Constant calculation method of net-following type electrochemical energy storage virtual inertia constant

Abstract

The invention relates to the technical field of electrochemical energy storage system control, in particular to a constant calculation method for a network-following type electrochemical energy storage virtual inertia constant. According to the method, by defining the virtual inertia constant and combining the power grid frequency and the stored energy change data, the stored energy change is accurately represented through preprocessing and thermal model analysis. And an objective function is constructed, optimization solution is carried out based on constraint conditions such as energy conservation and battery safety, constant virtual inertia is calculated in a segmented manner, and the effect is ensured through simulation verification and parameter adjustment. The method improves the calculation accuracy of the virtual inertia constant, optimizes the matching effect with the time-varying virtual inertia, guarantees the safe and stable operation of the energy storage device, prolongs the service life, effectively improves the frequency regulation capability of the power grid, and provides powerful support for the stable operation of the power grid.

Description

Technical Field

[0001] The invention relates to the technical field of electrochemical energy storage system control, and in particular to a method for calculating a constant virtual inertia constant of a grid-following electrochemical energy storage system. Background Art

[0002] With the increasing proportion of renewable energy generation in the power system, such as wind power and solar energy, its randomness and intermittency have brought many challenges to the stable operation of the power grid. As an effective means of regulation, electrochemical energy storage systems are increasingly widely used in power grids. In the grid-following operation mode, electrochemical energy storage systems need to simulate the inertial response characteristics of traditional synchronous generators to provide virtual inertial support and enhance the frequency stability of the power grid.

[0003] However, the current grid-following electrochemical energy storage virtual inertia control technology has some shortcomings. On the one hand, in the calculation process of the virtual inertia constant, there is a lack of comprehensive consideration of the characteristics of the energy storage device itself and the actual operating conditions of the power grid. During operation, the energy storage device will be affected by various factors such as battery aging, temperature changes, self-discharge, and internal resistance changes. These factors will cause the energy state and response characteristics of the energy storage device to change, thereby affecting the accuracy of the virtual inertia constant. On the other hand, the existing calculation methods usually do not effectively optimize and verify the constantized virtual inertia, and cannot ensure that it has a consistent grid support effect with the time-varying virtual inertia under different working conditions. It is also difficult to ensure that while meeting the grid frequency regulation needs, it is also difficult to take into account the safe and stable operation and service life of the energy storage device. Summary of the invention

[0004] In view of the above deficiencies in the prior art, the purpose of the present invention is to comprehensively consider the characteristics of the energy storage device and the power grid conditions, calculate the virtual inertia constant, optimize its matching with the time-varying virtual inertia, ensure the safety of the energy storage device, and improve the frequency stability of the power grid.

[0005] In order to achieve the above objectives, the present invention adopts the following technical solutions:

[0006] A method for calculating the constant of a virtual inertia constant of a grid-connected electrochemical energy storage system comprises the following steps:

[0007] S1. Define the virtual inertia constant. The virtual inertia constant (HESS) is a key parameter of the energy storage device's ability to respond to grid frequency changes. The virtual inertia constant is defined as the ratio of the change in energy released by the energy storage device to the change in system frequency.

[0008] In S1, the virtual inertia constant (HESS) is a key parameter of the energy storage device's ability to respond to grid frequency changes, and is expressed as:

[0009]

[0010] Among them, ΔE represents the change in energy released by the energy storage device; Δf represents the change in system frequency, which is measured by the grid frequency monitoring equipment and the unit is Hertz (Hz).

[0011] S2. Characterization of energy storage energy changes, combining the virtual inertia constant defined in S1 with the actual grid operation data to quantify the energy released by the energy storage device when the grid frequency changes;

[0012] S21. Preprocessing the grid frequency data, preprocessing the grid frequency data to obtain a frequency change sequence;

[0013] S22. synchronously process the energy data of the energy storage device, consider factors such as battery aging, temperature influence, self-discharge, internal resistance change, etc., use the thermal model method to estimate the energy state, and obtain the energy state sequence;

[0014] S23. Calculate the energy change, calculate the energy change released by the energy storage device within a certain period of time.

[0015] In S21, the step of preprocessing the power grid frequency data includes:

[0016] S211. Filter to remove high-frequency noise, select Butterworth filter to remove high-frequency noise, perform fast Fourier transform (FFT) on the power grid frequency data to obtain its spectrum, observe the obtained spectrum to determine the distribution range of the main noise frequency components, set the cutoff frequency and filter order of the Butterworth filter according to the distribution range of the noise frequency components, and after determining the cutoff frequency and filter order, use the Butterworth filter to process the power grid frequency data to remove the high-frequency noise components and obtain the frequency data after preliminary processing;

[0017] When setting the cutoff frequency of the Butterworth filter, make sure that the cutoff frequency of the Butterworth filter is slightly lower than the lower limit of the main noise frequency;

[0018] S212. Remove outliers. Use the 3σ principle to identify and remove outliers. Calculate the mean μ and standard deviation σ of the power grid frequency data. Data outside the range of (μ-3σ) to (μ+3σ) are considered outliers and removed to ensure the rationality and consistency of the data.

[0019] in,

[0020]

[0021] Where, M is the number of data points; fi is the grid frequency data, i∈M;

[0022] S213. Obtain the frequency change sequence. After being processed by S211 and S21, the processed grid frequency sequence {f(t)} is obtained. By calculating the frequency difference between adjacent time points, the frequency change sequence {Δf(t)} is obtained, that is, at time points t and t+Δt, the frequency change is: Δf(t)=f(t+Δt)-f(t), where Δt is the time interval.

[0023] In S22, the thermal model method includes:

[0024] S221. Construct a heat conduction equation, consider the processes of heat generation, heat conduction, heat convection and heat radiation inside the battery, and establish a thermal model including parameters such as battery temperature Tb, charge and discharge power P, ambient temperature Ta, battery size (length, width, height) and material thermal conductivity λ; the heat conduction equation is:

[0025]

[0026] Among them, ρ is the battery material density, c is the specific heat capacity, Q is the heat generation rate, h is the surface heat transfer coefficient, and A is the battery surface area;

[0027] S222. Measure the temperature rise rate of the battery at different charge and discharge powers Combined with the thermal model, the value of the heat generation rate Q is calculated;

[0028]

[0029] S223. Considering the mutual influence between thermal effect and electrical characteristics, a coupled thermal-electrical model is established. In the model, the change of battery temperature Tb will affect the battery's internal resistance R, open circuit voltage Voc and other electrical parameters. The model is expressed as:

[0030] R = R (T0) (1 + α (Tb - T0));

[0031] Voc=Voc(T0)(1+β(Tb-T0));

[0032] Where T0 is the reference temperature, α is the temperature coefficient, β is the voltage coefficient, R(T0) is the internal resistance at the reference temperature T0, Voc(T0) is the open circuit voltage at the reference temperature T0, and R(T0) is the internal resistance at the reference temperature T0;

[0033] S224. Energy state calculation: The energy state E(t) of the energy storage device at different time points is calculated by combining the thermal model with actual measurement data (such as battery temperature, charge and discharge power, etc.). The calculation formula of the energy state E(t) is:

[0034]

[0035] Where E0 is the initial energy of the battery, P(τ) is the charge and discharge power at time τ, and Q(τ) is the heat generation rate at time τ. By calculating the energy state E(t) at different time points, the energy state sequence {E(t)} can be obtained.

[0036] S225. Energy change calculation: In a certain time period [t1′, t′2], the energy change ΔE released by the energy storage device is calculated by the following formula:

[0037]

[0038] Wherein, α is the temperature coefficient, T is the ambient temperature, T0 is the reference temperature; E(t1′) and E(t′2) are the energy states of the energy storage device at time t1′ and t′2 calculated based on actual measurement data.

[0039] S3. Construct an objective function. According to the energy change representation in S2, construct an objective function to minimize the difference in energy change between the constantized and time-varying virtual inertias, and ensure the consistency of the constantized virtual inertia and the time-varying virtual inertia in terms of power grid support effects. The objective function is defined as:

[0040]

[0041] Hconst is the constantized virtual inertia, Hvar(t) is the time-varying virtual inertia; t0 and t1 are the endpoints of the considered time interval, λ1 and t2 are the weight coefficients;

[0042] t0 and t1 are determined according to the actual operation of the power grid and the working characteristics of the energy storage device, and λ1 and t2 are selected according to the importance attached to the differences between the constant virtual inertia and the time-varying virtual inertia.

[0043] S4. Optimize and solve the constraint conditions. On the basis of the objective function, the constraint conditions are used to ensure that the constantized virtual inertia matches the time-varying virtual inertia in terms of key indicators, so as to ensure the physical rationality and calculation accuracy of the constantization process;

[0044] The constraints in S4 include energy conservation constraints, battery safe operating area constraints, battery life constraints, temperature constraints, and internal resistance change constraints, etc.;

[0045] In the energy conservation constraint, the maximum charge and discharge power of the energy storage device is expressed as Pmax. The energy conservation constraint requires that at any time t, the energy change rate of the energy storage device satisfies: If the requirements are not met, the relevant parameters are adjusted; among them, the energy state sequence {E(t)} of the energy storage device obtained by the thermal model is used to calculate the energy change rate by numerical difference method Right now

[0046]

[0047] The state of charge (SOC) of the battery should satisfy SOCmin≤SOC≤SOCmax, where SOCmin and SOCmax are the minimum and maximum states of charge allowed by the battery, respectively; the state of charge of the battery is calculated based on the thermal model, that is, Where Emax is the maximum energy capacity of the battery;

[0048] The battery life constraint is: the actual number of cycles is less than the designed number of cycles of the battery, and the number of charge and discharge times of the battery is recorded to obtain the actual number of cycles;

[0049] Temperature constraint: the range of the battery operating temperature T′ is: T′∈[Tm′in,Tm′ax], where Tm′in is the minimum value of the battery operating temperature and Tm′ax is the maximum value of the battery operating temperature;

[0050] Internal resistance change constraint, the internal resistance R satisfies: R=R0(1+βt), R0 is the initial internal resistance of the battery, β is the internal resistance change rate, and t is time.

[0051] S5. Solve the constant virtual inertia in sections. According to the constraints of S4, the dynamic response process of the energy storage device is divided into a charging stage and a discharging stage, and the constant virtual inertia is solved respectively to adapt to the dynamic characteristics of the energy storage device in different stages.

[0052] The solution methods for the constant virtual inertia during the charging phase include:

[0053] Determine the charging time interval. The selection of the charging time interval needs to comprehensively consider the charging process of the energy storage device and the change of the grid frequency. The starting time of the charging time interval is the moment when the energy storage device starts charging and the grid frequency is relatively stable. The end time of the charging time interval is the moment when charging is completed or the grid frequency changes significantly again;

[0054] Calculate the constant virtual inertia during the charging phase. According to the law of conservation of energy, the ratio of the energy increase ΔEc of the energy storage device during the charging phase to the frequency change Δfc during the charging phase is the constant virtual inertia Hc during the charging phase, that is,

[0055] In S5, the method for solving the constant virtual inertia in the discharge stage includes:

[0056] Determine the discharge time interval. The discharge time interval is determined according to the discharge process of the energy storage device and the grid frequency. The discharge time interval is the moment when the energy storage device starts to discharge and the grid frequency is relatively stable. The discharge time interval is the moment when the discharge is completed or the grid frequency changes significantly.

[0057] Calculate the constant virtual inertia in the discharge phase. According to the law of conservation of energy, the ratio of the energy reduction ΔEd of the energy storage device in the discharge phase to the frequency change Δfd in the charging phase is the constant virtual inertia Hd in the charging phase, that is,

[0058] S6. Verification and adjustment: Apply the constantized virtual inertia obtained in S5 to the grid equivalent inertia expression, verify the effect of the constantized virtual inertia by simulating the system frequency response, and adjust relevant parameters as needed.

[0059] The S6 includes:

[0060] S61. Verify that the constantized virtual inertia of the charging stage and the constantized virtual inertia of the discharging stage obtained in S5 are applied to the expression of equivalent inertia of the power grid; for the charging stage, the expression of equivalent inertia of the power grid is expressed as Iceq=I0+HcΔfc, where Iceq is the equivalent inertia of the power grid in the charging stage, I0 is the initial inertia of the power grid, and Δfc is the frequency change in the charging stage; for the discharging stage, the expression of equivalent inertia of the power grid is Ideq=I0+HdΔfd, where Ideq is the equivalent inertia of the power grid in the discharging stage, and Δfd is the frequency change in the discharging stage;

[0061] S62. Simulate the system frequency response. Use power system simulation software to simulate the system frequency response. During the simulation, input grid parameters (such as grid resistance, inductance, etc.), energy storage device parameters (such as energy storage device capacity, internal resistance, etc.) and different frequency change scenarios (such as simulating grid frequency fluctuations under different load conditions);

[0062] S63. Check whether the frequency response meets expectations. Observe whether the grid frequency response meets expectations. The grid frequency response includes:

[0063] Frequency recovery time: whether the grid frequency can be restored to a stable frequency range within a specified time after being disturbed;

[0064] Frequency fluctuation amplitude: whether the fluctuation amplitude of the grid frequency during the regulation process is within the allowable range;

[0065] S64. Adjust, adjust, analyze the situation that does not meet expectations, and adjust the relevant parameters:

[0066] Adjust the time interval. If it is found that the time interval division of the charging stage or the discharging stage is unreasonable (such as the time interval is too long or too short), the time interval can be re-divided according to the frequency change law of the power grid and the actual working conditions of the energy storage device; if it is found that the charging time is too long, resulting in a long frequency recovery time, the charging time interval can be appropriately shortened, and other relevant parameters can be adjusted accordingly;

[0067] Adjust the constraint parameters. If a parameter in the constraint is found to be inappropriate, such as the maximum charge and discharge power is too large or too small, it can be adjusted according to the battery performance and the actual needs of the energy storage device. If it is found that the maximum charge and discharge power is too small and the energy storage device cannot effectively participate in the grid frequency regulation, the maximum charge and discharge power can be appropriately increased. For parameters such as the state of charge range, battery life, temperature range, and internal resistance change rate, if it is found that they do not conform to the actual situation, corresponding adjustments can also be made.

[0068] Recalculate the constant virtual inertia. After adjusting the relevant parameters, the constant virtual inertia needs to be recalculated. According to the method in S5, the time interval of the charging stage and the discharging stage is re-determined, the energy change and the frequency change are calculated, and then the new constant virtual inertia and are obtained.

[0069] Verify again, apply the new constantized virtual inertia to the grid equivalent inertia expression again, and verify it by simulating the system frequency response; repeat the above verification and adjustment process until a satisfactory result is obtained, that is, the grid frequency response meets the expected frequency recovery time and frequency fluctuation amplitude requirements.

[0070] In summary, due to the adoption of the above technical solution, the beneficial technical effects of the invention are:

[0071] Improve the accuracy of virtual inertia constant calculation. When calculating the virtual inertia constant, the present invention adopts a thermal model method, deeply analyzes factors such as battery aging, temperature, self-discharge and internal resistance change, constructs a heat conduction equation, considers heat generation, conduction, convection and radiation, and combines battery temperature, charge and discharge power and other parameters to accurately calculate the energy state. The heat generation rate is determined by measuring the temperature rise rate at different powers, and a coupled thermal-electric model is established to reflect the influence of thermal effects on electrical parameters, thereby accurately calculating the energy change, and finally achieving high precision in the calculation of the virtual inertia constant.

[0072] Optimize the matching effect of constantized virtual inertia and time-varying virtual inertia. When constructing the objective function, clarify the relationship between constantized virtual inertia and time-varying virtual inertia, and balance the energy change difference between the two at the endpoints of different time intervals through weight coefficients. When optimizing and solving constraints, multiple constraints such as energy conservation, battery safe working area, life, temperature and internal resistance change are used to ensure that the key indicators of constantized virtual inertia match the time-varying virtual inertia. For example, the energy conservation constraint ensures that the energy change rate of the energy storage device is reasonable, so that the energy storage device performs better in grid frequency regulation.

[0073] To ensure the safe and stable operation of the energy storage device and extend its service life, the energy conservation constraint requires that the energy change rate of the energy storage device meet specific conditions. The energy state sequence is calculated according to the thermal model, and the change rate is calculated by numerical difference to prevent abnormal energy changes. The battery state of charge (SOC) constraint keeps the SOC between the minimum and maximum values ​​allowed to avoid excessive charging and discharging. The battery life constraint ensures that the actual number of cycles is lower than the designed number by recording the number of charge and discharge times. The temperature constraint controls the battery operating temperature between and , and the internal resistance change constraint limits the range of internal resistance change, comprehensively ensuring the safety and stability of the energy storage device, reducing battery loss, and extending service life.

[0074] To improve the frequency regulation effect of the power grid, when solving the constant virtual inertia in segments, the time interval is determined according to the charging of the energy storage device and the power grid frequency during the charging phase. According to the law of conservation of energy, the constant virtual inertia is calculated as the ratio of the energy increase in the charging phase to the frequency change. The same is true for the discharge phase. Through verification and adjustment, the power grid frequency response is simulated to check whether the frequency recovery time and fluctuation amplitude meet the standards. According to the results, the time interval and constraint parameters are adjusted, and the constant virtual inertia is recalculated until the power grid frequency regulation requirements are met, effectively improving the power grid frequency stability and power quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 A logic block diagram of a method for calculating the constant of the virtual inertia constant of grid-connected electrochemical energy storage;

[0076] Figure 2 Logic block diagram of the method for characterizing energy changes in energy storage;

[0077] Figure 3 A flow chart of a method for verifying the effect of constantized virtual inertia by simulating system frequency response and adjusting related parameters as needed. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the invention more clear, the invention is further described in detail below in combination with the embodiments. It should be understood that the specific embodiments described here are only used to explain the invention and are not used to limit the invention.

[0079] like Figure 1 As shown, the constant calculation method of the virtual inertia constant of the grid-type electrochemical energy storage includes the following steps:

[0080] S1. Define the virtual inertia constant. The virtual inertia constant (HESS) is a key parameter of the energy storage device's ability to respond to grid frequency changes. The virtual inertia constant is defined as the ratio of the change in energy released by the energy storage device to the change in system frequency.

[0081] S2. Characterization of energy storage energy changes, combining the virtual inertia constant defined in S1 with the actual grid operation data to quantify the energy released by the energy storage device when the grid frequency changes;

[0082] S3. Construct an objective function. According to the energy change representation in S2, construct an objective function to minimize the difference in energy change between the constantized and time-varying virtual inertias, and ensure the consistency of the constantized virtual inertia and the time-varying virtual inertia in terms of power grid support effects;

[0083] S4. Optimize and solve the constraint conditions. On the basis of the objective function, the constraint conditions are used to ensure that the constantized virtual inertia matches the time-varying virtual inertia in terms of key indicators, so as to ensure the physical rationality and calculation accuracy of the constantization process;

[0084] S5. Solve the constant virtual inertia in sections. According to the constraints of S4, the dynamic response process of the energy storage device is divided into two stages (charging stage and discharging stage), and the constant virtual inertia is solved respectively to adapt to the dynamic characteristics of the energy storage device in different stages.

[0085] S6. Verification and adjustment: Apply the constantized virtual inertia obtained in S5 to the grid equivalent inertia expression, verify the effect of the constantized virtual inertia by simulating the system frequency response, and adjust relevant parameters as needed.

[0086] In S1, the virtual inertia constant (HESS) is a key parameter of the energy storage device's ability to respond to grid frequency changes, and is defined as the ratio of the energy change released by the energy storage device to the system frequency change, which can be expressed as:

[0087]

[0088] Among them, ΔE represents the change in energy released by the energy storage device; Δf represents the change in system frequency, which is measured by the grid frequency monitoring equipment and the unit is Hertz (Hz).

[0089] like Figure 2 As shown, the S2 includes:

[0090] S21. Preprocessing the grid frequency data, preprocessing the grid frequency data to obtain a frequency change sequence;

[0091] S22. synchronously process the energy data of the energy storage device, consider factors such as battery aging, temperature influence, self-discharge, internal resistance change, etc., use the thermal model method to estimate the energy state, and obtain the energy state sequence;

[0092] S23. Calculate the energy change, calculate the energy change released by the energy storage device within a certain period of time.

[0093] In S21, the step of extracting data features includes:

[0094] S211. Filter to remove high-frequency noise, select Butterworth filter to remove high-frequency noise, perform fast Fourier transform (FFT) on the power grid frequency data to obtain its spectrum, observe the obtained spectrum to determine the distribution range of the main noise frequency components, set the cutoff frequency and filter order of the Butterworth filter according to the distribution range of the noise frequency components, and after determining the cutoff frequency and filter order, use the Butterworth filter to process the power grid frequency data to remove the high-frequency noise components and obtain the frequency data after preliminary processing;

[0095] When setting the cutoff frequency of the Butterworth filter, make sure that the cutoff frequency of the Butterworth filter is slightly lower than the lower limit of the main noise frequency;

[0096] S212. Remove outliers. Use the 3σ principle to identify and remove outliers. Calculate the mean μ and standard deviation σ of the power grid frequency data. Data outside the range of (μ-3σ) to (μ+3σ) are considered outliers and removed to ensure the rationality and consistency of the data.

[0097] in,

[0098]

[0099] Where, M is the number of data points; fi is the grid frequency data, i∈M;

[0100] S213. Obtain the frequency change sequence. After being processed by S211 and S21, the processed grid frequency sequence {f(t)} is obtained. By calculating the frequency difference between adjacent time points, the frequency change sequence {Δf(t)} is obtained, that is, at time points t and t+Δt, the frequency change is: Δf(t)=f(t+Δt)-f(t), where Δt is the time interval.

[0101] In S22, the thermal model method includes:

[0102] S221. Construct a heat conduction equation, consider the processes of heat generation, heat conduction, heat convection and heat radiation inside the battery, and establish a thermal model including parameters such as battery temperature Tb, charge and discharge power P, ambient temperature Ta, battery size (length, width, height) and material thermal conductivity λ; the heat conduction equation is:

[0103]

[0104] Among them, ρ is the battery material density, c is the specific heat capacity, Q is the heat generation rate, h is the surface heat transfer coefficient, and A is the battery surface area;

[0105] S222. Measure the temperature rise rate of the battery at different charge and discharge powers Combined with the thermal model, the value of the heat generation rate Q is calculated;

[0106]

[0107] S223. Considering the mutual influence between thermal effect and electrical characteristics, a coupled thermal-electrical model is established. In the model, the change of battery temperature Tb will affect the battery's internal resistance R, open circuit voltage Voc and other electrical parameters. The model is expressed as:

[0108] R(Tb)=R(T0)(1+α(Tb-T0));

[0109] Voc(Tb)=Voc(T0)(1+β(Tb-T0));

[0110] In the formula, T0 is the reference temperature, α is the temperature coefficient, and β is the voltage coefficient;

[0111] S224. Energy state calculation: The energy state E(t) of the energy storage device at different time points is calculated by combining the thermal model with actual measurement data (such as battery temperature, charge and discharge power, etc.). The calculation formula of the energy state E(t) is:

[0112]

[0113] Where E0 is the initial energy of the battery, P(τ) is the charge and discharge power at time τ, and Q(τ) is the heat generation rate at time τ. By calculating the energy state E(t) at different time points, the energy state sequence {E(t)} can be obtained.

[0114] S225. Energy change calculation: In a certain time period [t1′, t′2], the energy change released by the energy storage device is calculated by the following formula:

[0115]

[0116] Wherein, α is the temperature coefficient, T is the ambient temperature, T0 is the reference temperature; E(t1′) and E(t′2) are the energy states of the energy storage device at time t1′ and t′2 calculated based on actual measurement data.

[0117] In S3, the objective function is defined as:

[0118]

[0119] Hconst is the constantized virtual inertia, Hvar(t) is the time-varying virtual inertia; t0 and t1 are the endpoints of the considered time interval, λ1 and t2 are the weight coefficients;

[0120] t0 and t1 are determined according to the actual situation of the power grid operation and the working characteristics of the energy storage device. If the energy storage device mainly participates in the power grid regulation during the daily peak and valley periods, t0 is set to a period before the peak and valley periods begin, and t1 is set to a period after the peak and valley periods end, so as to fully cover the working range of the energy storage device during the power grid frequency regulation process;

[0121] λ1 and t2 are selected according to the degree of emphasis on the differences between the constant virtual inertia and the time-varying virtual inertia. If more attention is paid to the consistency of the change rates of the two, they can be appropriately increased; if more emphasis is placed on the differences under different frequency changes, they can be appropriately increased.

[0122] The constraints in S4 include energy conservation constraints, battery safe operating area constraints, battery life constraints, temperature constraints, and internal resistance change constraints, etc.;

[0123] In the energy conservation constraint, the maximum charge and discharge power of the energy storage device is expressed as Pmax. The energy conservation constraint requires that at any time t, the energy change rate of the energy storage device satisfies: If the requirements are not met, the relevant parameters are adjusted; among them, the energy state sequence {E(t)} of the energy storage device obtained by the thermal model is used to calculate the energy change rate by numerical difference method. Right now

[0124]

[0125] The state of charge (SOC) of the battery should satisfy SOCmin≤SOC≤SOCmax, where SOCmin and SOCmax are the minimum and maximum states of charge allowed by the battery, respectively; the state of charge of the battery is calculated based on the thermal model, that is, Where Emax is the maximum energy capacity of the battery;

[0126] The battery life constraint is: the actual number of cycles is less than the designed number of cycles of the battery, and the number of charge and discharge times of the battery is recorded to obtain the actual number of cycles;

[0127] Temperature constraint: the range of the battery operating temperature T′ is: T′∈[T′min,T′max], where T′min is the minimum value of the battery operating temperature and T′max is the maximum value of the battery operating temperature;

[0128] Internal resistance change constraint, the internal resistance R satisfies: R=R0(1+βt), R0 is the initial internal resistance of the battery, β is the internal resistance change rate, and t is time.

[0129] In S5, the method for solving the constant virtual inertia in the charging stage includes:

[0130] Determine the charging time interval. The selection of the charging time interval needs to comprehensively consider the charging process of the energy storage device and the change of the grid frequency. The starting time of the charging time interval is the moment when the energy storage device starts charging and the grid frequency is relatively stable. The end time of the charging time interval is the moment when charging is completed or the grid frequency changes significantly again;

[0131] Calculate the constant virtual inertia during the charging phase. According to the law of conservation of energy, the ratio of the energy increase ΔEc of the energy storage device during the charging phase to the frequency change Δfc during the charging phase is the constant virtual inertia Hc during the charging phase, that is, The energy increase ΔEc of the energy storage device during the charging phase is calculated according to the energy change calculation formula in S225, and the frequency change Δfc during the charging phase is obtained according to the frequency change calculation formula in S213.

[0132] In S5, the method for solving the constant virtual inertia in the discharge stage includes:

[0133] Determine the discharge time interval. The discharge time interval is determined according to the discharge process of the energy storage device and the grid frequency. The discharge time interval is the moment when the energy storage device starts to discharge and the grid frequency is relatively stable. The discharge time interval is the moment when the discharge is completed or the grid frequency changes significantly.

[0134] Calculate the constant virtual inertia in the discharge phase. According to the law of conservation of energy, the ratio of the energy reduction ΔEd of the energy storage device in the discharge phase to the frequency change Δfd in the charging phase is the constant virtual inertia Hd in the charging phase, that is, The energy increase ΔEc of the energy storage device during the charging phase is calculated according to the energy change calculation formula in S225, and the frequency change Δfc during the charging phase is obtained according to the frequency change calculation formula in S213.

[0135] like Figure 3 As shown, the S6 includes:

[0136] S61. Verify that the constantized virtual inertia of the charging stage and the constantized virtual inertia of the discharging stage obtained in S5 are applied to the expression of equivalent inertia of the power grid; for the charging stage, the expression of equivalent inertia of the power grid is expressed as Iceq=I0+HcΔfc, where Iceq is the equivalent inertia of the power grid in the charging stage, I0 is the initial inertia of the power grid, and Δfc is the frequency change in the charging stage; for the discharging stage, the expression of equivalent inertia of the power grid is Ideq=I0+HdΔfd, where Ideq is the equivalent inertia of the power grid in the discharging stage, and Δfd is the frequency change in the discharging stage;

[0137] S62. Simulate the system frequency response. Use power system simulation software to simulate the system frequency response. During the simulation, input grid parameters (such as grid resistance, inductance, etc.), energy storage device parameters (such as energy storage device capacity, internal resistance, etc.) and different frequency change scenarios (such as simulating grid frequency fluctuations under different load conditions);

[0138] S63. Check whether the frequency response meets expectations. Observe whether the grid frequency response meets expectations. The grid frequency response includes:

[0139] Frequency recovery time: whether the grid frequency can be restored to a stable frequency range within a specified time after being disturbed. In some grid standards, the frequency is required to be restored to a stable frequency range of ±0.2Hz within 30 seconds;

[0140] Frequency fluctuation amplitude: whether the fluctuation amplitude of the power grid frequency during the adjustment process is within the allowable range. Generally, the frequency fluctuation amplitude is not allowed to exceed ±0.5Hz.

[0141] S64. Adjustment, analysis of situations that do not meet expectations. If the verification results do not meet expectations, the specific reasons need to be analyzed. If it is found that the frequency recovery time is too long, it may be due to inaccurate calculation of the constantized virtual inertia in the charging or discharging stage, or unreasonable setting of constraints (such as too strict or too loose maximum charge and discharge power limits, unreasonable state of charge range, etc.). If the frequency fluctuation amplitude is too large, it may be because the response speed of the energy storage device is not fast enough, or the grid parameter settings are inaccurate.

[0142] Adjust relevant parameters according to the analysis results:

[0143] Adjust the time interval. If it is found that the time interval division of the charging stage or the discharging stage is unreasonable (such as the time interval is too long or too short), the time interval can be re-divided according to the frequency change law of the power grid and the actual working conditions of the energy storage device; if it is found that the charging time is too long, resulting in a long frequency recovery time, the charging time interval can be appropriately shortened, and other relevant parameters can be adjusted accordingly;

[0144] Adjust the constraint parameters. If a parameter in the constraint is found to be inappropriate, such as the maximum charge and discharge power is too large or too small, it can be adjusted according to the battery performance and the actual needs of the energy storage device. If it is found that the maximum charge and discharge power is too small and the energy storage device cannot effectively participate in the grid frequency regulation, the maximum charge and discharge power can be appropriately increased. For parameters such as the state of charge range, battery life, temperature range, and internal resistance change rate, if it is found that they do not conform to the actual situation, corresponding adjustments can also be made.

[0145] Recalculate the constant virtual inertia. After adjusting the relevant parameters, the constant virtual inertia needs to be recalculated. According to the method in S5, the time interval of the charging stage and the discharging stage is re-determined, the energy change and the frequency change are calculated, and then the new constant virtual inertia and are obtained.

[0146] Verify again, apply the new constantized virtual inertia to the grid equivalent inertia expression again, and verify it by simulating the system frequency response; repeat the above verification and adjustment process until a satisfactory result is obtained, that is, the grid frequency response meets the expected frequency recovery time and frequency fluctuation amplitude requirements.

[0147] The above description is a preferred embodiment of the invention and is not intended to limit the invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention should be included in the protection scope of the invention.

Claims

1. A method for calculating the constant of the virtual inertia constant of a grid-connected electrochemical energy storage, characterized in that: The following steps are involved: S1. Define the virtual inertia constant. The virtual inertia constant is a key parameter of the energy storage device's ability to respond to grid frequency changes. The virtual inertia constant is defined as the ratio of the change in energy released by the energy storage device to the change in system frequency. S2. Characterization of energy storage energy changes, combining the virtual inertia constant defined in S1 with the actual grid operation data to quantify the energy released by the energy storage device when the grid frequency changes; S3. Construct an objective function. According to the energy change representation in S2, construct an objective function to minimize the difference in energy change between the constantized and time-varying virtual inertias, and ensure the consistency of the constantized virtual inertia and the time-varying virtual inertia in terms of power grid support effects; S4. Optimize and solve the constraint conditions. On the basis of the objective function, the constraint conditions are used to ensure that the constantized virtual inertia matches the time-varying virtual inertia in terms of key indicators, so as to ensure the physical rationality and calculation accuracy of the constantization process; S5. Solve the constant virtual inertia in sections. According to the constraints of S4, the dynamic response process of the energy storage device is divided into a charging stage and a discharging stage, and the constant virtual inertia is solved respectively to adapt to the dynamic characteristics of the energy storage device in different stages. S6. Verification and adjustment: Apply the constantized virtual inertia obtained in S5 to the grid equivalent inertia expression, verify the effect of the constantized virtual inertia by simulating the system frequency response, and adjust relevant parameters as needed.

2. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 1, characterized in that: In S1, the virtual inertia constant is a key parameter of the energy storage device's ability to respond to grid frequency changes, and is expressed as: Among them, ΔE represents the change in energy released by the energy storage device; Δf represents the change in system frequency.

3. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 1, characterized in that: The S2 includes: S21. Preprocessing the grid frequency data, preprocessing the grid frequency data to obtain a frequency change sequence; S22. Synchronously process the energy data of the energy storage device, consider the aging, temperature influence, self-discharge, and internal resistance change factors of the battery, use the thermal model method to estimate the energy state, and obtain the energy state sequence; S23. Calculate the energy change, calculate the energy change released by the energy storage device within a certain period of time.

4. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 3, characterized in that: In S21, the step of preprocessing the power grid frequency data includes: S211. Filter to remove high-frequency noise, select Butterworth filter to remove high-frequency noise, perform fast Fourier transform on the power grid frequency data to obtain its spectrum, observe the obtained spectrum to determine the distribution range of the main noise frequency components, set the cutoff frequency and filter order of the Butterworth filter according to the distribution range of the noise frequency components, and after determining the cutoff frequency and filter order, use the Butterworth filter to process the power grid frequency data to remove the high-frequency noise components and obtain the frequency data after preliminary processing; Among them, the cutoff frequency of the Butterworth filter should be slightly smaller than the lower limit of the main noise frequency; S212. Remove outliers. Use the 3σ principle to identify and remove outliers. Calculate the mean μ and standard deviation σ of the power grid frequency data. Data outside the range of (μ-3σ) to (μ+3σ) are considered outliers and removed to ensure the rationality and consistency of the data. in, Where, M is the number of data points; fi is the grid frequency data, i∈M; S213. Obtain the frequency change sequence. After being processed by S211 and S21, the processed grid frequency sequence {f(t)} is obtained. By calculating the frequency difference between adjacent time points, the frequency change sequence {Δf(t)} is obtained, that is, at time points t and t+Δt, the frequency change is: Δf(t)=f(t+Δt)-f(t), where Δt is the time interval.

5. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 3, characterized in that: In S22, the steps of the thermal model method include: S221. Construct a heat conduction equation, consider the heat generation, heat conduction, heat convection and heat radiation processes inside the battery, and establish a thermal model that includes battery temperature Tb, charge and discharge power P, ambient temperature Ta, battery size and material thermal conductivity λ; the heat conduction equation is: Among them, ρ is the battery material density, c is the specific heat capacity, Q is the heat generation rate, h is the surface heat transfer coefficient, and A is the battery surface area; S222. Measure the temperature rise rate of the battery at different charge and discharge powers Combined with the thermal model, the value of the heat generation rate Q is calculated; S223. Considering the mutual influence between thermal effect and electrical characteristics, a coupled thermal-electrical model is established. In the model, the change of battery temperature Tb will affect the internal resistance R and open circuit voltage Voc electrical parameters of the battery. The model is expressed as: R(Tb)=R(T0)(1+α(Tb-T0)); Voc(Tb)=Voc(T0)(1+β(Tb-T0)); In the formula, T0 is the reference temperature, α is the temperature coefficient, and β is the voltage coefficient; S224. Energy state calculation: The energy state E(t) of the energy storage device at different time points is calculated by combining the thermal model with the actual measurement data. The calculation formula of the energy state E(t) is: Where E0 is the initial energy of the battery, P(τ) is the charge and discharge power at time τ, and Q(τ) is the heat generation rate at time τ. By calculating the energy state E(t) at different time points, the energy state sequence {E(t)} can be obtained. S225. Energy change calculation: In a certain time period [t1′, t′2], the energy change released by the energy storage device is calculated by the following formula: Wherein, α is the temperature coefficient, T is the ambient temperature, T0 is the reference temperature; E(t1′) and E(t′2) are the energy states of the energy storage device at time t1′ and t′2 calculated based on actual measurement data.

6. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 1, characterized in that: In S3, the objective function is expressed as: Hconst is the constant virtual inertia, Hvar(t) is the time-varying virtual inertia; t0 and t1 are the endpoints of the considered time interval, and λ1 and t2 are weight coefficients.

7. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 1, characterized in that: The constraints in S4 include energy conservation constraints, battery safe operating area constraints, battery life constraints, temperature constraints and internal resistance change constraints; In the energy conservation constraint, the maximum charge and discharge power of the energy storage device is expressed as Pmax. The energy conservation constraint requires that at any time t, the energy change rate of the energy storage device satisfies: If the requirements are not met, the relevant parameters are adjusted; among them, the energy state sequence {E(t)} of the energy storage device obtained by the thermal model is used to calculate the energy change rate by numerical difference method. Right now The state of charge (SOC) of the battery should satisfy SOCmin≤SOC≤SOCmax, where SOCmin and SOCmax are the minimum and maximum states of charge allowed by the battery, respectively; the state of charge of the battery is calculated based on the thermal model, that is, Where Emax is the maximum energy capacity of the battery; The battery life constraint is that the actual number of cycles is less than the designed number of cycles of the battery, and the number of times the battery is charged and discharged is recorded to obtain the actual number of cycles; Temperature constraint: the range of the battery operating temperature T′ is: T′∈[Tm′in,Tm′ax], where Tm′in is the minimum value of the battery operating temperature and Tm′ax is the maximum value of the battery operating temperature; Internal resistance change constraint, the internal resistance R satisfies: R=R0(1+βt), R0 is the initial internal resistance of the battery, β is the internal resistance change rate, and t is time.

8. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 1, characterized in that: In S5, the method for solving the constant virtual inertia in the charging stage includes: Determine the charging time interval. The selection of the charging time interval needs to comprehensively consider the charging process of the energy storage device and the change of the grid frequency. The starting time of the charging time interval is the moment when the energy storage device starts charging and the grid frequency is relatively stable. The end time of the charging time interval is the moment when charging is completed or the grid frequency changes significantly again; Calculate the constant virtual inertia during the charging phase. According to the law of conservation of energy, the ratio of the energy increase ΔEc of the energy storage device during the charging phase to the frequency change Δfc during the charging phase is the constant virtual inertia Hc during the charging phase, that is, The solution methods for constant virtual inertia in the discharge stage include: Determine the discharge time interval. The discharge time interval is determined according to the discharge process of the energy storage device and the grid frequency. The discharge time interval is the moment when the energy storage device starts to discharge and the grid frequency is relatively stable. The discharge time interval is the moment when the discharge is completed or the grid frequency changes significantly. Calculate the constant virtual inertia in the discharge phase. According to the law of conservation of energy, the ratio of the energy reduction ΔEd of the energy storage device in the discharge phase to the frequency change Δfd in the charging phase is the constant virtual inertia Hd in the charging phase, that is, 9. The method for calculating the constant of the virtual inertia constant of the grid-connected electrochemical energy storage according to claim 1, characterized in that: The S6 includes: S61. Verify that the constantized virtual inertia of the charging stage and the constantized virtual inertia of the discharging stage obtained in S5 are applied to the expression of equivalent inertia of the power grid; for the charging stage, the expression of equivalent inertia of the power grid is expressed as Iceq=I0+HcΔfc, where Iceq is the equivalent inertia of the power grid in the charging stage, I0 is the initial inertia of the power grid, and Δfc is the frequency change in the charging stage; for the discharging stage, the expression of equivalent inertia of the power grid is Ideq=I0+HdΔfd, where Ideq is the equivalent inertia of the power grid in the discharging stage, and Δfd is the frequency change in the discharging stage; S62. Simulate the system frequency response. Use power system simulation software to simulate the system frequency response. During the simulation, input grid parameters, energy storage device parameters, and different frequency change scenarios; S63. Check whether the frequency response is as expected. The grid frequency response includes: Frequency recovery time: whether the grid frequency can be restored to a stable frequency range within a specified time after being disturbed; Frequency fluctuation amplitude: whether the fluctuation amplitude of the grid frequency during the regulation process is within the allowable range; S64. Adjustment: Analyze the situation that does not meet expectations and adjust the relevant parameters: Adjust the time interval. If it is found that the time interval division of the charging stage or the discharging stage is unreasonable, the time interval shall be re-divided according to the frequency change law of the power grid and the actual working conditions of the energy storage device. If it is found that the charging time is too long, resulting in a long frequency recovery time, the charging time interval shall be appropriately shortened, and other relevant parameters shall be adjusted accordingly. Adjust the constraint parameters. If a parameter in the constraint is found to be inappropriate, such as the maximum charge and discharge power is too large or too small, adjust it according to the battery performance and the actual needs of the energy storage device. If the maximum charge and discharge power is found to be too small and the energy storage device cannot effectively participate in the grid frequency regulation, increase the maximum charge and discharge power appropriately. For the state of charge range, battery life, temperature range and internal resistance change rate parameters, if it is found that they do not conform to the actual situation, corresponding adjustments can also be made. Recalculate the constantized virtual inertia. After adjusting the relevant parameters, the constantized virtual inertia needs to be recalculated. According to the method in S5, the time interval of the charging stage and the discharging stage is re-determined, the energy change and the frequency change are calculated, and then the new constantized virtual inertia and are obtained. S65. Verify again, apply the new constantized virtual inertia to the grid equivalent inertia expression again, and verify by simulating the system frequency response; repeat the above verification and adjustment process until a satisfactory result is obtained, that is, the grid frequency response meets the expected frequency recovery time and frequency fluctuation amplitude requirements.

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