Network construction type equipment optimization configuration method for improving quiescent voltage stability margin of system

Abstract

The invention provides a network construction type equipment optimal configuration method for improving the quiescent voltage stability margin of a system. The method comprises the following steps: acquiring power grid parameters and typical operation scene parameters of a power system; the minimum singular value of the system is obtained by calculating the Jacobian matrix of the power system; identifying weak load nodes of the system through the minimum singular value of the system and the minimum singular value criterion, and selecting candidate drop points of the network-forming equipment; and based on a system static voltage stability margin improvement demand, carrying out capacity optimization configuration on the network construction type equipment of the candidate drop points. According to the method, the configuration position is determined through the minimum singular value calculation of the system, the reactive voltage supporting capability is ensured to be efficiently exerted, and the static voltage stability margin of the load center power grid is remarkably improved; and the capacity is optimized based on the system static voltage stability margin demand, the equipment investment cost is reduced, the resource waste is reduced, the adaptability and reliability of the configuration scheme to the multi-scene operation of the power grid are improved, and reliable technical support is provided for ensuring the static voltage stability of the power system.

Description

Technical Field

[0001] This invention relates to the field of power technology, and specifically to a method for optimizing the configuration of grid-type equipment to improve the static voltage stability margin of a system. Background Technology

[0002] Driven by the demand for carbon reduction, energy construction and transformation in various regions have ushered in a new round of development. New energy sources, represented by wind power and photovoltaics, are being integrated into the power grid in large quantities. While promoting the green, low-carbon, and sustainable development of the power grid, this also poses challenges to its safe and stable operation. New energy sources are integrated into the grid through power electronic converters, resulting in significant differences in active and reactive power output and regulation characteristics compared to traditional generating units. As the installed capacity of new energy sources increases, the proportion of traditional generating units decreases, reducing the grid's dynamic reactive power reserve and increasing the risk of power outages caused by voltage instability. Load centers lacking conventional power support will exhibit a "hollowed-out" state, and under heavy load conditions, voltage stability problems will become more severe once disturbed or a fault occurs. Based on these issues, deploying dynamic reactive power compensation devices in key substations can effectively enhance the voltage stability of the system. Conventional dynamic reactive power compensation devices mainly include SVC (Static Var Compensator), SVG (Static Var Generator), and synchronous condensers. Among them, SVG, as a typical representative of the new generation of FACTS (Flexible AC Transmission Systems), is currently widely used in power systems. Configuring SVG as reactive power compensation for converter filters and step-up transformers can smooth voltage fluctuations under steady-state conditions and support voltage under transient conditions.

[0003] Grid-formed SVG is a synchronous reactive power compensation device that improves the overcurrent capacity of components, increases capacitor energy, and employs a grid-forming control strategy based on conventional SVG. When a system fault occurs, the grid-formed SVG acts as a relatively independent synchronous voltage source, providing instantaneous and rapid reactive power support, thus quickly supporting the system voltage. It outperforms conventional SVG in response speed and overload capacity, meeting both dynamic and static reactive power requirements of the power grid and becoming a key device for building new power systems. Currently, grid-formed SVG technology is still immature and lacks relevant standards. Systematic research on grid-formed SVG control strategies, optimal placement, and grid-connected capacity in conjunction with large power grids has not yet been conducted. The installation location and capacity of grid-formed SVG will have different impacts on system characteristics, and their effects on system stability will vary, becoming a challenge for grid-formed SVG grid connection in the context of "hollowed-out" power grids. This may limit the reactive power and voltage support potential of grid-formed SVGs and threaten the safe and stable supply of power.

[0004] In summary, there are currently few methods for equipment location and capacity determination that take into account the static voltage support capability of grid-type equipment. Further research is needed on voltage optimization configuration methods for grid-type equipment to improve static voltage support capability, so as to provide an optimized configuration method for grid-type equipment that can improve the static voltage stability margin of the system. Summary of the Invention

[0005] This invention addresses the problems existing in the prior art by providing a method for optimizing the configuration of network-type devices to reduce the computational load and ensure the interpretability of the results.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: This invention provides a method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system, comprising the following steps: Obtain power grid parameters and typical operating scenario parameters of the power system; Based on the power grid parameters and typical operating scenario parameters, the node admittance matrix of the power system is calculated and the Jacobian matrix is ​​obtained. The minimum singular value of the system is then calculated using the Jacobian matrix. By using the system minimum singular value and minimum singular value criterion, weak load nodes of the system are identified under multiple preset operating conditions, and candidate landing points of network-type equipment are selected based on the weak load nodes of the system. Based on the requirement to improve the static voltage stability margin of the system, and with the goal of minimizing the total capacity of the network-type equipment, the capacity of the network-type equipment at the candidate landing points is configured.

[0007] Optionally, obtaining the minimum singular value of the system includes the following steps: The node admittance matrix is ​​generated based on the power grid topology and branch parameters in the power grid parameters. Based on the node injection power, load power and initial voltage values ​​in the typical operating scenario parameters, a system power balance equation is constructed, and the system power balance equation is linearized to obtain the Jacobian matrix. The Jacobian matrix is ​​subjected to singular value decomposition, and the singular value with the smallest value is extracted from the decomposition result as the minimum singular value of the system.

[0008] Optionally, obtaining multiple operating conditions includes the following steps: The power system adjusts the output level of local generating units to multiple preset values, and at the same time controls the load power of each load node in the system according to the initial power factor, so as to obtain multiple operating conditions with different local generating unit output and load corresponding changes.

[0009] Optionally, identifying vulnerable load nodes in the system includes the following steps: For each load node under each operating condition, calculate the sensitivity of the system minimum singularity to changes in active power and reactive power at the load node. The minimum singularity criterion for each load node under the corresponding operating condition is calculated based on the active power change sensitivity and reactive power change sensitivity.

[0010] Optionally, selecting candidate landing points for network-type devices includes the following steps: Calculate the arithmetic mean of the minimum singular value criterion for each load node under all operating conditions; Sort the arithmetic mean of each load node in descending order; The number of candidate landing points is preset based on the scale of the power system. N Select the top results from the sorted list. N Each load node serves as a candidate landing point for network-type equipment; N It is a positive integer.

[0011] Optionally, capacity configuration includes the following steps: The objective function is to minimize the sum of the capacities of all candidate landing points connected to the network-type devices. The variables of the objective function are the capacity values ​​of the network-type devices at each candidate landing point, and the range of the values ​​of the variables is between the upper and lower limits of the rated capacity of the network-type devices.

[0012] Optionally, the constraints on the capacity configuration include location result constraints, power balance constraints, state variable constraints, control variable constraints, and static voltage stability margin constraints. The location selection result constraint is that network-type devices can only access confirmed candidate landing points; The power balance constraint is that the injected power of each node in the system after the network-type equipment is connected satisfies the power conservation of the load consumption power and the branch transmission power. The state variable constraint is that the state variable values ​​of the network-type device are between a preset upper limit and a lower limit; The control variable constraint is that the control variable values ​​of the network-type equipment are between a preset upper limit and a lower limit; The static voltage stability margin constraint is: the static voltage stability margin of the system after connecting to the network-type equipment meets the preset improvement requirements.

[0013] Optionally, obtaining the system's static voltage stability margin includes the following steps: The system's total load power is increased based on a constant power factor, and the change in the system's minimum singular value is monitored synchronously. When the minimum singularity of the system is detected to be less than the preset singularity, the total load power of the system is recorded and the total load power of the system is recorded as the system load carrying capacity. The system load carrying capacity is used as an indicator to characterize the static voltage stability margin of the system.

[0014] Optionally, configuring the capacity of the network-type devices at the candidate landing points further includes the following steps: Set initial capacity values ​​for the network-type equipment at each candidate landing point; Based on the initial capacity value, calculate the system load carrying capacity after the corresponding network-type equipment is connected; The system load carrying capacity is compared with the preset static voltage stability margin improvement target. If the improvement target is not met, the capacity value of the grid-type equipment is increased and the system load carrying capacity is recalculated until the system load carrying capacity meets the improvement target.

[0015] Optionally, during the capacity configuration process, when the capacity value of a network-type device with candidate landing points reaches the upper limit of the rated capacity of the network-type device, the node operation type corresponding to the network-type device is switched; based on the node operation type of the network-type device after the switch, the system load carrying capacity is recalculated and iterated until the system load carrying capacity meets the improvement target.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention determines the configuration location by calculating the minimum singular value of the system, identifies weak load nodes in the system under multiple operating conditions, ensures the efficient operation of their reactive power and voltage support capabilities, and significantly improves the static voltage stability margin of the load center power grid. Furthermore, it optimizes capacity based on the system's static voltage stability margin requirements, significantly reducing equipment investment costs and minimizing resource waste while meeting the requirements. Simultaneously, by conducting verification through multiple preset operating conditions covering different local unit output and load levels, it improves the adaptability and reliability of the configuration scheme to various power grid operating scenarios, providing reliable technical support for ensuring the static voltage stability of the power system. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is a flowchart of the optimized location strategy for networked devices based on the minimum singularity criterion according to an embodiment of the present invention; Figure 3This is a process for optimizing the capacity configuration of network-type equipment in accordance with the embodiments of the present invention to improve the static voltage stability margin; Figure 4 These are the system minimum singularity curves corresponding to different load amplification factors when the local unit is at 90% output in Example 1; Figure 5 This is the minimum singularity criterion result for each load bus when the local unit is operating at 90% capacity in Example 1; Figure 6 This is a schematic diagram of the candidate installation points for the mesh-type SVG in Example 1; Figure 7 This is a comparison chart of load-bearing capacity improvement under different local unit output levels in Example 1; Figure 8 This is a graph showing the relationship between the capacity change of a single SVG device and its load-bearing capacity in Example 1. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0021] It is worth noting that, unless otherwise specified, the methods used in this invention are all conventional methods; and the raw materials and equipment used are all conventional commercially available products, and their sources are not specifically limited.

[0022] It should also be noted that, for ease of understanding, the method steps in the specific embodiments of the present invention are described in a certain order, but those skilled in the art can change the order of the steps according to actual needs, so this should not be used as a limiting condition; further, in the description of the following specific embodiments, the superscripts and subscripts of each parameter should be understood as distinguishing marks of similar identifiers in accordance with common interpretations, representing parameters of the related or corresponding devices, and should not be understood as specific models or special marks.

[0023] Please see Figures 1-3 As shown, this embodiment provides a method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of the system, which includes the following steps: 1. A method for optimizing the location of network-type equipment based on the minimum singularity criterion; Combination Figure 2 As shown, when obtaining the power grid parameters and typical operating scenario parameters of the power system, it is first necessary to obtain the power grid topology based on the local power grid settings used in this embodiment; secondly, it is necessary to combine historical operating states to provide a stable operating state of the power system that is representative of daily operation and reflects the core operating characteristics. The parameters include power output parameters: referring to the node injection power of local units in the system, covering different output levels of local units. These output levels correspond to common states of the power grid such as full power generation, power maintenance, and local unit energy replenishment due to fluctuations in new energy output; load level parameters: referring to the initial load power of each load node in the system, covering typical load states such as load flat periods and load peak periods; voltage reference parameters: referring to the initial voltage value of each node in the system, corresponding to the initial state of stable operation of the power grid voltage under typical scenarios.

[0024] Based on grid parameters and parameters of typical operating scenarios, the node admittance matrix of the power system is calculated and the Jacobian matrix is ​​obtained. The minimum singular value of the system is then calculated using the Jacobian matrix.

[0025] Obtaining the minimum singular value of the system involves the following steps: Generate the node admittance matrix based on the power grid topology and branch parameters in the power grid parameters; When selecting the locations for reactive power compensation devices, the first consideration is the system's ability to resist voltage shifts under quantified operating scenarios. This involves identifying the system strength under the worst-case disturbance conditions and then determining the appropriate locations for the reactive power compensation devices. Based on the node injection power, load power, and initial voltage values ​​in typical operating scenario parameters, a system power balance equation is constructed; corresponding to the nodes... Injection power , Representation; Node load , In other words, the power grid's first... The power balance equations for each node are as follows: ; In the formula, , They are nodes The imbalance between active and reactive power; , They are nodes and The voltage amplitude; , They are nodes and The electrical conductance and susceptance between them; For nodes and The phase angle difference between them.

[0026] The system power balance equations are linearized, resulting in the Jacobian matrix on the system side, which is expressed as: ; In the formula, =[ , ] This represents the voltage response output phasor in polar coordinates. =[ , ] This represents the power perturbation vector in polar coordinates; Represents the Jacobian matrix. , , and This represents its sub-block matrix; Δδ is the phase angle change of a single non-equilibrium node relative to the system's equilibrium node. ΔU This represents the voltage magnitude change at a single unbalanced node. ΔP , ΔQ These are the disturbance changes in active and reactive power injected into a single node, respectively.

[0027] Jacobian matrix It also represents the sensitivity matrix of the voltage response of each node in the system to power disturbances. When the system is subjected to a certain uncertain disturbance, i.e. The direction of the perturbation vector is uncertain, and its ability to resist voltage shift should be considered to overcome perturbations that may induce the worst voltage shift. Mathematically, this is determined by a matrix. Maximum singular value quantization, i.e.: ; In the formula, σ max {}and σ min {} denote the maximum and minimum singular values ​​respectively; ||•||2 denotes the 2-norm of the vector. The reciprocal of the minimum singular value of a matrix [ σ min ] -1 The physical meaning of is the system gain against the worst power disturbance, characterizing the maximum sensitivity of the system voltage response to all uncertain power disturbances. Therefore, the system strength in the disturbance rejection performance dimension should be determined by the minimum singular value of the Jacobian matrix (sensitivity matrix). σ min ] -1 Quantification, σ minA higher voltage indicates a lower sensitivity of the system voltage to power disturbances and a stronger system.

[0028] The singular value decomposition of the Jacobian matrix is ​​calculated as follows: ; In the formula, m Jacobian matrix J dimensionality; V and U All are orthogonal matrices of order m×m; For singular values δ i The diagonal matrix formed Singular vectors v i and u i They are respectively V and U middle δ i The corresponding column vector.

[0029] Under a certain operating condition, extract The minimum singular value reflects the system's ability to withstand the worst voltage deviations under current operating conditions. However, when a singular value approaches zero, any small power disturbance will cause a large change in the system's state variables, indicating that the static voltage stability limit has been reached. At this point, the system response is primarily determined by the minimum singular value. and its corresponding left singular vector and right singular vector Decide.

[0030] Therefore, the distance between the minimum singular value of the Jacobian matrix and zero can characterize the static voltage stability margin of the system. The condition for the system to reach the static stability boundary is satisfied at the current operating point as follows: ; Given the system's Jacobian matrix and performing singular decomposition, perturbing the load nodes allows us to determine the impact of changes in active and reactive power on the minimum singular values. For systems with a large number of nodes, numerical methods can be used to obtain approximate results for the minimum singular values ​​on control variables. Minimum singular value sensitivity characterizes the degree of influence of system variables on the voltage stability of the power system at the current operating point. When the minimum singular values ​​have high sensitivity to the control variables of a certain load node, such as active or reactive power, it means that power perturbations at that node are more likely to cause the system to reach the static voltage stability boundary.

[0031] By using the system's minimum singular value and the minimum singular value criterion, vulnerable load nodes in the system are identified under multiple preset operating conditions. Specifically, for each load node under each operating condition, the sensitivity of the system's minimum singular value to changes in active and reactive power is calculated, and a minimum singular value criterion that comprehensively considers the sensitivity of the minimum singular value to active and reactive power changes and the load level is presented. ; For each load node under each operating condition, the sensitivity of the system's minimum singular value to changes in active and reactive power is calculated. Specifically, a minimum singular value criterion is calculated for each load node. The bus corresponding to the maximum value of the criterion exhibits both high sensitivity and heavy load characteristics at the current operating point and is considered a weak bus. Considering that the minimum singular value criterion only reflects the characteristics of a single system operating state, multiple operating conditions need to be set. The system adjusts the output levels of local generating units to multiple preset values, while simultaneously controlling the load power of each load node within the system to adjust according to the initial power factor. This yields multiple operating conditions with varying local generating unit output and load changes, covering different local generating unit output and load levels, in order to identify weak buses under various scenarios. By changing the local generating unit output levels and setting the entire network load to increase at a constant power factor, the minimum singularity criterion for each load node under different operating conditions is calculated, and the optimal compensation node is determined by ranking them. Under each scenario, the entire network load increases according to a constant power factor, and the load power can be expressed as: ; In the formula, This is the load amplification factor. and yes Node under operating conditions i The initial active and reactive loads, It is a collection of load buses.

[0032] thus, Under operating conditions, the node i The minimum singular value criterion is: ; Calculate the arithmetic mean of the minimum singular value criterion for each load node under all operating conditions; calculate the arithmetic mean for all load buses. ,exist Find the minimum singular value criterion set under each operating condition. and obtained Nodes under various working conditions The average value of the minimum singular value criterion The arithmetic mean of each load node is sorted in descending order, as follows: ; Candidate landing points for grid-type equipment are selected based on weak load nodes in the system; the number of candidate landing points is preset according to the power system scale. N Therefore, in the site selection stage, the former can be selected. N Each node serves as a candidate landing point for the network-type SVG. N It is a positive integer.

[0033] By calculating the minimum singularity criterion for each load bus under various operating conditions, the weak load nodes of the system can be identified. Then, a grid-type SVG can be configured at the node where the load is located, which can maximize its reactive voltage support capability and effectively improve the static voltage stability of the current system.

[0034] 2. A method for optimizing the capacity configuration of grid-type equipment to improve static voltage stability margin; Combination Figure 3 As shown, based on the requirement to improve the system's static voltage stability margin, and with the goal of minimizing the total capacity of grid-type equipment, the capacity of grid-type equipment at candidate landing points is configured. Specifically, in the capacity determination stage, for load center grids with hollowed-out power sources, the goal is to minimize the total capacity of grid-type SVG to meet the requirements for improving static voltage stability margin. This is achieved by adding grid-type SVGs to weak load nodes to improve the static voltage stability margin of the receiving-end system.

[0035] Obtain the grid structure parameters and the SVG model parameters of the grid structure, and provide the operating parameters such as local power output and load level under the initial operating conditions.

[0036] The system increases the total load power based on a constant power factor, and the change in the system's minimum singular value is monitored synchronously. First, the active and reactive power of the load are increased according to the initial power factor, and the minimum singular value continuously decreases. When the system's minimum singular value is found to be less than a preset singular value, the total load power is recorded and recorded as the system's load carrying capacity. In this embodiment, the point where the minimum singular value approaches zero is considered the limit point of static stability, and the load active power at the point where the system's minimum singular value is zero is defined as the load carrying capacity. That is, the system load carrying capacity is used as an indicator of the system's static voltage stability margin. The total load power of the system when the minimum singular value is zero, increased according to the constant power factor, is the load carrying capacity, representing the static voltage stability margin before connection to the grid-type SVG. .

[0037] The sum of the network-type device capacities accessed by all candidate landing points The objective function is to minimize the total capacity of the networked SVG at each candidate landing point. The variables in the objective function are the capacity values ​​of the networked devices at each candidate landing point, and the values ​​of these variables range between the upper and lower limits of the rated capacity of the networked devices. Therefore, the objective function is constructed with the goal of minimizing the total capacity of the networked SVG to satisfy the system's static stability improvement constraints: ; In the formula, For nodes i The capacity of the connected network-type SVG This represents the number of load nodes in the system.

[0038] The constraints mainly include inequalities related to location result constraints, power balance constraints, state variable constraints, control variable constraints, and static voltage margin index improvement constraints.

[0039] The location selection constraint is that network-type devices can only access confirmed candidate landing points, expressed as: ; In the formula, Location of SVG access nodes in a network configuration; It is the set of all candidate nodes.

[0040] The power balance constraint is: after connecting network-type devices, the injected power of each node in the system satisfies the power consumption of the load and the transmission power of the branches; the power balance constraint expression of the system after connecting network-type SVG is: ; The state variable constraints are as follows: the state variable values ​​of network-type devices are between preset upper and lower limits; the state variable constraint expressions for network-type SVGs are as follows: ; The control variable constraints are as follows: for network-type devices, the control variable values ​​are between preset upper and lower limits; the control variable constraint expression for network-type SVG is: ; In the formula, , and They are nodes The configuration capacity of active power injection, reactive power injection and reactive power compensation devices; , and Representing the connection nodes respectively and nodes The branch conductance, susceptance, and phase angle difference; the subscripts max and min represent the upper and lower limits, respectively.

[0041] The static voltage stability margin constraint is: the static voltage stability margin of the system after connecting network-type equipment meets the preset improvement requirement; the expression for the static voltage stability margin index improvement constraint is: ; In the formula, , The static voltage stability margin indicators are defined before and after connecting to the grid-type SVG. The goal is to improve the static voltage stability margin.

[0042] The overall optimization model is as follows: ; Based on the above model, in the solution process, the initial capacity values ​​are first set for the network-type devices at each candidate landing point. Then, based on the initial capacity value, the system load carrying capacity after the corresponding network-type equipment is connected is calculated; the system load carrying capacity is compared with the preset static voltage stability margin improvement target. If the improvement target is not met, the capacity value of the network-type equipment is increased and the system load carrying capacity is recalculated until the system load carrying capacity meets the improvement target.

[0043] Based on the above, after selecting candidate landing points for the mesh-type SVG, an optimizer is used to solve for the minimum total capacity that satisfies the requirement of improved static voltage stability margin. The optimizer can be, for example, a particle swarm optimizer, a genetic algorithm optimizer, or an interior-point optimizer.

[0044] Furthermore, during the optimization process, if the connected network-type SVG reaches its capacity limit, the node operation type corresponding to the network-type device is switched, such as changing it from a PV node to a PQ node, and the system load carrying capacity after the network-type SVG is connected is recalculated. Monitor whether the static voltage stability margin requirement is met. If the conditions are not met, the capacity of the network-type SVG in the aforementioned steps is increased to continue solving the problem, iterating until the system load carrying capacity meets the improvement target.

[0045] Example 1; In the IEEE-39 system, units outside the balancing node are considered local units. As the load's active and reactive power increases according to the initial power factor, the minimum singular value continuously decreases, and the point approaching zero is the static stability limit. For example... Figure 4 As shown, taking 90% of the local unit output as an example, when the load amplification factor is 1.195, the system reaches the static stability boundary, and the corresponding system load carrying capacity is 7285MW.

[0046] Taking a local unit output level of 90% as an example, the results of the minimum singularity criterion are presented. It is found that as the load power increases, the sensitivity criterion values ​​of each load node generally rise. (Reference) Figure 5 As shown, under various load levels, the sensitivity indices of nodes 20, 8, 4, 15, 16, and 3 are ranked highest, and their average values ​​of the singularity criterion are also the largest.

[0047] by Figure 6 Taking the local power grid node diagram shown as an example, the power supply nodes outside the slack node are local power sources. The operating conditions are set as follows: the output of the local power source is 100%, 90%, 80%, and 70% of its original output. Under different local power source outputs, the load power increases according to the power factor until the system reaches the static voltage stability limit. Combining the above operating conditions, various load levels, and generator outputs, the average value of the minimum singularity criterion is obtained. The average values ​​of the minimum singularity criterion for all load nodes are sorted, and the top 6 nodes are found to be nodes [20, 8, 4, 15, 16, 3]. Therefore, the nodes selected by these six dashed boxes are candidate landing points for grid-type SVG installation.

[0048] The load carrying capacity is defined as the active power of the load at the operating point where the minimum singular value of the system is zero. The load carrying capacity of the system before and after compensation using the minimum singular value criterion method is compared. When the installed SVG reaches its capacity limit, it is converted from a PV node to a PQ node.

[0049] The control result of the reactive power loop is set to maintain the grid connection point voltage. Upcc =1.07 pu, the capacity of a single SVG in a network configuration is Q i =300Mvar, the nodes with the largest singular value criterion averages of 20, 8, and 4 are incorporated into the SVG, by Figure 7 It can be seen that the load-bearing capacity is improved under different working conditions.

[0050] refer to Figure 8 As shown, when the capacity of a single SVG device increases, the system is less likely to reach the static stability boundary, and the load-bearing capacity is improved. Therefore, subsequent capacity configuration needs to be determined in conjunction with the increased load-bearing capacity requirements. Taking a load-bearing capacity increase of 300MW as an example, and setting the operating condition as 80% local power output, the optimal configuration scheme for the grid-type SVG capacity is shown in Table 1 below: Grid-type SVG Site Selection and Capacity Determination Scheme.

[0051] Table 1 Location and Capacity Scheme for Networked SVG When optimizing the capacity configuration of a grid-type SVG, different initial operating states, such as local power output, initial load level, and grid structure, will affect the final configuration result. Therefore, it is necessary to select several typical initial operating states, especially scenarios where the system load carrying capacity is reduced, such as low power output, peak load period, and planned maintenance, and give a capacity configuration scheme for the grid-type SVG by comprehensively considering the static stability margin improvement requirements of several scenarios.

[0052] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, and is not intended to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions made by those skilled in the art to the technical solution of the present invention do not depart from the essence and scope of the technical solution of the present invention.

Claims

1. A method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system, characterized in that: Includes the following steps: Obtain power grid parameters and typical operating scenario parameters of the power system; Based on the power grid parameters and typical operating scenario parameters, the node admittance matrix of the power system is calculated and the Jacobian matrix is ​​obtained. The minimum singular value of the system is then calculated using the Jacobian matrix. By using the system minimum singular value and minimum singular value criterion, weak load nodes of the system are identified under multiple preset operating conditions, and candidate landing points of network-type equipment are selected based on the weak load nodes of the system. Based on the requirement to improve the static voltage stability margin of the system, and with the goal of minimizing the total capacity of the network-type equipment, the capacity of the network-type equipment at the candidate landing points is configured.

2. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 1, characterized in that: Obtaining the minimum singular value of the system involves the following steps: The node admittance matrix is ​​generated based on the power grid topology and branch parameters in the power grid parameters. Based on the node injection power, load power and initial voltage values ​​in the typical operating scenario parameters, a system power balance equation is constructed, and the system power balance equation is linearized to obtain the Jacobian matrix. The Jacobian matrix is ​​subjected to singular value decomposition, and the singular value with the smallest value is extracted from the decomposition result as the minimum singular value of the system.

3. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 1, characterized in that: Obtaining multiple operating conditions involves the following steps: The power system adjusts the output level of local generating units to multiple preset values, and at the same time controls the load power of each load node in the system according to the initial power factor, so as to obtain multiple operating conditions with different local generating unit output and load corresponding changes.

4. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 1, characterized in that: Identifying vulnerable load nodes in the system includes the following steps: For each load node under each operating condition, calculate the sensitivity of the system minimum singularity to changes in active power and reactive power at the load node. The minimum singularity criterion for each load node under the corresponding operating condition is calculated based on the active power change sensitivity and reactive power change sensitivity.

5. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 4, characterized in that: Selecting candidate landing sites for network-type devices includes the following steps: Calculate the arithmetic mean of the minimum singular value criterion for each load node under all operating conditions; Sort the arithmetic mean of each load node in descending order; The number of candidate landing points is preset based on the scale of the power system. N Select the top results from the sorted list. N Each load node serves as a candidate landing point for network-type equipment; N It is a positive integer.

6. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 1, characterized in that: Capacity configuration includes the following steps: The objective function is to minimize the sum of the capacities of all candidate landing points connected to the network-type devices. The variables of the objective function are the capacity values ​​of the network-type devices at each candidate landing point, and the range of the values ​​of the variables is between the upper and lower limits of the rated capacity of the network-type devices.

7. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 1, characterized in that: The constraints on capacity configuration include location result constraints, power balance constraints, state variable constraints, control variable constraints, and static voltage stability margin constraints. The location selection result constraint is that network-type devices can only access confirmed candidate landing points; The power balance constraint is that the injected power of each node in the system after the network-type equipment is connected satisfies the power conservation of the load consumption power and the branch transmission power. The state variable constraint is that the state variable values ​​of the network-type device are between a preset upper limit and a lower limit; The control variable constraint is that the control variable values ​​of the network-type equipment are between a preset upper limit and a lower limit; The static voltage stability margin constraint is: the static voltage stability margin of the system after connecting to the network-type equipment meets the preset improvement requirements.

8. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 7, characterized in that: Obtaining the static voltage stability margin of the system includes the following steps: The system's total load power is increased based on a constant power factor, and the change in the system's minimum singular value is monitored synchronously. When the minimum singularity of the system is detected to be less than the preset singularity, the total load power of the system is recorded and the total load power of the system is recorded as the system load carrying capacity. The system load carrying capacity is used as an indicator to characterize the static voltage stability margin of the system.

9. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 8, characterized in that: The capacity configuration of the network-type devices for the candidate landing points also includes the following steps: Set initial capacity values ​​for the network-type equipment at each candidate landing point; Based on the initial capacity value, calculate the system load carrying capacity after the corresponding network-type equipment is connected; The system load carrying capacity is compared with the preset static voltage stability margin improvement target. If the improvement target is not met, the capacity value of the grid-type equipment is increased and the system load carrying capacity is recalculated until the system load carrying capacity meets the improvement target.

10. The method for optimizing the configuration of network-type equipment to improve the static voltage stability margin of a system according to claim 9, characterized in that: During capacity configuration, when the capacity value of a network-type device with candidate landing points reaches the upper limit of the rated capacity of the network-type device, the node operation type corresponding to the network-type device is switched; based on the node operation type of the network-type device after switching, the system load carrying capacity is recalculated and iterated until the system load carrying capacity meets the improvement target.

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