New energy reactive power configuration optimization method and system

Abstract

The invention discloses a new energy reactive power configuration optimization method and system. The method comprises the steps of obtaining a new energy starting combination, a load level and an operation mode data set under a grid structure in a target power grid after new energy access; calculating a dynamic voltage stability index of a new energy near-region power grid node of the operation mode data set, and judging the dynamic voltage stability index; when the dynamic voltage stability index is lower than the first threshold value, judging whether the dynamic voltage stability index is lower than a second threshold value or not; and configuring the capacity of the static var generator according to a first strategy if the capacity of the static var generator is lower than the second threshold value, and configuring the capacity of the static var generator according to a second strategy if the capacity of the static var generator is higher than the second threshold value, thereby completing new energy reactive configuration optimization. Through the new energy reactive power configuration optimization method and system, accurate analysis and optimization of transient stability characteristics of the power grid can be realized, and the safety and stability of the power grid are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy reactive power configuration optimization, and particularly to a new energy reactive power configuration optimization method and system. Background Art

[0002] With the transformation of the global energy structure and the vigorous development of clean energy, the large-scale access of new energy such as wind power and photovoltaic power to the power grid has become a trend. However, after the new energy is connected to the power grid, it will have an impact on the transient stability characteristics of the power grid, which is a problem that cannot be ignored. Transient stability is the ability of the power grid to quickly restore the stable operation state after being affected by faults or disturbances, and it is crucial for ensuring the safety of the power grid.

[0003] After the new energy is connected to the power grid, the transient stability characteristics of the power grid will change. On the one hand, the output of new energy is random, intermittent and unpredictable, which increases the power fluctuation of the power grid and impacts the transient stability of the power grid. On the other hand, the access location and scale of new energy will also affect the transient stability characteristics of the power grid. Due to the uneven distribution of wind and light resources in the power grid, the access location and scale of new energy to the power grid need to be optimized according to specific situations.

[0004] At different locations where new energy is connected to the power grid, its impact on the transient stability characteristics of the power grid will also be different. For a specific power grid, the strength of the power grid framework structure is also different at different locations. Therefore, when new energy is connected to the power grid, it is necessary to comprehensively consider factors such as the power grid framework structure, the distribution and scale of new energy, etc., and select the optimal access location and method to minimize the impact on the transient stability characteristics of the power grid. Summary of the Invention

[0005] The purpose of this part is to outline some aspects of the embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this part, as well as in the abstract and title of the present application, to avoid obscuring the purpose of this part, the abstract and the title, and such simplifications or omissions shall not be used to limit the scope of the present invention.

[0006] In view of the above existing problems, the present invention is proposed.

[0007] Therefore, the present invention provides a new energy reactive power configuration optimization method and system, which can solve the problems mentioned in the background art.

[0008] To solve the above technical problems, the present invention provides the following technical solutions. A new energy reactive power configuration optimization method includes:

[0009] Obtaining an operating mode data set of the new energy startup combination, load level, and grid framework structure in the target power grid after the new energy is connected;

[0010] Calculate the dynamic voltage stability index of the new energy near - area grid nodes in the operating mode data set, and judge the dynamic voltage stability index;

[0011] When the dynamic voltage stability index is lower than the first threshold, at the same time, judge whether the dynamic voltage stability index is lower than the second threshold;

[0012] If it is lower than the second threshold, configure the capacity of the static var generator according to the first strategy; if it is higher than the second threshold, configure the capacity of the static var generator according to the second strategy, and complete the optimization of new energy reactive power configuration.

[0013] As a preferred solution of the new energy reactive power configuration optimization method described in the present invention, wherein: the acquisition of the operating mode data set under the new energy start - up combination, load level and grid structure in the target grid after new energy access includes:

[0014] There are N wind farms W i , where i = 1, 2,..., N and M photovoltaic power stations S j , where j = 1, 2,..., M;

[0015] The new energy start - up combination is expressed as follows:

[0016]

[0017] Among them, t represents the time point, i represents the wind farm number, W i (t) represents the starting capacity of wind farm i at time t, and the starting capacity of the photovoltaic power station is represented by the variable S j (t), which represents the power generation power of photovoltaic power station j at time t;

[0018] Record the total load of the power system as L(t), which changes with time and user demand;

[0019] The grid structure is represented by the line resistance R ij and reactance X ij , the resistance R ij and reactance X ij represent the line parameters connecting node i and node j;

[0020] Record each operating mode as a data, and the specific representation is as follows:

[0021]

[0022] The operating mode data set is a set of data corresponding to each operating mode record.

[0023] As a preferred solution of the new - energy reactive - power configuration optimization method described in the present invention, wherein: calculating the dynamic voltage stability index of the new - energy near - area power - grid nodes of the operation - mode data set and judging the dynamic voltage stability index includes:

[0024] Establish the state - space equation of the power system containing new energy:

[0025]

[0026] V k (t)=Cx(t)+Du(t)

[0027] Wherein, x(t) is the system - state vector, including the node - voltage phase angle, frequency and the state variables of the new - energy generator; A, B, C, D are matrices related to the network - structure parameters respectively; u(t) includes the control input of the static var generator reactive - power compensation equipment; d(t) represents the disturbance signal, including the influence of new - energy output fluctuation and load change L(t); V k (t) is the voltage amplitude of node k, that is, the output vector, represents the time - change rate of each system - state variable.

[0028] As a preferred solution of the new - energy reactive - power configuration optimization method described in the present invention, wherein: calculating the dynamic voltage stability index of the new - energy near - area power - grid nodes of the operation - mode data set and judging the dynamic voltage stability index further includes:

[0029] For each operation mode, obtain the node - voltage response V k (t) at each moment through the state - space equation of the power system containing new energy;

[0030] Preset the comprehensive dynamic voltage stability margin index DVS k , which is expressed as follows:

[0031] DVS k =w1T r +w2|V min，k |+w3Δf

[0032]

[0033] Wherein, t0 is the disturbance - start moment, t f is the simulation - end moment or the time when the voltage recovers to the normal range, T r is the time required for the voltage to recover from the lowest point after the disturbance to the normal range; |V min，k | is the maximum amplitude of the voltage drop of node k; Δf is the maximum value of the frequency deviation; w1, w2, w3 are weight factors.

[0034] As a preferred solution of the new - energy reactive - power configuration optimization method described in the present invention, where: when the dynamic voltage stability index is lower than the first threshold, and the judgment of whether the dynamic voltage stability index is lower than the second threshold includes:

[0035] If the dynamic voltage stability index is higher than the first threshold, it is determined that the dynamic voltage of the power grid at the new - energy access location is stable;

[0036] If the dynamic voltage stability index is lower than the first threshold, it is determined that the dynamic voltage of the power grid at the new - energy access location is unstable, and further judge whether the dynamic voltage stability index is lower than the second threshold; the first threshold is greater than the second threshold.

[0037] As a preferred solution of the new - energy reactive - power configuration optimization method described in the present invention, where: the capacity of configuring the static var generator by the first strategy includes:

[0038]

[0039] where K1, K2, K3, K4 are weight coefficients; (DVS k −θ1) represents the degree to which the voltage stability margin is lower than the threshold; ∑ i W i (t) is the total output of the wind farm; ∑ j S j (t) is the total output of the photovoltaic power station; L(t) is the current total load of the system.

[0040] As a preferred solution of the new - energy reactive - power configuration optimization method described in the present invention, where: the capacity of configuring the static var generator by the second strategy includes:

[0041]

[0042] where, represents the proportion of adjusting the compensation amount according to the position of the voltage stability margin relative to the threshold range, K5, K6, K7, K8 are weight coefficients; (DVS k −θ1) represents the degree to which the voltage stability margin is lower than the threshold; ∑ i W i (t) is the total output of the wind farm; ∑ j S j (t) is the total output of the photovoltaic power station; L(t) is the current total load of the system.

[0043] A new - energy reactive - power configuration optimization system, characterized in that it includes:

[0044] A data acquisition module for acquiring a dataset of new energy startup combinations, load levels, and operating modes under the grid structure in the target power grid after new energy access;

[0045] A first judgment module for calculating the dynamic voltage stability index of the new energy near - area grid nodes of the operating mode dataset and judging the dynamic voltage stability index;

[0046] A second judgment module for, when the dynamic voltage stability index is lower than a first threshold, simultaneously judging whether the dynamic voltage stability index is lower than a second threshold;

[0047] An adjustment module for, if it is lower than the second threshold, configuring the capacity of the static var generator according to a first strategy, and if it is higher than the second threshold, configuring the capacity of the static var generator according to a second strategy to complete the optimization of new energy reactive power configuration.

[0048] A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that when the processor executes the computer program, the steps of the method described above are implemented.

[0049] A computer - readable storage medium, having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method described above are implemented.

[0050] Advantages of the present invention: The present invention proposes a method and system for optimizing new energy reactive power configuration, which acquires a dataset of new energy startup combinations, load levels, and operating modes under the grid structure in the target power grid after new energy access; calculates the dynamic voltage stability index of the new energy near - area grid nodes of the operating mode dataset and judges the dynamic voltage stability index; when the dynamic voltage stability index is lower than a first threshold, simultaneously judges whether the dynamic voltage stability index is lower than a second threshold; if it is lower than the second threshold, configures the capacity of the static var generator according to a first strategy, and if it is higher than the second threshold, configures the capacity of the static var generator according to a second strategy to complete the optimization of new energy reactive power configuration. Through the method and system for optimizing new energy reactive power configuration of the present invention, accurate analysis and optimization of the transient stability characteristics of the power grid can be realized, improving the safety and stability of the power grid. At the same time, the method can also flexibly select different optimization strategies according to specific situations to meet different requirements. Description of the Drawings

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts. Among them:

[0052] Figure 1 Flowchart of a method for optimizing new - energy reactive power configuration and a system provided by an embodiment of the present invention;

[0053] Figure 2 Schematic diagram of the configuration of a method for optimizing new - energy reactive power configuration and a system provided by an embodiment of the present invention, (a) Voltage instability before configuring SVG at new - energy station 1, (b) Voltage stability after configuring SVG at new - energy station 1;

[0054] Figure 3 Internal structure diagram of a computer device of a method for optimizing new - energy reactive power configuration and a system provided by an embodiment of the present invention. Detailed implementation manners

[0055] To make the above - mentioned objects, features, and advantages of the present invention more obvious and understandable, the following will describe the detailed implementation manners of the present invention with reference to the accompanying drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0056] Embodiment 1

[0057] Refer to Figures 1-3 , which is the first embodiment of the present invention. This embodiment provides a method for optimizing new - energy reactive power configuration and a system, including a method for optimizing new - energy reactive power configuration and a system for optimizing new - energy reactive power configuration. Among them, a method for optimizing new - energy reactive power configuration includes:

[0058] S101, obtaining an operation - mode data set of new - energy startup combinations, load levels, and grid structures in the target power grid after new - energy access;

[0059] Among them, obtaining an operation - mode data set of new - energy startup combinations, load levels, and grid structures in the target power grid after new - energy access includes:

[0060] Suppose there are N wind farms W i , where i = 1, 2,..., N and M photovoltaic power plants S j , where j = 1, 2,..., M;

[0061] The new - energy startup combination is expressed as follows:

[0062]

[0063] Among them, t represents a time point, i represents the wind - farm number, and W i(t) represents the generating capacity of wind farm i at time t, and the generating capacity of the PV power station is represented by the variable S j (t), representing the power generation of PV power station j at time t;

[0064] The total load of the power system is denoted as L(t), which varies with time and user demand;

[0065] The grid structure is represented by the line resistance R ij and reactance X ij for representation. The resistance R ij and reactance X ij represent the line parameters connecting node i and node j;

[0066] Each operation mode is represented as a data for recording, specifically as follows:

[0067]

[0068] The operation mode dataset is a set of data corresponding to each operation mode record.

[0069] It should be noted that by collecting data on the startup combinations of new energy sources, power system operators can more accurately understand the power generation capabilities of each wind farm and PV power station, thereby formulating a more reasonable energy dispatch plan. This can not only ensure the stable operation of the power system, but also improve the utilization rate of new energy sources and reduce energy waste. Load level data is crucial for power system load forecasting. By collecting and analyzing historical load data, a more accurate load forecasting model can be established, thereby predicting future load demands in advance and providing strong support for the dispatch and operation of the power system. The grid structure is the backbone of the power system and has an important impact on the stability and security of the power system. By collecting and analyzing operation mode data under the grid structure, information such as the grid topology and line parameters can be deeply understood, thereby discovering weak links in the grid and providing data support for grid planning and transformation.

[0070] When a fault occurs in the power system, the operation mode dataset can provide rich fault information to help operators quickly locate the cause of the fault and formulate effective fault handling measures. This is of great significance for improving the reliability and security of the power system. As the proportion of new energy sources in the power system continues to increase, how to effectively absorb new energy has become an important issue. By collecting and analyzing new energy startup combinations and operation mode data, a more reasonable new energy absorption strategy can be formulated, improving the new energy absorption capacity and promoting the sustainable development of new energy.

[0071] In summary, obtaining the new energy starting-up combination, load level, and operation mode dataset under the grid structure in the target power grid after new energy access is of great significance for the planning, dispatching, operation, and maintenance of the power system.

[0072] S102, calculate the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset, and judge the dynamic voltage stability index;

[0073] Among them, calculating the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset and judging the dynamic voltage stability index includes:

[0074] Establish the state space equation of the power system containing new energy:

[0075]

[0076] V k (t) = Cx(t) + Du(t)

[0077] Among them, x(t) is the system state vector, including the node voltage phase angle, frequency, and the state variables of the new energy generator. A, B, C, and D are matrices related to the network structure parameters respectively. u(t) includes the control input of the static var generator reactive power compensation equipment; d(t) represents the disturbance signal, including the influence of new energy output fluctuations and load changes L(t); V k (t) is the voltage amplitude of node k, that is, the output vector, represents the time change rate of each system state variable.

[0078] Furthermore, calculating the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset and judging the dynamic voltage stability index also includes:

[0079] For each operation mode, obtain the node voltage response V k (t) at each moment through the state space equation of the power system containing new energy;

[0080] Preset the comprehensive dynamic voltage stability margin index DVS k , which is expressed as follows:

[0081] DVS k = w1T r + w2|V min，k | + w3Δf

[0082]

[0083] Among them, t0 is the starting moment of the disturbance, and t f is the end moment of the simulation or the time when the voltage returns to the normal range, Tr is the time required for the voltage to recover from the lowest point after the disturbance to the normal range; |V min,k | is the maximum amplitude of the voltage drop at node k; Δf is the maximum value of the frequency deviation; w1, w2, and w3 are weighting factors.

[0084] It should be noted that in practical applications, by calculating and judging the dynamic voltage stability indexes of the nodes in the power grid near the new energy, important reference bases can be provided for the stable operation of the power system. This calculation method and judgment standard not only consider the influence of the new energy output fluctuation and load change on the power grid stability, but also by introducing the comprehensive dynamic voltage stability margin index DVS k , comprehensively consider multiple factors such as voltage recovery time, voltage drop amplitude, and frequency deviation, so as to more comprehensively evaluate the dynamic voltage stability of the power grid.

[0085] In actual operation, by collecting the power grid operation data, establishing the state space equation of the power system containing new energy, and simulating various operation modes, the node voltage response V at each moment can be obtained kt . Then, according to the preset comprehensive dynamic voltage stability margin index DVS k , evaluate and judge the dynamic voltage stability of the power grid. If the value of DVS k is relatively large, it indicates that the power grid can quickly return to stability after being disturbed, and the dynamic voltage stability is good; otherwise, it indicates that the dynamic voltage stability of the power grid is poor, and corresponding measures need to be taken for improvement.

[0086] In addition, by calculating and judging the dynamic voltage stability indexes of the nodes in the power grid near the new energy, useful references can also be provided for the planning and design of the power system. For example, when planning the new energy access to the power grid, according to the dynamic voltage stability indexes of the nodes, the optimal location and capacity of the new energy access can be determined, so as to ensure the stable operation of the power grid. At the same time, in the daily operation of the power system, by real-time monitoring and warning of the dynamic voltage stability indexes, potential problems in the power grid operation can be discovered and processed in a timely manner, ensuring the safe and reliable operation of the power system.

[0087] In summary, by calculating and judging the dynamic voltage stability indexes of the nodes in the power grid near the new energy, useful references and supports can be provided for the stable operation, planning, and design of the power system. With the rapid development of new energy and the continuous upgrading of the power system, the application prospect of this method will be broader and broader.

[0088] S103. When the dynamic voltage stability index is lower than the first threshold, simultaneously judge whether the dynamic voltage stability index is lower than the second threshold;

[0089] In S104, if it is lower than the second threshold, configure the capacity of the static var generator according to the first strategy; if it is higher than the second threshold, configure the capacity of the static var generator according to the second strategy, thus completing the optimization of new energy reactive power configuration.

[0090] Furthermore, when the dynamic voltage stability index is lower than the first threshold, determining whether the dynamic voltage stability index is lower than the second threshold simultaneously includes:

[0091] If the dynamic voltage stability index is higher than the first threshold, it is determined that the dynamic voltage of the power grid at the new energy access location is stable;

[0092] If the dynamic voltage stability index is lower than the first threshold, it is determined that the dynamic voltage of the power grid at the new energy access location is unstable, and further determine whether the dynamic voltage stability index is lower than the second threshold; the first threshold is greater than the second threshold.

[0093] Configuring the capacity of the static var generator according to the first strategy includes:

[0094]

[0095] wherein, K1, K2, K3, K4 are weight coefficients; (DVS k -θ1) represents the degree to which the voltage stability margin is lower than the threshold; ∑ i W i (t) is the total output of the wind farm; ∑ j S j (t) is the total output of the photovoltaic power station; L(t) is the current total load of the system.

[0096] Configuring the capacity of the static var generator according to the second strategy includes:

[0097]

[0098] wherein, represents the ratio of adjusting the compensation amount according to the position of the voltage stability margin relative to the threshold range, K5, K6, K7, K8 are weight coefficients; (DVS k -θ1) represents the degree to which the voltage stability margin is lower than the threshold; ∑ i W i (t) is the total output of the wind farm; ∑ j S j (t) is the total output of the photovoltaic power station; L(t) is the current total load of the system.

[0099] In an alternative embodiment, the setting of the weight coefficients K1, K2, K3, K4 needs to be based on the characteristics of the power system and practical experience, as well as the sensitivity analysis of factors such as dynamic voltage stability margin, load changes, and new energy output changes. It can be solved through the following specific steps:

[0100] When setting the weight coefficient K1, refer to historical data or determine through simulation tests how much the margin value can be improved by increasing the unit reactive power compensation under the low voltage stability margin condition. Or it can be adjusted according to the static voltage characteristics (such as load-voltage curve) and dynamic characteristics (such as response speed) of the system.

[0101] When setting the weight coefficients K2 and K3, these two coefficients are related to the total output of the wind farm and the photovoltaic power station, and reflect the degree of influence of the output fluctuation of new energy power generation on voltage stability.

[0102] It can be calculated by statistically analyzing the correlation between the change of new energy output and the decrease of voltage stability margin, and comprehensively considering the impedance characteristics of the power grid. Or assume that the influence of new energy output fluctuation on voltage stability is large, then the corresponding coefficient value is higher; otherwise it is lower.

[0103] When setting the weight coefficient K4, this coefficient is used to describe the relationship between the change of system load and the SVG configuration requirement.

[0104] According to the trend of voltage drop when the load increases and the trend of voltage rise when the load decreases, combined with the voltage support ability of the power grid, determine the value of this coefficient. Or if the load increase causes a significant voltage reduction, then K4 should be set to a relatively large positive number so as to increase the SVG compensation when the load increases.

[0105] It should be noted that in the actual setting process, more factors may need to be considered, such as the importance of nodes, the performance limitations of SVG equipment itself, cost-benefit, etc., and the optimal coefficient value is obtained through multiple simulation verifications and optimization iterations. In addition, in some advanced algorithms, these coefficients may be dynamically adjusted to adapt to the changes in real-time operating conditions.

[0106] When setting the weight coefficient K5, the sensitivity of the system's demand for additional reactive power compensation in the higher voltage stability margin range can be referred to. It is also possible to determine the SVG compensation ratio corresponding to the unit margin gain when the margin value increases from θ1 to θ2 by comparing the relationship curves between different dynamic voltage stability margins and SVG compensation amounts.

[0107] When setting the weight coefficients K6 and K7, these two coefficients reflect the degree of influence of the total output of the wind farm and the photovoltaic power station on the voltage stability of the system. The influence of the change of new energy output on voltage stability still needs to be considered under the condition of higher margin.

[0108] Based on historical data or simulation test results, analyze the degree of influence of new energy power generation fluctuation on SVG configuration requirement in the higher voltage stability margin range, so as to adjust the values of these two coefficients.

[0109] When setting the weight coefficient K8, this coefficient is used to describe the influence degree of load change on the SVG configuration requirement under the medium voltage stability margin level of the system.

[0110] According to the trend of voltage drop caused by load increase and the grid impedance characteristics, calculate how this coefficient should reflect the relationship between load change and SVG compensation amount.

[0111] If the influence of load change on voltage stability is relatively small at this time, then the value of k8 may be relatively low; otherwise, it is relatively high.

[0112] It should be noted that in the specific setting process, these coefficients usually need to be iteratively optimized multiple times by combining on-site measured data, simulation models, and expert experience, and may also need to consider the static and dynamic stability of the power grid, node importance, and SVG equipment. These coefficients are used to adapt to the real-time power grid operation status and conditions.

[0113] To sum up, the present invention proposes a new energy reactive power configuration optimization method, which obtains the new energy startup combination, load level, and operation mode data set under the grid structure in the target power grid after new energy access; calculates the dynamic voltage stability index of the new energy near-area grid nodes of the operation mode data set, and judges the dynamic voltage stability index; when the dynamic voltage stability index is lower than the first threshold, simultaneously judges whether the dynamic voltage stability index is lower than the second threshold; if it is lower than the second threshold, configures the capacity of the static var generator according to the first strategy, if it is higher than the second threshold, configures the capacity of the static var generator according to the second strategy, and completes the new energy reactive power configuration optimization. Through the new energy reactive power configuration optimization method and system of the present invention, accurate analysis and optimization of the transient stability characteristics of the power grid can be realized, and the safety and stability of the power grid can be improved. At the same time, this method can also flexibly select different optimization strategies according to specific situations to meet different requirements.

[0114] In a preferred embodiment, a new energy reactive power configuration optimization system includes:

[0115] A data acquisition module, which is used to acquire the new energy startup combination, load level, and operation mode data set under the grid structure in the target power grid after new energy access;

[0116] A first judgment module, which is used to calculate the dynamic voltage stability index of the new energy near-area grid nodes of the operation mode data set and judge the dynamic voltage stability index;

[0117] A second judgment module, which is used to judge whether the dynamic voltage stability index is lower than the second threshold when the dynamic voltage stability index is lower than the first threshold;

[0118] An adjustment module is used to configure the capacity of the static var generator according to the first strategy if it is lower than the second threshold, and configure the capacity of the static var generator according to the second strategy if it is higher than the second threshold, so as to complete the optimization of new energy reactive power configuration.

[0119] The above-mentioned unit modules can be embedded in the processor in the computer device in hardware form or independent of the processor, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0120] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 3 shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a new energy reactive power configuration optimization method. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covered on the display screen, or a button, a trackball, or a touchpad set on the housing of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0121] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the following steps are realized:

[0122] Obtain the operating mode data set of the new energy start-up combination, load level, and grid structure in the target power grid after new energy access;

[0123] Calculate the dynamic voltage stability index of the new energy near-region grid nodes of the operating mode data set, and judge the dynamic voltage stability index;

[0124] When the dynamic voltage stability index is lower than the first threshold, simultaneously judge whether the dynamic voltage stability index is lower than the second threshold;

[0125] If it is lower than the second threshold, configure the capacity of the static var generator according to the first strategy, and if it is higher than the second threshold, configure the capacity of the static var generator according to the second strategy, so as to complete the optimization of new energy reactive power configuration.

[0126] Example 2

[0127] Reference Figures 1-3 , which is an embodiment of the present invention, provides a new energy reactive power configuration optimization method and system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through comparative experiments.

[0128] Taking the power grid data of a new energy intensive access area in the south of China as an example, applying the method of the present invention, by judging the dynamic voltage stability index of the new energy connection point, the weak new energy power stations with voltage stability are selected, and their SVG configuration schemes are optimized to optimize the reactive power voltage stability of the nearby power grid.

[0129] Through the AC fault scanning analysis of different nodes of the power grid under different operation modes, the dynamic voltage stability indexes of each new energy power station are obtained.

[0130] The power grid has new energy power stations 1, 2, and 3 connected to the grid at the 220 kV level. Three faults are made at the connection points. Through calculation, the fault critical clearing times to ensure voltage stability are 8 cycles, 8 cycles, and 9 cycles, and the SVG capacity configured for the new energy power stations is determined.

[0131] Table 1 Optimization Scheme of Dynamic Reactive Power Compensation Device for New Energy Power Stations

[0132] Installed capacity MVA SVG configured capacity MVA New energy power station 1 100 15 New energy power station 2 150 22.5 New energy power station 3 150 22.5

[0133] By judging the dynamic voltage stability index of the new energy power station and optimizing its reactive power configuration scheme, the ability of the new energy power station to resist fault impact can be significantly improved. It can be seen from Figure 2 that for the three faults at the connection point of the new energy power station 1 before optimization, the voltage becomes unstable when the fault duration is 9 cycles. After optimizing its SVG configuration scheme and connecting an SVG device with a capacity of 15 MVA, the voltage can remain stable when the fault duration is 9 cycles.

[0134] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not restrictive. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered within the scope of the claims of the present invention.

[0135] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code. The solutions in the embodiments of the present application can be implemented in various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0136] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0137] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means realizes the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process. Thus, the instructions executed on the computer or other programmable device provide steps for realizing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0139] Although the preferred embodiments of the present application have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0140] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to include these modifications and variations.

Claims

1. A method for optimizing the reactive power configuration of new energy, characterized in that, Including: Obtain the operation mode dataset of new energy start-up combinations, load levels, and grid structures in the target power grid after new energy access; Calculate the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset, and judge the dynamic voltage stability index; When the dynamic voltage stability index is lower than the first threshold, simultaneously judge whether the dynamic voltage stability index is lower than the second threshold; If it is lower than the second threshold, configure the capacity of the static var generator according to the first strategy. If it is higher than the second threshold, configure the capacity of the static var generator according to the second strategy to complete the optimization of new energy reactive power configuration.

2. The new energy reactive power configuration optimization method according to claim 1, wherein, The obtaining of the operation mode dataset of new energy start-up combinations, load levels, and grid structures in the target power grid after new energy access includes: There are N wind farms W i , where i = 1, 2, …, N and M photovoltaic power stations S j , where j = 1, 2, …, M; The new energy start-up combination is represented as follows: Among them, t represents the time point, i represents the wind farm number, and W i (t) represents the installed capacity of wind farm i at time t, and the installed capacity of the PV power station is represented by the variable S j (t), representing the power generation of PV power station j at time t; Denote the total load of the power system as L(t), which changes with time and user demand; Line resistance R of the grid structure ij and reactance X ij are represented. The resistance R ij and reactance X ij represent the line parameters connecting node i and node j; Record each operation mode as a data, and the specific representation is as follows: The operation mode dataset is a set of data corresponding to each operation mode.

3. The new energy reactive power configuration optimization method according to claim 2, wherein The calculating of the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset and the judging of the dynamic voltage stability index include: Establish the state space equation of the power system containing new energy: V k y(t) = Cx(t) + Du(t) Among them, \(x(t)\) is the system state vector, including the node voltage phase angle, frequency, and the state variables of the new energy generator. \(A\), \(B\), \(C\), and \(D\) are matrices related to the network structure parameters respectively. \(u(t)\) includes the control input of the static var generator reactive power compensation equipment; \(d(t)\) represents the disturbance signal, which contains the influence of the new energy output fluctuation and the load change \(L(t)\); \(V_k(t)\) is the voltage amplitude of node \(k\), that is, the output vector. It represents the time change rate of each state variable of the system.

4. The new energy reactive power configuration optimization method according to claim 3, wherein, The calculating of the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset and the judging of the dynamic voltage stability index also include: For each operating mode, the node voltage response V at each moment is obtained through the state space equation of the power system with new energy sources k (t); Preset comprehensive dynamic voltage stability margin index DVS k , which is expressed as follows: DVS k = w1T r + w2|V min,k | + w3Δf where t0 is the start time of the disturbance, t f is the end time of the simulation or the time when the voltage recovers to the normal range, T r is the time required for the voltage to recover from the lowest point after the disturbance to the normal range; |V min,k | is the maximum amplitude of the voltage drop at node k; Δf is the maximum value of the frequency deviation; w1, w2, w3 are weighting factors.

5. The new energy reactive power configuration optimization method according to claim 4, wherein The when the dynamic voltage stability index is lower than the first threshold, simultaneously judging whether the dynamic voltage stability index is lower than the second threshold includes: If the dynamic voltage stability index is higher than the first threshold, it is determined that the dynamic voltage of the power grid at the new energy access location is stable; If the dynamic voltage stability index is lower than the first threshold, it is determined that the dynamic voltage of the power grid at the new energy access location is unstable, and further judge whether the dynamic voltage stability index is lower than the second threshold; the first threshold is greater than the second threshold.

6. The new energy reactive power configuration optimization method according to claim 5, wherein The first strategy for configuring the capacity of the static var generator includes: Among them, K1, K2, K3, and K4 are weighting coefficients; (DVS k -θ1) represents the degree to which the voltage stability margin is lower than the threshold; ∑ i W i (t) is the total output of the wind farm; ∑ j S j (t) is the total output of the PV power station; L(t) is the current total load of the system.

7. The new energy reactive power configuration optimization method according to claim 6, wherein The configuring the capacity of the static var generator according to the second strategy includes: Among them, represents the ratio of adjusting the compensation amount according to the position of the voltage stability margin relative to the threshold range, and K5, K6, K7, and K8 are weighting factors; (DVS k -θ1) represents the degree to which the voltage stability margin is lower than the threshold; ∑ i W i (t) is the total output of the wind farm; ∑ j S j (t) is the total output of the PV power station; L(t) is the current total load of the system.

8. A new energy reactive power configuration optimization system, characterized in that The including: A data acquisition module for obtaining the operation mode dataset of new energy start-up combinations, load levels, and grid structures in the target power grid after new energy access; A first judgment module for calculating the dynamic voltage stability index of the new energy near-region grid nodes in the operation mode dataset and judging the dynamic voltage stability index; A second judgment module for when the dynamic voltage stability index is lower than the first threshold, simultaneously judging whether the dynamic voltage stability index is lower than the second threshold; An adjustment module for, if it is lower than the second threshold, configuring the capacity of the static var generator according to the first strategy, and if it is higher than the second threshold, configuring the capacity of the static var generator according to the second strategy to complete the optimization of new energy reactive power configuration.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.

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