An optimization method for inertia evaluation considering different frequency response characteristic data ranges

Abstract

This invention discloses an inertia assessment optimization method considering different frequency response characteristic data ranges, belonging to the field of control technology. It optimizes the assessment method by processing easily measurable frequency characteristic data. For cases where complete frequency response characteristic data is known, a step-by-step inertia assessment method based on sliding window technology is proposed; for cases where only partial frequency response characteristic data is available, an inertia assessment method based on piecewise polynomial fitting is proposed. Finally, a typical high-proportion new energy power system model was built on an electromagnetic transient software simulation platform to verify the proposed inertia assessment optimization method based on different frequency response characteristic data ranges. The results show that it can effectively reduce the impact of time factors and errors on the results, while greatly improving the accuracy of inertia assessment, and has good practical engineering application value.

Description

Technical Field

[0001] This invention belongs to the field of ultra-wideband communication technology, and in particular relates to an inertia evaluation and optimization method that takes into account different frequency response characteristic data ranges. Background Technology

[0002] With the large-scale integration of new energy sources and energy storage power electronic equipment into the grid, and the implementation of high-capacity inter-regional DC transmission technology, the traditional power system dominated by conventional synchronous generator units is gradually transforming into a power system with a high proportion of new energy sources, primarily wind and solar turbines. Compared to the traditional power system, in a power system with a high proportion of new energy sources, synchronous generators are gradually being replaced by converter interface power supplies with almost zero inertia, resulting in a relatively reduced moment of inertia and a weakened system inertia response capability. This reduction in inertia level leads to significant changes in the system's frequency characteristics after disturbances: on the one hand, new energy units, through power electronic components, are decoupled from the AC system frequency and cannot provide inertia support for the system; on the other hand, the output power of new energy units often depends on natural conditions such as wind speed and irradiance, exhibiting strong fluctuations and intermittent characteristics. If the output fluctuates over a wide range, the system may experience regional grid stability problems due to a lack of inertia support, which in turn restricts the proportion of new energy units in the system. Under the combined effect of these two aspects, the integration of large-scale new energy units will cause the system to exhibit "low inertia" characteristics, and the frequency regulation capability will also be significantly weakened. The rate of change of frequency (RoCoF), a metric for measuring a system's frequency regulation capability, increases significantly as inertia decreases. A large RoCoF can not only lead to uncontrollable system frequency response but may even cause pole slippage in synchronous machines, resulting in internal structural damage. To prevent the damage to power system stability caused by low inertia levels, extensive research on the theory and evaluation of inertia has been conducted both domestically and internationally in recent years. Excellent inertia assessment strategies help measure power system stability and provide early warnings for systems with low inertia levels. Summary of the Invention

[0003] The purpose of this invention is to provide an optimized inertia assessment method that considers different frequency response characteristic data ranges. Addressing the shortcomings of existing algorithms, this invention optimizes the traditional inertia assessment method for different application scenarios, reducing the adverse impact of time factors on the assessment method. For scenarios requiring analysis of partial frequency data, a total inertia assessment method based on piecewise polynomial fitting is proposed; for scenarios requiring analysis of the complete frequency response curve, a step-by-step inertia assessment method based on sliding window technology is proposed.

[0004] To achieve the above objectives, the present invention provides an inertia evaluation optimization method that takes into account different frequency response characteristic data ranges, comprising the following steps:

[0005] Step 1: Calculate the theoretical inertia time constant of the system;

[0006] Step 2: Calculate the system's inertia time constant;

[0007] Step 3: Calculate the error of the inertia assessment method;

[0008] Step 4: Optimize the inertia evaluation method to account for the errors of inertia evaluation methods with different frequency response characteristic data ranges.

[0009] Preferably, the specific calculation process of the system's theoretical inertia time constant in step 1 is as follows:

[0010] S11. Calculate the inertia time constant H. The inertia time constant H is the time that the generator set can sustain using only its stored kinetic energy to provide energy for its rated capacity. It is defined as the ratio of the rotor kinetic energy of the generator at its rated mechanical angular velocity to the generator's rated capacity. The specific expression is as follows:

[0011]

[0012] Among them, E k Let J be the rotational kinetic energy stored by the rotor rotation of a single generator, J be the moment of inertia of the synchronous generator, ω be the angular frequency of the generator, and S be the rotational kinetic energy stored by the rotor rotation of a single generator. B This is the system's rated capacity;

[0013] S12. Calculate the total system inertia. For power systems with a high proportion of renewable energy integration, the total system inertia is expressed as the sum of the rotational kinetic energies of all types of generating units. The specific expression is as follows:

[0014]

[0015] Among them, E sys H is the theoretical inertia of the system. Gi S represents the inertia time constant of each synchronous generator unit in the system. Gi For the capacity of each synchronous generator unit in the system, the inertia support provided by the new energy generator units is mainly virtual inertia, H. Nj Set the virtual inertia time constant value for each new energy unit in the system; S Nj E represents the capacity of each new energy unit in the system. IMk This represents the total inertia of the asynchronous units in the system.

[0016] S13. When considering different types of generators with different inertia values ​​and inertia-time constant values ​​as a whole, the theoretical inertia-time constant of the system is calculated using the following formula:

[0017]

[0018] Among them, Esys S is the theoretical inertia of the system. Bi S represents the rated capacity of each synchronous generator unit in the system. Bj This refers to the rated capacity of each new energy unit in the system.

[0019] Preferably, the specific calculation process for the system's inertia time constant in step 2 is as follows:

[0020] S21. Find the expression for the system's inertial response, as follows:

[0021]

[0022] Among them, P m P e S represents the mechanical and electromagnetic power of the system; DΔω represents the damping power of the system, and S... B f is the system's rated capacity. n The system's rated frequency;

[0023] S22. Simplify the left-hand side of step S21 to ΔP, and calculate the system's calculated inertia time constant and calculated inertia value. The specific formulas are as follows:

[0024]

[0025]

[0026] Among them, H Csys E represents the system's calculated inertia time constant. Csys f is the computational inertia of the system. n The system's rated frequency; ΔP is the power imbalance of the entire system; df / dt is the rate of frequency change at the system nodes; S B This refers to the system's rated capacity.

[0027] Preferably, the error of the inertia assessment method is an important criterion for measuring the accuracy of inertia assessment, and the specific calculation method is as follows:

[0028]

[0029] Among them, E Csys E is the computational inertia of the system. sys Let ε represent the theoretical inertia of the system, and let ε represent the error of the inertia assessment method.

[0030] Preferably, the optimization in step 4 is an optimization of the system's calculated inertia time constant in step 2. The optimization methods include a step-by-step inertia evaluation method based on sliding window technology and an inertia evaluation method based on piecewise polynomial fitting.

[0031] Preferably, the specific process of the step-by-step inertia evaluation method based on sliding window technology is as follows:

[0032] 1) Obtain the equivalent inertia curve of the system;

[0033] 2) Apply a sliding window processing to the equivalent inertia curve. Let the window length be l and the initial sampled data be i. Then the sliding window takes values ​​from the i-th data point to the (i+l-1)-th data point. Calculate the variance of the data within this window, denoted as S. i 2 Variance is expressed as

[0034]

[0035] In the formula, N is the total amount of data from the start sampling time to the end sampling time, i.e., the length of the sliding window; E i It is the calculated inertia for each sample in the sliding window; E A It is the average calculated inertia over the entire sliding window period;

[0036] 3) Traverse the frequency response characteristic data to obtain the variance of all windows with a window length of l. Select the window with the lowest variance value and use the average value of all inertia values ​​within that window as the system's calculated inertia. The system's calculated inertia is expressed as...

[0037]

[0038] Among them, E Csys E is the computational inertia of the system. A It is the average calculated inertia over the entire sliding window period, E t It represents all the computational inertia within the sliding window, and N is the total amount of data from the start sampling time to the end sampling time, which is the length of the sliding window.

[0039] Preferably, the specific process for obtaining the equivalent inertia curve of the system is as follows:

[0040] 1) Collect the frequency response data of the system and plot the frequency characteristic curve;

[0041] 2) Calculate the slope value of two points within each sampling step of the frequency response curve as df / dt within that sampling step, and plot the frequency change rate curve of df / dt with respect to time t;

[0042] 3) Calculate the system's calculated inertia E in each step using the df / dt obtained in each step, and plot the equivalent inertia curve of E as a function of time t.

[0043] Preferably, the inertia evaluation method based on piecewise polynomial fitting is specifically implemented as follows:

[0044] 1) Determine the order of the piecewise polynomial fitting and determine the coefficients A0, A1, and B. i The estimated value of the delay time t1 is used as the initial value for the iteration;

[0045] 2) Obtain the optimal estimate of each coefficient through multiple fitting iterations;

[0046] 3) Take coefficient A1 as the system's frequency change rate and substitute it into the system's calculated inertia formula to obtain the system's calculated inertia. The system inertia calculation formula is as follows:

[0047]

[0048] Among them, H Csys E represents the system's calculated inertia time constant. Csys f is the computational inertia of the system. n The system's rated frequency; ΔP is the power imbalance of the entire system; df / dt is the rate of frequency change at the system nodes; S B This refers to the system's rated capacity.

[0049] Therefore, the present invention employs the above-mentioned inertia evaluation optimization method that takes into account different frequency response characteristic data ranges, and has the following beneficial effects:

[0050] (1) The traditional inertia assessment method has been optimized for different application scenarios, reducing the adverse impact of time factors on the assessment method;

[0051] (2) For cases where complete frequency response characteristic data is known, a step-by-step inertia evaluation method based on sliding window technology is proposed. That is, the system inertia is calculated and processed within each sampling step, avoiding the influence of the initial disturbance error in the data and the unknown first frequency modulation time on the results. For cases where only partial frequency response characteristic data is available, an inertia evaluation method based on piecewise polynomial fitting is proposed. This method considers the change in the order of the system frequency model before and after the first frequency modulation, thereby reducing the influence of the number of fitting steps on the results.

[0052] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0053] Figure 1 This is a diagram illustrating the step-by-step inertia evaluation process based on sliding window technology in this invention.

[0054] Figure 2 This is a simulation example diagram of the three-machine, nine-node system of the present invention;

[0055] Figure 3 The following is a simulation result diagram of the step-by-step inertia evaluation strategy in the example embodiment;

[0056] Figure 4This is a comparison chart of errors in the step-by-step inertia evaluation strategy for the example implementation.

[0057] Figure 5 The diagram shows the segmented frequency response curve of an example. Detailed Implementation

[0058] Example

[0059] The following detailed description of embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0060] An inertia evaluation optimization method considering different frequency response characteristic data ranges includes the following steps:

[0061] Step 1: Calculate the theoretical inertia time constant of the system;

[0062] Step 2: Calculate the system's inertia time constant;

[0063] Step 3: Calculate the error of the inertia assessment method; the error of the inertia assessment method is an important standard for measuring the accuracy of inertia assessment. The specific calculation method is as follows:

[0064]

[0065] Among them, E Csys E is the computational inertia of the system. sys Let ε represent the theoretical inertia of the system, and let ε represent the error of the inertia assessment method.

[0066] Step 4: Optimize the inertia evaluation method to account for the errors of inertia evaluation methods with different frequency response characteristic data ranges.

[0067] The specific calculation process of the system's theoretical inertia time constant in step 1 is as follows:

[0068] S11. Calculate the inertia time constant H. The inertia time constant H is the time that the generator set can sustain using only its stored kinetic energy to provide energy for its rated capacity. It is defined as the ratio of the rotor kinetic energy of the generator at its rated mechanical angular velocity to the generator's rated capacity. The specific expression is as follows:

[0069]

[0070] Among them, E k Let J be the rotational kinetic energy stored by the rotor rotation of a single generator, J be the moment of inertia of the synchronous generator, ω be the angular frequency of the generator, and S be the rotational kinetic energy stored by the rotor rotation of a single generator. B This is the system's rated capacity;

[0071] S12. Calculate the total system inertia. For power systems with a high proportion of renewable energy integration, the total system inertia is expressed as the sum of the rotational kinetic energies of all types of generating units. The specific expression is as follows:

[0072]

[0073] Among them, E sys H is the theoretical inertia of the system. Gi S represents the inertia time constant of each synchronous generator unit in the system. Gi For the capacity of each synchronous generator unit in the system, the inertia support provided by the new energy generator units is mainly virtual inertia, H. Nj Set the virtual inertia time constant value for each new energy unit in the system; S Nj E represents the capacity of each new energy unit in the system. IMk This represents the total inertia of the asynchronous units in the system.

[0074] S13. When considering different types of generators with different inertia values ​​and inertia-time constant values ​​as a whole, the theoretical inertia-time constant of the system is calculated using the following formula:

[0075]

[0076] Among them, E sys S is the theoretical inertia of the system. Bi S represents the rated capacity of each synchronous generator unit in the system. Bj This refers to the rated capacity of each new energy unit in the system.

[0077] The specific calculation process for the system's inertia time constant in step 2 is as follows:

[0078] S21. Find the expression for the system's inertial response, as follows:

[0079]

[0080] Among them, P m P e S represents the mechanical and electromagnetic power of the system; DΔω represents the damping power of the system, and S... B f is the system's rated capacity. n The system's rated frequency;

[0081] S22. Simplify the left-hand side of step S21 to ΔP, and calculate the system's calculated inertia time constant and calculated inertia value. The specific formulas are as follows:

[0082]

[0083]

[0084] Among them, H Csys E represents the system's calculated inertia time constant. Csys f is the computational inertia of the system. n The system's rated frequency; ΔP is the power imbalance of the entire system; df / dt is the rate of frequency change at the system nodes; S B This refers to the system's rated capacity.

[0085] The optimization in step 4 is an optimization of the system's calculated inertia time constant in step 2. The optimization methods include the step-by-step inertia evaluation method based on sliding window technology and the inertia evaluation method based on piecewise polynomial fitting.

[0086] like Figure 1 The specific process of the step-by-step inertia evaluation method based on sliding window technology is as follows:

[0087] 1) Obtain the equivalent inertia curve of the system;

[0088] 2) Apply a sliding window processing to the equivalent inertia curve. Let the window length be l and the initial sampled data be i. Then the sliding window takes values ​​from the i-th data point to the (i+l-1)-th data point. Calculate the variance of the data within this window, denoted as S. i 2 The variance can be expressed as:

[0089]

[0090] In the formula: N is the total amount of data from the start sampling time to the end sampling time, i.e., the length of the sliding window; E i It is the calculated inertia for each sample in the sliding window; E A It is the average calculated inertia over the entire sliding window period;

[0091] 3) Traverse the frequency response characteristic data to obtain the variance of all windows with a window length of l. Take the window with the lowest variance value and use the average value of all inertia in that window as the system's calculated inertia. The system's calculated inertia is expressed as:

[0092]

[0093] Among them, E Csys E is the computational inertia of the system. A It is the average calculated inertia over the entire sliding window period, E t It represents all the computational inertia within the sliding window, and N is the total amount of data from the start sampling time to the end sampling time, which is the length of the sliding window.

[0094] The specific process for obtaining the equivalent inertia curve of the system is as follows:

[0095] 1) Collect the frequency response data of the system and plot the frequency characteristic curve;

[0096] 2) Calculate the slope value of two points within each sampling step of the frequency response curve as df / dt within that sampling step, and plot the frequency change rate curve of df / dt with respect to time t;

[0097] 3) Calculate the system's calculated inertia E in each step using the df / dt obtained in each step, and plot the equivalent inertia curve of E as a function of time t.

[0098] The specific process of the inertia evaluation method based on piecewise polynomial fitting is as follows:

[0099] 1) Determine the order of the piecewise polynomial fitting and determine the coefficients A0, A1, and B. i The estimated value of the delay time t1 is used as the initial value for the iteration;

[0100] 2) Obtain the optimal estimate of each coefficient through multiple fitting iterations;

[0101] 3) Take coefficient A1 as the system's frequency change rate and substitute it into the system's calculated inertia formula to obtain the system's calculated inertia. The system inertia calculation formula is as follows:

[0102]

[0103] Among them, H Csys E represents the system's calculated inertia time constant. Csys f is the computational inertia of the system. n The system's rated frequency; ΔP is the power imbalance of the entire system; df / dt is the rate of frequency change at the system nodes; S B This refers to the system's rated capacity.

[0104] The proposed inertia evaluation optimization method, which takes into account data ranges of response characteristics at different frequencies, was verified by simulation, as follows:

[0105] like Figure 2 As shown, a three-machine, nine-node example was used on the Matlab platform for verification. Three sets of comparative experiments were conducted on this example, and the specific data of the unit are shown in Table 1.

[0106] Table 1

[0107] <![CDATA[H1 / s]]> 3.89 7.78 3.89 <![CDATA[H2 / s]]> 2.49 2.49 2.49 <![CDATA[H3 / s]]> / / / ΔP / MW 100 100 50 t / s 6 6 6 <![CDATA[S B1 / MW]]> 1000 1000 1000 <![CDATA[S B2 / MW]]> 400 400 400 <![CDATA[S B3 / MW]]> 600 600 600 <![CDATA[E sys / MW*s]]> 4886 8776 4886

[0108] Reference Figure 3In the three cases shown in the table above, the time from the occurrence of the disturbance to the first drop in frequency to its lowest point is taken, and the rate of change of system frequency and the calculated inertia are obtained in each time step with a step size of 0.05 seconds. The simulation results in the three cases show that the data error in the early stage of the disturbance and the intervention of frequency modulation will lead to a large error in the inertia assessment.

[0109] Referring to Table 2, to reduce the impact of the aforementioned time factor on the evaluation results, data within 1 second, 0.75 seconds, and 0.05 to 0.75 seconds after the disturbance occurred were used for calculation, with the unit of the calculated system inertia being MW*s. It can be seen that under various value selection strategies, the difference between the calculated inertia and the theoretical inertia was narrowed to an acceptable range.

[0110] Table 2

[0111] 0s-1s 6379.8 10236.0 6068.0 error 30.57％ 16.63％ 24.20％ 0s-0.75s 5878.8 9891.0 5638.2 error 20.32％ 12.70％ 15.39％ 0.05s-0.75s 5618.6 9701.3 5458.9 error 15.00％ 10.54％ 11.7％

[0112] Referring to Table 3, to address the shortcomings of the previous evaluation strategy, a sliding window technique was incorporated into the algorithm. The calculated inertia of the system was calculated for three conditions with window lengths of 10, 30, and 50, where the unit of calculated inertia is MW*s. The table shows that the error range for inertia assessment under the three conditions is 0.50% to 6.92%. Compared to the evaluation strategy without the sliding window technique, the error is relatively small. Furthermore, the sliding window technique eliminates the need to filter data within a specific time range to reduce error, thus minimizing the interference of time factors on inertia assessment. This verifies the correctness of the step-by-step inertia assessment strategy based on the sliding window technique.

[0113] Table 3

[0114]

[0115]

[0116] Referring to Table 4, the system under the three operating conditions was fitted three to six times. Due to space limitations, only the fitting results for case one are presented here. The fitting results show that the polynomial fitting method processes the data as a whole curve and is not affected by the selection of the time interval. Different fitting orders lead to different systematic errors in the calculation results. The optimal fitting order is concentrated between fourth and fifth orders due to differences in system topology, but the most suitable fitting order cannot be determined. To optimize the impact of the fitting order on the results, the system frequency response curve should be further fitted to a piecewise polynomial function.

[0117] Table 4

[0118]

[0119] Refer to Table 5 and Figure 4 Similarly, the system under the three operating conditions was fitted three to six times. Due to space limitations, only the fitting results for case one are presented here. A comparison of the fitting results with the errors under the three operating conditions shows that fitting the frequency response curve to a piecewise polynomial function not only kept the error at an extremely low level but also reduced the impact of the fitting order on the results, thereby significantly improving the accuracy of inertia level estimation.

[0120] Table 5

[0121]

[0122] In particular, even if the frequency response characteristic data is only a part of the curve, a rough assessment of the system inertia can still be made. Taking Case 1 as an example, the frequency response curve is divided into three parts: A, B, and C, corresponding to 0.2 seconds - 0.6 seconds, 0.6 seconds - 1 second, and 1 second - 1.4 seconds after the disturbance, respectively. Figure 5 As shown in Table 6, even with only partial frequency response data available, the inertia assessment method based on piecewise polynomial fitting can still provide a relatively accurate assessment of the system inertia, demonstrating the effectiveness of the optimization strategy proposed in this paper.

[0123] Table 6

[0124]

[0125]

[0126] Therefore, this invention employs the aforementioned inertia assessment optimization method that considers different frequency response characteristic data ranges. By processing easily measurable frequency characteristic data, the assessment method is optimized. For cases where complete frequency response characteristic data is known, a step-by-step inertia assessment method based on sliding window technology is proposed; for cases where only partial frequency response characteristic data is available, an inertia assessment method based on piecewise polynomial fitting is proposed. Finally, a typical high-proportion new energy power system model was built on an electromagnetic transient software simulation platform to verify the proposed inertia assessment optimization method based on different frequency response characteristic data ranges. The results show that it can effectively reduce the impact of time factors and errors on the results, while greatly improving the accuracy of inertia assessment, demonstrating good practical engineering application value.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An inertia evaluation and optimization method considering data ranges of response characteristics at different frequencies, characterized in that, Includes the following steps: Step 1: Calculate the theoretical inertia time constant of the system; Step 2: Calculate the system's inertia time constant; Step 3: Calculate the error of the inertia assessment method; Step 4: Optimize the inertia evaluation method to account for the errors in inertia evaluation methods that take into account different frequency response characteristic data ranges; The optimization in step 4 is an optimization of the system's inertia time constant calculation in step 2. The optimization methods include a step-by-step inertia evaluation method based on sliding window technology and an inertia evaluation method based on piecewise polynomial fitting. Specifically, the step-by-step inertia evaluation method based on sliding window technology is used when the complete frequency response characteristic data is known; the inertia evaluation method based on piecewise polynomial fitting is used when only partial frequency response characteristic data is available. The specific process of the step-by-step inertia evaluation method based on sliding window technology is as follows: 1) Obtain the equivalent inertia curve of the system; 2) Apply a sliding window processing to the equivalent inertia curve. Let the window length be l and the initial sampled data be i. Then the sliding window takes values ​​from the i-th data point to the (i+l-1)-th data point. Calculate the variance of the data within this window, denoted as S. i 2 Variance is expressed as In the formula, N is the total amount of data from the start sampling time to the end sampling time, i.e., the length of the sliding window; E i It is the calculated inertia for each sample in the sliding window; E A It is the average calculated inertia over the entire sliding window period; 3) Traverse the frequency response characteristic data to obtain the variance of all windows with a window length of l. Select the window with the lowest variance value and use the average value of all inertia values ​​within that window as the system's calculated inertia. The system's calculated inertia is expressed as... in, E Csys For the system's computational inertia, E A It is the average calculated inertia over the entire sliding window period. E t It represents all the computational inertia within the sliding window, and N is the total amount of data from the start sampling time to the end sampling time, which is the length of the sliding window. The specific process of the inertia evaluation method based on piecewise polynomial fitting is as follows: 1) Determine the order of the piecewise polynomial fitting and determine the coefficients A0, A1, and B. i The estimated value of the delay time t1 is used as the initial value for the iteration; 2) Obtain the optimal estimates of each coefficient through multiple fitting iterations; 3) Take coefficient A1 as the system's frequency change rate and substitute it into the system's calculated inertia formula to obtain the system's calculated inertia. The system inertia calculation formula is as follows: Among them, H Csys E represents the system's calculated inertia time constant. Csys f is the computational inertia of the system. n The system's rated frequency; ΔP is the power imbalance of the entire system; df / dt is the rate of frequency change at the system nodes; S B This refers to the system's rated capacity.

2. The inertia evaluation and optimization method considering different frequency response characteristic data ranges according to claim 1, characterized in that, The specific calculation process for the theoretical inertia time constant of the system in step 1 is as follows: S11. Calculate the inertia time constant. H， Inertia time constant H This is the duration for which a generator set can sustain power at its rated capacity using only its stored kinetic energy. It is defined as the ratio of the generator's rotor kinetic energy at its rated mechanical angular velocity to its rated capacity. The specific expression is as follows: Among them, E k Let J be the rotational kinetic energy stored by the rotor rotation of a single generator, J be the moment of inertia of the synchronous generator, ω be the angular frequency of the generator, and S be the rotational kinetic energy stored by the rotor rotation of a single generator. B This is the system's rated capacity; S12. Calculate the total system inertia. For power systems with a high proportion of renewable energy integration, the total system inertia is expressed as the sum of the rotational kinetic energies of all types of generating units. The specific expression is as follows: in, E sys The theoretical inertia of the system; H Gi Let be the inertia time constant of each synchronous generator unit in the system; S Gi For the capacity of each synchronous generator unit in the system, the inertia support provided by the new energy generator units is mainly virtual inertia. H Nj Set the virtual inertia time constant value for each new energy unit in the system; S Nj The capacity of each new energy unit in the system, E IMk This represents the total inertia of the asynchronous units in the system. S13. When considering different types of generators with different inertia values ​​and inertia-time constant values ​​as a whole, the theoretical inertia-time constant of the system is calculated using the following formula: Among them, E sys The theoretical inertia of the system, S Bi S represents the rated capacity of each synchronous generator unit in the system. Bj This refers to the rated capacity of each new energy unit in the system.

3. The inertia evaluation and optimization method considering different frequency response characteristic data ranges according to claim 2, characterized in that, The specific calculation process for the system's inertia time constant in step 2 is as follows: S21. Find the expression for the system's inertial response, as follows: in, P m , P e For the system's mechanical power and electromagnetic power, DΔω S is the damping power of the system. B f is the system's rated capacity. n The system's rated frequency; S22. The left-hand side of step S21 above can be simplified to Δ P The calculated inertia time constant and calculated inertia value of the system are obtained using the following formulas. in, H Csys The calculated inertia time constant of the system; E Csys The calculated inertia of the system; f n The system's rated frequency; Δ P This represents the power imbalance of the entire system. df / dt The rate of change of the frequency of the system nodes; S B This refers to the system's rated capacity.

4. The inertia evaluation and optimization method considering different frequency response characteristic data ranges according to claim 3, characterized in that, The error of the inertia assessment method is an important criterion for measuring the accuracy of inertia assessment. The specific calculation method is as follows: in, E Csys For the system's computational inertia, E sys The theoretical inertia of the system, This indicates the error in the inertia assessment method.

5. The inertia evaluation and optimization method considering different frequency response characteristic data ranges according to claim 4, characterized in that, The specific process for obtaining the equivalent inertia curve of the system is as follows: 1) Collect the frequency response data of the system and plot the frequency characteristic curve; 2) Calculate the slope value of two points within each sampling step of the frequency response curve as df / dt within that sampling step, and plot the frequency change rate curve of df / dt with respect to time t; 3) Calculate the system's calculated inertia E in each step using the df / dt obtained in each step, and plot the equivalent inertia curve of E as a function of time t.

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