A method for evaluating the grid inertia stability region considering the aging of energy storage batteries

Abstract

This invention relates to the field of power system frequency stabilization strategy technology, and discloses a method for assessing the grid inertia stability domain considering the aging of energy storage batteries. First, a battery peak power decay model is established under a long-term grid inertia support scenario. Then, a dynamic calculation method for the upper limit of ISR (Inertia Sequence Ratio) is proposed by combining the staged decay characteristics and changing trends of battery peak power. Simultaneously, load forecast data is used to optimize the solution of the lower limit of ISR under frequency stability constraints, forming a complete ISR profile. This profile can be used to assess the current grid inertia level, quantitatively evaluate the grid's inertia safety, ensure that the grid can maintain the frequency change rate and frequency deviation within a stable range, and promptly alert the grid control center to take measures when inertia is insufficient to ensure grid frequency safety. The ISR constructed by this invention fully considers the impact of battery aging on inertia assessment, enabling the grid to provide timely early warnings and adjust inertia strategies.

Description

Technical Field

[0001] This invention relates to the field of power system frequency stability strategy technology, specifically to a grid inertia stability domain assessment method that takes into account the aging of energy storage batteries. Background Technology

[0002] With the increasing proportion of new energy sources and the decreasing proportion of synchronous generators, the power grid's frequency regulation capability is reduced, posing challenges to its safe and stable operation. Inertia level is a decisive factor in power system frequency stability, and ensuring the grid's inertia level is crucial for ensuring grid frequency stability. In recent years, battery energy storage systems have played an important role in grid frequency stability, effectively solving the problem of insufficient inertia support for new energy power generation. However, the frequent charging and discharging of battery energy storage systems under continuous small disturbances accelerates battery aging, leading to a decrease in peak power. Ignoring the impact of battery aging on the grid's inertia level may result in an inflated inertia assessment, thus affecting the grid's timely early warning and inertia adjustment.

[0003] To avoid this problem, establishing a grid inertia assessment system that fully considers battery aging has become a crucial technical challenge. Currently, the following are the key technical challenges in long-term grid inertia assessment that takes into account energy storage battery aging:

[0004] 1) Dynamically calculate the upper and lower limits of grid inertia stability: Since the real-time operating status of the grid and batteries is constantly changing, in order to ensure that the grid has sufficient inertia to support frequency stability, it is crucial to update the boundary of the grid inertia stability domain in real time, taking into account factors such as grid load fluctuations and the intermittency of renewable energy.

[0005] 2) Assessment of Peak Power Degradation in Long-Cycle Energy Storage Batteries: Battery lifespan is affected by various factors, such as temperature, cycle count, and changes in battery state of energy. Therefore, accurately assessing battery state is crucial for defining the upper boundary of the grid's ISR (Inertia Stability Region).

[0006] 3) Accuracy of load forecasting: Grid inertia assessment requires combining short-term and long-term load forecasting techniques. Under the premise of satisfying frequency stability constraints, optimization algorithms are used to assess the minimum inertia requirement of the grid in the future time period. Therefore, the accuracy of load forecasting directly affects the solution of the lower boundary of ISR.

[0007] When implementing a long-term grid inertia assessment method that takes into account the aging of energy storage batteries, the following main challenges are encountered: First, the accuracy of the ISR boundary solution. If the grid ISR solution is too conservative, it will waste inertia resources and lead to economic losses; conversely, if the grid ISR boundary is set too aggressively, it will threaten grid frequency security. Therefore, a scientifically sound and reasonable ISR solution is crucial. Second, the accuracy of the peak power decay model for energy storage batteries. Establishing an accurate battery aging model is very important and requires combining actual operating data and experimental verification to improve the model's accuracy. Third, system feasibility and real-time performance. The grid inertia assessment method that takes into account the long-term aging of batteries needs to be verified in a real system and possess real-time performance and feasibility to be effective in practical applications.

[0008] The existing measures and their shortcomings are as follows:

[0009] Existing measure 1 uses power spectral density based on frequency disturbance events to evaluate the system inertia when the power grid is operating stably. This measure requires a large number of data samples to evaluate the inertia requirement when the power grid is operating stably through reinforcement learning using frequency events. However, data sampling is difficult and computational costs are high when applying it to actual optimization control, making it difficult to implement.

[0010] Existing measure 2 uses the system frequency change rate index to calculate the critical value of inertia. Although this measure can greatly simplify the calculation process by using only the frequency deviation rate to predict the critical value of grid inertia, the evaluation results are one-sided and cannot reflect the impact of inertia level on the lowest frequency point, thus bringing certain frequency stability risks.

[0011] Existing measure 3 configures the grid inertia level and verifies the effect by constructing an actual grid model. This measure can comprehensively reflect the integrated characteristics of the grid by constructing an actual grid frequency response model. However, the model used is usually of a high order and is computationally complex, making it difficult to evaluate the application online.

[0012] Existing measure 4 is based on a Bayesian parameter evaluation model to achieve online evaluation of system inertia under uncertainty conditions. The Bayesian parameter evaluation method used in this measure mainly relies on massive parameters such as frequency and active power, and then conducts a large amount of network training to analyze the relationship between these data and grid inertia. However, establishing patterns solely through data is quite complex and increases the difficulty of operation.

[0013] Existing measure 5 comprehensively considers the frequency change rate and frequency stability boundary to establish a minimum inertia model for the power grid in islanded and grid-connected modes. This measure mainly focuses on the assessment of the minimum inertia requirement of the power grid from the perspective of frequency dynamic characteristics calculation at the system level. However, it ignores the constraints of environmental factors and their own energy storage status on the output level of new energy sources and energy storage batteries, and gives less consideration to the maximum inertia support capacity of power grid equipment, which affects the accuracy of the assessment. Summary of the Invention

[0014] To address the aforementioned problems, the present invention aims to provide a grid inertia stability domain assessment method that takes into account the aging of energy storage batteries. By incorporating battery aging into the grid inertia assessment system, this method effectively reflects the declining trend of the battery's maximum inertia support capacity, avoids the problem of inflated inertia assessments due to neglecting battery aging, prevents untimely grid early warnings and delays in system inertia adjustments, and effectively improves the operational reliability of the system. The technical solution is as follows:

[0015] A method for evaluating the grid inertia stability domain considering energy storage battery aging includes the following steps:

[0016] Step 1: Establish a battery peak power decay model under the scenario of long-term grid inertia support;

[0017] Step 2: Calculate the grid inertia stability region taking into account long-term battery aging.

[0018] The calculation of inertia is used to describe the magnitude of the inertia of various devices in the power grid. Combined with the staged decay characteristics and changing trends of battery peak power, the upper boundary value of the power grid ISR is calculated. The lower boundary value of the power grid ISR under the frequency stability constraint is optimized by using load forecast data, thus forming a complete power grid ISR profile.

[0019] Furthermore, step 1 specifically involves:

[0020] The increase in active power of the energy storage battery during the inertia support process is expressed as:

[0021]

[0022] In the formula: P BESS (t) represents the output power of the energy storage battery at time t; K B (μ) is the inertial response coefficient of the energy storage battery; μ is the SOE of the energy storage battery; df(t) / dt is the RoCoF of the power grid at time t;

[0023] The discharge coefficient K was established using the SOE value of the energy storage battery as the independent variable. d and charging coefficient K c The relationship between the dependent and dependent variables is as follows:

[0024]

[0025] Where: K Bmax The maximum value of the inertial response coefficient of the energy storage battery; μ min and μ max These are the minimum and maximum values ​​of the SOE safety range for energy storage batteries, respectively; μ low and μ highThese represent the smaller and larger values ​​of SOE for the energy storage battery, respectively; n is the adaptive coefficient of the curve.

[0026] The SOE of an energy storage battery is determined by the dynamic change in the output force supported by the battery's inertia, i.e.

[0027]

[0028] Furthermore, step 2 specifically involves:

[0029] Step 2.1: The time-domain expression of the power grid frequency response characteristics is:

[0030]

[0031] Where: R is the droop coefficient; D is the system damping coefficient; ω n ζ represents the natural oscillation frequency; ζ represents the damping ratio; ω represents the natural oscillation frequency. r Indicates the damping frequency; α and The algebraic expression t is set up to facilitate solving; d The steady-state frequency deviation corresponds to the time point; ΔP represents the grid power disturbance.

[0032] The detailed expressions for the various coefficients in equation (4) are as follows:

[0033]

[0034] In the formula: where F H T represents the turbine coefficient of the generator set; R R is the reheat time constant of the synchronous generator; R is the droop coefficient of the governor; K g H represents the proportion of generator capacity to grid capacity, and H is the total system inertia time constant.

[0035] The expression for the system's total inertial time constant H is:

[0036] H = H g +H re +H BESS (6)

[0037] Where: H g H is the total inertial time constant of the synchronous generator; re H represents the total virtual inertial time constant of the new energy source; BESS The total virtual inertial time constant of the energy storage battery;

[0038] Step 2.2: Calculate the upper boundary value of the power grid ISR:

[0039] The power degradation of the energy storage battery in the inertia support is caused by calendar aging and cycle aging, and their expressions are as follows:

[0040]

[0041] Where: PL d_cal This refers to the degree of peak power degradation of the energy storage battery under calendar aging; PL d_cyc The degree of peak power degradation under cyclic aging of energy storage batteries; PL total M represents the degree of total peak power degradation due to battery aging. SOE The average value of the SOE sequence during the process of providing inertial support for the energy storage battery; d is the battery's operating time; cd i and nc i These represent the battery charge / discharge depth and cycle number of the i-th cycle after decomposition using the Matlab rainflow counting program; N is the total number of cycle groups decomposed by the rainflow counting method.

[0042] The expression for the change in the upper boundary of the grid ISR, taking into account the aging of energy storage batteries, is as follows:

[0043]

[0044] In the formula: Calculate the upper boundary value of the inertia of the power grid; This refers to the rated capacity of the synchronous generator; This refers to the rated capacity of the new energy generating units; This refers to the rated capacity of the energy storage battery.

[0045] Step 2.3: Calculate the lower boundary value of the power grid ISR:

[0046] The lower boundary optimization model for power grid ISR is constructed with the minimum power grid inertia time constant as the objective function and the frequency stability index as the constraint condition:

[0047]

[0048] In the formula: R max The maximum value of RoCoF after a power disturbance occurs in the power grid; Δf max Δf represents the maximum allowable frequency deviation after a power disturbance in the power grid, i.e., the frequency stability boundary. d This represents the actual frequency deviation.

[0049] After obtaining the minimum inertia time constant of the power grid, the minimum calculated inertia value of the power grid can be further calculated based on the real-time output of each generator unit:

[0050]

[0051] In the formula: The lower boundary value for calculating the inertia of the power grid; H min To optimize the solution of the minimum inertia time constant of the power grid; P SGFor real-time output of conventional generator sets; P re Provide real-time power output for new energy generating units; P BESS To provide real-time power output for energy storage batteries;

[0052] Step 2.4: Assuming conventional generator sets output constant power according to their rated capacity, new energy generator sets output power according to their planned output curves, and energy storage batteries output power based on the inertia support model, the real-time calculated inertia of the power grid is obtained as follows:

[0053] E sys =H g P SG +H re P re +H BESS P BESS (11).

[0054] Furthermore, after step 2, the method further includes: determining the grid inertia stability based on the relative magnitude of the real-time calculated inertia and the grid ISR lower boundary value, specifically: calculating the inertia stability margin.

[0055]

[0056] In the formula: SR sys For inertia stability margin;

[0057] SR sys A larger value indicates a higher grid inertia level than the lower boundary value of the grid ISR; when SR sys When the inertia is less than 0%, the grid inertia is lower than the lower boundary value of the grid ISR, the grid frequency becomes unstable, and the grid needs to increase the overall inertia level to restore frequency stability.

[0058] The beneficial effects of this invention are:

[0059] 1) Based on the grid inertia support principle of battery energy storage system, this invention establishes a model to evaluate the long-term peak power decay of battery. By taking battery aging into consideration in the grid inertia evaluation system, it can effectively reflect the downward trend of the maximum inertia support capacity of battery, avoid the problem of inflated inertia evaluation due to ignoring battery aging, prevent untimely grid early warning and delay in system inertia adjustment, and effectively improve the operational reliability of the system.

[0060] 2) This invention proposes an ISR evaluation method based on the grid frequency response model based on the phased characteristics of battery peak power decay. Specifically, the upper boundary of ISR is updated by using the battery peak power decay trend, while the load forecast result is used as input, and the lower boundary of ISR under frequency stability constraints is dynamically evaluated by an optimization algorithm to obtain the complete ISR. The grid frequency response model is easier to operate and implement in practice than the data-driven method in considering the comprehensive characteristics of the grid.

[0061] 3) The ISR energy and inertia stability margin index constructed in this invention can quickly and quantitatively describe the real-time power grid inertia security, and the results are more intuitive. Attached Figure Description

[0062] Figure 1 This is a flowchart of the grid inertia stability domain assessment method that takes into account the aging of energy storage batteries, as described in this invention.

[0063] Figure 2 This is data on power grid disturbances.

[0064] Figure 3 This is a curve for evaluating the degradation of battery peak power under long-cycle inertia support.

[0065] Figure 4 This is the result of the power grid ISR assessment.

[0066] Figure 5(a) shows the results of power grid frequency risk analysis under different strategies: ISR assessment results.

[0067] Figure 5(b) shows the results of power grid frequency risk analysis under different strategies: dynamics after frequency disturbance. Detailed Implementation

[0068] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0069] To address the impact of energy storage battery aging on grid frequency stability, this invention proposes a grid inertia stability domain assessment method that takes into account energy storage battery aging. The flowchart is shown below. Figure 1 As shown, this invention first establishes a battery peak power decay model under a long-term power grid inertia support scenario. Then, combining the staged decay characteristics and changing trends of battery peak power, a dynamic calculation method for the upper limit of ISR is proposed. Simultaneously, load forecast data is used to optimize the solution of the lower limit of ISR under frequency stability constraints, forming a complete ISR profile. This profile can be used to assess the current inertia level of the power grid, quantitatively evaluate the inertia safety level of the power grid, ensure that the power grid can maintain the frequency change rate and frequency deviation within a stable range, and promptly alert the power grid control center to take measures when inertia is insufficient, ensuring power grid frequency safety. The ISR constructed in this invention fully considers the impact of battery aging on inertia assessment, enabling the power grid to provide timely early warning and adjust inertia strategies.

[0070] 1. Inertia Support Model for Battery Energy Storage System

[0071] The process of inertia support by the energy storage battery simulates the intrinsic response of a synchronous generator in the power grid. Since the rotor speed cannot change abruptly, the time delay keeps the mechanical power constant, while increasing the electromagnetic power causes the generator speed to decrease, thereby releasing rotational kinetic energy and reducing the grid's RoCoF. The increase in active power of the energy storage battery during this process can be expressed as:

[0072]

[0073] In the formula: P BESS (t) represents the output power of the energy storage battery at time t; K B (μ) is the inertial response coefficient of the energy storage battery. To avoid the rapid depletion or saturation of the energy storage battery's SOE (Stete of Energy) during long-term inertial support, this coefficient is adaptively adjusted with the SOE. μ is the SOE of the energy storage battery; df(t) / dt is the grid RoCoF (rate-of-change-of-frequency) at time t.

[0074] The inertial response coefficient of an energy storage battery is determined by the discharge coefficient K. d and charging coefficient K c Composition: When the SOE of energy storage is low, K c Set to the maximum value to allow the energy storage battery to support the grid inertia as much as possible during charging, while reducing K. d The setting decreases as the SOE of the energy storage battery decreases; when the SOE of the energy storage battery is high, K... d Set to the maximum value to maximize the inertia supported by the energy storage battery during discharge, while K... c The value is set relatively small and decreases as the SOE of the energy storage increases. K is established with the SOE value of the energy storage battery as the independent variable. d and K c The relationship between the dependent and dependent variables is as follows:

[0075]

[0076] Where: K Bmax The maximum value of the inertial response coefficient of the energy storage battery; μ min and μ max These are the minimum and maximum values ​​of the SOE safety range for energy storage batteries, respectively; μ low and μ high These represent the smaller and larger values ​​of SOE for the energy storage battery, respectively; n is the adaptive coefficient of the curve.

[0077] The SOE of an energy storage battery is determined by the dynamic change in the output force supported by the battery's inertia, i.e.

[0078]

[0079] In the formula: Δt is the charging and discharging time of the energy storage battery; E BESS This refers to the capacity of the energy storage battery.

[0080] 2. Evaluation method for grid inertia stability region considering long-term battery aging

[0081] In order to better reflect the system scale and describe the overall frequency regulation capability of the power grid, this invention uses calculated inertia (i.e., the capacity of each generator multiplied by the inertial time constant corresponding to the generator, with the unit being MWs) to describe the magnitude of the inertia of various devices in the power grid.

[0082] The time-domain expression for the power grid frequency response characteristics is:

[0083]

[0084] Where: R is the droop coefficient; D is the system damping coefficient; ω n ζ represents the natural oscillation frequency; ζ represents the damping ratio; ω represents the natural oscillation frequency. r Indicates the damping frequency; α and The algebraic expression t is set up to facilitate solving; d This represents the time corresponding to the steady-state frequency deviation.

[0085] The detailed expressions for the various coefficients in equation (4) are as follows:

[0086]

[0087] In the formula: where F H T represents the turbine coefficient of the generator set; R R is the reheat time constant of the synchronous generator; R is the droop coefficient of the governor; K g Let H be the ratio of generator capacity to grid capacity, and H be the total system inertia time constant.

[0088] The expression for the system's total inertial time constant H is:

[0089] H = H g +H re +H BESS (6)

[0090] Where: H g H is the total inertial time constant of the synchronous generator; re H represents the total virtual inertial time constant of the new energy source; BESS This represents the total virtual inertial time constant of the energy storage battery.

[0091] (1) Calculation of the upper boundary value of the power grid ISR.

[0092] The upper boundary of the grid's Inertia Resistance Ratio (ISR) is primarily determined by the rated capacity of each generator unit and the peak power of the energy storage batteries affected by aging degradation. The power degradation of the energy storage batteries in the inertia support is typically caused by calendar aging and cyclic aging, expressed as follows:

[0093]

[0094] Where: PLd_cal The percentage of peak power degradation under calendar aging of the energy storage battery; PL d_cyc The percentage of peak power degradation under cyclic aging of the energy storage battery; PL total M represents the degree of total peak power degradation due to battery aging. SOE The average value of the SOE sequence during the process of providing inertial support for the energy storage battery; d is the battery's operating time; cd i and nc i These represent the battery charge / discharge depth and cycle number of the i-th cycle after decomposition using the Matlab rainflow counting program; N is the total number of cycle groups decomposed by the rainflow counting method.

[0095] After obtaining the battery power degradation results, the upper boundary change of the grid ISR considering the aging of the energy storage battery can be further obtained, and its expression is:

[0096]

[0097] In the formula: Calculate the upper boundary value of the inertia of the power grid; This refers to the rated capacity of the synchronous generator; This refers to the rated capacity of the new energy generating units; This refers to the rated capacity of the energy storage battery.

[0098] (2) Calculation of the lower boundary value of the power grid ISR.

[0099] The total inertial time constant H of the synchronous generator in a conventional synchronous generator set in a new power system g Generally, the inertial time constant of the power grid is relatively fixed, while the inertial time constant of the new energy generating units and energy storage batteries can be adjusted and controlled by the converter. Therefore, the determination of the lower boundary is mainly determined by the new energy generating units and energy storage batteries together. This invention constructs a lower boundary optimization model for the power grid ISR with the minimum inertial time constant of the power grid as the objective function and the frequency stability index as the constraint condition:

[0100]

[0101] In the formula: R max The maximum value of RoCoF after a power disturbance occurs in the power grid; Δf max Δf represents the maximum allowable frequency deviation after a power disturbance in the power grid, i.e., the frequency stability boundary. d This represents the actual frequency deviation.

[0102] After obtaining the minimum inertia time constant of the power grid, the minimum calculated inertia value of the power grid can be further calculated based on the real-time output of each generator unit:

[0103]

[0104] In the formula: The lower boundary value for calculating the inertia of the power grid; H min To optimize the solution of the minimum inertia time constant of the power grid; P SG For real-time output of conventional generator sets; P re Provide real-time power output for new energy generating units; P BESS It provides real-time power output for energy storage batteries.

[0105] (3) Real-time calculation of the inertia value of the power grid.

[0106] The real-time calculated inertia of the power grid is related to the inertial time constant and real-time output of each generator unit. This invention assumes that conventional generator units output constant power according to their rated power, new energy generator units output according to their planned output curves, and energy storage batteries output power based on the inertia support model. Thus, the real-time calculated inertia of the power grid is obtained as follows:

[0107] E sys =H g P SG +H re P re +H BESS P BESS (11)

[0108] The real-time ISR of the power grid can be obtained by calculating the upper and lower boundary values ​​of the inertia. When the power grid suddenly faces an inertia shortage during long-term operation, it can make predictions based on the ISR assessment results and adjust its control strategy in advance, thereby avoiding RoCoF and Δf. d Exceeding the stable range.

[0109] (4) Method for determining the stability of power grid inertia.

[0110] Whether the power grid inertia is stable or not depends on the relative magnitude of the real-time calculated inertia and the lower bound of the ISR, i.e.

[0111]

[0112] In the formula: SR sys For inertia stability margin, SR sys A larger value indicates a higher level of grid inertia than the lower boundary of ISR. When SR sys A value less than 0% indicates that the grid inertia is below the critical ISR value, resulting in grid frequency instability. The overall inertia level of the grid needs to be increased to restore frequency stability. This indicator can be used to further quantify the current grid inertia level and more accurately control system operation stability.

[0113] 3. Example:

[0114] The total capacity of the synchronous generator sets in the calculation examples set in this invention (Total of 5 units), and the parameters of each synchronous generator set are set to F. H =0.3, T R =8s, R=0.083, K g =0.08, H g =5s; Total capacity of new energy units H re =4.8s; Total capacity of energy storage battery H BESS =4.8s.

[0115] (1) Battery peak power decay over long cycle

[0116] First, the power disturbance is input into the grid SFR model to obtain system frequency fluctuation data. The predicted power disturbance in the example analysis is randomly generated, such as... Figure 2 The figure shows the power disturbance of the power grid over 24 hours.

[0117] Then, the power disturbance data is input into the SFR model, and the model is supported by the inertia of the energy storage battery (parameter set to K). Bmax =30; μ min ~μ max The value ranges from 0.1 to 0.9; μ low ~μ high The SOE of the battery is calculated using a value of 0.25 to 0.75 (n = 25), and the battery cycle aging information is statistically analyzed using the rainflow counting method. Since battery degradation assessment is typically measured in years, it is often difficult to obtain SOE data from energy storage batteries that provide long-term inertia support during testing. Therefore, this embodiment uses a rolling cycle approach to extend the SOE calculated by the inertia support model to a longer testing period and inputs it into the rainflow counting program to evaluate the battery power degradation results over a longer period. This method can more realistically reflect the performance degradation of the battery during long-term use. By substituting the SOE cycle information from a longer period into the battery peak power degradation model, the following can be obtained: Figure 3 The diagram shows an estimated peak power degradation trend for the battery. Under inertia-supported scenarios, the peak power degradation of the energy storage battery will reach approximately 2.4% after 10 years.

[0118] (2) Evaluation results of grid inertia stability region considering battery aging

[0119] The ISR evaluation model proposed in this invention is used to evaluate the calculated inertia in real time. To discuss the impact of battery peak power decay on grid ISR, this invention studies the ISR changes of the grid under the same power disturbance at different battery aging levels. Figure 4 The results of the grid ISR quantitative assessment are presented, showing the battery's performance under the same power disturbance (time step 1s, duration 10h) after aging for 1 month, 6 months, and 120 months. Figure 4 As shown, the upper boundary of the power grid's Inertia Resistance (ISR) gradually decreases with battery aging. With increasing operating time, the upper boundary of ISR gradually decreases from approximately 525 MWs (after one month of battery aging) to around 500 MWs (after 120 months of aging), meaning that the actual operating inertia of the power grid may increasingly approach its maximum critical value. From the perspective of economic operation of the power grid, when the inertia level is stable (the actual power grid inertia is far from the lower boundary of ISR), the power grid can adjust the real-time inertia based on the gradually decreasing upper boundary of ISR, placing it at a relatively central position within the entire ISR. This ensures a certain inertia stability margin while reducing power waste and saving generator output costs.

[0120] Figures 5(a) and (b) illustrate the risks associated with grid ISR assessment without considering battery aging. When the lower boundary of grid ISR increases with power disturbances, neglecting battery aging can lead to an inflated upper boundary assessment of ISR. For example, when the lower boundary of ISR exceeds the true upper boundary of ISR (as shown by the gray line in Figure 5(a)), the grid may misjudge that its inertia reserve is sufficient, thus missing the adjustment opportunity and causing system RoCoF and Δf to increase. d Exceeding the grid's specified limits. The evaluation method proposed in this invention can provide timely warnings to the grid before the lower boundary of the Inertia Stability Margin (ISR) exceeds the upper boundary, enabling the system to take proactive measures, such as issuing instructions to increase the virtual inertia provided by new energy sources, increasing generator frequency regulation reserves, or implementing orderly load shedding in advance, to ensure the grid's inertia stability margin (SR) in such a state. sys >0%, meaning the real-time inertia of the power grid is located within the unlined area in Figure 5(a). To further illustrate the effectiveness of the method, a power step disturbance of 0.09 pu was applied to the power grid at t = 6s. The response of the power grid frequency under different strategies is shown in Figure 5(b). As can be seen from Figure 5(b), if the power grid ISR assessment does not take battery aging into account, the insufficient power grid inertia level at this time will not be perceived, that is, no stabilization measures will be taken, leading to RoCoF and Δf. d Exceeding the limit; however, under the ISR effect evaluated by the method proposed in this invention, the power grid can promptly detect insufficient inertia and reduce the equivalent power disturbance to 0.06 pu through stabilization measures (in this example, an orderly load reduction measure is adopted), thereby restoring SR. sys By bringing the index to a level greater than 0%, the frequency index is ensured to meet stability constraints. Therefore, the evaluation results of this invention help the power grid to more comprehensively assess its inertia reserve status and improve the reliability of power grid operation.

[0121] The proposed method for assessing the grid inertia stability domain considering battery aging effectively reflects the declining trend of the battery's maximum inertia support capacity by establishing a battery peak power attenuation model. Combined with the grid frequency response model and load forecast data, an optimization algorithm dynamically assesses the grid ISR boundary, further using the inertia stability margin index to quantify the grid inertia stability domain. This invention considers the impact of peak power attenuation caused by battery aging on the grid's inertia support capacity. By assessing the grid ISR, real-time inertia levels can be dynamically monitored, preventing overestimation of grid inertia due to neglecting battery aging, avoiding untimely warnings and delays in inertia adjustment, thereby improving the system's operational reliability.

Claims

1. A method for evaluating the grid inertia stability domain considering the aging of energy storage batteries, characterized in that, Includes the following steps: Step 1: Establish a battery peak power decay model under the scenario of long-term grid inertia support; Step 2: Calculate the grid inertia stability region taking into account long-term battery aging; The computational inertia is used to describe the magnitude of the inertia of various devices in the power grid. Combined with the stage decay characteristics and changing trend of battery peak power, the upper boundary value of the power grid ISR is calculated. The lower boundary value of the power grid ISR under the frequency stability constraint is optimized by using load forecast data, thus forming a complete power grid ISR profile. Step 2 is as follows: Step 2.1: The time-domain expression of the power grid frequency response characteristics is: （4） In the formula: R This is the adjustment coefficient; D The system damping coefficient; ω n Indicates the natural oscillation frequency; ζ Indicates the damping ratio; ω r Indicates the damping frequency; α and φ The algebraic expression is set up to facilitate the solution; t d This represents the time corresponding to the steady-state frequency deviation. For grid power disturbance; The detailed expressions for the various coefficients in equation (4) are as follows: （5） In the formula: F H This indicates the turbine coefficient of the generator set; T R This represents the reheat time constant of the synchronous generator; R This refers to the droop coefficient of the speed controller; K g The proportion of generator capacity to grid capacity. H The total inertial time constant of the system; Among them, the total inertial time constant of the system H The expression is: （6） In the formula: H g The total inertial time constant of the synchronous generator; H re The total virtual inertial time constant of the new energy source; H BESS The total virtual inertial time constant of the energy storage battery; Step 2.2: Calculate the upper boundary value of the power grid ISR: The power degradation of the energy storage battery in the inertia support is caused by calendar aging and cycle aging, and their expressions are as follows: （7） In the formula: PL d_cal This represents the degree of peak power degradation of the energy storage battery under calendar aging. PL d_cyc This refers to the degree of peak power degradation under cyclic aging of the energy storage battery. PL total This represents the degree of total peak power degradation due to battery aging. M SOE The average value of the SOE sequence during the process of providing inertial support for energy storage batteries; d This refers to the battery's runtime. cd i and nc i These are the numbers after decomposition using the Matlab rainflow counting program. i The depth of charge / discharge and the number of cycles of the battery in the group cycle; N This represents the total number of cycle groups in the rainflow counting method. The expression for the change in the upper boundary of the grid ISR, taking into account the aging of energy storage batteries, is as follows: （8） In the formula: Calculate the upper boundary value of the inertia of the power grid; This refers to the rated capacity of the synchronous generator; This refers to the rated capacity of the new energy generating units; This refers to the rated capacity of the energy storage battery. Step 2.3: Calculate the lower boundary value of the power grid ISR: The lower boundary optimization model for power grid ISR is constructed with the minimum power grid inertia time constant as the objective function and the frequency stability index as the constraint condition: （9） In the formula: R max The maximum value of RoCoF after a power disturbance occurs in the power grid; Δ f max This represents the maximum permissible frequency deviation after a power disturbance in the power grid, i.e., the frequency stability boundary; Δ f d This represents the actual frequency deviation. After obtaining the minimum inertia time constant of the power grid, the minimum calculated inertia value of the power grid can be further calculated based on the real-time output of each generator unit: （10） In the formula: Calculate the lower boundary value of the inertia of the power grid; H min To optimize the solution of the minimum inertial time constant of the power grid; P SG Real-time output of conventional generator sets; P re Provide real-time power output for new energy generating units; P BESS To provide real-time power output for energy storage batteries; Step 2.4: Assuming conventional generator sets output constant power according to their rated capacity, new energy generator sets output power according to their planned output curves, and energy storage batteries output power based on the inertia support model, the real-time calculated inertia of the power grid is obtained as follows: （11）。 2. The grid inertia stability domain assessment method considering energy storage battery aging according to claim 1, characterized in that, Step 1 is as follows: The increase in active power of the energy storage battery during the inertia support process is expressed as: （1） In the formula: P BESS ( t ) is the first t The energy storage battery output at all times; K B ( μ ) represents the inertial response coefficient of the energy storage battery; μ SOE for energy storage batteries; d f ( t ) / d t For the first t RoCoF (Road CoF) of the power grid at any time; The discharge coefficient was established using the SOE value of the energy storage battery as the independent variable. K d and charging coefficient K c The relationship between the dependent and dependent variables is as follows: （2） In the formula: K Bmax This represents the maximum value of the inertial response coefficient of the energy storage battery. μ min and μ max These are the minimum and maximum values ​​of the SOE safety range for energy storage batteries, respectively. μ low and μ high These are the smaller and larger values ​​of SOE for energy storage batteries, respectively. n The adaptive coefficient of the curve; The SOE of an energy storage battery is determined by the dynamic change in the output force supported by the battery's inertia, i.e. （3） In the formula: Δ t The charging and discharging time of the energy storage battery; E BESS This refers to the capacity of the energy storage battery.

3. The grid inertia stability domain assessment method considering energy storage battery aging according to claim 1, characterized in that, Step 2 is followed by: determining the grid inertia stability based on the relative magnitude of the real-time calculated inertia and the grid ISR lower boundary value, specifically: calculating the inertia stability margin. （12） In the formula: SR sys For inertia stability margin; SR sys A larger value indicates a higher level of grid inertia than the lower boundary value of grid ISR; when SR sys When the inertia is less than 0%, the grid inertia is lower than the lower boundary value of the grid ISR, the grid frequency becomes unstable, and the grid needs to increase the overall inertia level to restore frequency stability.

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