Method and device for improving distributed stability of new energy power system

Abstract

The application relates to a distributed stability performance improvement method and device of a new energy power system, wherein the method comprises the following steps: determining dynamic interaction gains between new energy equipment and a power grid at a grid connection point of the new energy equipment in the new energy power system; establishing a local gain condition and a boundary condition thereof which meet preset small signal stability performance requirements; and using the local gain condition and the boundary condition to constrain the dynamic interaction gains between each new energy equipment and the power grid at the grid connection point of the new energy equipment, so that closed-loop poles avoid the right half plane of a complex plane and an area which does not meet the preset stability performance requirements. Thus, the problems in the prior art that, due to the fact that a centralized analysis method depends on a complete system model and global operation information, the calculation complexity is high, the scalability is poor, and it is difficult to meet the requirements of fast, flexible and scalable small signal stability analysis in a large-scale new energy access scene, and a distributed analysis method cannot explicitly and quantitatively represent and guarantee system small signal stability performance indexes are solved.

Description

Technical Field

[0001] This application relates to the field of new energy power system technology, and in particular to a method and apparatus for improving the distributed stability performance of a new energy power system. Background Technology

[0002] Currently, the power system is gradually transforming from a traditional form dominated by synchronous generators to a new form with a high proportion of renewable energy integration. Renewable energy typically requires power electronic converters for grid connection, and its dynamic characteristics are mainly determined by the control structure, control parameters, and grid connection conditions. With the continuous increase in the installed capacity of renewable energy, the system's equivalent inertia, damping support capacity, and dynamic regulation characteristics are undergoing significant changes, making the small-signal stability problem of the power system increasingly prominent, manifesting in various forms such as low-frequency oscillations and wide-frequency oscillations. Therefore, how to effectively analyze the small-signal stability of renewable energy power systems and improve their stability performance is a crucial technical issue of concern to both academia and industry.

[0003] In related technologies, centralized analysis methods are often used to analyze the small-signal stability of new energy power systems. These methods include solving for closed-loop system eigenvalues ​​and ensuring system oscillation damping by constructing closed-loop pole distribution constraints. Centralized analysis is well-suited for scenarios with small system scale, clear model structure, and available global information. Meanwhile, distributed analysis frameworks offer a feasible approach to solving the small-signal stability analysis problem of large-scale new energy power systems.

[0004] However, in related technologies, centralized analysis methods rely on complete system models and global operational information. In scenarios with an increasing number of new energy devices, diversified control types, and frequent changes in operating conditions, the computational complexity is high and the scalability is poor. This makes it difficult to meet the needs of fast, flexible, and scalable small signal stability analysis in large-scale new energy access scenarios. Meanwhile, decentralized analysis methods focus on providing qualitative criteria for system stability and cannot clearly and quantitatively characterize and guarantee the small signal stability performance indicators of the system. These issues urgently need to be addressed. Summary of the Invention

[0005] This application provides a distributed stability performance enhancement method and device for new energy power systems to solve the problems in related technologies, such as the high computational complexity and poor scalability of centralized analysis methods due to their reliance on complete system models and global operating information, which makes it difficult to meet the needs of fast, flexible and scalable small signal stability analysis in large-scale new energy access scenarios, and the inability of distributed analysis methods to clearly and quantitatively characterize and guarantee the small signal stability performance indicators of the system.

[0006] The first aspect of this application provides a distributed stability performance enhancement method for a new energy power system, comprising the following steps: determining the dynamic interaction gain between new energy equipment in the new energy power system and the power grid at its connection point; establishing local gain conditions and boundary conditions that meet preset small-signal stability performance requirements; and using the local gain conditions and boundary conditions to constrain the dynamic interaction gain between each new energy equipment and the power grid at its connection point, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements.

[0007] Through the aforementioned technical means, the embodiments of this application can determine the dynamic interaction gain between each new energy device and the power grid in a decentralized manner, and establish local gain conditions and boundary conditions that meet the preset small-signal stability performance. The dynamic interaction gain of each device is independently constrained, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements. Thus, without relying on the complete system global model, decentralized analysis of the small-signal stability characteristics of the new energy power system can be achieved. This has stronger scalability and engineering applicability, and improves the system's ability to meet small-signal stability performance. The small-signal stability performance requirements at the system level are transformed into local gain constraints at the device side, thereby providing quantifiable decentralized constraints for the design of new energy control structures, control parameter tuning, and grid connection stability assessment.

[0008] Optionally, in one embodiment of this application, determining the dynamic interaction gain between the new energy equipment in the new energy power system and the power grid at its connection point includes: determining the dynamic interaction relationship between the new energy equipment and the power grid at its connection point; and determining the dynamic interaction gain between the new energy equipment in the new energy power system and the power grid at its connection point based on the dynamic interaction relationship.

[0009] Through the above-mentioned technical means, the embodiments of this application can determine the interaction gain between the dynamics of the new energy equipment and the equivalent dynamics of the power grid from the dynamic interaction relationship between the new energy equipment and the power grid at its connection point. Thus, without the need for global system information, the inherent correlation mechanism between the dynamic interaction gain between the new energy equipment and the power grid at its connection point and the distribution of closed-loop poles of the system can be revealed.

[0010] Optionally, in one embodiment of this application, the expression for the local gain condition is: , in, For the first i Local gain between Taiwan's new energy equipment and the power grid at its connection point. This refers to the dynamic inverse of the aforementioned new energy equipment. For the network matrix i There are diagonal elements, where Γ is a forbidden region in the complex plane; The boundary conditions are as follows: , Wherein, B is the finite boundary of the forbidden region of the complex plane.

[0011] Through the above-mentioned technical means, the embodiments of this application can construct local gain conditions that meet the preset small-signal stability performance requirements, so as to constrain the dynamic interaction gain between each new energy device and the power grid at its grid connection point, thereby achieving effective constraints on the distribution of closed-loop poles in the system, and constructing boundary conditions to transform the regional verification problem into a boundary verification problem, thereby improving the feasibility of the project.

[0012] Optionally, in one embodiment of this application, constraining the dynamic interaction gain between each new energy device and its grid connection point using the local gain condition and its boundary conditions includes: determining a small-signal stability performance index based on at least one of the system operating conditions, stability standards, or engineering design requirements of the new energy power system, and defining a complex plane forbidden region and its finite boundary based on the small-signal stability performance index; calculating or identifying a network matrix based on at least one of the network topology, line parameters, and operating point information of the new energy power system to extract the local network parameters corresponding to each grid connection node; substituting the device dynamic model into the local gain condition and the boundary conditions based on the local network parameters to obtain inequality constraints regarding device dynamics; and solving for the inequality constraints for each new energy device.

[0013] Through the above-mentioned technical means, the embodiments of this application can map the small-signal stability performance index to the finite boundary of the complex plane forbidden region according to the small-signal stability standard. Then, combined with local network parameters, the device dynamic model is substituted into the boundary conditions to generate inequality constraints. The constraints are then solved for each device separately, so that each new energy device only needs to obtain local network parameters and its own dynamic model to independently solve the range of control parameters that meet the stability performance requirements, without relying on the global system model and operating information. This reduces the computational complexity and communication requirements. At the same time, by updating the local network parameters and forbidden region definition in real time, it can adapt to the dynamic changes in the system operating conditions, thereby improving flexibility and scalability.

[0014] Optionally, in one embodiment of this application, the method further includes: if the current dynamics of any new energy device do not satisfy the local gain condition and the boundary condition, then adjusting the control structure or control parameters of the any new energy device until all devices satisfy the inequality constraint.

[0015] Through the above-mentioned technical means, the embodiments of this application can adjust the control structure or parameters only for the device when the device does not dynamically meet the local gain conditions and its boundary conditions, without affecting the other devices. This achieves the restoration and optimization of the overall system stability through local correction, significantly enhances the system's adaptability and robustness, and effectively ensures the small-signal stability of the power system under large-scale new energy access scenarios.

[0016] A second aspect of this application provides a distributed stability performance enhancement device for a new energy power system, comprising: a determination module for determining the dynamic interaction gain between new energy equipment in the new energy power system and the power grid at its connection point; an establishment module for establishing local gain conditions and boundary conditions that meet preset small-signal stability performance requirements; and an enhancement module for using the local gain conditions and boundary conditions to constrain the dynamic interaction gain between each new energy equipment and the power grid at its connection point, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements.

[0017] Through the aforementioned technical means, the embodiments of this application can determine the dynamic interaction gain between each new energy device and the power grid in a decentralized manner, and establish local gain conditions and boundary conditions that meet the preset small-signal stability performance. The dynamic interaction gain of each device is independently constrained, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements. Thus, without relying on the complete system global model, decentralized analysis of the small-signal stability characteristics of the new energy power system can be achieved. This has stronger scalability and engineering applicability, and improves the system's ability to meet small-signal stability performance. The small-signal stability performance requirements at the system level are transformed into local gain constraints at the device side, thereby providing quantifiable decentralized constraints for the design of new energy control structures, control parameter tuning, and grid connection stability assessment.

[0018] Optionally, in one embodiment of this application, the determining module includes: a first determining unit, configured to determine the dynamic interaction relationship between the new energy equipment and the power grid at its connection point; and a second determining unit, configured to determine the dynamic interaction gain between the new energy equipment in the new energy power system and the power grid at its connection point based on the dynamic interaction relationship.

[0019] Through the above-mentioned technical means, the embodiments of this application can determine the interaction gain between the dynamics of the new energy equipment and the equivalent dynamics of the power grid from the dynamic interaction relationship between the new energy equipment and the power grid at its connection point. Thus, without the need for global system information, the inherent correlation mechanism between the dynamic interaction gain between the new energy equipment and the power grid at its connection point and the distribution of closed-loop poles of the system can be revealed.

[0020] Optionally, in one embodiment of this application, the expression for the local gain condition is: , in, For the first i Local gain between Taiwan's new energy equipment and the power grid at its connection point. This refers to the dynamic inverse of the aforementioned new energy equipment. For the network matrix i There are diagonal elements, where Γ is a forbidden region in the complex plane; The boundary conditions are as follows: , Wherein, B is the finite boundary of the forbidden region of the complex plane.

[0021] Through the above-mentioned technical means, the embodiments of this application can construct local gain conditions that meet the preset small-signal stability performance requirements, so as to constrain the dynamic interaction gain between each new energy device and the power grid at its grid connection point, thereby achieving effective constraints on the distribution of closed-loop poles in the system, and constructing boundary conditions to transform the regional verification problem into a boundary verification problem, thereby improving the feasibility of the project.

[0022] Optionally, in one embodiment of this application, the enhancement module includes: a third determining unit, configured to determine a small-signal stability performance index based on at least one of the system operating conditions, stability standards, or engineering design requirements of the new energy power system, and define a complex plane forbidden region and its finite boundary based on the small-signal stability performance index; an extraction unit, configured to calculate or identify a network matrix based on at least one of the network topology, line parameters, and operating point information of the new energy power system, to extract the local network parameters corresponding to each grid-connected node; a fourth determining unit, configured to substitute the device dynamic model into the local gain condition and the boundary condition based on the local network parameters, to obtain inequality constraints regarding device dynamics; and a solving unit, configured to solve for each new energy device to satisfy the inequality constraints.

[0023] Through the above-mentioned technical means, the embodiments of this application can map the small-signal stability performance index to the finite boundary of the complex plane forbidden region according to the small-signal stability standard. Then, combined with local network parameters, the device dynamic model is substituted into the boundary conditions to generate inequality constraints. The constraints are then solved for each device separately, so that each new energy device only needs to obtain local network parameters and its own dynamic model to independently solve the range of control parameters that meet the stability performance requirements, without relying on the global system model and operating information. This reduces the computational complexity and communication requirements. At the same time, by updating the local network parameters and forbidden region definition in real time, it can adapt to the dynamic changes in the system operating conditions, thereby improving flexibility and scalability.

[0024] Optionally, in one embodiment of this application, the enhancement module further includes: an adjustment unit, configured to adjust the control structure or control parameters of any new energy device if the current dynamic of any new energy device does not satisfy the local gain condition and the boundary condition, until all devices satisfy the inequality constraint.

[0025] Through the above-mentioned technical means, the embodiments of this application can adjust the control structure or parameters only for the device when the device does not dynamically meet the local gain conditions and its boundary conditions, without affecting the other devices. This achieves the restoration and optimization of the overall system stability through local correction, significantly enhances the system's adaptability and robustness, and effectively ensures the small-signal stability of the power system under large-scale new energy access scenarios.

[0026] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the distributed stability performance improvement method for a new energy power system as described in the above embodiments.

[0027] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for improving the distributed stability performance of a new energy power system.

[0028] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the above-described method for improving the distributed stability performance of a new energy power system.

[0029] This application's embodiments can determine the dynamic interaction gain between each new energy device and the power grid in a decentralized manner, and establish local gain conditions and boundary conditions that meet preset small-signal stability performance. Independent constraints are applied to the dynamic interaction gain of each device, ensuring that closed-loop poles avoid the right half-plane of the complex plane and regions that do not meet preset stability performance requirements. This allows for decentralized analysis of the small-signal stability characteristics of new energy power systems without relying on a complete global system model. It offers stronger scalability and engineering applicability, improves the system's ability to meet small-signal stability performance, and transforms system-level small-signal stability performance requirements into device-side local gain constraints. This provides quantifiable decentralized constraints for new energy control structure design, control parameter tuning, and grid connection stability assessment. Therefore, it solves the problems in related technologies where centralized analysis methods rely on complete system models and global operational information, resulting in high computational complexity and poor scalability, making it difficult to meet the needs of fast, flexible, and scalable small-signal stability analysis in large-scale new energy access scenarios. Furthermore, decentralized analysis methods cannot clearly and quantitatively characterize and guarantee system small-signal stability performance indicators.

[0030] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0031] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a distributed stability performance improvement method for a new energy power system according to an embodiment of this application; Figure 2 This is a schematic diagram of local gain conditions provided according to an embodiment of this application; Figure 3 This is a schematic diagram of a distributed stability performance enhancement device for a new energy power system according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0032] Figure label: 10-Distributed stability enhancement device for new energy power systems; 100-Determining module, 200-Establishing module, 300-Enhancing module; 401-Memory, 402-Processor, 403-Communication interface. Detailed Implementation

[0033] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0034] The following description, with reference to the accompanying drawings, illustrates a method and apparatus for improving the distributed stability performance of a new energy power system according to embodiments of this application. In response to the aforementioned background technologies, centralized analysis methods, relying on complete system models and global operational information, suffer from high computational complexity and poor scalability, making it difficult to meet the needs of rapid, flexible, and scalable small-signal stability analysis in large-scale renewable energy integration scenarios. Meanwhile, decentralized analysis methods cannot clearly and quantitatively characterize and guarantee the small-signal stability performance indicators of the system. This application provides a decentralized stability performance improvement method for renewable energy power systems. This method can determine the dynamic interaction gain between each renewable energy device and the grid in a decentralized manner, and establish local gain conditions and boundary conditions that satisfy preset small-signal stability performance. Independent constraints are applied to the dynamic interaction gain of each device, ensuring that closed-loop poles avoid the right half-plane of the complex plane and regions that do not meet preset stability performance requirements. Thus, decentralized analysis of the small-signal stability characteristics of renewable energy power systems is achieved without relying on a complete global system model. This method has stronger scalability and engineering applicability, improves the system's ability to meet small-signal stability performance, and transforms the system-level small-signal stability performance requirements into local gain constraints on the device side. This provides quantifiable decentralized constraints for renewable energy control structure design, control parameter tuning, and grid connection stability assessment. This solves the problems in related technologies, such as the high computational complexity and poor scalability of centralized analysis methods due to their reliance on complete system models and global operational information, which makes it difficult to meet the needs of fast, flexible, and scalable small signal stability analysis in large-scale new energy access scenarios, while decentralized analysis methods cannot clearly and quantitatively characterize and guarantee the small signal stability performance indicators of the system.

[0035] Specifically, Figure 1 This is a flowchart illustrating a method for improving the distributed stability performance of a new energy power system, as provided in an embodiment of this application.

[0036] like Figure 1 As shown, the method for improving the distributed stability performance of this new energy power system includes the following steps: In step S101, the dynamic interaction gain between the new energy equipment in the new energy power system and the power grid at its connection point is determined.

[0037] It is understood that in the embodiments of this application, the dynamic interaction gain can characterize the coupling strength between the dynamic output characteristics of the new energy equipment and the grid impedance characteristics at the grid connection point, and the dynamic interaction gain is the local gain.

[0038] The distribution of closed-loop poles directly determines the small-signal dynamic characteristics of the system. When the system has poles located in the right half of the complex plane, the system will experience small-signal instability. When the closed-loop poles of the system are all located in the left half of the complex plane, but are close to the imaginary axis, have insufficient damping, or have a slow dynamic decay rate, the system may still experience weakly damped oscillations, threatening the safe and stable operation of the power system.

[0039] In practical implementation, this application embodiment can start from the dynamic interaction relationship between new energy equipment and the power grid at its connection point, define and characterize the dynamic interaction gain between the dynamics on the new energy equipment side and the equivalent dynamics on the power grid side, i.e., local gain. An intrinsic correlation between local gain and closed-loop pole distribution is established, enabling decentralized analysis of the small-signal stability characteristics of the new energy power system without relying on a complete global system model. The local gain analysis method can reveal the intrinsic correlation mechanism between the dynamic interaction gain of new energy equipment and the power grid at its connection point and the system's closed-loop pole distribution without requiring global system information.

[0040] For example, embodiments of this application can establish a dynamic interaction model between the equipment and the power grid based on local measurement information such as port voltage and current of the new energy equipment, combined with the equivalent impedance or admittance characteristics of the power grid at the grid connection point, and then extract the dynamic interaction gain that reflects the interaction strength.

[0041] The embodiments of this application can determine the dynamic interaction gain between new energy equipment and the power grid at its connection point in a new energy power system, so as to quantitatively describe the small-signal stability performance requirements of the system. The system-level stability problem is transformed into local gain calculation at the device level. Each new energy device only needs to complete the gain determination based on local measurable information, thus avoiding dependence on the global system model.

[0042] Optionally, in one embodiment of this application, determining the dynamic interaction gain between new energy equipment in a new energy power system and the power grid at its connection point includes: determining the dynamic interaction relationship between the new energy equipment and the power grid at its connection point; and determining the dynamic interaction gain between the new energy equipment in a new energy power system and the power grid at its connection point based on the dynamic interaction relationship.

[0043] It is understood that the dynamic interaction relationship in the embodiments of this application can be understood as the dynamic reversal of the new energy device port. Equivalent dynamic of the grid at the connection point The new dynamics formed by "combining" near a given running point, i.e. ...

[0044] In practical implementation, the embodiments of this application can interconnect new energy devices through a power network and perform small-signal modeling near a given operating point. By eliminating load nodes or passive network nodes, the system can be equivalent to a networked dynamic system that retains only the grid-connected nodes of new energy devices. Assume the system has a total of... n If each grid-connected node is connected to one new energy device, then the small-signal relationship on the network side can be represented as: (1) in, It is the active power vector of the nodes. It is the nodal work angle vector. It is the nodal power perturbation vector. It is a network matrix. Definition: A network matrix containing the total number of nodes in the system. n Each node connects to only one new energy device, and the device dynamics are as follows: Therefore, the device transfer function matrix can be constructed as follows: (2) Device transfer function matrix D( s The input to ) is the power perturbation vector of each node, and the output is the virtual phase angle vector of each node, that is: (3) The characteristic equation of the closed-loop model of the system composed of equations (1) and (3) is: (4) The roots of equation (4) are the closed-loop poles of the system. The distribution of the closed-loop poles directly determines the small-signal dynamic performance of the system. When the closed-loop poles are located in the right half of the complex plane, the system experiences small-signal instability; when the closed-loop poles are located in the left half of the complex plane, but their positions do not meet the preset stability performance requirements, the system may have problems such as insufficient stability margin, slow dynamic decay rate, and weak oscillation damping.

[0045] To quantitatively describe the small-signal stability requirements of the system, the region Γ in the complex plane is defined as the "forbidden region". The forbidden region can be selected according to actual engineering needs. For example, it can be selected as the right half of the complex plane to characterize the small-signal stability requirements; it can also be selected as a weakly damped region or a region with insufficient dynamic decay rate near the imaginary axis to characterize specific small-signal stability requirements.

[0046] According to Gael's disk theorem, if the characteristic equation satisfies the strict diagonal dominance condition within the domain Γ, then equation (4) has no roots within the domain, meaning the closed-loop poles of the system are strictly distributed outside the domain: Let If the matrix satisfies equation (5) within its domain, then equation (6) holds.

[0047] (5) (6) Therefore, it can be concluded that the closed-loop poles of the system are strictly located outside the forbidden region Γ, and the system satisfies the small-signal stability or small-signal stability performance requirement defined by Γ. Based on the above analysis, the first... i The local gain between the new energy equipment and the power grid at its connection point is .

[0048] This application embodiment can start from the dynamic interaction relationship between new energy equipment and the power grid at its connection point, determine the interaction gain between the dynamics on the new energy equipment side and the equivalent dynamics on the power grid side, and thus reveal the intrinsic correlation mechanism between the dynamic interaction gain between new energy equipment and the power grid at its connection point and the distribution of closed-loop poles of the system without the need for global system information.

[0049] In step S102, local gain conditions and their boundary conditions that satisfy the preset small-signal stability performance requirements are established.

[0050] It is understood that the preset small-signal stability performance requirements in the embodiments of this application can be small-signal stability-related indicators such as oscillation damping ratio, stability margin, and dynamic attenuation rate. The preset small-signal stability performance requirements can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0051] In practical implementation, the embodiments of this application can construct local gain conditions that meet preset small-signal stability performance requirements. Local gain conditions can constrain the dynamic interaction gain between each new energy device and the power grid at its connection point, ensuring that closed-loop poles avoid the right half-plane of the complex plane and regions that do not meet preset stability performance requirements, thereby improving the system's small-signal stability performance. In practical engineering applications, directly solving the local gain conditions may face a significant computational burden. To improve engineering feasibility, the embodiments of this application can propose local gain conditions and their boundary conditions, transforming the regional verification problem into a boundary verification problem.

[0052] The embodiments of this application can analytically transform the preset small signal stability performance requirements into local gain conditions and boundary conditions on the device side, realizing the decomposition of stability performance indicators from the system global to the device local, so that each new energy device can independently determine whether its own dynamics meet the system stability requirements, and realizing decentralized and quantifiable guarantee of stability performance.

[0053] Optionally, in one embodiment of this application, the expression for the local gain condition is: , in, For the firsti Local gain between Taiwan's new energy equipment and the power grid at its connection point. For the dynamic inversion of new energy equipment For the network matrix i There are diagonal elements, where Γ is a forbidden region in the complex plane; The boundary conditions are: , Where B is the finite boundary of the forbidden region in the complex plane.

[0054] It is understood that, in the embodiments of this application, the dynamic inverse of the new energy equipment refers to the inverse of the equipment transfer function, reflecting the contribution form of the equipment's dynamic characteristics in the feedback loop. The i-th diagonal element of the network matrix is ​​the i-th element in the network. i The self-admittance component of a node characterizes the local equivalent characteristics of the network topology and line parameters at that node; the complex plane forbidden region can be understood as a region defined on the complex plane according to the preset small-signal stability performance requirements, into which closed-loop poles are not allowed to enter.

[0055] In actual implementation, the embodiments of this application can define the first... i The local gain between the new energy equipment and the power grid at its connection point is The local gain condition is proposed: For characteristic equation (4), it is assumed that matrix D(s) is non-singular in the forbidden region Γ. If the local network of each new energy device and its access point satisfies the local gain condition (equation (7)), then the closed-loop poles of the system will strictly not fall into the forbidden region Γ.

[0056] (7) Based on the above analysis, the determination of the local gain condition can be distributed across each new energy device. Geometrically speaking, as... Figure 2 As shown, the local gain condition sets forth design requirements: the dynamic inverse of each device Strictly avoid falling into the category of With the center as the center, Within the dynamically infeasible region of radius, where For the network matrix i The diagonal element, in physical terms, is the first element in the network. i The node's "self-admittance".

[0057] For new energy systems, their network topology and parameter characteristics typically remain stable during operation. Therefore, as long as each new energy device ensures that its dynamic characteristics match the local network characteristics of the access point and meets the design requirements of the local gain condition, the system's closed-loop poles can be prevented from falling into the preset forbidden region, thus effectively constraining the distribution of the system's closed-loop poles. In practical engineering applications, since the forbidden region is a continuous region in the complex plane, directly solving the complex variable function modulus inequality corresponding to the local gain condition usually faces a significant computational burden. To improve engineering feasibility, this application further proposes boundary conditions for the local gain condition, transforming the regional verification problem into a boundary verification problem. Assumption Matrix If the diagonal elements of the forbidden domain Γ are all non-zero, then the verification of the local gain condition only needs to be performed on the boundary B of Γ, as shown in Equation (8).

[0058] (8) Specifically, when the matrix If all diagonal elements are non-zero in the region Γ, then the function... The function is parsed within the forbidden region Γ. According to the maximum modulus principle, the function... The minimum value of occurs on the boundary B of region Γ. Therefore, if inequality (8) is satisfied on boundary B, the local gain condition can be strictly guaranteed to hold throughout the entire forbidden region Γ.

[0059] Further analysis reveals that, since the dynamics of new energy equipment in engineering are typically strictly rational functions (such as droop control and virtual synchronous machine control), when hour, This means the function The minimum value can only occur on the finite boundary B of the region Γ. f Therefore, the boundary conditions are further transformed into equation (9).

[0060] (9) The embodiments of this application can construct local gain conditions and their boundary conditions that meet the preset small-signal stability performance requirements, so as to constrain the dynamic interactive gain between each new energy device and the power grid at its grid connection point, thereby achieving effective constraints on the distribution of closed-loop poles in the system, and constructing boundary conditions to transform the regional verification problem into a boundary verification problem, thereby improving the feasibility of the project.

[0061] In step S103, the local gain condition and its boundary condition are used to constrain the dynamic interactive gain between each new energy device and the power grid at its connection point, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements.

[0062] It is understood that the preset stability performance requirements in the embodiments of this application can be selected according to actual engineering needs. For example, they can be selected as the right half-plane of the complex plane to characterize the small signal stability requirements, or they can be selected as the weakly damped region or the region with insufficient dynamic attenuation rate near the imaginary axis to characterize specific small signal stability performance requirements. The preset stability performance requirements can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here.

[0063] In actual implementation, the embodiments of this application can transform local gain conditions into dynamic interactive gain constraints on each new energy device and the power grid at its connection point, thereby becoming a powerful tool to guide the design or parameter tuning of new energy control structures, and has great potential for engineering applications.

[0064] For example, in this embodiment, the current local gain can be calculated for each new energy device. If the device does not meet the local gain condition within the complex plane forbidden region, its dynamic characteristics can be changed by adjusting the device control parameters to make the device meet the local gain condition. If all devices in the system meet their respective local gain conditions, it is ensured that all closed-loop poles are located in the left half-plane of the complex plane and within the region that meets the preset stability performance requirements.

[0065] The embodiments of this application can independently constrain the dynamic interactive gain of each device using local gain conditions and their boundary conditions, thereby avoiding dependence on global models and operational information. This results in low computational complexity and strong scalability. At the same time, it ensures that closed-loop poles avoid the right half-plane of the complex plane and regions that do not meet the preset stability performance requirements. This achieves quantitative guarantee and improvement of system stability performance under a distributed framework, and can adapt to the small signal stability analysis needs of a large number of devices in large-scale new energy access scenarios.

[0066] Optionally, in one embodiment of this application, the dynamic interaction gain between each new energy device and its grid connection point is constrained using local gain conditions and their boundary conditions. This includes: determining a small-signal stability performance index based on at least one of the system operating conditions, stability standards, or engineering design requirements of the new energy power system, and defining a complex plane forbidden region and its finite boundary based on the small-signal stability performance index; calculating or identifying a network matrix based on at least one of the network topology, line parameters, and operating point information of the new energy power system to extract the local network parameters corresponding to each grid connection node; substituting the device dynamic model into the local gain conditions and boundary conditions based on the local network parameters to obtain inequality constraints regarding device dynamics; and solving for the inequality constraints for each new energy device.

[0067] It is understood that the small-signal stability performance indicators in the embodiments of this application can be closed-loop pole damping ratio, dynamic decay rate, and stability margin, etc., which can be set by those skilled in the art according to the actual situation, and no specific restrictions are made here. The system operating condition can be the current operating status of the system, such as the power generation level, load level, and power flow distribution; the stability standard refers to the quantitative requirements for small-signal stability specified in industry standards or power grid operation guidelines; the engineering design requirements refer to the more specific stability quality requirements proposed in specific projects based on equipment characteristics, power grid structure, etc.

[0068] In actual implementation, the embodiments of this application can construct dynamic constraints for new energy equipment based on the above-mentioned local gain conditions and their boundary conditions.

[0069] Specifically, based on the system operating conditions, stability standards, or engineering design requirements, small-signal stability performance indicators are determined, and the complex plane forbidden region is defined accordingly. and its finite boundary Based on the network topology, line parameters, and operating point information of the new energy access system, calculate or identify the network matrix. And extract the local network parameters corresponding to each grid-connected node. as well as .

[0070] Furthermore, by substituting the equipment dynamic model into the local gain conditions and boundary conditions, inequality constraints concerning equipment dynamics are obtained. These inequalities are then solved for each new energy device.

[0071] This application embodiment can map the small-signal stability performance index to the finite boundary of the complex plane forbidden region based on the small-signal stability standard. Then, combined with local network parameters, the device dynamic model is substituted into the boundary conditions to generate inequality constraints. The constraints are then solved for each device separately. This allows each new energy device to independently solve the range of control parameters that meet the stability performance requirements by only obtaining local network parameters and its own dynamic model, without relying on the global system model and operating information. This reduces computational complexity and communication requirements. At the same time, by updating local network parameters and forbidden region definitions in real time, it can adapt to the dynamic changes in system operating conditions, improving flexibility and scalability.

[0072] Optionally, in one embodiment of this application, the method further includes: if the current dynamics of any new energy device do not meet the local gain condition and boundary condition, then adjusting the control structure or control parameters of any new energy device until all devices meet the inequality constraints.

[0073] It is understood that adjusting the control structure in the embodiments of this application can be understood as changing the control architecture of the new energy equipment converter; adjusting the control parameters can be understood as modifying the proportional coefficient, integral coefficient, filter time constant and other parameter values ​​of the controller under the current control structure.

[0074] In actual implementation, the embodiments of this application can continuously monitor whether each new energy device meets the local gain conditions and its boundary conditions. If the current dynamic of a certain device does not meet the local gain conditions and its boundary conditions, its control structure or control parameters are adjusted until all devices meet the corresponding local gain conditions and its boundary conditions.

[0075] For example, in embodiments of this application, when the current dynamics of a device do not meet the local gain conditions and their boundary conditions, the device control parameters can be adjusted, such as adjusting the current loop proportional coefficient, the voltage loop integral coefficient, or switching the control mode. The adjustment process only involves that device and does not require communication with other devices or retuning of the global control strategy until all devices meet their respective inequality constraints.

[0076] The embodiments of this application can adjust the control structure or parameters only for the device when the device dynamically fails to meet the local gain conditions and its boundary conditions, without affecting the other devices. This achieves the restoration and optimization of the overall system stability through local correction, significantly enhancing the system's adaptability and robustness, and effectively ensuring the small-signal stability of the power system under large-scale new energy access scenarios.

[0077] The decentralized stability performance enhancement method for new energy power systems proposed in this application can determine the dynamic interaction gain between each new energy device and the grid in a decentralized manner, and establish local gain conditions and boundary conditions that meet the preset small-signal stability performance. Independent constraints are applied to the dynamic interaction gain of each device, ensuring that closed-loop poles avoid the right half-plane of the complex plane and regions that do not meet the preset stability performance requirements. This allows for decentralized analysis of the small-signal stability characteristics of the new energy power system without relying on a complete global system model. It has stronger scalability and engineering applicability, and improves the system's ability to meet small-signal stability performance. The system-level small-signal stability performance requirements are transformed into local gain constraints on the device side, providing quantifiable decentralized constraints for new energy control structure design, control parameter tuning, and grid connection stability assessment. This solves the problems in related technologies where centralized analysis methods rely on complete system models and global operational information, resulting in high computational complexity and poor scalability, making it difficult to meet the needs of fast, flexible, and scalable small-signal stability analysis in large-scale new energy access scenarios. Furthermore, decentralized analysis methods cannot clearly and quantitatively characterize and guarantee the small-signal stability performance indicators of the system.

[0078] Next, referring to the accompanying drawings, a distributed stability improvement device for a new energy power system according to an embodiment of this application is described.

[0079] Figure 3 This is a schematic diagram of the structure of the distributed stability performance enhancement device for a new energy power system according to an embodiment of this application.

[0080] like Figure 3 As shown, the distributed stability performance enhancement device 10 of the new energy power system includes: a determination module 100, an establishment module 200, and an enhancement module 300.

[0081] Among them, the determination module 100 is used to determine the dynamic interaction gain between new energy equipment in the new energy power system and the power grid at its connection point.

[0082] Module 200 is used to establish local gain conditions and their boundary conditions that meet the preset small-signal stability performance requirements.

[0083] The enhancement module 300 is used to constrain the dynamic interactive gain between each new energy device and the power grid at its connection point by utilizing local gain conditions and their boundary conditions, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements.

[0084] Optionally, in one embodiment of this application, the determining module 100 includes: a first determining unit and a second determining unit.

[0085] The first determining unit is used to determine the dynamic interaction relationship between the new energy equipment and the power grid at its connection point.

[0086] The second determining unit is used to determine the dynamic interaction gain between new energy equipment in the new energy power system and the power grid at its connection point, based on the dynamic interaction relationship.

[0087] Optionally, in one embodiment of this application, the expression for the local gain condition is: , in, For the first i Local gain between Taiwan's new energy equipment and the power grid at its connection point. For the dynamic inversion of new energy equipment For the network matrix i There are diagonal elements, where Γ is a forbidden region in the complex plane; The boundary conditions are: , Where B is the finite boundary of the forbidden region in the complex plane.

[0088] Optionally, in one embodiment of this application, the enhancement module 300 includes: a third determining unit, an extraction unit, a fourth determining unit, and a solving unit.

[0089] The third determining unit is used to determine the small-signal stability performance index based on at least one of the system operating conditions, stability standards, or engineering design requirements of the new energy power system, and to define the complex plane forbidden region and its finite boundary based on the small-signal stability performance index.

[0090] The extraction unit is used to calculate or identify the network matrix based on at least one of the network topology, line parameters and operating point information of the new energy power system, so as to extract the local network parameters corresponding to each grid-connected node.

[0091] The fourth determining unit is used to substitute the device dynamic model into the local gain condition and boundary condition based on the local network parameters to obtain inequality constraints on the device dynamics.

[0092] The solution unit is used to solve for the inequality constraints of each new energy device.

[0093] Optionally, in one embodiment of this application, the lifting module 300 further includes an adjustment unit.

[0094] The adjustment unit is used to adjust the control structure or control parameters of any new energy device if the current dynamic of any new energy device does not meet the local gain condition and boundary condition, until all devices meet the inequality constraints.

[0095] It should be noted that the foregoing explanation of the distributed stability performance enhancement method for new energy power systems also applies to the distributed stability performance enhancement device for new energy power systems in this embodiment, and will not be repeated here.

[0096] The decentralized stability performance enhancement device for new energy power systems proposed in this application can determine the dynamic interaction gain between each new energy device and the grid in a decentralized manner, and establish local gain conditions and boundary conditions that meet the preset small-signal stability performance. It independently constrains the dynamic interaction gain of each device, ensuring that closed-loop poles avoid the right half-plane of the complex plane and regions that do not meet the preset stability performance requirements. This allows for decentralized analysis of the small-signal stability characteristics of the new energy power system without relying on a complete global system model. It has stronger scalability and engineering applicability, and improves the system's ability to meet small-signal stability performance. It transforms the system-level small-signal stability performance requirements into local gain constraints on the device side, thus providing quantifiable decentralized constraints for new energy control structure design, control parameter tuning, and grid connection stability assessment. This solves the problems in related technologies where centralized analysis methods rely on complete system models and global operational information, resulting in high computational complexity and poor scalability, making it difficult to meet the needs of fast, flexible, and scalable small-signal stability analysis in large-scale new energy access scenarios. Furthermore, decentralized analysis methods cannot clearly and quantitatively characterize and guarantee the small-signal stability performance indicators of the system.

[0097] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0098] When the processor 402 executes the program, it implements the distributed stability performance improvement method for the new energy power system provided in the above embodiments.

[0099] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0100] The memory 401 is used to store computer programs that can run on the processor 402.

[0101] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0102] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0103] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0104] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0105] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for improving the distributed stability performance of a new energy power system.

[0106] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described method for improving the distributed stability performance of a new energy power system.

[0107] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0108] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0109] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0110] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0111] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0112] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0113] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0114] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for improving the distributed stability performance of a new energy power system, characterized in that, Includes the following steps: Determine the dynamic interaction gain between new energy equipment in a new energy power system and the power grid at its connection point; Establish local gain conditions and their boundary conditions that meet the preset small-signal stability performance requirements; The local gain condition and its boundary conditions are used to constrain the dynamic interactive gain between each new energy device and the power grid at its connection point, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements.

2. The method according to claim 1, characterized in that, The determination of the dynamic interaction gain between new energy equipment in a new energy power system and the power grid at its connection point includes: Determine the dynamic interaction relationship between the new energy equipment and the power grid at its connection point; The dynamic interaction gain between the new energy equipment in the new energy power system and the power grid at its connection point is determined based on the dynamic interaction relationship.

3. The method according to claim 1, characterized in that, The expression for the local gain condition is: ， in, For the first i Local gain between Taiwan's new energy equipment and the power grid at its connection point. This refers to the dynamic inverse of the aforementioned new energy equipment. For the network matrix i There are diagonal elements, where Γ is a forbidden region in the complex plane; The boundary conditions are as follows: ， Wherein, B is the finite boundary of the forbidden region of the complex plane.

4. The method according to claim 1, characterized in that, The method of constraining the dynamic interaction gain between each new energy device and its grid connection point using the local gain condition and its boundary conditions includes: Based on at least one of the system operating conditions, stability standards, or engineering design requirements of the new energy power system, determine the small-signal stability performance index, and define the complex plane forbidden region and its finite boundary based on the small-signal stability performance index. The network matrix is ​​calculated or identified based on at least one of the network topology, line parameters and operating point information of the new energy power system, so as to extract the local network parameters corresponding to each grid-connected node. Based on the local network parameters, the device dynamics model is substituted into the local gain condition and the boundary condition to obtain inequality constraints regarding device dynamics. Solve for the inequality constraints for each new energy device.

5. The method according to claim 4, characterized in that, Also includes: If the current dynamics of any new energy device do not meet the local gain condition and the boundary condition, then the control structure or control parameters of the new energy device are adjusted until all devices meet the inequality constraints.

6. A distributed stability performance enhancement device for a new energy power system, characterized in that, include: The determination module is used to determine the dynamic interaction gain between new energy equipment in a new energy power system and the power grid at its connection point. A module is established to establish local gain conditions and their boundary conditions that meet the preset small-signal stability performance requirements; The enhancement module is used to constrain the dynamic interactive gain between each new energy device and the power grid at its connection point using the local gain conditions and their boundary conditions, so that the closed-loop poles avoid the right half-plane of the complex plane and the region that does not meet the preset stability performance requirements.

7. The apparatus according to claim 6, characterized in that, The determining module includes: The first determining unit is used to determine the dynamic interaction relationship between the new energy equipment and the power grid at its connection point; The second determining unit is used to determine the dynamic interaction gain between the new energy equipment in the new energy power system and the power grid at its connection point, based on the dynamic interaction relationship.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the distributed stability performance improvement method for a new energy power system as described in any one of claims 1-5.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the distributed stability performance improvement method for new energy power systems as described in any one of claims 1-5.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the distributed stability performance improvement method for new energy power systems as described in any one of claims 1-5.

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