Virtual inertia adjusting method, device and equipment based on real-time control and predictive optimization

Abstract

The invention relates to a virtual inertia adjustment method, device and equipment based on real-time control and predictive optimization. The method comprises the following steps: acquiring parameter data and a first constraint condition of an alternating-current and direct-current hybrid power grid; virtual inertia is calculated according to the parameter data; according to the energy storage residual electric quantity, the energy storage power, the actual output power and the virtual inertia, a first constraint condition is adopted for judgment to obtain a judgment result; and if the judgment result does not meet the first constraint condition, correcting the virtual inertia to obtain the optimized virtual rotational inertia. According to the method, the virtual inertia is calculated by obtaining parameter data, whether the parameter data and the virtual inertia meet the first constraint condition is judged, whether the virtual inertia meets the virtual rotational inertia required by the power grid or not is further judged, and if not, the virtual inertia is corrected to obtain the optimized virtual rotational inertia required by the power grid. The virtual rotational inertia of the power grid is adjusted in real time according to the power grid operation state.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power grid, and particularly relates to a virtual inertia regulation method, device and equipment based on real-time control and prediction optimization. BACKGROUND

[0002] In the operation of AC-DC hybrid power grid (or multi-region hybrid power grid), the traditional generator set provides rotational inertia to help maintain the stability of the power grid. However, due to the access of renewable energy (wind power, photovoltaic, etc.) and energy storage systems, the traditional generator set gradually withdraws, resulting in a decrease in the rotational inertia of the power grid. Virtual rotational inertia generally refers to the inertia response of a traditional synchronous generator simulated by a power electronic device to help the power grid maintain stability during frequency fluctuations. The AC-DC power grid can include an AC subnetwork and a DC subnetwork, such as a VSC-HVDC connected power grid. The grid-forming energy storage can be regarded as a power system based on a voltage source converter VSC, which can actively support and regulate the frequency and voltage of the power grid. The power electronic device included in the grid-forming energy storage system can simulate the inertia response of a traditional synchronous generator, i.e., provide virtual rotational inertia.

[0003] However, the virtual rotational inertia required by the power grid is not constant, but is dynamically adjusted according to the operating state of the power grid, so the virtual rotational inertia provided by the grid-forming energy storage system is also a dynamic process. Specifically, the virtual rotational inertia is closely related to factors such as frequency deviation, frequency change rate, power balance, and energy storage system state (SOC, power limit). SUMMARY

[0004] The present application provides a virtual inertia regulation method, device and equipment based on real-time control and prediction optimization, which is used to solve the technical problem that the virtual rotational inertia of the existing power grid cannot be adjusted in real time according to the operating state of the power grid.

[0005] In order to achieve the above-mentioned purpose, the present application provides the following technical scheme:

[0006] On the one hand, a virtual inertia regulation method based on real-time control and prediction optimization is provided, comprising the following steps:

[0007] Obtaining parameter data and first constraint conditions of an AC-DC hybrid power grid, wherein the parameter data includes basic inertia, frequency deviation, frequency deviation weight coefficient, frequency deviation change rate, frequency change rate weight coefficient, active power deviation, energy storage remaining power, energy storage power and actual output power;

[0008] According to the basic inertia, the frequency deviation, the frequency deviation weight coefficient, the frequency deviation change rate, the frequency change rate weight coefficient and the active power deviation, the virtual inertia is calculated.

[0009] According to the energy storage remaining power, the energy storage power, the actual output power and the virtual inertia, the first constraint condition is judged to obtain a judgment result; if the judgment result does not satisfy the first constraint condition, the virtual inertia is modified to obtain an optimized virtual rotational inertia.

[0010] Preferably, the virtual inertia is modified to obtain an optimized virtual rotational inertia, including:

[0011] The energy storage power, the actual output power, the damping coefficient and the angular frequency deviation at each sampling time are obtained; according to the energy storage power, the actual output power, the damping coefficient and the angular frequency deviation at each sampling time, the current virtual inertia corresponding to the sampling time is calculated;

[0012] The function parameters, the second constraint condition and the prediction time domain are obtained; according to the function parameters, the prediction time domain, the second constraint condition and the current virtual inertia corresponding to each sampling time, a solver is used to calculate to obtain an optimal virtual inertia sequence, and the first optimization result of the optimal virtual inertia sequence is taken as an inertia optimization reference value;

[0013] The current virtual inertia is modified to follow the inertia optimization reference value in an error feedback correction mode to obtain an optimized virtual rotational inertia satisfying the additional constraint and the first constraint condition.

[0014] Preferably, the virtual inertia adjustment method based on real-time control and prediction optimization includes: according to the function parameters, the prediction time domain, the second constraint condition and the current virtual inertia corresponding to each sampling time, a solver is used to calculate a target function to obtain an optimal virtual inertia sequence; the target function is:

[0015]

[0016]

[0017] The second constraint condition is:

[0018]

[0019] In the formula, Q is a frequency deviation weight, R is a control amount change weight, N is the total number of the prediction time domain, J ac (k) is the current virtual inertia to be optimized at the kth sampling time, ΔJ ac (k) is the optimized inertia difference at the kth sampling time, Δω is the angular frequency deviation, i is the ith prediction time domain, J min is the minimum value of the virtual inertia, J max is the maximum value of the virtual inertia, T s is the sampling time, Pess P is the energy storage output power control amount m P is the actual output power e SOC is the energy storage power min SOC is the minimum value of the energy storage remaining power max SOC is the maximum value of the energy storage remaining power, k is the number of sampling time, E ess P is the energy storage capacity ess max J is the maximum value of the energy storage output power control amount.

[0020] Preferably, the virtual inertia adjustment method based on real-time control and prediction optimization comprises: according to the energy storage power, the actual output power, the damping coefficient and the angular frequency deviation of each sampling time, a real-time adjustment formula is adopted to calculate the current virtual inertia corresponding to the sampling time; the real-time adjustment formula is:

[0021]

[0022] J is the virtual inertia, D is the damping coefficient, and ac J(k) is the current virtual inertia at the kth sampling time, P m P(k) is the actual output power at the kth sampling time, P e SOC(k) is the energy storage power at the kth sampling time, and Δω(k) is the angular frequency deviation at the kth sampling time, D is a coefficient greater than 0 ac D is the damping coefficient.

[0023] Preferably, the expression of the error feedback correction method is: J is the virtual inertia, D is the damping coefficient, and ac J(k) is the current virtual inertia at the kth sampling time, which is also the virtual inertia to be optimized; J(k) is the inertia optimization reference value at the kth sampling time, k p K is the proportional gain;

[0024] The additional constraint is:

[0025]

[0026] J is the virtual inertia, D is the damping coefficient, and ac J(k) is the current virtual inertia at the kth sampling time, P m P(k) is the actual output power at the kth sampling time, P e SOC(k) is the energy storage power at the kth sampling time, and Δω(k) is the angular frequency deviation at the kth sampling time, D is a coefficient greater than 0 ac D is the damping coefficient, J minJ is the minimum value of the virtual inertia. max This represents the maximum value of the virtual inertia.

[0027] Preferably, the virtual inertia adjustment method based on real-time control and predictive optimization includes: calculating the virtual inertia using a dynamic adjustment formula based on the base inertia, the frequency deviation, the frequency deviation weighting coefficient, the frequency deviation change rate, the frequency change rate weighting coefficient, and the active power deviation; the dynamic adjustment formula is:

[0028] The virtual inertia is calculated using a dynamic adjustment formula based on the basic inertia, the frequency deviation, the frequency deviation weighting coefficient, the frequency deviation change rate, the frequency change rate weighting coefficient, and the active power deviation; the dynamic adjustment formula is as follows.

[0029] Preferably, the first constraint condition is:

[0030]

[0031] In the formula, J ac For virtual inertia, J min J is the minimum value of the virtual inertia. max P represents the maximum value of the virtual inertia, t is the total sampling time, τ is the sampling time, and P is the maximum value of the virtual inertia. ess P is the output power control quantity for energy storage. m P represents the actual output power. e For energy storage power, SOC min SOC is the minimum remaining energy storage capacity. max E represents the maximum remaining energy storage capacity, SOC represents the remaining energy storage capacity, SOC0 represents the initial remaining energy storage capacity, and E represents the maximum remaining energy storage capacity. ess For energy storage capacity, P ess max This represents the maximum value of the energy storage output power control quantity.

[0032] On the other hand, a virtual inertia adjustment device based on real-time control and predictive optimization is provided, including a data acquisition module, a virtual inertia calculation module and a correction and optimization module;

[0033] The data acquisition module is used to acquire parameter data and first constraint conditions of the AC / DC hybrid power grid. The parameter data includes basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, active power deviation, remaining energy storage capacity, energy storage power, and actual output power.

[0034] The virtual inertia calculation module is used to calculate and obtain the virtual inertia based on the basic inertia, the frequency deviation, the frequency deviation weighting coefficient, the frequency deviation change rate, the frequency change rate weighting coefficient, and the active power deviation.

[0035] The correction and optimization module is used to make a judgment based on the remaining energy storage capacity, the energy storage power, the actual output power, and the virtual inertia using the first constraint condition, and obtain a judgment result; if the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain an optimized virtual rotational inertia.

[0036] Preferably, the correction and optimization module includes a calculation submodule, a reference value acquisition submodule, and a correction and optimization submodule;

[0037] The calculation submodule is used to obtain the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; and to calculate the current virtual inertia corresponding to that sampling moment based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment.

[0038] The reference value acquisition submodule is used to acquire function parameters, the second constraint condition, and the prediction time domain; and to calculate the optimal virtual inertia sequence using a solver based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time. The first optimization result of the optimal virtual inertia sequence is used as the inertia optimization reference value.

[0039] The correction and optimization submodule is used to correct the current virtual inertia by following the inertia optimization reference value using an error feedback correction method, so as to obtain an optimized virtual rotational inertia that satisfies the additional constraints and the first constraint conditions.

[0040] On the other hand, a terminal device is provided, including a processor and a memory;

[0041] The memory is used to store program code and transmit the program code to the processor;

[0042] The processor is configured to execute the virtual inertia adjustment method based on real-time control and predictive optimization as described above, according to the instructions in the program code.

[0043] This invention relates to a virtual inertia adjustment method, apparatus, and device based on real-time control and predictive optimization. The virtual inertia adjustment method includes acquiring parameter data and a first constraint condition of an AC / DC hybrid power grid. The parameter data includes basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, active power deviation, remaining energy storage capacity, energy storage power, and actual output power. Virtual inertia is calculated based on the basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, and active power deviation. The remaining energy storage capacity, energy storage power, actual output power, and virtual inertia are used to make a judgment based on the first constraint condition, resulting in a judgment result. If the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain an optimized virtual rotational inertia.

[0044] As can be seen from the above technical solutions, this application has the following advantages: The virtual inertia adjustment method based on real-time control and predictive optimization calculates the virtual inertia by acquiring parameter data, and then determines whether the calculated virtual inertia meets the virtual rotational inertia required by the power grid based on whether the parameter data and the virtual inertia meet the first constraint condition. If not, the virtual inertia is corrected to obtain the optimized virtual rotational inertia required by the power grid, thereby realizing the real-time adjustment of the virtual rotational inertia of the power grid according to the power grid operating status. This solves the technical problem that the virtual rotational inertia of the existing power grid cannot be adjusted in real time according to the power grid operating status.

[0045] This virtual inertia adjustment device based on real-time control and predictive optimization obtains virtual inertia by acquiring parameter data through a data acquisition module, a virtual inertia calculation module, and a correction and optimization module. Then, it determines whether the calculated virtual inertia meets the virtual rotational inertia required by the power grid based on whether the parameter data and virtual inertia meet the first constraint condition. If not, the virtual inertia is corrected to obtain the optimized virtual rotational inertia required by the power grid, thereby realizing the real-time adjustment of the virtual rotational inertia of the power grid according to the power grid's operating status. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0047] Figure 1 This is a flowchart illustrating the steps of the virtual inertia adjustment method based on real-time control and predictive optimization described in the embodiments of this application.

[0048] Figure 2This is a schematic diagram of the framework of the virtual inertia adjustment device based on real-time control and predictive optimization as described in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the terminal device described in an embodiment of this application. Detailed Implementation

[0050] To make the inventive objectives, features, and advantages of this application more apparent and understandable, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this application, and not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0051] In the description of the embodiments of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0052] In the embodiments of this application, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application according to the specific circumstances.

[0053] This application provides a virtual inertia adjustment method, apparatus, and device based on real-time control and predictive optimization, which solves the technical problem that the virtual rotational inertia of existing power grids cannot be adjusted in real time according to the power grid operating status.

[0054] Example 1:

[0055] Figure 1 This is a flowchart illustrating the steps of the virtual inertia adjustment method based on real-time control and predictive optimization described in the embodiments of this application.

[0056] like Figure 1 As shown in the figure, this application provides a virtual inertia adjustment method based on real-time control and predictive optimization, including the following steps:

[0057] S1. Obtain the parameter data and first constraint conditions of the AC / DC hybrid power grid. The parameter data includes the basic inertia J. base Frequency deviation Δf, frequency deviation weighting coefficient K f Frequency deviation change rate df / dt, frequency change rate weighting coefficient K df Active power deviation ΔP, remaining energy storage capacity SOC, energy storage power P e and actual output power P m .

[0058] It should be noted that step S1 is to obtain the parameter data and the first constraint conditions of the AC / DC hybrid power grid, so as to provide data for subsequent steps.

[0059] In this embodiment of the application, the first constraint is:

[0060]

[0061] In the formula, J ac For virtual inertia, J min J is the minimum value of the virtual inertia. max P represents the maximum value of the virtual inertia, t is the total sampling time, τ is the sampling time, and P is the maximum value of the virtual inertia. ess P is the output power control quantity for energy storage. m P represents the actual output power. e For energy storage power, SOC min SOC is the minimum remaining energy storage capacity. max E represents the maximum remaining energy storage capacity, SOC represents the remaining energy storage capacity, SOC0 represents the initial remaining energy storage capacity, and E represents the maximum remaining energy storage capacity. ess For energy storage capacity, P ess max This represents the maximum value of the energy storage output power control quantity. The remaining energy storage capacity is expressed as a percentage (%), and the energy storage capacity is expressed in Wh.

[0062] S2. The virtual inertia is obtained by calculating the basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, and active power deviation.

[0063] It should be noted that in step S2, the virtual inertia is calculated based on the basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, and active power deviation obtained in step S1. This virtual inertia provides data for subsequent judgment on whether the first constraint condition is met, ensuring that the obtained virtual inertia enables the energy storage system's inertia regulation to achieve instantaneous stability, long-term performance optimization, and good collaborative optimization of multi-regional AC / DC hybrid power grids. This virtual inertia regulation method based on real-time control and predictive optimization first calculates the virtual inertia based on the acquired parameter data, providing data for subsequent preliminary judgment on whether the virtual inertia meets the first constraint condition, thus ensuring that the virtual inertia meets the virtual rotational inertia required by the power grid.

[0064] In this embodiment, the virtual inertia adjustment method based on real-time control and predictive optimization includes: calculating the virtual inertia using a dynamic adjustment formula based on the base inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, and active power deviation; the dynamic adjustment formula is:

[0065]

[0066] In the formula, J ac For virtual inertia, J base Based on the inertia, K f K is the frequency deviation weighting coefficient, Δf is the frequency deviation, and K is the frequency deviation weighting coefficient. df K is the frequency change rate weighting coefficient, df / dt is the frequency deviation change rate, and K is the frequency deviation weighting coefficient. p Here, is the power imbalance weighting coefficient, and ΔP is the active power deviation. The unit of foundation inertia is kg·m. 2 .

[0067] S3. Based on the remaining energy storage capacity, energy storage power, actual output power, and virtual inertia, a judgment is made using the first constraint condition to obtain the judgment result; if the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain the optimized virtual rotational inertia.

[0068] It should be noted that in step S3, the remaining energy storage capacity, energy storage power, actual output power, and virtual inertia obtained from steps S1 and S2 are judged according to the first constraint condition to obtain the judgment result. If the judgment result satisfies the first constraint condition, the virtual inertia calculated in step S2 is used as the virtual rotational inertia required by the power grid; if the judgment result does not satisfy the first constraint condition, the virtual inertia is corrected to obtain an optimized virtual rotational inertia that meets the virtual rotational inertia required by the power grid. The optimized virtual rotational inertia obtained by this virtual inertia adjustment method based on real-time control and predictive optimization combines the advantages of both, improves accuracy and adaptability to different scenarios. This virtual inertia adjustment method based on real-time control and predictive optimization performs better in terms of real-time performance, stability, and multi-constraint processing, while also considering the physical limitations of the energy storage system in AC / DC hybrid power grids, such as the remaining energy storage capacity (SOC) and power limitations.

[0069] This application provides a virtual inertia adjustment method based on real-time control and predictive optimization. The method includes acquiring parameter data and a first constraint condition of an AC / DC hybrid power grid. The parameter data includes basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, active power deviation, remaining energy storage capacity, energy storage power, and actual output power. Virtual inertia is calculated based on the basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, and active power deviation. The remaining energy storage capacity, energy storage power, actual output power, and virtual inertia are used to make a judgment based on the first constraint condition, resulting in a judgment result. If the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain an optimized virtual rotational inertia. This virtual inertia adjustment method based on real-time control and predictive optimization calculates virtual inertia by acquiring parameter data. Then, it determines whether the calculated virtual inertia meets the virtual rotational inertia required by the power grid based on whether the parameter data and virtual inertia satisfy the first constraint condition. If not, the virtual inertia is corrected to obtain the optimized virtual rotational inertia required by the power grid. This method realizes the real-time adjustment of the power grid's virtual rotational inertia according to the power grid's operating status, solving the technical problem that the virtual rotational inertia of the existing power grid cannot be adjusted in real time according to the power grid's operating status.

[0070] In one embodiment of this application, modifying the virtual inertia to obtain an optimized virtual rotational inertia includes:

[0071] S31. Obtain the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; calculate the current virtual inertia corresponding to that sampling moment based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment.

[0072] S32. Obtain the function parameters, the second constraint condition, and the prediction time domain; calculate the optimal virtual inertia sequence using the solver based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time, and use the first optimization result of the optimal virtual inertia sequence as the inertia optimization reference value;

[0073] S33. The current virtual inertia is corrected by following the inertia optimization reference value using an error feedback correction method to obtain an optimized virtual rotational inertia that satisfies the additional constraints and the first constraint conditions.

[0074] It should be noted that the optimized virtual rotational inertia obtained by correcting the virtual inertia can be obtained within a hierarchical framework. This framework includes a real-time control layer and a predictive optimization layer. The real-time control layer responds quickly to frequency changes, while the predictive optimization layer performs rolling optimization, considering future states and constraints. The hierarchical framework ensures both real-time performance and the ability to handle multiple constraints. The real-time control layer and the predictive optimization layer need to work together. The parameters optimized by the predictive optimization layer (such as the first optimization result of the optimal virtual inertia sequence) serve as the reference input for the real-time control layer (such as the inertia optimization reference value), ensuring the stability of the obtained optimized virtual rotational inertia. In this embodiment, the real-time control layer is used to calculate the current virtual inertia corresponding to each sampling moment based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment in step S31, achieving high-frequency adjustment of the virtual inertia and quickly suppressing frequency fluctuations. The predictive optimization layer is used to perform low-frequency rolling optimization based on the current virtual inertia obtained in step S31, executing step S32 to obtain the inertia optimization reference value.

[0075] In the embodiments of this application, in step S31, the current virtual inertia corresponding to each sampling moment is calculated using a real-time adjustment formula based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; the real-time adjustment formula is:

[0076]

[0077] In the formula, J ac (k) represents the current virtual inertia at the k-th sampling time, P m (k) represents the actual output power at the k-th sampling time, P e (k) represents the stored energy power at the k-th sampling time, and Δω(k) represents the angular frequency deviation at the k-th sampling time. For coefficients greater than 0, D ac is the damping coefficient. Where, This setting prevents the denominator of the formula from being zero during real-time adjustments.

[0078] It should be noted that the real-time control layer is used to ensure the asymptotic stability of the power grid dynamics. In the real-time control function of the real-time control layer, the state variable x(k) = Δω(k) is defined, where Δω(k) is the angular frequency deviation at the k-th sampling time; a positive definite function is chosen. Discretely differentiating the positive definite function, we obtain the derivative function as follows: The requirement is that ΔV ≤ 0 to ensure the state variables do not diverge and thus maintain stability. A real-time adjustment formula can be derived from the dynamic model of the virtual moment of inertia. The expression for the dynamic model of the virtual moment of inertia is:

[0079]

[0080] In the formula, J ac Let D be the virtual moment of inertia (kg·m²), Δω be the angular frequency deviation (rad / s), and D be the virtual moment of inertia (kg·m²). ac P is the damping coefficient. m (energy storage power), P e This represents the actual output power.

[0081] In the embodiments of this application, in step S32, the optimal virtual inertia sequence is obtained by calculating the objective function of the solver based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time; the objective function is:

[0082]

[0083]

[0084] The second constraint is:

[0085]

[0086] In the formula, Q is the frequency deviation weight, R is the control quantity change weight, N is the total number of predictions in the time domain, and J is the control quantity change weight. ac (k) represents the current virtual inertia to be optimized at the k-th sampling time, ΔJ ac (k) represents the optimized inertia difference at the k-th sampling time, Δω represents the angular frequency deviation, and i represents the i-th prediction time domain. min J is the minimum value of the virtual inertia. max T represents the maximum value of the virtual inertia. s For sampling time, P ess P is the output power control quantity for energy storage. m P represents the actual output power. e For energy storage power, SOC min SOC is the minimum remaining energy storage capacity. max The maximum value of the remaining energy storage capacity is given by E, where SOC is the remaining energy storage capacity, k is the number of sampling times, and E is the maximum value of the remaining energy storage capacity.ess For energy storage capacity, P ess max This represents the maximum value of the energy storage output power control quantity.

[0087] It should be noted that in step S32, the objective function represents minimizing the frequency deviation and the control quantity change. The frequency deviation weight Q=1, and the control quantity change weight R=0.1. Based on the current virtual inertia at each sampling time obtained in step S31, the current state of the power grid and the disturbance prediction in the prediction time domain are updated in real time. Then, based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time, the objective function of the solver is used to calculate and obtain the optimal virtual inertia sequence. The optimal virtual inertia sequence can be understood as follows: for each prediction time domain, the objective function of step S32 is used for optimization calculation to obtain an optimized virtual inertia J. ac (k+i), N predictions in the time domain yield N optimized virtual inertia, and N optimized virtual inertia J ac (k+1), ..., J ac (k+i), ..., J ac The optimal virtual inertia sequence is composed of (k+N).

[0088] In the embodiments of this application, in step S33, the expression for the error feedback correction method is: In the formula, J ac (k) represents the current virtual inertia at the k-th sampling time, and is also the virtual inertia to be optimized; The inertia optimization reference value at the k-th sampling time, k p For proportional gain;

[0089] Additional constraints are:

[0090]

[0091] In the formula, J ac (k) represents the current virtual inertia at the k-th sampling time, P m (k) represents the actual output power at the k-th sampling time, P e (k) represents the stored energy power at the k-th sampling time, and Δω(k) represents the angular frequency deviation at the k-th sampling time. For coefficients greater than 0, D ac J is the damping coefficient. min J is the minimum value of the virtual inertia. max This represents the maximum value of the virtual inertia. Wherein, the proportional gain k... p =0.5.

[0092] It should be noted that in step S33, the optimized virtual rotational inertia is obtained through a collaborative mechanism between the predictive optimization layer and the real-time control layer. Specifically, the current virtual inertia J at the k-th sampling time of the real-time control layer is... ac (k) The inertia optimization reference value at the kth sampling time of the prediction optimization layer needs to be tracked. The inertia optimization reference value at the kth sampling time is corrected by error feedback correction so that the optimized virtual rotation inertia of the optimization result satisfies the additional constraints.

[0093] In the embodiments of this application, the virtual inertia adjustment method based on real-time control and predictive optimization is used at a rated frequency of 50Hz, a load surge of 15MW, and an initial... The AC subgrid, the energy storage system with a capacity of 20MW / 40MWh and SOC∈[20%, 90%], and N=10, Q=1, R=0.1, J min =100 and J max Using parameters such as =1000 as an example, in steps S1 and S2, the grid frequency f drops to 49.2Hz, with a recovery time >5s; after optimization in step S3: the grid frequency f recovers to 49.8Hz, with a recovery time <2s; the virtual moment of inertia J... ac The dynamic adjustment range is 280. The remaining SOC of the energy storage is maintained between 25% and 88%, without exceeding the limit.

[0094] Example 2:

[0095] Figure 2 This is a schematic diagram of the framework of the virtual inertia adjustment device based on real-time control and predictive optimization described in the embodiments of this application.

[0096] like Figure 2 As shown, this application provides a virtual inertia adjustment device based on real-time control and predictive optimization, including a data acquisition module 10, a virtual inertia calculation module 20, and a correction and optimization module 30.

[0097] The data acquisition module 10 is used to acquire parameter data and first constraint conditions of the AC / DC hybrid power grid. The parameter data includes basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, active power deviation, remaining energy storage capacity, energy storage power and actual output power.

[0098] The virtual inertia calculation module 20 is used to calculate and obtain the virtual inertia based on the basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, and active power deviation.

[0099] The correction and optimization module 30 is used to make a judgment based on the remaining energy storage capacity, energy storage power, actual output power and virtual inertia using the first constraint condition, and obtain the judgment result; if the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain the optimized virtual rotational inertia.

[0100] It should be noted that the content of the modules in the device of Embodiment 2 has been described in the steps of the method of Embodiment 1, and the content of the virtual inertia adjustment device module based on real-time control and predictive optimization will not be repeated in this embodiment. In this embodiment, the virtual inertia adjustment device based on real-time control and predictive optimization calculates the virtual inertia by acquiring parameter data through a data acquisition module, a virtual inertia calculation module, and a correction and optimization module. Then, based on whether the parameter data and the virtual inertia meet the first constraint condition, it is determined whether the calculated virtual inertia meets the virtual rotational inertia required by the power grid. If it does not meet the constraint condition, the virtual inertia is corrected to obtain the optimized virtual rotational inertia required by the power grid, thereby realizing the real-time adjustment of the virtual rotational inertia of the power grid according to the power grid operating status.

[0101] In this embodiment, the correction and optimization module 30 includes a calculation submodule, a reference value acquisition submodule, and a correction and optimization submodule;

[0102] The calculation submodule is used to obtain the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; and calculates the current virtual inertia corresponding to that sampling moment based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment.

[0103] The reference value acquisition submodule is used to acquire function parameters, the second constraint condition, and the prediction time domain. Based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time, the solver calculates and obtains the optimal virtual inertia sequence. The first optimization result of the optimal virtual inertia sequence is used as the inertia optimization reference value.

[0104] The correction and optimization submodule is used to correct the current virtual inertia by following the inertia optimization reference value using an error feedback correction method, so as to obtain an optimized virtual rotational inertia that satisfies the additional constraints and the first constraint conditions.

[0105] Example 3:

[0106] Figure 3 This is a schematic diagram of the terminal device described in an embodiment of this application.

[0107] like Figure 3 As shown, this application provides a terminal device, including a processor and a memory;

[0108] Memory is used to store program code and transfer the program code to the processor;

[0109] The processor is used to execute the virtual inertia adjustment method based on real-time control and predictive optimization according to the instructions in the program code.

[0110] It should be noted that the processor is used to execute the steps in the above-described embodiment of a virtual inertia adjustment method based on real-time control and predictive optimization, according to the instructions in the program code. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described system / device embodiments.

[0111] For example, a computer program can be divided into one or more modules / units, one or more of which are stored in memory and executed by a processor to complete this application. One or more modules / units can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in a terminal device.

[0112] Terminal devices can be computing devices such as desktop computers, laptops, handheld computers, and cloud servers. Terminal devices may include, but are not limited to, processors and memory. Those skilled in the art will understand that this does not constitute a limitation on the terminal device, which may include more or fewer components than illustrated, or combinations of certain components, or different components. For example, a terminal device may also include input / output devices, network access devices, buses, etc.

[0113] The processor referred to can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0114] Memory can be an internal storage unit of a terminal device, such as a hard drive or RAM. Memory can also be an external storage device, such as a plug-in hard drive, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, memory can include both internal and external storage units. Memory is used to store computer programs and other programs and data required by the terminal device. Memory can also be used to temporarily store data that has been output or will be output.

[0115] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between apparatuses or units through some interfaces, and may be electrical, mechanical, or other forms.

[0117] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0119] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A virtual inertia adjustment method based on real-time control and predictive optimization, characterized in that, Includes the following steps: Obtain parameter data and first constraint conditions for the AC / DC hybrid power grid. The parameter data includes basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, active power deviation, remaining energy storage capacity, energy storage power, and actual output power. The virtual inertia is calculated based on the basic inertia, the frequency deviation, the frequency deviation weighting coefficient, the frequency deviation change rate, the frequency change rate weighting coefficient, and the active power deviation. The judgment is made based on the remaining energy storage capacity, the energy storage power, the actual output power, and the virtual inertia using the first constraint condition, and a judgment result is obtained; if the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain an optimized virtual rotational inertia.

2. The virtual inertia adjustment method based on real-time control and predictive optimization according to claim 1, characterized in that, The virtual inertia is corrected to obtain the optimized virtual rotational inertia, which includes: Obtain the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; calculate the current virtual inertia corresponding to each sampling moment based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; Obtain the function parameters, the second constraint, and the prediction time domain; calculate the optimal virtual inertia sequence using a solver based on the function parameters, the prediction time domain, the second constraint, and the current virtual inertia corresponding to each sampling time; and use the first optimization result of the optimal virtual inertia sequence as the inertia optimization reference value. The current virtual inertia is corrected by using an error feedback correction method to follow the inertia optimization reference value, thereby obtaining an optimized virtual rotational inertia that satisfies the additional constraints and the first constraint condition.

3. The virtual inertia adjustment method based on real-time control and predictive optimization according to claim 2, characterized in that, include: The optimal virtual inertia sequence is obtained by calculating the objective function of the solver based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time. The objective function is: The second constraint is: In the formula, Q is the frequency deviation weight, R is the control quantity change weight, N is the total number of predictions in the time domain, and J is the control quantity change weight. ac (k) represents the current virtual inertia to be optimized at the k-th sampling time, ΔJ ac (k) represents the optimized inertia difference at the k-th sampling time, Δω represents the angular frequency deviation, and i represents the i-th prediction time domain. min J is the minimum value of the virtual inertia. max T represents the maximum value of the virtual inertia. s For sampling time, P ess P is the output power control quantity for energy storage. m P represents the actual output power. e For energy storage power, SOC min SOC is the minimum remaining energy storage capacity. max The maximum value of the remaining energy storage capacity is given by E, where SOC is the remaining energy storage capacity, k is the number of sampling times, and E is the maximum value of the remaining energy storage capacity. ess For energy storage capacity, P ess max This represents the maximum value of the energy storage output power control quantity.

4. The virtual inertia adjustment method based on real-time control and predictive optimization according to claim 2, characterized in that, include: The current virtual inertia corresponding to each sampling moment is calculated using a real-time adjustment formula based on the stored energy power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; the real-time adjustment formula is: In the formula, J ac (k) represents the current virtual inertia at the k-th sampling time, P m (k) represents the actual output power at the k-th sampling time, P e (k) represents the stored energy power at the k-th sampling time, and Δω(k) represents the angular frequency deviation at the k-th sampling time. For coefficients greater than 0, D ac is the damping coefficient.

5. The virtual inertia adjustment method based on real-time control and predictive optimization according to claim 2, characterized in that, The expression for the error feedback correction method is: In the formula, J ac (k) represents the current virtual inertia at the k-th sampling time, and is also the virtual inertia to be optimized; The inertia optimization reference value at the k-th sampling time, k p For proportional gain; The additional constraint is: In the formula, J ac (k) represents the current virtual inertia at the k-th sampling time, P m (k) represents the actual output power at the k-th sampling time, P e (k) represents the stored energy power at the k-th sampling time, and Δω(k) represents the angular frequency deviation at the k-th sampling time. For coefficients greater than 0, D ac J is the damping coefficient. min J is the minimum value of the virtual inertia. max This represents the maximum value of the virtual inertia.

6. The virtual inertia adjustment method based on real-time control and predictive optimization according to any one of claims 1-5, characterized in that, include: The virtual inertia is calculated using a dynamic adjustment formula based on the fundamental inertia, the frequency deviation, the frequency deviation weighting coefficient, the frequency deviation change rate, the frequency change rate weighting coefficient, and the active power deviation; the dynamic adjustment formula is: In the formula, J ac For virtual inertia, J base Based on the inertia, K f K is the frequency deviation weighting coefficient, Δf is the frequency deviation, and K is the frequency deviation weighting coefficient. df K is the frequency change rate weighting coefficient, df / dt is the frequency deviation change rate, and K is the frequency deviation weighting coefficient. p ΔP is the power imbalance weighting coefficient, and ΔP is the active power deviation.

7. The virtual inertia adjustment method based on real-time control and predictive optimization according to any one of claims 1-5, characterized in that, The first constraint is: In the formula, J ac For virtual inertia, J min J is the minimum value of the virtual inertia. max P represents the maximum value of the virtual inertia, t is the total sampling time, τ is the sampling time, and P is the maximum value of the virtual inertia. ess P is the output power control quantity for energy storage. m P represents the actual output power. e For energy storage power, SOC min SOC is the minimum remaining energy storage capacity. max E represents the maximum remaining energy storage capacity, SOC represents the remaining energy storage capacity, SOC0 represents the initial remaining energy storage capacity, and E represents the maximum remaining energy storage capacity. ess For energy storage capacity, P ess max This represents the maximum value of the energy storage output power control quantity.

8. A virtual inertia adjustment device based on real-time control and predictive optimization, characterized in that, include: Data acquisition module, virtual inertia calculation module, and correction and optimization module; The data acquisition module is used to acquire parameter data and first constraint conditions of the AC / DC hybrid power grid. The parameter data includes basic inertia, frequency deviation, frequency deviation weighting coefficient, frequency deviation change rate, frequency change rate weighting coefficient, active power deviation, remaining energy storage capacity, energy storage power, and actual output power. The virtual inertia calculation module is used to calculate and obtain the virtual inertia based on the basic inertia, the frequency deviation, the frequency deviation weighting coefficient, the frequency deviation change rate, the frequency change rate weighting coefficient, and the active power deviation. The correction and optimization module is used to make a judgment based on the remaining energy storage capacity, the energy storage power, the actual output power, and the virtual inertia using the first constraint condition, and obtain a judgment result; if the judgment result does not meet the first constraint condition, the virtual inertia is corrected to obtain an optimized virtual rotational inertia.

9. The virtual inertia adjustment device based on real-time control and predictive optimization according to claim 8, characterized in that, The correction and optimization module includes a calculation submodule, a reference value acquisition submodule, and a correction and optimization submodule. The calculation submodule is used to obtain the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment; and to calculate the current virtual inertia corresponding to that sampling moment based on the stored power, actual output power, damping coefficient, and angular frequency deviation at each sampling moment. The reference value acquisition submodule is used to acquire function parameters, the second constraint condition, and the prediction time domain; and to calculate the optimal virtual inertia sequence using a solver based on the function parameters, the prediction time domain, the second constraint condition, and the current virtual inertia corresponding to each sampling time. The first optimization result of the optimal virtual inertia sequence is used as the inertia optimization reference value. The correction and optimization submodule is used to correct the current virtual inertia by following the inertia optimization reference value using an error feedback correction method, so as to obtain an optimized virtual rotational inertia that satisfies the additional constraints and the first constraint conditions.

10. A terminal device, characterized in that, Including the processor and memory; The memory is used to store program code and transmit the program code to the processor; The processor is configured to execute the virtual inertia adjustment method based on real-time control and predictive optimization as described in any one of claims 1-7, according to the instructions in the program code.

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