, and execute S210.

[0120] S209. No adjustment is required;

[0121] S210. In response to the next adjustment of the inverter filter parameters of each distributed power source, new constraints are constructed based on the preset index deviation range, the updated preset index threshold and the resonance risk index, and added to the multi-objective impedance matching equation.

[0122] Figure 3 This is a schematic diagram of the structure of the medium and low voltage distribution network coordinated control device provided in the embodiments of this application, as shown below. Figure 3 As shown, the medium- and low-voltage distribution network coordinated control device 30 provided in this embodiment includes:

[0123] The data acquisition module 301 is used to acquire broadband impedance spectrum data of medium-voltage busbar synchronously collected from medium- and low-voltage distribution networks, as well as harmonic emission characteristic data of low-voltage distributed power sources. The broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band of the preset harmonic order, and the harmonic emission characteristic data includes the harmonic emission intensity of each distributed power source connected to the medium- and low-voltage distribution network.

[0124] The equation construction module 302 is used to construct a multi-objective impedance matching equation based on broadband impedance spectrum data. The multi-objective impedance matching equation includes an impedance matching degree objective and an equipment capacity constraint objective.

[0125] The matrix determination module 303 is used to determine the medium-voltage side harmonic admittance compensation matrix of the medium- and low-voltage distribution network based on the multi-objective impedance matching equation.

[0126] The coefficient calculation module 304 is used to dynamically calculate the harmonic responsibility coefficient of each distributed power source based on the harmonic admittance compensation matrix and harmonic emission characteristic data.

[0127] The parameter adjustment module 305 is used to adjust the inverter filter parameters of each distributed power source based on the harmonic responsibility coefficient.

[0128] In one possible implementation, the equation building module 302 is also used to express the formula: min∑|Z m (f)·Y A (f)-1|, construct the impedance matching degree objective of the multi-objective impedance matching equation;

[0129] In the above formula, Z m (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f;

[0130] In one possible implementation, the equation building module 302 is also used to express the formula: min∑|Y A (f)|, constructing the equipment capacity constraint objective of the multi-objective impedance matching equation;

[0131] In the above formula, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0132] In one possible implementation, the matrix determination module 303 is also used to obtain a preset value range as a constraint condition for the value of the harmonic admittance compensation matrix in the multi-objective impedance matching equation; based on the value constraint condition, the impedance matching degree optimization objective and the equipment capacity constraint objective are mapped to the Ising model of the quantum annealing machine, and the medium-voltage side harmonic admittance compensation matrix is ​​obtained by solving.

[0133] In one possible implementation, the coefficient calculation module 304 is also used to calculate λ using formula λ. i =(H i ·Y A ) / (∑H i ·|Y A |), calculate the harmonic liability factor;

[0134] In the above formula, λ i H is the harmonic liability factor for the i-th distributed power source. i For the harmonic emission characteristics data of the i-th distributed power source, Y A This is the harmonic admittance compensation matrix.

[0135] In one possible implementation, the coefficient calculation module 304 is also used to obtain the harmonic current phase angle and admittance matrix phase angle of the distributed power source; and to correct the harmonic liability coefficient based on the harmonic current phase angle and admittance matrix phase angle.

[0136] In one possible implementation, the parameter adjustment module 305 is further configured to sort each distributed power source in descending order of harmonic responsibility coefficients to obtain a priority list; obtain the resonance risk index; and adjust the inverter filter parameters of the distributed power sources in the priority list according to the resonance risk index and a preset index threshold.

[0137] In one possible implementation, the parameter adjustment module 305 is also used to adjust the parameters according to the formula: β=∑|Z new (f)·Y A (f)-1|, calculate the resonance risk index;

[0138] In the above formula, β is the resonance risk index, and Z new (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

[0139] In one possible implementation, the equation construction module 302 is further configured to update the preset index threshold based on the magnitude of the resonance risk index and the preset index threshold; in response to the next adjustment of the inverter filter parameters of each distributed power source, new constraints are constructed based on the preset index deviation range, the updated preset index threshold and the resonance risk index, and added to the multi-objective impedance matching equation.

[0140] In one possible implementation, the equation construction module 302 is further configured to: if the resonance risk index is less than the product of a preset index threshold and a first adjustment coefficient within a series of preset control cycles, then multiply the preset index threshold by a second adjustment coefficient to obtain an updated preset index threshold, wherein the first adjustment coefficient is less than the second adjustment coefficient, and both the first and second adjustment coefficients are greater than 0 and less than 1; if the resonance risk index is greater than the product of a preset index threshold and a third adjustment coefficient within a single control cycle, then multiply the preset index threshold by a fourth adjustment coefficient to obtain an updated preset index threshold, wherein both the third and fourth adjustment coefficients are greater than 1, and the fourth adjustment coefficient is less than the third adjustment coefficient.

[0141] The medium- and low-voltage distribution network coordinated control device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effect are similar, and will not be described in detail here.

[0142] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 4 As shown, the electronic device 40 provided in this embodiment includes at least one processor 401 and a memory 402. Optionally, the electronic device 40 further includes a communication component 403. The processor 401, memory 402, and communication component 403 are connected via a bus 404.

[0143] In a specific implementation, at least one processor 401 executes computer execution instructions stored in memory 402, causing at least one processor 401 to perform the above-described method.

[0144] The specific implementation process of processor 401 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0145] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0146] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage device.

[0147] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0148] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0149] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the above-described method.

[0150] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0151] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0152] The division of units is merely a logical functional division; in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0153] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0154] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0155] If a function is implemented as 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 technical solution of this invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0156] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0157] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A method for coordinated control of medium and low voltage distribution networks, characterized in that, include: Acquire broadband impedance spectrum data of medium-voltage busbar synchronously collected from medium- and low-voltage distribution networks, and harmonic emission characteristic data of low-voltage distributed power sources. The broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band of the preset harmonic order, and the harmonic emission characteristic data includes the harmonic emission intensity of each distributed power source connected to the medium- and low-voltage distribution network. Based on the broadband impedance spectrum data, a multi-objective impedance matching equation is constructed, which includes an impedance matching degree objective and a device capacity constraint objective. Based on the multi-objective impedance matching equation, the harmonic admittance compensation matrix of the medium- and low-voltage distribution network on the medium- and low-voltage side is determined. Based on the harmonic admittance compensation matrix and the harmonic emission characteristic data, the harmonic responsibility coefficient of each distributed power source is dynamically calculated. Based on the harmonic responsibility coefficient, the inverter filtering parameters of each distributed power source are adjusted.

2. The method according to claim 1, characterized in that, Based on the broadband impedance spectrum data, the impedance matching degree objective of the multi-objective impedance matching equation is constructed as follows: Using the formula: min∑|Z m (f)·Y A (f)-1|, construct the impedance matching degree target of the multi-objective impedance matching equation; In the above formula, Z m (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f; Accordingly, based on the broadband impedance spectrum data, the equipment capacity constraint targets for constructing the multi-objective impedance matching equation include: Through the formula: min∑|Y A (f)|, construct the equipment capacity constraint objective of the multi-objective impedance matching equation; In the above formula, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

3. The method according to claim 2, characterized in that, Based on the multi-objective impedance matching equation, the medium-voltage side harmonic admittance compensation matrix of the medium- and low-voltage distribution network is determined, including: A preset range of values ​​is obtained as a constraint condition for the value of the harmonic admittance compensation matrix in the multi-target impedance matching equation. Based on the aforementioned value constraints, the impedance matching optimization objective and the equipment capacity constraint objective are mapped to the Ising model of the quantum annealing machine, and the medium-voltage side harmonic admittance compensation matrix is ​​obtained by solving.

4. The method according to claim 1, characterized in that, The step of dynamically calculating the harmonic liability coefficient of each distributed power source based on the harmonic admittance compensation matrix and the harmonic emission characteristic data includes: Through formula λ i =(H i ·Y A ) / (∑H i ·|Y A |), calculate the harmonic liability factor; In the above formula, λ i H is the harmonic liability factor for the i-th distributed power source. i For the harmonic emission characteristics data of the i-th distributed power source, Y A This is the harmonic admittance compensation matrix.

5. The method according to claim 4, characterized in that, The method further includes: Obtain the phase angle of the harmonic current and the phase angle of the admittance matrix of the distributed power source; The harmonic liability coefficient is corrected based on the phase angle of the harmonic current and the phase angle of the admittance matrix.

6. The method according to claim 1, characterized in that, The adjustment of inverter filter parameters for each distributed power source based on the harmonic responsibility coefficient includes: Based on the order of harmonic responsibility coefficients from largest to smallest, each distributed power source is sorted sequentially to obtain a priority list; Obtain the resonance risk index; Based on the resonance risk index and the preset index threshold, the inverter filter parameters of the distributed power sources in the priority list are adjusted.

7. The method according to claim 6, characterized in that, The acquisition of the resonance risk index includes: Using the formula: β=∑|Z new (f)·Y A (f)-1|, calculate the resonance risk index; In the above formula, β is the resonance risk index, and Z new (f) shows the broadband impedance spectrum data, Y A (f) represents the admittance component of the harmonic admittance compensation matrix at frequency f.

8. The method according to claim 6 or 7, characterized in that, The method further includes: The preset index threshold is updated based on the magnitude of the resonance risk index and the preset index threshold. In response to the next adjustment of the inverter filter parameters of each distributed power source, new constraints are constructed based on the preset index deviation range, the updated preset index threshold, and the resonance risk index, and added to the multi-objective impedance matching equation.

9. The method according to claim 8, characterized in that, The step of updating the preset index threshold based on the magnitude of the resonance risk index and the preset index threshold includes: If the resonance risk index is less than the product of the preset index threshold and the first adjustment coefficient within a set number of consecutive adjustment cycles, then the preset index threshold is multiplied by the second adjustment coefficient to obtain an updated preset index threshold. The first adjustment coefficient is less than the second adjustment coefficient, and both the first and second adjustment coefficients are greater than 0 and less than 1. If the resonance risk index is greater than the product of the preset index threshold and the third adjustment coefficient within a single control cycle, then the preset index threshold is multiplied by the fourth adjustment coefficient to obtain an updated preset index threshold. Both the third and fourth adjustment coefficients are greater than 1, and the fourth adjustment coefficient is less than the third adjustment coefficient.

10. A medium- and low-voltage distribution network coordinated control device, characterized in that, include: The data acquisition module is used to acquire broadband impedance spectrum data of medium-voltage busbar synchronously collected from medium- and low-voltage distribution networks, as well as harmonic emission characteristic data of low-voltage distributed power sources. The broadband impedance spectrum data includes the impedance and phase angle of the harmonic frequency band of the preset harmonic order, and the harmonic emission characteristic data includes the harmonic emission intensity of each distributed power source connected to the medium- and low-voltage distribution network. The equation construction module is used to construct a multi-objective impedance matching equation based on the broadband impedance spectrum data. The multi-objective impedance matching equation includes an impedance matching degree objective and a device capacity constraint objective. The matrix determination module is used to determine the medium-voltage side harmonic admittance compensation matrix of the medium- and low-voltage distribution network based on the multi-objective impedance matching equation. The coefficient calculation module is used to dynamically calculate the harmonic responsibility coefficient of each distributed power source based on the harmonic admittance compensation matrix and the harmonic emission characteristic data. The parameter adjustment module is used to adjust the inverter filter parameters of each distributed power source based on the harmonic responsibility coefficient.

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