Power grid inertia stability domain evaluation method considering aging of energy storage battery

Abstract

The invention relates to the technical field of power system frequency stabilization strategies, and discloses a power grid inertia stability domain evaluation method considering energy storage battery aging. The method comprises the following steps: firstly, establishing a battery peak power attenuation model under a power grid long-period inertia support scene, then providing an ISR upper limit dynamic calculation method in combination with a staged attenuation characteristic and a change trend of the battery peak power, and meanwhile, optimizing and solving an ISR lower limit under a frequency stability constraint condition by utilizing load prediction data to form a complete ISR contour, the method can be used for evaluating the current inertia level of the power grid, quantitatively evaluating the inertia safety degree of the power grid, ensuring that the frequency change rate and the frequency deviation of the power grid can be kept in a stable range, and reminding a power grid control center to take measures in time when the inertia is insufficient to ensure the frequency safety of the power grid. According to the ISR constructed by the method, the influence of battery aging on inertia evaluation is fully considered, so that the power grid can perform early warning and adjust the inertia strategy in time.

Description

Technical Field

[0001] The present invention relates to the technical field of power system frequency stabilization strategies, and in particular to a power grid inertia stability domain assessment method taking into account energy storage battery aging. Background Art

[0002] As the proportion of renewable energy continues to increase, the proportion of synchronous generators decreases, the frequency regulation capacity of the power grid decreases, and its safe and stable operation is challenged. The inertia level is a decisive factor in the stability of the power system frequency. Ensuring the inertia level of the power grid is crucial to ensuring the stability of the power grid frequency. In recent years, battery energy storage systems have played an important role in the stability of power grid frequency, effectively solving the problem of lack of inertia support for renewable energy power generation. However, the frequent charging and discharging of battery energy storage systems in the inertia response under continuous small disturbances accelerates battery aging and causes its peak power to decay. If the impact of battery aging on the inertia level of the power grid is ignored, the inertia assessment may be falsely high, which in turn affects the timely warning and inertia adjustment of the power grid.

[0003] In order to avoid this problem, establishing a grid inertia assessment system that fully takes into account battery aging has become an important technical key. At present, there are the following technical keys for long-term grid inertia assessment taking 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 boundaries of the grid inertia stability domain in real time based on factors such as grid load fluctuations and intermittent renewable energy.

[0005] 2) Evaluation of peak power attenuation of long-cycle energy storage batteries: The life of the battery is affected by many factors, such as temperature, number of cycles, changes in battery energy state, etc. Therefore, accurately evaluating the battery state is crucial to defining the upper boundary of the grid ISR (Inertia Stability Region).

[0006] 3) Accuracy of load forecasting: Grid inertia assessment needs to combine short-term and long-term load forecasting techniques, and use optimization algorithms to assess the minimum inertia requirements of the grid in the future time period while meeting frequency stability constraints. Therefore, the accuracy of load forecasting directly affects the solution of the ISR lower bound.

[0007] When implementing the long-term inertia assessment method for the power grid that takes into account the aging of energy storage batteries, the following major difficulties are also faced: First, the accuracy of the ISR boundary solution. If the power grid ISR solution is too conservative, it will waste inertia resources and bring operating economic losses; on the contrary, if the power grid ISR boundary setting is too aggressive, it will threaten the frequency safety of the power grid. Therefore, it is very important to solve the ISR scientifically and reasonably. The second is the accuracy of the peak power attenuation model of the energy storage battery. It is very important to establish an accurate battery aging model, which needs to be combined with actual operating data and experimental verification to improve the accuracy of the model. The third is the feasibility and real-time issues of the system. The power grid inertia assessment method that takes into account the long-term aging of the battery needs to be verified in the actual system and have real-time 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 during stable operation of the power grid. This measure uses reinforcement learning through frequency events to evaluate the inertia demand during stable operation of the power grid, which requires a large number of data samples. However, applying it to actual optimization control has certain difficulties in data sampling and high computing cost, making it difficult to implement.

[0010] Existing measure 2 uses the system frequency change rate indicator to calculate the inertia critical value. Although this measure only uses the frequency deviation rate to predict the grid inertia critical value, which can greatly simplify the calculation process, the evaluated results are one-sided and cannot reflect the impact of the 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 constructs an actual grid frequency response model that can more comprehensively reflect the comprehensive characteristics of the grid, but the model usually used has a high order and complex calculations, making it difficult to evaluate and apply online.

[0012] Existing measure 4 is based on the Bayesian parameter evaluation model to achieve online evaluation of system inertia under uncertain conditions. The Bayesian parameter evaluation method adopted by this measure mainly relies on massive frequency, active power and other parameters, and then conducts a large amount of network training to analyze the relationship between these data and grid inertia. However, it is relatively complicated to establish rules only through data, which increases the difficulty of operation.

[0013] Existing measure 5 comprehensively considers the frequency change rate and frequency stability boundary, and establishes a minimum inertia model for evaluating the power grid in island and grid-connected modes. This measure mainly evaluates the minimum inertia demand of the power grid based on the calculation of frequency dynamic characteristics at the system level, but ignores the constraints of environmental factors and their own energy storage status on the output level of new energy and energy storage batteries, and gives less consideration to the maximum inertia support capacity of power grid equipment, which affects the accuracy of the evaluation. Summary of the invention

[0014] In view of the above problems, the purpose of the present invention is to provide a grid inertia stability domain assessment method taking into account the aging of energy storage batteries, taking battery aging into account in the grid inertia assessment system, which can effectively reflect the trend of the decline in the maximum inertia support capacity of the battery, avoid the problem of falsely high inertia assessment due to ignoring battery aging, prevent the grid from being late in early warning and delaying the system inertia adjustment, and effectively improve the system's operational reliability. The technical solution is as follows:

[0015] A method for evaluating the inertia stability domain of a power grid taking into account the aging of energy storage batteries comprises the following steps:

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

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

[0018] The calculated inertia is used to describe the inertia of various devices in the power grid. The upper boundary value of the power grid ISR is calculated based on the staged attenuation characteristics and change trend of the battery peak power. The load forecast data is used to optimize the lower boundary value of the power grid ISR under the frequency stability constraint condition to form a complete power grid ISR profile.

[0019] Furthermore, step 1 is specifically as follows:

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

[0021]

[0022] Where: P BESS (t) is the output of the energy storage battery at the tth moment; K B (μ) is the inertia 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 the tth moment;

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

[0024]

[0025] Where: K Bmax is the maximum value of the inertia response coefficient of the energy storage battery; μ min and μ max are the minimum and maximum values ​​of the SOE safety range of the energy storage battery; μ low and μ highare the smaller and larger values ​​of the SOE of the energy storage battery respectively; n is the adaptive coefficient of the curve;

[0026] The SOE of the energy storage battery is determined by the dynamic change of the output supported by the inertia of the energy storage battery, that is,

[0027]

[0028] Furthermore, step 2 is specifically as follows:

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

[0030]

[0031] Where: R is the adjustment coefficient; D is the system damping coefficient; ω n represents the natural oscillation frequency; ζ represents the damping ratio; ω r represents the damping frequency; α and Algebraic formulas are provided to facilitate solving; t d is the frequency steady-state deviation corresponding to the moment; ΔP is the power disturbance of the power grid;

[0032] The detailed expressions of various coefficients in formula (4) are:

[0033]

[0034] In the formula: F H Indicates the turbine factor of the generator set; T R is the reheating time constant of the synchronous generator; R is the speed regulator adjustment coefficient; K g is the ratio of the generator capacity to the grid capacity, H is the total inertia time constant of the system;

[0035] Among them, the total inertia time constant H of the system is expressed as:

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

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

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

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

[0040]

[0041] Where: PL d_cal PL is the peak power attenuation degree of the energy storage battery under calendar aging; d_cyc PL is the peak power attenuation degree of the energy storage battery under cycle aging; total is the total peak power attenuation degree caused by the aging of the energy storage battery; M SOE The average value of the SOE sequence in the process of providing inertial support for the energy storage battery; d is the operating time of the battery; cd i and nc i are the battery charge and discharge depth and cycle number of the i-th group of cycles after decomposition by the Matlab rain flow counting program; N is the total number of cycle groups decomposed by the rain flow counting method;

[0042] The calculation of the upper boundary change of the grid ISR taking into account the aging of the energy storage battery is expressed as:

[0043]

[0044] Where: Calculate the upper boundary value of inertia for the power grid; is the rated capacity of the synchronous generator; is the rated capacity of the new energy unit; is the rated capacity of the energy storage battery;

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

[0046] Taking the minimum inertia time constant of the power grid as the objective function and the frequency stability index as the constraint condition, the power grid ISR lower boundary optimization model is constructed as follows:

[0047]

[0048] Where: R max is the maximum value of RoCoF after the power disturbance occurs in the power grid; Δf max Δf is the maximum allowable frequency deviation after a power disturbance occurs in the power grid, i.e., the frequency stability boundary; d is 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 according to the real-time output of each generator set:

[0050]

[0051] Where: Calculate the lower boundary value of inertia for the power grid; H min is the minimum inertia time constant of the power grid solved by optimization; P SGReal-time output for conventional generator sets; P re Provide real-time power for new energy units; BESS Provide power to energy storage batteries in real time;

[0052] Step 2.4: Assume that the conventional generator sets output at a constant rate according to the rated power, the new energy generator sets output according to the planned output curve, and the energy storage batteries output according to the inertia support model, and then the real-time calculated inertia of the power grid is obtained as:

[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: judging the inertia stability of the power grid according to the relative size between the real-time calculated inertia and the lower boundary value of the power grid ISR, specifically: calculating the inertia stability margin:

[0055]

[0056] Where: SR sys is the inertia stability margin;

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

[0058] The beneficial effects of the present invention are:

[0059] 1) Based on the grid inertia support principle of the battery energy storage system, the present invention establishes a model for evaluating the long-term peak power attenuation of the battery, and takes battery aging into consideration in the grid inertia evaluation system, which can effectively reflect the trend of the decline in the maximum inertia support capacity of the battery, avoids the problem of inflated inertia evaluation due to ignoring battery aging, prevents untimely grid early warning and delays in system inertia adjustment, and effectively improves the operational reliability of the system.

[0060] 2) According to the phased characteristics of battery peak power attenuation, the present invention proposes an ISR evaluation method based on the power grid frequency response model. Specifically, the upper boundary of the ISR is updated by using the battery peak power attenuation trend. At the same time, the load forecast result is used as input, and the optimization algorithm is used to dynamically evaluate the lower boundary of the ISR under the frequency stability constraint, so as to obtain a complete ISR. Compared with the data-driven method, the power grid frequency response model is easier to operate and implement in practice when considering the comprehensive characteristics of the power grid.

[0061] 3) The ISR energy and inertia stability margin index constructed by the present invention can quickly and quantitatively describe the real-time power grid inertia safety, and the result is more intuitive. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 The present invention is a flow chart of a method for evaluating the inertia stability domain of a power grid taking into account the aging of energy storage batteries.

[0063] Figure 2 is the grid power disturbance data.

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

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

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

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

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

[0069] In order to deal with the impact of energy storage battery aging on grid frequency stability, the present invention proposes a grid inertia stability domain assessment method taking into account energy storage battery aging. The flow chart is as follows: Figure 1 As shown. First, a battery peak power attenuation model is established under the scenario of long-term inertia support of the power grid. Then, a dynamic calculation method for the upper limit of the ISR is proposed in combination with the staged attenuation characteristics and change trend of the battery peak power. At the same time, the load forecast data is used to optimize the ISR lower limit under the frequency stability constraint to form a complete ISR profile, which can be used to evaluate the current inertia level of the power grid, quantitatively evaluate the inertia safety of the power grid, ensure that the power grid can maintain the frequency change rate and frequency deviation within a stable range, and promptly remind the power grid control center to take measures when the inertia is insufficient to ensure the frequency safety of the power grid. The ISR constructed by the present invention fully considers the impact of battery aging on inertia assessment, so that the power grid can timely warn and adjust the inertia strategy.

[0070] 1. Inertia support model of battery energy storage system

[0071] The process of energy storage battery inertia support simulates the inherent response of the synchronous generator in the power grid. Since the rotor speed cannot change suddenly, the time delay keeps the mechanical power constant, and the increase of electromagnetic power makes the unit speed decrease, thereby releasing the rotational kinetic energy and reducing the RoCoF of the power grid. The active power increment of the energy storage battery in this process can be expressed as:

[0072]

[0073] Where: P BESS (t) is the output of the energy storage battery at the tth moment; K B (μ) is the inertia response coefficient of the energy storage battery. In order to avoid the rapid exhaustion or saturation of the SOE (Stete of Energy) of the energy storage battery during the long-term inertia support, the coefficient is adaptively adjusted with the change of SOE; μ is the SOE of the energy storage battery; df(t) / dt is the RoCoF (rate-of-change-of-frequency) of the power grid at the tth moment.

[0074] The inertia response coefficient of the energy storage battery is determined by the discharge coefficient K d and charging factor K c Composition: When the energy storage SOE is low, K c Set it to the maximum value so that the energy storage battery can support the grid inertia as much as possible when charging, and reduce K d Set and decrease with the decrease of energy storage battery SOE; when the energy storage SOE is high, K d Set to the maximum value to support the inertia as much as possible when the energy storage battery is discharged, and K c The K value is set small and decreases with the increase of energy storage SOE. d and K c The relationship for the dependent variable is as follows:

[0075]

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

[0077] The SOE of the energy storage battery is determined by the dynamic change of the output supported by the inertia of the energy storage battery, that is,

[0078]

[0079] Where: Δt is the charging and discharging time of the energy storage battery; E BESS is the energy storage battery capacity.

[0080] 2. Grid inertia stability domain assessment method considering long-term battery aging

[0081] In order to better reflect the system scale and describe the overall frequency control capability of the power grid, the present invention adopts calculated inertia (i.e., the capacity of each generator multiplied by the inertia time constant corresponding to the generator, in MWs) to describe the inertia size of various devices in the power grid.

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

[0083]

[0084] Where: R is the adjustment coefficient; D is the system damping coefficient; ω n represents the natural oscillation frequency; ζ represents the damping ratio; ω r represents the damping frequency; α and Algebraic formulas are provided to facilitate solving; t d is the time corresponding to the frequency steady-state deviation.

[0085] The detailed expressions of various coefficients in formula (4) are:

[0086]

[0087] In the formula: F H Indicates the turbine factor of the generator set; T R is the reheating time constant of the synchronous generator; R is the speed regulator adjustment coefficient; K g is the ratio of the generator capacity to the grid capacity, and H is the total inertia time constant of the system.

[0088] Among them, the total inertia time constant H of the system is expressed as:

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

[0090] Where: H g is the total inertia time constant of the synchronous generator; H re is the total virtual inertia time constant of new energy; H BESS is the total virtual inertia 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 ISR is mainly determined by the rated capacity of each generator set and the peak power of the energy storage battery affected by aging. The power attenuation of the energy storage battery in the inertial support is usually caused by calendar aging and cycle aging, and their expressions are:

[0093]

[0094] Where: PLd_cal is the peak power attenuation degree of the energy storage battery under calendar aging (%); PL d_cyc is the peak power attenuation degree of the energy storage battery under cycle aging (%); PL total is the total peak power attenuation degree caused by the aging of the energy storage battery; M SOE The average value of the SOE sequence in the process of providing inertial support for the energy storage battery; d is the operating time of the battery; cd i and nc i are the battery charge and discharge depth and cycle number of the i-th cycle after decomposition by the Matlab rain flow counting program; N is the total number of cycle groups decomposed by the rain flow counting method.

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

[0096]

[0097] Where: Calculate the upper boundary value of inertia for the power grid; is the rated capacity of the synchronous generator; is the rated capacity of the new energy unit; is the rated capacity of the energy storage battery.

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

[0099] Total inertia time constant H of synchronous generators of conventional synchronous generator sets in new power systems g Generally, it is relatively fixed, while the inertia time constant of the new energy unit and the energy storage battery can be adjusted and controlled by the converter. Therefore, the establishment of the lower boundary is mainly determined by the new energy unit and the energy storage battery. The present invention takes the minimum inertia time constant of the power grid as the objective function and the frequency stability index as the constraint condition to construct the power grid ISR lower boundary optimization model as follows:

[0100]

[0101] Where: R max is the maximum value of RoCoF after the power disturbance occurs in the power grid; Δf max Δf is the maximum allowable frequency deviation after a power disturbance occurs in the power grid, i.e., the frequency stability boundary; d is 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 according to the real-time output of each generator set:

[0103]

[0104] Where: Calculate the lower boundary value of inertia for the power grid; H min is the minimum inertia time constant of the power grid solved by optimization; P SG Real-time output for conventional generator sets; P re Provide real-time power for new energy units; BESS Provide real-time power to energy storage batteries.

[0105] (3) The power grid calculates the inertia value in real time.

[0106] The real-time calculated inertia of the power grid is related to the inertia time constant and real-time output of each generator set. The present invention assumes that the conventional generator set outputs at a constant rate according to the rated power, the new energy generator set outputs according to the planned output curve, and the energy storage battery outputs according to the inertia support model, and then 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, the power grid can make a prediction based on the ISR evaluation results and adjust the control strategy in advance to avoid the RoCoF and Δf d Out of stability range.

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

[0110] The determination of whether the grid inertia is stable or not depends on the relative size of the real-time calculated inertia and the lower limit of the ISR, that is,

[0111]

[0112] Where: SR sys is the inertia stability margin, SR sys The larger the value, the higher the grid inertia level is above the ISR lower boundary. sys When it is <0%, it means that the grid inertia is lower than the ISR critical value, the grid frequency is unstable, and the grid needs to increase the overall inertia level to restore frequency stability. With the help of this indicator, the current grid inertia level can be further quantified to more accurately control the stability of system operation.

[0113] 3. Example:

[0114] The total capacity of the synchronous generator set in the calculation example of the present invention (Total 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 long-cycle peak power attenuation

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

[0117] Then, the power disturbance data is input into the SFR model, and the energy storage battery inertia support model (parameters are set to K Bmax =30; μ min ~μ max The value is 0.1~0.9; μ low ~μ high The value is 0.25~0.75; n=25) to calculate the battery SOE, and use the rain flow counting method to count the battery cycle aging information. Since battery attenuation evaluation is usually measured in years, it is often difficult to obtain SOE data for long-term inertia support provided by energy storage batteries during testing. Therefore, this embodiment uses a rolling cycle method to extend the SOE calculated by the inertia support model to a longer test period, and inputs it into the rain flow counting program to evaluate the battery power attenuation results over a longer period. This method can more truly reflect the performance degradation of the battery during long-term use. By substituting longer-period SOE cycle information into the battery peak power attenuation model, the following can be obtained: Figure 3 The battery peak power attenuation trend is estimated as shown. Under the inertial support scenario, the peak power attenuation of the energy storage battery will reach about 2.4% after 10 years.

[0118] (2) Grid inertia stability domain assessment results taking battery aging into account

[0119] The ISR evaluation model proposed in the present invention is used to evaluate the calculated inertia in real time. In order to discuss the impact of battery peak power attenuation on the ISR of the power grid, the present invention studies the ISR changes when the power grid faces the same power disturbance under different battery aging degrees. Figure 4 The results of the quantitative evaluation of the ISR of the power grid when the battery is facing the same power disturbance (time step of 1s and duration of 10h) after aging for 1 month, 6 months, and 120 months are shown. Figure 4 As shown in the figure, the upper boundary of the grid ISR will gradually decay as the battery ages. As the operating time increases, the upper boundary of the ISR gradually decreases from about 525MWs (battery aging for 1 month) to about 500MWs (aging for 120 months), which means that the actual grid inertia operation may be getting closer and closer to the maximum critical value of inertia. From the perspective of grid economic operation, when the inertia level is in a stable state (the actual grid inertia is far from the lower boundary of the ISR), the grid can adjust the real-time inertia according to the gradually decreasing upper boundary of the ISR so that it is located in a relatively central position of the entire ISR. This can not only ensure a certain inertia stability margin, but also reduce power waste and save the output cost of the generator set.

[0120] Figures 5(a) and (b) show the risks of grid ISR assessment without considering battery aging. When the grid ISR lower boundary increases with the increase of power disturbance, if battery aging is not taken into account, the ISR upper boundary assessment will be falsely high. For example, when the ISR lower boundary exceeds the actual ISR upper boundary (as shown in the gray dashed area in Figure 5(a)), the grid will misjudge that the inertia reserve is sufficient, and thus miss the adjustment opportunity, which will cause the system RoCoF and Δf d The evaluation method proposed in this invention can timely warn the power grid before the lower boundary of ISR exceeds the upper boundary, so that the system can take measures in advance, such as issuing instructions to increase the virtual inertia provided by new energy, increase the frequency regulation standby of generators, or reduce load in advance in an orderly manner, to ensure the inertia stability margin SR of the power grid when this state occurs. sys >0%, that is, the real-time inertia of the power grid is located in the unlined area of ​​Figure 5(a). To further illustrate the effect of the method, a power step disturbance of 0.09pu is applied to the power grid at t=6s, and the response of the power grid frequency under different strategies is shown in Figure 5(b). As shown in Figure 5(b), if the power grid ISR evaluation does not take battery aging into account, it will not perceive that the power grid inertia level is insufficient at this time, that is, no stabilization measures will be taken, resulting in RoCoF and Δf d Under the ISR evaluated by the method proposed in this invention, the power grid can timely sense the inertia shortage and reduce the equivalent power disturbance to 0.06pu through stabilization measures (in this example, orderly load reduction measures are taken in advance), thereby restoring SR. sys The index is increased to a level greater than 0%, ensuring that the frequency index meets the stability constraint. Therefore, the evaluation result of the present invention helps the power grid to more comprehensively judge the inertia reserve situation and improve the reliability of power grid operation.

[0121] In the grid inertia stability domain assessment method taking into account battery aging proposed in the present invention, by establishing a battery peak power attenuation model, the trend of the battery's maximum inertia support capacity decreasing is effectively reflected. In combination with the grid frequency response model and load forecast data, the optimization algorithm is used to dynamically assess the grid ISR boundary, thereby further using the inertia stability margin index to quantitatively assess the grid inertia stability domain situation. The present invention takes into account the impact of peak power attenuation caused by battery aging on the grid inertia support capacity. By evaluating the grid ISR, the real-time inertia level can be dynamically monitored to prevent the grid inertia from being over-assessed due to ignoring battery aging, avoid untimely warnings and inertia adjustment delays, and thus improve the system's operational reliability.

Claims

1. A method for evaluating the inertia stability domain of a power grid taking into account the aging of energy storage batteries, characterized in that: The following steps are involved: Step 1: Establish a battery peak power attenuation model under the long-term inertia support scenario of the power grid; Step 2: Calculate the grid inertia stability region taking into account long-term battery aging The calculated inertia is used to describe the inertia of various devices in the power grid. The upper boundary value of the power grid ISR is calculated based on the staged attenuation characteristics and change trend of the battery peak power. The load forecast data is used to optimize the lower boundary value of the power grid ISR under the frequency stability constraint condition to form a complete power grid ISR profile.

2. The method for evaluating the inertia stability domain of a power grid taking into account the aging of energy storage batteries according to claim 1, characterized in that: Step 1 is as follows: The active power increment of the energy storage battery during the process of energy storage battery inertia support is expressed as: Where: P BESS (t) is the output of the energy storage battery at the tth moment; K B (μ) is the inertia 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 the tth moment; The discharge coefficient K is established with the SOE value of the energy storage battery as the independent variable. d and charging factor K c The relationship for the dependent variable is as follows: Where: K Bmax is the maximum value of the inertia response coefficient of the energy storage battery; μ min and μ max are the minimum and maximum values ​​of the SOE safety range of the energy storage battery; μ low and μ high are the smaller and larger values ​​of the SOE of the energy storage battery respectively; n is the adaptive coefficient of the curve; The SOE of the energy storage battery is determined by the dynamic change of the output supported by the inertia of the energy storage battery, that is, Where: Δt is the charging and discharging time of the energy storage battery; E BESS is the energy storage battery capacity.

3. The method for evaluating the inertia stability domain of a power grid taking into account the aging of energy storage batteries according to claim 1, characterized in that: Step 2 is as follows: Step 2.1: The time domain expression of the grid frequency response characteristic is: Where: R is the adjustment coefficient; D is the system damping coefficient; ω n represents the natural oscillation frequency; ζ represents the damping ratio; ω r represents the damping frequency; α and Algebraic formulas are provided to facilitate solving; t d is the frequency steady-state deviation corresponding to the moment; ΔP is the power disturbance of the power grid; The detailed expressions of various coefficients in formula (4) are: In the formula: F H Indicates the turbine factor of the generator set; T R is the reheating time constant of the synchronous generator; R is the speed regulator adjustment coefficient; K g is the ratio of the generator capacity to the grid capacity, H is the total inertia time constant of the system; Among them, the total inertia time constant H of the system is expressed as: H=H g +H re +H BESS (6) Where: H g is the total inertia time constant of the synchronous generator; H re is the total virtual inertia time constant of new energy; H BESS is the total virtual inertia time constant of the energy storage battery; Step 2.2: Calculate the upper boundary value of the power grid ISR: The power attenuation of the energy storage battery in the inertial support is caused by calendar aging and cycle aging, and their expressions are: Where: PL d_cal PL is the peak power attenuation degree of the energy storage battery under calendar aging; d_cyc PL is the peak power attenuation degree of the energy storage battery under cycle aging; total is the total peak power attenuation degree caused by the aging of the energy storage battery; M SOE The average value of the SOE sequence in the process of providing inertial support for the energy storage battery; d is the operating time of the battery; cd i and nc i are the battery charge and discharge depth and cycle number of the i-th group of cycles after decomposition by the Matlab rain flow counting program; N is the total number of cycle groups decomposed by the rain flow counting method; The calculation of the upper boundary change of the grid ISR taking into account the aging of the energy storage battery is expressed as: Where: Calculate the upper boundary value of inertia for the power grid; is the rated capacity of the synchronous generator; is the rated capacity of the new energy unit; is the rated capacity of the energy storage battery; Step 2.3: Calculate the lower boundary value of the power grid ISR: Taking the minimum inertia time constant of the power grid as the objective function and the frequency stability index as the constraint condition, the power grid ISR lower boundary optimization model is constructed as follows: Where: R max is the maximum value of RoCoF after the power disturbance occurs in the power grid; Δf max Δf is the maximum allowable frequency deviation after a power disturbance occurs in the power grid, i.e., the frequency stability boundary; d is 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 according to the real-time output of each generator set: Where: Calculate the lower boundary value of inertia for the power grid; H min is the minimum inertia time constant of the power grid solved by optimization; P SG Real-time output for conventional generator sets; P re Provide real-time power for new energy units; BESS Provide power to energy storage batteries in real time; Step 2.4: Assume that the conventional generator sets output at a constant rate according to the rated power, the new energy generator sets output according to the planned output curve, and the energy storage batteries output according to the inertia support model, and then the real-time calculated inertia of the power grid is obtained as: E sys =H g P SG +H re P re +H BESS P BESS (11)。 4. The method for evaluating the inertia stability region of a power grid taking into account the aging of energy storage batteries according to claim 3 is characterized in that: After step 2, the method further includes: judging the inertia stability of the power grid according to the relative size between the real-time calculated inertia and the lower boundary value of the power grid ISR, specifically: calculating the inertia stability margin: Where: SR sys is the inertia stability margin; SR sys The larger the value, the higher the grid inertia level is than the grid ISR lower boundary value; when SR sys When <0%, the grid inertia is lower than the grid ISR lower boundary value, the grid frequency becomes unstable, and the grid needs to increase the overall inertia level to restore frequency stability.

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