Inertia support capability constraint-considered dynamic partition optimization method for sending-end power grid

Abstract

The invention provides a sending-end power grid dynamic partition optimization method considering inertia supporting capacity constraint, and belongs to the technical field of power system engineering, and the method comprises the steps: obtaining the working data of generators of a sending-end power grid, analyzing and comparing the motor inertia of different generators based on the working data, and constructing a dynamic inertia model, determining inertia response characteristics of different generators under different disturbances, integrating the inertia response characteristics of all generators to obtain power grid inertia characteristics of the sending-end power grid, further obtaining inertia supporting capacity of the sending-end power grid, and evaluating power grid toughness of the sending-end power grid; according to the power grid toughness, a dynamic constraint condition is set, a sending-end power grid is dynamically partitioned, and a dynamic partitioning scheme is optimized by applying an optimization algorithm, so that an optimal partitioning scheme is obtained, the stability and the flexibility of the power grid are improved, the response capability of the power grid to frequency fluctuation and sudden load change is enhanced, and the reliability and the adaptability of a power system are improved. And effective integration of renewable energy sources is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of power system engineering, and in particular to a method for optimizing dynamic partitioning of a sending-end power grid considering inertia support capacity constraints. Background Art

[0002] With the rapid development of renewable energy, traditional power systems are facing challenges such as frequency fluctuations and unstable power supply. The motor inertia of generators plays a key role in the frequency stability of the power grid, especially when there are sudden load changes or power generation fluctuations. Inertia can provide the necessary support to maintain the stability of the power grid. Traditional methods mainly rely on static models, usually only considering the inertia characteristics of a single generator, lacking a comprehensive analysis of the overall inertia of the power grid, and often evaluating the resilience of the power grid through empirical rules, lacking systematic dynamic analysis and quantitative models.

[0003] Therefore, the present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account the inertia support capacity constraint. Summary of the Invention

[0004] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid that takes into account inertia support capacity constraints. By collecting generator operating data, analyzing their motor inertia, constructing a dynamic inertia model, and integrating the response characteristics of different generators under disturbances, the overall inertia characteristics of the power grid are derived. Based on these characteristics, the inertia support capacity and resilience of the power grid are evaluated, dynamic constraints are set, and dynamic partitioning of the power grid is performed. Finally, the optimal partitioning scheme is determined through an optimization algorithm to improve the stability and flexibility of the power grid, enhance the power grid's response capability to frequency fluctuations and sudden load changes, improve the reliability and adaptability of the power system, and promote the effective integration of renewable energy.

[0005] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints, comprising:

[0006] Step 1: Obtaining the operating data of the generators of the sending-end power grid, analyzing and comparing the motor inertias of different generators based on the operating data, and then constructing a dynamic inertia model;

[0007] Step 2: Determine the inertia response characteristics of different generators under different disturbances based on the dynamic inertia model, and obtain the grid inertia characteristics of the sending-end grid by integrating the inertia response characteristics of all generators;

[0008] Step 3: Determine the inertia support capability of the sending-end power grid based on the power grid inertia characteristics, and evaluate the power grid resilience of the sending-end power grid based on the inertia support capability;

[0009] Step 4: Dynamic constraints are set according to the grid resilience, the sending-end grid is dynamically partitioned, and an optimization algorithm is applied to optimize the dynamic partitioning scheme to obtain the optimal partitioning scheme.

[0010] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid considering inertia support capacity constraints. The method obtains operating data of generators in the sending-end power grid, analyzes and compares the motor inertias of different generators based on the operating data, and then constructs a dynamic inertia model, including:

[0011] Calculating the motor inertia of the corresponding generator based on the working data, performing statistical analysis on the motor inertia, and determining the inertia distribution of different generators;

[0012] The key variables of the generator are determined according to the inertia distribution, and then the relationship equations are determined to construct a dynamic inertia model.

[0013] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method determines key variables of a generator based on the inertia distribution, further determines a relationship equation, and constructs a dynamic inertia model, including:

[0014] determining a first relationship between motor inertia and generator type based on the inertia distribution;

[0015] determining a second relationship between the motor inertia and the load based on a change in inertia distribution under different load conditions;

[0016] Comparing the motor inertias between different operating states to determine a third relationship between the operating state and the motor inertia;

[0017] Based on the first, second and third relationships, key variables are determined and a relationship equation is established. The first, second and third relationships are combined using the relationship equation to construct a dynamic inertia model.

[0018] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method determines the inertial response characteristics of different generators under different disturbances based on the dynamic inertia model, and synthesizes the inertial response characteristics of all generators to obtain the grid inertia characteristics of the sending-end power grid, including:

[0019] Obtaining existing disturbances in the power grid at the sending end, converting the existing disturbances into disturbance data and inputting them into a dynamic inertia model, which outputs speed change and frequency change responses of each generator;

[0020] determining an inertial response of each generator based on the speed change and frequency change responses;

[0021] Analyzing the characteristics of the inertial response to obtain inertial response characteristics, selecting corresponding virtual inertial devices from a power generation characteristic-virtual device table based on the inertial response characteristics, combining the inertial response characteristics with virtual inertial technology, and establishing a virtual response model for each virtual inertial device;

[0022] The disturbance data is input into the virtual response model, which outputs the virtual response. The virtual response is combined with the inertia response to calculate the grid inertia characteristics of the sending-end grid.

[0023] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method inputs disturbance data into a virtual response model, which outputs a virtual response. The virtual response is combined with the inertia response to calculate the inertia characteristics of the sending-end power grid, including:

[0024] Combine the virtual response with the inertial response to form a comprehensive response, and evaluate the effective inertia of the sending-end power grid based on the comprehensive response;

[0025] The recovery performance of the power grid at the sending end is evaluated based on the frequency change response, and the inertia characteristics of the power grid are determined in combination with the effective inertia and the recovery performance.

[0026] The present invention provides a method for dynamically partitioning a sending-end power grid considering inertia support capacity constraints, deriving the inertia support capacity of the sending-end power grid based on the inertia characteristics of the power grid, and evaluating the power grid resilience of the sending-end power grid based on the inertia support capacity, including:

[0027] Determine the resilience evaluation index of the sending-end power grid from the power grid resilience index table, and evaluate the power grid resilience of the sending-end power grid by combining the inertia support capacity and the resilience evaluation index:

[0028] Where R represents the grid resilience score of the sending power grid; w1 represents the importance of recovery time and frequency deviation for the grid resilience score; w2 represents the importance of redundancy for the resilience score; w3 represents the importance of backup capacity for the resilience score; w4 represents the importance of inertia support capacity for the resilience score; T m Indicates the mechanical recovery time of the sending-end power grid; T e Indicates the electronic recovery time of the sending-end power grid; T c Indicates the control recovery time of the sending-end power grid; f max (t) represents the maximum frequency value of the power grid at the sending end at time t; f no Indicates the standard frequency of the sending-end power grid; e -λt represents the time attenuation influence factor; k represents the rate of change of frequency deviation over time; Y represents the residual influence value; N r2 Indicates the number of redundant components in the sending-end power grid; N r1 represents the total number of components in the sending-end power grid; P b Indicates the backup power supply power of the sending-end grid; P lorepresents the actual load of the sending power grid; H represents the inertia support capacity of the sending power grid; α represents the nonlinear index of the recovery time and frequency deviation on the power grid resilience score; β represents the nonlinear index of redundancy on the power grid resilience score; γ represents the nonlinear index of backup capacity on the power grid resilience score; δ represents the nonlinear index of inertia support capacity on the power grid resilience score.

[0029] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid considering inertia support capacity constraints. Dynamic constraints are set according to the grid resilience, the sending-end power grid is dynamically partitioned, and an optimization algorithm is applied to optimize the dynamic partitioning scheme to obtain the optimal partitioning scheme, including:

[0030] Collecting historical operating data of the sending power grid, determining electrical characteristics of the sending power grid based on the historical operating data, and identifying key nodes related to grid resilience;

[0031] According to the inertia characteristics of the power grid and historical operation data, dynamic constraints and optimization targets corresponding to each key node are set, and the sending-end power grid is dynamically partitioned based on the dynamic constraints and optimization targets;

[0032] An optimization algorithm is selected according to the optimization goal to optimize the dynamic partitioning scheme and obtain the best partitioning scheme.

[0033] The present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method sets dynamic constraints and optimization targets corresponding to each key node based on the inertia characteristics of the power grid and historical operating data, including:

[0034] Perform characteristic analysis on the frequency change response corresponding to the key nodes and set the first dynamic constraint condition;

[0035] determining a power balance of the sending-end power grid based on historical load and historical power generation in the historical operation data, and deriving a second dynamic constraint condition;

[0036] An optimization objective is set based on the first dynamic constraint condition and the second dynamic constraint condition.

[0037] Compared with the existing technology, the beneficial effects of this application are as follows: by collecting generator operating data, analyzing its motor inertia, constructing a dynamic inertia model, and integrating the response characteristics of different generators under disturbances, the overall inertia characteristics of the power grid are obtained. Based on these characteristics, the inertia support capacity and resilience of the power grid are evaluated, dynamic constraints are set and dynamic partitioning of the power grid is performed. Finally, the optimal partitioning scheme is determined through the optimization algorithm to improve the stability and flexibility of the power grid, enhance the power grid's response capability to frequency fluctuations and load mutations, improve the reliability and adaptability of the power system, and promote the effective integration of renewable energy.

[0038] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0039] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0041] Figure 1 It is a flow chart of a method for dynamic partitioning optimization of a sending-end power grid considering inertia support capacity constraints provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0042] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0043] Example 1:

[0044] The embodiment of the present invention provides a method for optimizing the dynamic partitioning of the sending-end power grid considering the inertia support capacity constraint, such as Figure 1 Shown, including:

[0045] Step 1: Obtaining the operating data of the generators of the sending-end power grid, analyzing and comparing the motor inertias of different generators based on the operating data, and then constructing a dynamic inertia model;

[0046] Step 2: Determine the inertia response characteristics of different generators under different disturbances based on the dynamic inertia model, and obtain the grid inertia characteristics of the sending-end grid by integrating the inertia response characteristics of all generators;

[0047] Step 3: Determine the inertia support capability of the sending-end power grid based on the power grid inertia characteristics, and evaluate the power grid resilience of the sending-end power grid based on the inertia support capability;

[0048] Step 4: Dynamic constraints are set according to the grid resilience, the sending-end grid is dynamically partitioned, and an optimization algorithm is applied to optimize the dynamic partitioning scheme to obtain the optimal partitioning scheme.

[0049] In this embodiment, the operating data refers to various types of data collected during the operation of the generator, such as the speed, power generation, load conditions, etc. For example, the operating data of a generator includes the speed (such as 1500rpm), power generation (such as 100MW) under different loads, and the frequency of load changes.

[0050] In this embodiment, the motor inertia is a physical quantity that measures the generator's ability to resist speed changes, usually expressed in seconds (s), and reflects the generator's ability to store energy. For example, a generator's motor inertia is 5s, which means that the generator can maintain its speed change when a disturbance occurs in the power grid.

[0051] In this embodiment, the input and output of the dynamic inertia model are the input of the model as the key variable, and the output is the dynamic response of the generator, such as speed change and frequency change. For example, the input is the load change rate and the initial speed of the generator, and the output is the speed and frequency change curve of the system after the disturbance.

[0052] In this embodiment, the operating data of the generators is obtained, the motor inertia of each generator is calculated, and statistical analysis is performed to determine its inertia distribution. Based on the inertia distribution, key variables are identified, and corresponding relationship equations are constructed to form a dynamic inertia model. By analyzing the inertia distribution, a relationship between the motor inertia and the generator type, load conditions, and operating status is established. By comparing the inertia changes under different conditions, key variables are identified, and relationship equations are constructed. These relationships are integrated to form a dynamic inertia model.

[0053] In this embodiment, the inertial response is the response of the generator based on its inertia characteristics when it is disturbed, which is usually manifested as changes in speed and frequency. For example, after a sudden load change, the inertial response of the generator may be manifested as a process of gradually recovering after a decrease in speed.

[0054] In this embodiment, the inertial response characteristic is a characteristic parameter describing the inertial response, such as the maximum response amplitude, recovery time, etc. For example, the inertial response characteristic of a certain generator is "high frequency response, low amplitude fluctuation".

[0055] In this embodiment, the grid inertia characteristic is the overall inertial performance of the grid when disturbed, including the inertial response of the generator and the contribution of the virtual inertial device. For example, by combining the inertial response and virtual response of the generator, the total inertia characteristic of the grid is calculated to be "high inertia, low frequency fluctuation".

[0056] In this embodiment, the inertial response emphasizes the instantaneous natural reaction mechanism, and the inertial response characteristics focus on the specific manifestations and parameters of this reaction. The grid inertia characteristics is a more macro concept, involving the inertial capacity of the entire grid and its impact on frequency stability.

[0057] In this embodiment, the inertia support capability refers to the effective inertia provided by the generators and virtual inertial devices when the power grid is subjected to disturbances, which affects the frequency stability and responsiveness of the power grid. For example, if the total effective inertia of the generators and virtual inertial devices of a power grid is 1200 MW·s, this indicates that the power grid can provide relatively strong frequency support during load changes or faults.

[0058] In this embodiment, grid resilience refers to the ability of the power system to maintain stable operation, recover quickly, and adapt to changes when subjected to various disturbances (such as natural disasters, equipment failures, load fluctuations, etc.). Resilience not only includes the recovery speed of the grid after the disturbance occurs, but also involves its stability and adaptability to parameters such as frequency and power during the disturbance.

[0059] In this embodiment, the evaluation index is determined from the grid resilience index table, and a comprehensive evaluation is performed in combination with the inertia support capability and other factors (such as recovery time, frequency deviation, redundancy and backup capability), and the grid resilience score R is calculated by a formula.

[0060] In this embodiment, the dynamic partitioning scheme divides the power grid into multiple dynamic areas based on the operating characteristics and dynamic constraints of the power grid for better management and optimization. For example, the power grid is divided into a main network, a regional network and a distribution network. Each area is dynamically adjusted according to the load characteristics and power generation capacity. According to the load changes and power generation conditions, the power grid is divided into high-load areas and low-load areas for easy scheduling.

[0061] In this embodiment, the optimal partitioning scheme is the optimal grid partitioning scheme obtained after processing by the optimization algorithm, which can effectively improve the resilience and operating efficiency of the grid. For example, the partitioning scheme obtained by the optimization algorithm reasonably distributes the load of a key node to ensure that the frequency can be maintained stable under high load conditions. Under specific conditions, the communication mode of certain substations is adjusted to reduce the impact of faults on the overall grid.

[0062] The working principle and beneficial effects of the above technical solution are: by collecting generator operating data, analyzing its motor inertia, constructing a dynamic inertia model, and integrating the response characteristics of different generators under disturbances, the overall inertia characteristics of the power grid are obtained. Based on these characteristics, the inertia support capacity and resilience of the power grid are evaluated, dynamic constraints are set and the power grid is dynamically partitioned. Finally, the optimal partitioning scheme is determined through an optimization algorithm to improve the stability and flexibility of the power grid, enhance the power grid's response ability to frequency fluctuations and load mutations, improve the reliability and adaptability of the power system, and promote the effective integration of renewable energy.

[0063] Example 2:

[0064] An embodiment of the present invention provides a method for optimizing dynamic partitioning of a sending-end power grid considering inertia support capacity constraints. The method obtains operating data of generators in the sending-end power grid, analyzes and compares the motor inertias of different generators based on the operating data, and then constructs a dynamic inertia model. The method includes:

[0065] Calculating the motor inertia of the corresponding generator based on the working data, performing statistical analysis on the motor inertia, and determining the inertia distribution of different generators;

[0066] The key variables of the generator are determined according to the inertia distribution, and then the relationship equations are determined to construct a dynamic inertia model.

[0067] In this embodiment, the statistical analysis is to perform statistical processing on the collected motor inertia data to understand its distribution characteristics and patterns. For example, by performing statistics on the motor inertia of multiple generators, it is found that the mean is 4.5s and the standard deviation is 1.2s.

[0068] In this embodiment, the inertia distribution is the distribution of motor inertia values ​​of different generators, which is usually represented by a histogram or a probability distribution function. For example, a histogram of a group of generator inertias is drawn, which shows that the inertia of most generators is concentrated between 3-6s.

[0069] In this embodiment, key variables are variables closely related to generator performance, such as load, speed, power generation, etc. For example, when constructing a dynamic inertia model, key variables may include load change rate, generator speed change, etc.

[0070] In this embodiment, the relational equation is a mathematical equation that describes the relationship between input and output and is used to predict system behavior. For example, a linear regression model can be used to establish the relational equation: Δf = k·ΔP, where Δf is the frequency change, ΔP is the power change, and k is a proportional constant.

[0071] The working principle and beneficial effects of the above technical solution are: by obtaining the operating data of the generator, calculating the motor inertia of each generator, and performing statistical analysis to determine its inertia distribution, key variables are identified based on the inertia distribution, and corresponding relationship equations are constructed to form a dynamic inertia model. This model can reflect the inertia characteristics of different generators under various operating conditions, providing a basis for subsequent dynamic analysis, optimizing the operation strategy of the power grid, improving its response capability to disturbances, and thereby enhancing the stability and reliability of the power system.

[0072] Example 3:

[0073] An embodiment of the present invention provides a method for optimizing dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method determines key variables of a generator based on the inertia distribution, further determines a relationship equation, and constructs a dynamic inertia model, including:

[0074] determining a first relationship between motor inertia and generator type based on the inertia distribution;

[0075] determining a second relationship between the motor inertia and the load based on a change in inertia distribution under different load conditions;

[0076] Comparing the motor inertias between different operating states to determine a third relationship between the operating state and the motor inertia;

[0077] Based on the first, second and third relationships, key variables are determined and a relationship equation is established. The first, second and third relationships are combined using the relationship equation to construct a dynamic inertia model.

[0078] In this embodiment, the first relationship is the relationship between the motor inertia and the generator type, indicating that different types of generators (such as synchronous generators, asynchronous generators, gas turbines, etc.) have different inertia characteristics. For example, the inertia of synchronous generators is usually large and suitable for large-scale power generation, while the inertia of asynchronous generators is small and suitable for fast response application scenarios.

[0079] In this embodiment, the second relationship is the relationship between motor inertia and load conditions, which refers to the inertia performance of the generator and its changes under different loads. For example, under heavy load conditions, the inertia of the generator may exhibit greater energy storage capacity, while under light load conditions, the inertia may decrease, affecting the frequency response of the system.

[0080] In this embodiment, the third relationship is the change relationship of the motor inertia under different operating states (such as normal operation, fault state, load fluctuation, etc.), reflecting the dynamic characteristics of the generator under different operating conditions. For example, in a fault state, the inertia of the generator may decrease because its output power is limited, resulting in increased frequency fluctuations.

[0081] In this embodiment, the change in inertia distribution refers to how the inertia value distribution of the generator changes under different loads or operating conditions, reflecting the adaptability and dynamic response capability of the system. For example, under high load conditions, the inertia distribution of the generator may shift toward a high inertia value, indicating a stronger energy storage capability, while under low load conditions it may shift toward a low inertia value.

[0082] In this embodiment, the key variables are variables related to motor inertia, generator type, load conditions and operating status. These variables are the basis for constructing relational equations. For example, the key variables may include generator type, load change rate, operating status (such as normal, fault, overload), etc.

[0083] In this embodiment, the relational equation is a mathematical equation that describes the relationship between the above-mentioned key variables and the motor inertia. For example, the following relational equation can be established: for the first relationship: J = f(T), where J is the inertia and T is the generator type; for the second relationship: J = g(L), where L is the load; for the third relationship: J = h(S), where S is the operating status.

[0084] In this embodiment, the dynamic inertia model is constructed by combining the above relationship equations to comprehensively consider the generator type, load and operating status to predict system performance. For example, the dynamic inertia model is: J dynamic =k1·f(t)+k2·g(L)+k3·h(S), where k1, k2, and k3 are weight coefficients, reflecting the influence of each factor on inertia.

[0085] The working principle and beneficial effects of the above technical solution are: by analyzing the inertia distribution, establishing the relationship between motor inertia and generator type, load conditions and operating status, and by comparing the inertia changes under different conditions, identifying key variables, constructing relationship equations, and integrating these relationships to form a dynamic inertia model, so as to more comprehensively describe the dynamic characteristics of the generator, enhance the grid's response capability under different operating conditions, and improve the stability and adaptability of the power system.

[0086] Example 4:

[0087] An embodiment of the present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method determines the inertial response characteristics of different generators under different disturbances based on the dynamic inertia model, and synthesizes the inertial response characteristics of all generators to obtain the grid inertia characteristics of the sending-end power grid, including:

[0088] Obtaining existing disturbances in the power grid at the sending end, converting the existing disturbances into disturbance data and inputting them into a dynamic inertia model, which outputs speed change and frequency change responses of each generator;

[0089] determining an inertial response of each generator based on the speed change and frequency change responses;

[0090] Analyzing the characteristics of the inertial response to obtain inertial response characteristics, selecting corresponding virtual inertial devices from a power generation characteristic-virtual device table based on the inertial response characteristics, combining the inertial response characteristics with virtual inertial technology, and establishing a virtual response model for each virtual inertial device;

[0091] The disturbance data is input into the virtual response model, which outputs the virtual response. The virtual response is combined with the inertia response to calculate the grid inertia characteristics of the sending-end grid.

[0092] In this embodiment, the presence of disturbance refers to various interference events occurring in the sending-end power grid, such as sudden load changes, generator failures, or external power grid fluctuations. For example, the instantaneous power demand of a power grid increases due to an increase in load, causing the grid frequency to decrease.

[0093] In this embodiment, the disturbance data is the data that converts the existing disturbance into data that can be used for model input, which generally includes the amplitude, duration and time series of the disturbance. For example, in the case of a sudden load change, the recorded disturbance data may include time series data of power change from 100MW to 150MW.

[0094] In this embodiment, the frequency change response is the frequency change reaction of the power grid to the disturbance, which is usually represented by a frequency change curve over time. For example, after a sudden load change, the power grid frequency drops from 50 Hz to 49.5 Hz and recovers to 50 Hz after a few seconds.

[0095] In this embodiment, the speed change is the response change of the generator speed to the disturbance, which is usually represented by a speed change curve over time. For example, after a sudden load change, the generator speed drops from 1500 rpm to 1480 rpm and then returns to 1500 rpm.

[0096] In this embodiment, the characteristic analysis is an in-depth analysis of the inertial response to identify its characteristics, such as response speed, amplitude, and recovery time. For example, the analysis finds that the inertial response characteristic of a certain generator is fast recovery, with a response time of less than 2 seconds and an amplitude change of no more than 5%.

[0097] In this embodiment, the power generation characteristic-virtual device table is a database containing characteristics of different generators or virtual devices, which is used to select a suitable virtual inertial device. For example, the table lists the characteristics of various virtual inertial devices, such as response time, output power, inertia value, etc.

[0098] In this embodiment, the virtual inertia device is a device or algorithm that simulates the inertia characteristics of a real generator and is used to enhance the stability of the power grid. For example, a virtual inertia device can quickly provide additional power support when the grid frequency drops to simulate the inertial response.

[0099] In this embodiment, the input of the virtual response model is disturbance data, typically a time series of load changes or frequency changes; the output is a virtual response, which represents the response of the virtual inertial device to the input disturbance. For example, the input is load mutation data, and the output is the power support curve provided by the virtual inertial device.

[0100] In this embodiment, the virtual response is the response of the virtual inertial device to the disturbance, which is usually manifested as additional power provided when the grid frequency changes. For example, when the grid frequency drops, the virtual response may be to provide an additional 20MW of power to help restore the frequency.

[0101] The working principle and beneficial effects of the above technical solution are as follows: the disturbance data of the sending-end power grid is input into the dynamic inertia model to obtain the speed and frequency change responses of each generator. Based on these changes, the inertia response of each generator is determined and the characteristics are analyzed. Based on the analysis results, the corresponding virtual inertia device is selected from the power generation characteristics-virtual device table to construct a virtual response model. The disturbance data is input into the model, the virtual response is output, and the overall inertia characteristics of the power grid are calculated in combination with the inertia response, thereby enhancing the power grid's response capability to disturbances and improving the stability and flexibility of the system.

[0102] Example 5:

[0103] An embodiment of the present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. Disturbance data is input into a virtual response model, which outputs a virtual response. The virtual response is combined with the inertia response to calculate the inertia characteristics of the sending-end power grid, including:

[0104] Combine the virtual response with the inertial response to form a comprehensive response, and evaluate the effective inertia of the sending-end power grid based on the comprehensive response;

[0105] The recovery performance of the power grid at the sending end is evaluated based on the frequency change response, and the inertia characteristics of the power grid are determined in combination with the effective inertia and the recovery performance.

[0106] In this embodiment, the comprehensive response is an overall response formed by combining the virtual response and the inertial response, reflecting the comprehensive dynamic characteristics of the power grid under disturbance. For example, in the case of a sudden load change, the inertial response of the generator causes the speed to drop, and the virtual inertial device provides additional power support. The final comprehensive response may be manifested as a smooth recovery curve of the frequency.

[0107] In this embodiment, the effective inertia is the inertia value exhibited by the power grid when it is disturbed, taking into account the combined effects of the generator and the virtual inertia device. It is generally used to assess the stability of the power grid. For example, if the generator inertia of a power grid is 1000MW·s and the effective inertia provided by the virtual inertia device is 200MW·s, the effective inertia of the power grid is 1200MW·s.

[0108] In this embodiment, the recovery performance is the ability of the power grid to recover to normal operating conditions after being disturbed, and is usually measured by the frequency recovery time and amplitude fluctuation. For example, after a sudden load change, the time it takes for the power grid frequency to recover from 49.5 Hz to 50 Hz is 3 seconds, and the frequency fluctuation amplitude does not exceed 0.2 Hz, which indicates that the power grid has good recovery performance.

[0109] The working principle and beneficial effects of the above technical solution are: combining virtual response with inertial response to form a comprehensive response, and then evaluating the effective inertia of the sending-end power grid. By analyzing the frequency change response, the recovery performance of the power grid is further evaluated. Combining the effective inertia and recovery performance, the inertial characteristics of the power grid are finally determined to fully reflect the dynamic performance and stability of the power grid under disturbances, help optimize the power grid design and operation strategy, and improve the power grid's adaptability to disturbances and recovery speed.

[0110] Example 6:

[0111] An embodiment of the present invention provides a method for dynamically partitioning and optimizing a sending-end power grid considering inertia support capacity constraints. The method derives the inertia support capacity of the sending-end power grid based on the inertia characteristics of the power grid, and evaluates the grid resilience of the sending-end power grid based on the inertia support capacity. The method includes:

[0112] Determine the resilience evaluation index of the sending-end power grid from the power grid resilience index table, and evaluate the power grid resilience of the sending-end power grid by combining the inertia support capacity and the resilience evaluation index:

[0113] Where R represents the grid resilience score of the sending power grid; w1 represents the importance of recovery time and frequency deviation for the grid resilience score; w2 represents the importance of redundancy for the resilience score; w3 represents the importance of backup capacity for the resilience score; w4 represents the importance of inertia support capacity for the resilience score; T m Indicates the mechanical recovery time of the sending-end power grid; T e Indicates the electronic recovery time of the sending-end power grid; T c Indicates the control recovery time of the sending-end power grid; f max (t) represents the maximum frequency value of the power grid at the sending end at time t; f no Indicates the standard frequency of the sending-end power grid; e -λt represents the time attenuation influence factor; k represents the rate of change of frequency deviation over time; Y represents the residual influence value; N r2 Indicates the number of redundant components in the sending-end power grid; N r1 represents the total number of components in the sending-end power grid; P b Indicates the backup power supply power of the sending-end grid; P lorepresents the actual load of the sending power grid; H represents the inertia support capacity of the sending power grid; α represents the nonlinear index of the recovery time and frequency deviation on the power grid resilience score; β represents the nonlinear index of redundancy on the power grid resilience score; γ represents the nonlinear index of backup capacity on the power grid resilience score; δ represents the nonlinear index of inertia support capacity on the power grid resilience score.

[0114] In this embodiment, the resilience assessment index is a series of quantitative indicators used to evaluate the recovery ability and stability of the power grid after a disturbance, usually including recovery time, frequency deviation, redundancy, backup capacity and inertia support capacity, etc. For example, in the power grid resilience index table, the following indicators may be listed: recovery time: the time required for the power grid to recover from the disturbance to the normal state, frequency deviation: the deviation of the power grid frequency from the standard frequency during the disturbance, redundancy: the ratio of the number of redundant components in the power grid to the total number of components, backup capacity: the ratio of the backup power supply power to the actual load, inertia support capacity: the size of the effective inertia of the power grid.

[0115] In this embodiment, the grid resilience index table is a table containing multiple grid resilience related indicators, which is used to comprehensively evaluate the resilience characteristics of the grid. For example, the table header corresponds to the indicator recovery time, and the description corresponds to the time (seconds) from disturbance to normal state.

[0116] The working principle and beneficial effects of the above technical solution are: by determining the evaluation indicators from the grid resilience index table, combining the inertia support capacity with other factors (such as recovery time, frequency deviation, redundancy and backup capacity) for comprehensive evaluation, and calculating the grid resilience score R through the formula, comprehensively considering the weights and nonlinear effects of various indicators, quantifying the resilience performance of the sending-end grid in the face of disturbances, helping to identify potential weaknesses and improvement directions, thereby improving the stability and recovery capabilities of the grid in emergencies.

[0117] Example 7:

[0118] An embodiment of the present invention provides a method for optimizing dynamic partitioning of a sending-end power grid considering inertia support capacity constraints. Dynamic constraints are set according to the grid resilience, the sending-end power grid is dynamically partitioned, and an optimization algorithm is applied to optimize the dynamic partitioning scheme to obtain an optimal partitioning scheme, including:

[0119] Collecting historical operating data of the sending power grid, determining electrical characteristics of the sending power grid based on the historical operating data, and identifying key nodes related to grid resilience;

[0120] According to the inertia characteristics of the power grid and historical operation data, dynamic constraints and optimization targets corresponding to each key node are set, and the sending-end power grid is dynamically partitioned based on the dynamic constraints and optimization targets;

[0121] An optimization algorithm is selected according to the optimization goal to optimize the dynamic partitioning scheme and obtain the best partitioning scheme.

[0122] In this embodiment, the historical operation data is the operation record of the power grid over the past period of time, including load changes, power generation, frequency fluctuations, fault events, recovery time, etc. For example, hourly load data, such as the load curve of a certain day, fault event records, the number of faults and recovery time of a substation in the past year, frequency fluctuation data, and frequency deviation within a specific time period.

[0123] In this embodiment, electrical characteristics describe electrical parameters exhibited by the power grid during operation, such as impedance, power factor, frequency response, and inertia. For example, impedance refers to the impedance value of a transmission line (e.g., 0.1+j0.05Ω), power factor refers to the power factor of a substation being 0.95, and inertia refers to the effective inertia of the power grid being 1500MW·s.

[0124] In this embodiment, key nodes are nodes in the power grid that have a greater impact on resilience, usually key substations, major transmission lines or load centers, such as major substations (such as the main substation in a city), important transmission lines (such as high-voltage lines connecting two major cities), and large load centers (such as industrial parks or large commercial areas).

[0125] In this embodiment, the dynamic constraint conditions are conditions that must be followed during the operation of the power grid, and include a first dynamic constraint condition and a second dynamic constraint condition.

[0126] In this embodiment, the optimization goal is the goal that is desired to be achieved in the operation of the power grid, which generally includes improving resilience, reducing operating costs, optimizing resource allocation, etc. For example, improving resilience: ensuring that the recovery time of the power grid in the event of a fault does not exceed 5 minutes in the design; reducing costs: minimizing the operating costs of the power grid while meeting safety constraints; optimizing resource allocation: reasonably arranging the output of the generator sets to meet the load demand.

[0127] In this embodiment, the frequency change response of the key nodes is analyzed, a first dynamic constraint condition is set, the historical load and power generation conditions are analyzed through historical operation data, the power balance of the sending-end power grid is determined, and a second dynamic constraint condition is obtained. The optimization target is set based on these two constraints.

[0128] The working principle and beneficial effects of the above technical solution are: collecting historical operating data of the sending-end power grid, analyzing electrical characteristics and identifying key nodes, setting dynamic constraints and optimization goals for each key node based on the inertial characteristics and historical data of the power grid, dividing the power grid through a dynamic partitioning method, and using an optimization algorithm to select the best partitioning scheme to achieve improved power grid operation efficiency and stability, effectively identify key nodes in the power grid, optimize power grid partitioning, improve the system's operating efficiency and responsiveness, and thus enhance the overall resilience and stability of the power grid.

[0129] Example 8:

[0130] The embodiment of the present invention provides a method for optimizing the dynamic partitioning of a sending-end power grid taking into account inertia support capacity constraints. The method sets dynamic constraints and optimization objectives corresponding to each key node based on the inertia characteristics of the power grid and historical operating data, including:

[0131] Perform characteristic analysis on the frequency change response corresponding to the key nodes and set the first dynamic constraint condition;

[0132] determining a power balance of the sending-end power grid based on historical load and historical power generation in the historical operation data, and deriving a second dynamic constraint condition;

[0133] An optimization objective is set based on the first dynamic constraint condition and the second dynamic constraint condition.

[0134] In this embodiment, characteristic analysis is performed on the frequency change response of key nodes under different disturbances to understand their dynamic characteristics and stability. Frequency response time is the time required for the frequency of a key node to return to a stable state after a disturbance. Frequency deviation is the maximum deviation of the frequency from the normal value (such as 50Hz) after the disturbance occurs. Inertia impact is analyzed to analyze the impact of the inertia of the key node on the frequency change. The larger the inertia, the smaller the frequency change. Control strategy is evaluated to evaluate the effectiveness of existing frequency control strategies (such as automatic generation control AGC) at key nodes. For example, at a key substation, after a disturbance, the frequency returned to normal within 5 seconds, with a maximum deviation of 0.3Hz and an inertia of 1000MW·s.

[0135] In this embodiment, the first dynamic constraint condition is a constraint condition set based on the frequency change response characteristics to ensure the frequency stability of the key node under disturbance, the frequency recovery time constraint: the frequency recovery time of the key node shall not exceed the set value (such as 5 seconds), the frequency deviation constraint: the frequency deviation of the key node during the disturbance shall not exceed a certain threshold (such as ±0.5Hz). For example, the first dynamic constraint condition can be set as: the frequency deviation of the key node shall not exceed ±0.5Hz, and the recovery time shall not exceed 5 seconds.

[0136] In this embodiment, historical load and historical power generation are historical load data and historical power generation data used to analyze the power balance state of the power grid. Historical load: power demand data in the past period of time (such as hourly load curve); historical power generation: power generation data in the same time period, including the output of renewable energy and traditional power generation. For example, in a certain time period, the historical load is 500MW, and the historical power generation is 550MW, which shows the power balance state of the power grid in that period.

[0137] In this embodiment, the second dynamic constraint is a constraint condition derived from power balance analysis to ensure that the power grid satisfies power balance during operation. The power balance constraint is: at any time, the total power generation power of the power grid must be equal to or greater than the total load power (taking losses into account). The power generation capacity constraint is: the output of the generator set should meet the load demand and safety margin. For example, the second dynamic constraint condition can be set as: in each time period, the power generation power must be greater than or equal to 110% of the load power.

[0138] In this embodiment, the optimization target is an optimization target set based on the first and second dynamic constraints to improve the stability and economy of the power grid, improve resilience: ensure the frequency stability and rapid recovery capability of the power grid at key nodes, optimize economy: minimize power generation costs while meeting power balance and frequency constraints, optimize resource allocation: reasonably dispatch power generation resources to ensure power generation capacity during high load periods. For example, the optimization target can be set as: minimize the overall power generation cost while meeting the frequency recovery time of no more than 5 seconds and power balance, and ensure that the frequency deviation of key nodes does not exceed ±0.5Hz.

[0139] The working principle and beneficial effects of the above technical solution are: perform characteristic analysis on the frequency change response of key nodes, set the first dynamic constraint condition, analyze the historical load and power generation conditions through historical operation data, determine the power balance of the sending-end power grid, and derive the second dynamic constraint condition. Based on these two constraints, set the optimization target to ensure the stability and reliability of the power grid under dynamic conditions, and ensure the power balance and frequency stability of the power grid under different operating conditions, thereby improving the safety and resilience of the power grid and reducing the risk of failure.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A dynamic partitioning optimization method for the sending-end power grid considering inertia support capacity constraints is characterized by: include: Step 1: Obtaining the operating data of the generators of the sending-end power grid, analyzing and comparing the motor inertias of different generators based on the operating data, and then constructing a dynamic inertia model; Step 2: Determine the inertia response characteristics of different generators under different disturbances based on the dynamic inertia model, and obtain the grid inertia characteristics of the sending-end grid by integrating the inertia response characteristics of all generators; Step 3: Determine the inertia support capability of the sending-end power grid based on the power grid inertia characteristics, and evaluate the power grid resilience of the sending-end power grid based on the inertia support capability; Step 4: Dynamic constraints are set according to the grid resilience, the sending-end grid is dynamically partitioned, and an optimization algorithm is applied to optimize the dynamic partitioning scheme to obtain the optimal partitioning scheme.

2. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 1 is characterized in that: Obtaining generator operating data of the sending-end power grid, analyzing and comparing motor inertias of different generators based on the operating data, and then constructing a dynamic inertia model, including: Calculating the motor inertia of the corresponding generator based on the working data, performing statistical analysis on the motor inertia, and determining the inertia distribution of different generators; The key variables of the generator are determined according to the inertia distribution, and then the relationship equations are determined to construct a dynamic inertia model.

3. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 2 is characterized in that: The key variables of the generator are determined based on the inertia distribution, and then the relationship equations are determined to construct a dynamic inertia model, including: determining a first relationship between motor inertia and generator type based on the inertia distribution; determining a second relationship between the motor inertia and the load based on a change in inertia distribution under different load conditions; Comparing the motor inertias between different operating states to determine a third relationship between the operating state and the motor inertia; Based on the first, second and third relationships, key variables are determined and a relationship equation is established. The first, second and third relationships are combined using the relationship equation to construct a dynamic inertia model.

4. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 1 is characterized in that: Based on the dynamic inertia model, the inertia response characteristics of different generators under different disturbances are determined, and the inertia response characteristics of all generators are integrated to obtain the grid inertia characteristics of the sending-end grid, including: Obtaining existing disturbances in the power grid at the sending end, converting the existing disturbances into disturbance data and inputting them into a dynamic inertia model, which outputs speed change and frequency change responses of each generator; determining an inertial response of each generator based on the speed change and frequency change responses; Analyzing the characteristics of the inertial response to obtain inertial response characteristics, selecting corresponding virtual inertial devices from a power generation characteristic-virtual device table based on the inertial response characteristics, combining the inertial response characteristics with virtual inertial technology, and establishing a virtual response model for each virtual inertial device; The disturbance data is input into the virtual response model, which outputs the virtual response. The virtual response is combined with the inertia response to calculate the grid inertia characteristics of the sending-end grid.

5. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 4 is characterized in that: The disturbance data is input into the virtual response model, which outputs the virtual response. The virtual response is combined with the inertia response to calculate the inertia characteristics of the sending-end power grid, including: Combine the virtual response with the inertial response to form a comprehensive response, and evaluate the effective inertia of the sending-end power grid based on the comprehensive response; The recovery performance of the power grid at the sending end is evaluated based on the frequency change response, and the inertia characteristics of the power grid are determined in combination with the effective inertia and the recovery performance.

6. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 1 is characterized in that: Determining the inertia support capability of the sending-end power grid according to the power grid inertia characteristics, and evaluating the power grid resilience of the sending-end power grid according to the inertia support capability, including: Determine the resilience evaluation index of the sending-end power grid from the power grid resilience index table, and evaluate the power grid resilience of the sending-end power grid by combining the inertia support capacity and the resilience evaluation index: Where R represents the grid resilience score of the sending power grid; w1 represents the importance of recovery time and frequency deviation for the grid resilience score; w2 represents the importance of redundancy for the resilience score; w3 represents the importance of backup capacity for the resilience score; w4 represents the importance of inertia support capacity for the resilience score; T m Indicates the mechanical recovery time of the sending-end power grid; T e Indicates the electronic recovery time of the sending-end power grid; T c Indicates the control recovery time of the sending-end power grid; f max (t) represents the maximum frequency value of the power grid at the sending end at time t; f no Indicates the standard frequency of the sending-end power grid; e -λt represents the time attenuation influence factor; k represents the rate of change of frequency deviation over time; Y represents the residual influence value; N r2 Indicates the number of redundant components in the sending-end power grid; N r1 represents the total number of components in the sending-end power grid; P b Indicates the backup power supply power of the sending-end grid; P lo represents the actual load of the sending power grid; H represents the inertia support capacity of the sending power grid; α represents the nonlinear index of the recovery time and frequency deviation on the power grid resilience score; β represents the nonlinear index of redundancy on the power grid resilience score; γ represents the nonlinear index of backup capacity on the power grid resilience score; δ represents the nonlinear index of inertia support capacity on the power grid resilience score.

7. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 1 is characterized in that: Dynamic constraints are set according to the grid resilience, the sending-end grid is dynamically partitioned, and an optimization algorithm is applied to optimize the dynamic partitioning scheme to obtain the optimal partitioning scheme, including: Collecting historical operating data of the sending power grid, determining electrical characteristics of the sending power grid based on the historical operating data, and identifying key nodes related to grid resilience; According to the inertia characteristics of the power grid and historical operation data, dynamic constraints and optimization targets corresponding to each key node are set, and the sending-end power grid is dynamically partitioned based on the dynamic constraints and optimization targets; An optimization algorithm is selected according to the optimization goal to optimize the dynamic partitioning scheme and obtain the best partitioning scheme.

8. The method for dynamic partitioning optimization of the sending-end power grid considering inertia support capacity constraints according to claim 4 is characterized in that: The dynamic constraints and optimization objectives for each key node are set based on the grid inertia characteristics and historical operating data, including: Perform characteristic analysis on the frequency change response corresponding to the key nodes and set the first dynamic constraint condition; determining a power balance of the sending-end power grid based on historical load and historical power generation in the historical operation data, and deriving a second dynamic constraint condition; An optimization objective is set based on the first dynamic constraint condition and the second dynamic constraint condition.

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