A method and apparatus for supplementary frequency control of aggregated energy storage and load response

Abstract

This application discloses a supplementary frequency control method and apparatus for aggregated energy storage and load response. The method includes: constructing a model framework for supplementary frequency control based on an equivalent frequency model of a power system with high renewable energy penetration; defining a power disturbance observer and a two-layer model predictive control unit within the model framework to obtain a target equivalent frequency model; obtaining observation values ​​through the power disturbance observer; inputting the sampled discrete frequency deviation and observation values ​​into the two-layer model predictive control unit to obtain a control trajectory for the aggregated energy storage system and the load; and correcting the control trajectory for control errors to obtain the final control trajectory, thereby regulating the actual output of the aggregated energy storage and the load response. This application effectively solves the frequency security problem faced by low-inertia power grids under islanding events by constructing a target equivalent frequency model including a power disturbance observer and a two-layer model predictive control unit and rationally allocating load response and energy storage output.

Description

Technical Field

[0001] This application relates to the field of microgrid technology, and in particular to a supplementary frequency control method and apparatus for aggregated energy storage and load response. Background Technology

[0002] In severe weather, the output power of renewable energy sources will be severely limited, and physical damage to external transmission lines may occur, potentially leading to unexpected power system isolation and resulting in long-term severe power shortages. Under such power disturbances, low-inertia systems relying solely on a subset of unchanged synchronous generators for frequency regulation often experience more severe frequency deviations. Therefore, high-proportion renewable energy power systems under severe weather conditions require a series of SFC (Supplemental Frequency Control) methods.

[0003] Aggregated energy storage systems are one of the most common types of distributed energy storage systems (SFCs). By combining multiple sets of distributed energy storage, aggregated energy storage can quickly provide a large amount of power to suppress frequency drops, while also meeting the plug-and-play requirements of the internal energy storage. Currently, it is assumed that combining aggregated energy storage with generators can control the system frequency within a specified range, ignoring the possibility that power shortages under unintentional islanding events may exceed the dynamic adjustment capabilities of both.

[0004] In addition to aggregated energy storage, SFC should also consider load response to prevent excessive frequency deviation. One of the most widely used methods is UFLS (under-frequency load shedding), where load shedding is divided into several stages, and load is shed when the frequency drops above a certain threshold. However, due to fixed parameters, UFLS cannot actively respond to power disturbances or cooperate with other frequency regulation units.

[0005] Therefore, how to coordinate the load response and aggregated energy storage output in SFC under severe weather conditions to produce positive effects is an urgent problem to be solved. Summary of the Invention

[0006] To address the aforementioned technical problems, embodiments of this application provide a supplementary frequency control method and apparatus for aggregated energy storage and load response.

[0007] Firstly, in order to solve the above-mentioned technical problems, this application provides a supplementary frequency control method for aggregated energy storage and load response, comprising:

[0008] A model framework for supplementary frequency control is constructed based on the equivalent frequency model of a power system with high renewable energy penetration.

[0009] Within the model framework, a power perturbation observer and a two-layer model prediction control unit are defined to obtain the target equivalent frequency model.

[0010] The target equivalent frequency model is used to obtain the observed values ​​through the power perturbation observer;

[0011] The discrete frequency deviation is sampled, and the discrete frequency deviation and the observed value are input into the two-layer model prediction and control unit to obtain the control trajectory for the aggregated energy storage system and the load.

[0012] The control trajectory is corrected for control errors to obtain the final control trajectory, which is then used to regulate the actual output of the aggregated energy storage and the load response.

[0013] The beneficial effects are:

[0014] In the technical solution provided by the embodiments of this application, a model framework for supplementary frequency control is constructed based on the equivalent frequency model of a power system with high renewable energy penetration. Within this model framework, a power disturbance observer and a two-layer model predictive control unit are defined to construct a target equivalent frequency model for frequency regulation of the load and aggregated energy storage. During supplementary frequency control, the target equivalent frequency model is used to obtain observed values ​​through the power disturbance observer; discrete frequency deviations are sampled, and the discrete frequency deviations and observed values ​​are input into the two-layer model predictive control unit to obtain control trajectories for the aggregated energy storage system and the load; control error correction is applied to the control trajectory to obtain the final control trajectory, which is then used to regulate the actual output of the aggregated energy storage and the load response. Thus, this application can rationally allocate load response and energy storage output during frequency regulation, considering load response characteristics and the aggregated energy storage system. This effectively solves the frequency security problem faced by low-inertia power grids under islanding events, achieves a more proactive frequency regulation effect, and obtains control strategies capable of coping with various power disturbances, thereby ensuring the safe operation of power systems with high renewable energy penetration.

[0015] In a second aspect, the present invention provides a supplementary frequency control device for aggregated energy storage and load response, comprising a construction unit, a model definition unit, an observation unit, a processing unit, and a correction unit;

[0016] The building block is used to construct a model framework for supplementary frequency control based on the equivalent frequency model of a power system with high renewable energy penetration.

[0017] The model definition unit is used to define a power perturbation observer and a two-layer model prediction control unit within the model framework to obtain the target equivalent frequency model.

[0018] An observation unit is used to obtain observation values ​​through the power perturbation observer using the target equivalent frequency model.

[0019] The processing unit is used to sample discrete frequency deviations and input the discrete frequency deviations and the observed values ​​into the two-layer model prediction and control unit to obtain the control trajectory for the aggregated energy storage system and the load.

[0020] The correction unit is used to correct the control error of the control trajectory to obtain the final control trajectory, so as to regulate the actual output of the aggregated energy storage and the load response based on the final control trajectory.

[0021] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0023] Figure 1 This is a flowchart illustrating a supplementary frequency control method for aggregated energy storage and load response, as shown in an exemplary embodiment of this application.

[0024] Figure 2 This is a schematic diagram of the target equivalent frequency model in an exemplary embodiment of this application;

[0025] Figure 3 This is a diagram illustrating the tracking effect of a power disturbance observer under different power disturbance magnitudes in an exemplary embodiment provided in this application.

[0026] Figure 4 This is a control block diagram of a two-layer model prediction control unit in an exemplary embodiment of this application;

[0027] Figure 5 This demonstrates the control effect of the target equivalent frequency model under different power disturbances in an exemplary embodiment of this application.

[0028] Figure 6 This is a schematic diagram comparing the frequency evolution between the target equivalent frequency model provided in this application and different SFCs;

[0029] Figure 7 This is a schematic diagram comparing the output power of the target equivalent frequency model provided in this application with different SFCs;

[0030] Figure 8 This is a block diagram illustrating a supplementary frequency control device for aggregated energy storage and load response, as shown in an exemplary embodiment of this application.

[0031] Figure 9 This is a schematic diagram of the structure of a computer system suitable for implementing the electronic devices of the present application embodiments. Detailed Implementation

[0032] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0033] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0034] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0035] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0036] By coordinating load response and aggregated energy storage output in SFC (Supply-Fueled Controller), the system is expected to achieve more proactive frequency regulation under severe weather conditions. However, this collaboration presents several challenges. The lack of global observability of the power system under severe weather conditions will prevent the timely implementation of coordination strategies. More effective frequency regulation requires rapid adjustment of load response and aggregated energy storage output based on power changes within the system, necessitating accurate power disturbance data to guide their operation. Another challenge is the difficulty in coordinating multiple units with inconsistent control steps.

[0037] To address the above-mentioned problems, embodiments of this application propose a supplementary frequency control method and apparatus for aggregated energy storage and load response. This mainly relates to the supplementary frequency control technology for aggregated energy storage and load response based on two-layer model predictive control, which is included in microgrid technology. These embodiments will be described in detail below.

[0038] Please refer to the following first. Figure 1 , Figure 1 This is a flowchart illustrating a supplementary frequency control method for aggregated energy storage and load response, as shown in an exemplary embodiment of this application. The method can be specifically executed by a server, which can be a standalone server or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms. No limitation is imposed here.

[0039] like Figure 1 As shown in an exemplary embodiment, the supplementary frequency control method for aggregated energy storage and load response may include steps S101 to S105, which are described in detail below:

[0040] Step S101: Construct a model framework for supplementary frequency control based on the equivalent frequency model of a power system with high renewable energy penetration.

[0041] The internal frequency evolution equation for a power system with high renewable energy penetration is:

[0042]

[0043] In the formula: H is the system inertia, D is the load damping coefficient; ΔP g ΔP e ΔP l These represent the power changes of the generator, aggregated energy storage, and load, respectively; ΔP d It represents system power interference; Δf represents system frequency variation.

[0044] As shown in the above equation, the frequency evolution trend can be evaluated based on different inputs to the aggregated energy storage system and the load, thereby guiding their coordination to improve frequency regulation performance. It is important to note that in addition to the two control inputs mentioned above, ΔP also needs to be obtained simultaneously. d and ΔP g Due to severe communication degradation in inclement weather, ΔP could not be directly measured. d and ΔP gTherefore, this embodiment constructs a supplementary frequency control model framework based on the equivalent frequency model of a power system with high renewable energy penetration, and constructs a target equivalent frequency model based on this model framework. The target equivalent frequency model is used to obtain system power interference and frequency changes, so as to achieve a reasonable allocation of load response and energy storage output during frequency regulation.

[0045] Step S102: Define a power perturbation observer and a two-layer model prediction control unit within the model framework to obtain the target equivalent frequency model.

[0046] Please see Figure 2 , Figure 2 This is a schematic diagram of the target equivalent frequency model in an exemplary embodiment of this application. For example... Figure 2 As shown, based on the traditional SFC, the target equivalent frequency model in this embodiment adds an SMO (Sliding Mode Observer) for rapid convergence evaluation of power disturbances and a dual-layer MPC (Model Predictive Control), which can effectively coordinate the operation of the aggregated energy storage system and the load response in frequency regulation. Therefore, this embodiment needs to define a power disturbance observer and a dual-layer model predictive control unit within the model framework when constructing the target equivalent frequency model.

[0047] Step S103: Using the target equivalent frequency model, the observed values ​​are obtained through the power perturbation observer.

[0048] Step S104: Sample the discrete frequency deviation, input the discrete frequency deviation and the observed value into the two-layer model prediction control unit to obtain the control trajectory for the aggregated energy storage system and the load.

[0049] After constructing the target equivalent frequency model, it is applied to the supplementary frequency control of aggregated energy storage and load response. First, the power disturbance observer of the target equivalent frequency model is used to obtain observations. Then, the observations, along with the discrete frequency deviation sampled from the traditional SFC structure of the target equivalent frequency model, are input to the two-layer model predictive control unit (MMC) to obtain the control trajectory for the aggregated energy storage system and the load. This leverages the MPC's ability to predict the evolution trend of the next time-time frequency under different control inputs, thus forming a control trajectory to guide the coordination of the aggregated energy storage system and the load. Furthermore, considering the continuous discrete control problems caused by the different control steps of the two systems, the discrete frequency deviation is simultaneously input to the MMC to form a more reasonable control trajectory.

[0050] Step S105: Correct the control error of the control trajectory to obtain the final control trajectory, and adjust the actual output and load response of the aggregated energy storage based on the final control trajectory.

[0051] In this embodiment, the error caused by the mismatch between the load and aggregated energy storage and the control trajectory is corrected by the control error correction method, so as to obtain the final control trajectory.

[0052] As can be seen from the above, in the method provided in this embodiment, on the one hand, a model framework for supplementary frequency control is constructed by constructing an equivalent frequency model based on a power system with high renewable energy penetration, and a power disturbance observer and a two-layer model predictive control unit are defined within the model framework to construct a target equivalent frequency model for frequency regulation of load and aggregated energy storage. The two-layer model predictive control unit is used to solve the continuous discrete control problem caused by its different control step size.

[0053] On the other hand, supplementary frequency control utilizes a target equivalent frequency model and obtains observed values ​​through a power disturbance observer. Discrete frequency deviations are sampled, and the discrete frequency deviations and observed values ​​are input into a two-layer model predictive control unit to obtain a control trajectory for the aggregated energy storage system and the load. Control error correction is applied to the control trajectory to obtain the final control trajectory, which is then used to regulate the actual output of the aggregated energy storage and the load response. Thus, this application can rationally allocate load response and energy storage output during frequency regulation, considering load response characteristics and the aggregated energy storage system. This effectively addresses the frequency security issues faced by low-inertia power grids under islanding events, achieving a more proactive frequency regulation effect and obtaining a control strategy capable of handling various power disturbances, thereby ensuring the safe operation of power systems with high renewable energy penetration.

[0054] in addition, Figure 2 The structural parameters of the target equivalent frequency model in the illustrated embodiment can be shown in Table 1 below.

[0055] Table 1:

[0056]

[0057]

[0058] In an exemplary embodiment provided in this application, the applied target equivalent frequency model, compared to the traditional SFC, is equipped with a power disturbance observer and a two-layer model prediction control unit, which includes an upper-layer model prediction control and a lower-layer model prediction control. Therefore, the specific steps for constructing the target equivalent frequency model may include:

[0059] Within the model framework, a power disturbance observer is defined based on the state equation of a power system with high renewable energy penetration. The power disturbance observer is suitable for high-power disturbances.

[0060] Define the first cost function of the upper-level model predictive control of the two-level model predictive control unit;

[0061] Configure the first constraint condition for the predictive control of the upper-level model;

[0062] Based on the first cost function and the first constraint condition, the upper-level model predictive control is defined to obtain the target upper-level model predictive control.

[0063] Define the second cost function of the lower-level model predictive control in the two-level model predictive control unit;

[0064] Configure the second constraint condition for the predictive control of the lower-level model;

[0065] Based on the second cost function and the second constraint, the lower-level model predictive control is defined to obtain the target lower-level model predictive control.

[0066] The target equivalent frequency model is constructed based on the power disturbance observer, the upper-level model predictive control of the target, and the lower-level model predictive control of the target.

[0067] In another exemplary embodiment provided in this application, the specific steps for defining a power perturbation observer within the model framework and obtaining the target power perturbation observer may include:

[0068] Based on the state equations of power systems with high renewable energy penetration, a state-space model is derived:

[0069]

[0070] y = Cx

[0071] In the formula, the state variable x = [Δf ΔP] e ] T The system disturbance d = ΔP d The system output y = Δf, where Δf is the system frequency change. The coefficient matrices A, B, C, and R are represented as follows:

[0072] B = [0 0] T

[0073] R = [-1 / 2H 0] T C = [1 0] T

[0074] In the formula, R e and τ e N represents the time constant and descent coefficient of energy storage within aggregated energy storage. e It refers to the amount of energy stored in aggregated energy storage;

[0075] The power disturbance observer is defined using a state-space model, resulting in the target power disturbance observer:

[0076]

[0077]

[0078] In the formula, L = [l1 l2] T The observation gain matrix, These are the observed values ​​of x, d, and y, respectively.

[0079] In this embodiment, after deriving the state-space model based on the state equation of a power system with high renewable energy penetration, a corresponding power disturbance observer can be designed by combining the state-space model and the common variable Δf to quickly and accurately evaluate ΔP. d The power disturbance observer can be designed as follows:

[0080]

[0081] The designed power disturbance observer compares And x is used to provide an estimate of the system state, and the system state error characteristics are as follows: ˙ Down:

[0082]

[0083] in

[0084] like Figure 3 As shown, Figure 3 This is a tracking effect diagram of a power disturbance observer under different power disturbance magnitudes in an exemplary embodiment provided in this application. In this embodiment, the power disturbance observation is a sliding mode observer (SMO). Figure 3 The tracking performance of power disturbance observations under different power disturbance magnitudes is shown, and compared with a Romberg observer with the same parameters. The two different power disturbance magnitudes are 1.5 MW (dashed line) and 3.5 MW (solid line). For the former, both observers can track power disturbances down to Δf. thr Previously, power disturbances were assessed. However, when a large power disturbance occurred, Δf dropped to Δf within 0.1 s. thr The Romberg observer fails to converge to a steady state within a specified time, causing the MPC to coordinate the aggregation of energy storage and loads under erroneous power disturbances. In contrast, the sliding mode observer's estimated power disturbances still keep pace with the actual values ​​in a timely manner. Clearly, the faster convergence and better dynamic performance of the sliding mode observer applied in this application are particularly suitable for high-power disturbance scenarios.

[0085] Thus, through the above embodiments, this application can observe system power interference by constructing a power disturbance observer, thus compensating for the deficiency that direct measurement is impossible due to severe communication degradation under adverse weather conditions.

[0086] The ability of MPC to predict the evolution trend of the next time frequency under different control inputs is utilized to form a control trajectory to guide the coordination of the aggregated energy storage system and the load. Furthermore, considering the continuous discrete control problem caused by the different control steps of the two, the target equivalent frequency model provided in this application is equipped with a two-layer model predictive control unit. The two-layer model predictive control unit is constructed by defining the second cost function of the upper-layer model predictive control and the second cost function of the lower-layer model predictive control, as well as configuring the first constraint condition of the upper-layer model predictive control and the second constraint condition of the lower-layer model predictive control.

[0087] In another exemplary embodiment provided in this application, the specific steps for defining the first cost function of the upper-level model predictive control may include:

[0088] Obtain the first correspondence between the prediction frequency deviation of the upper-level model predictive control and the discharge power and load response of aggregated energy storage;

[0089] The first cost function for predictive control of the upper-level model is defined based on the first correspondence.

[0090] In this embodiment, by sampling time t up For discrete-time systems, the equations of the power perturbation observer in the above embodiments can be reformulated in state-space form, resulting in:

[0091]

[0092] In the formula: Matrix A up B up ,R up Specifically:

[0093]

[0094] MPC optimizes the control window (N) c The control trajectory under (N) ensures the prediction window (N) p The predicted system state under condition N is close to the setpoint. p The value must be greater than N c .

[0095] Then, the output from the aggregated energy storage and k-(k+N) c The load response under (k+1)-(k+N) conditions determines the (k+1)-(k+N)-(k+N)-(k+N)-(k+1 ... p The prediction frequency bias of the upper-level model predictive control under the given conditions can be expressed as:

[0096]

[0097] In the formula:

[0098]

[0099] The F matrix contains the predicted state set of the frequency deviation from the next sampling time to its prediction window, which is related to the frequency deviation of the current step, the control trajectory to the control window, and the power disturbance. When k=1,

[0100] The first correspondence between the prediction frequency deviation of the upper-level model predictive control and the discharge power and load response of aggregated energy storage is obtained through the above calculation formulas, and the first cost function J of the upper-level model predictive control is defined accordingly. up .

[0101]

[0102] Where R up It is a symmetric positive definite matrix with dimension N. c , used to determine the weights of the control inputs.

[0103] In addition, the specific steps for configuring the first constraint condition of the upper-level model predictive control may include:

[0104] Configure the power limit of aggregated energy storage, the power limit of the load, and the frequency deviation constraint of the upper-level model predictive control to form the first constraint condition;

[0105] The power limit for aggregated energy storage is as follows:

[0106]

[0107] In the formula, p ei.min and p ei.max These are the minimum and maximum output power values ​​of the i-th energy storage unit in the aggregated energy storage system, respectively.

[0108] The power limit for the load response is 0 ≤ ΔP l ≤ΔP lmax ΔP lmax It is the maximum load that can be removed;

[0109] Frequency deviation constraint is Δf min ≤F≤Δf thr , Δf min This is the minimum allowed frequency.

[0110] Thus, this application defines and constrains the upper-level model predictive control through the predictive characteristics of the upper-level model predictive control and the data relationship with the power disturbance observer through the above embodiments, so as to obtain the target upper-level model predictive control, so as to optimize the output and load response of the aggregated energy storage system when supplementing frequency control and achieve frequency stability.

[0111] In another exemplary embodiment provided in this application, the specific steps for defining the second cost function of the lower-level model predictive control may include:

[0112] Obtain the second correspondence between the state of charge and output power of each energy storage unit in the aggregated energy storage;

[0113] The second cost function for predictive control of the lower-level model is defined based on the second correspondence.

[0114] In this embodiment, the aggregated energy storage system output under step k, optimized by the upper-level model predictive control, is further input to the lower-level model predictive control to specifically allocate the output power of each energy storage unit, thereby achieving SoC balance. Each step of the upper-level model predictive control corresponds to N of the lower-level model predictive control. s This means that there are N steps. s Each energy storage output power in each step should be guided by the same aggregated energy storage system output. Unlike upper-level model predictive control, the step size of lower-level model predictive control is represented by l.

[0115] To design a lower-level model predictive control capable of balancing the power of the SoCs among energy storage systems, we first analyze the relationship between the SoC and the output power of each energy storage system, which can be expressed as:

[0116]

[0117] Under the control of the lower-level model predictive control, which is determined by the output power of each energy storage unit, the prediction discrepancy of the SoC is as follows:

[0118]

[0119] in

[0120]

[0121]

[0122] For the formula representing the relationship between the SoC and the output power of each energy storage, α low and β low The calculation method and the construction of the upper-level model for predictive control α up and β up same.

[0123] The second correspondence between the state of charge and output power of each energy storage unit in the aggregated energy storage is obtained through the above calculation formulas, and the second cost function J of the lower-level model predictive control is defined accordingly. low :

[0124]

[0125] In addition, the specific steps for configuring the second constraint condition for the lower-level model predictive control may include:

[0126] Configure the lower-level model predictive control of aggregated energy storage and the output power relationship between individual energy storage units in aggregated energy storage, the power limit of energy storage and the slope rate constraint of energy storage to form the first constraint condition;

[0127] Among them, the output power relationship between aggregated energy storage and the individual energy storage units within aggregated energy storage is as follows:

[0128] for have

[0129]

[0130] Energy storage power limit is

[0131] The slope constraint for energy storage is Δp is the rate of change of the output power of the i-th energy storage unit in the l-th step. ei.min and Δp ei.max It is a climbing speed limit.

[0132] Thus, this application, through the above embodiments, considers the relationship between lower-level model predictive control and upper-level model predictive control, as well as the characteristics of the lower-level control itself, to realize the definition of the target lower-level model predictive control, which is used to construct a two-level model predictive control unit to balance the SoCs with different energy storage when supplementing frequency control.

[0133] Therefore, a two-layer model predictive control unit is formed by defining the cost function and constraints, consisting of upper-layer and lower-layer model predictive control. This unit is then applied to the optimal allocation of load response and energy storage output during frequency regulation using a target equivalent frequency model. (See also...) Figure 4 , Figure 4 This is a control block diagram of a two-layer model prediction control unit in an exemplary embodiment of this application. For example... Figure 4 As shown, the discrete frequency deviation Δf obtained from traditional SFC sampling k and the observed The input is fed into the upper-level model predictive control unit of the two-layer model predictive control system to optimize the output of the aggregated energy storage system and the k-th order load response, taking ΔP respectively. e k and ΔP l k In order to achieve frequency stability. This value is further input into the lower-level model predictive control and used as a reference value, controlled by the step size t. low In KT up up to (k+1)t upThe output power of energy storage is allocated within a certain time period, aiming to balance the respective SoCs of different energy storage systems.

[0134] In an exemplary embodiment provided in this application, the specific steps for correcting control errors in the control trajectory to obtain the final control trajectory may include:

[0135] The objective function is obtained by correcting the load shedding dimensions.

[0136] The aggregated energy storage and the output power relationship between the individual energy storage units in the aggregated energy storage are transformed into a form that combines the penalty function with the constraint, resulting in the transformed second constraint.

[0137] The control trajectory is updated based on the objective function and the transformed second constraint to obtain the final control trajectory.

[0138] For the load, since the load cannot be cut off in any size, it must be the minimum cut-off load ΔP. l min The load cannot operate exactly according to the control trajectory because it is an integer multiple of u, making it difficult to adjust by setting u. up The relevant linear constraints are used to handle this constraint. Therefore, in this embodiment, the control error correction for the load is implemented as a load shedding size correction:

[0139]

[0140] For aggregated energy storage systems, inconsistencies in the control step sizes of the upper and lower layer model predictive control will lead to constraint conflicts. Therefore, in this embodiment, the constraint condition of aggregated energy storage and the output power relationship between individual energy storage units in the aggregated energy storage is transformed into a form combining a penalty function and constraints to ensure that the sum of the output power of the energy storage units can approximate the specified output power of the aggregated energy storage, expressed as:

[0141]

[0142] The output power of each energy storage unit, obtained by solving the objective function with corrected load shedding dimensions and the corrected constraints, is considered as... Therefore, for Actual output of aggregated energy storage for The content represented, not ΔP e k .

[0143] Thus, through the above embodiments, this application corrects the load shedding size and constraint conditions for the errors caused by the mismatch between the load and aggregated energy storage and the control trajectory by controlling the error correction method, thereby obtaining a more suitable final control trajectory, achieving a more active frequency regulation effect, and realizing the safe operation of the power system with high renewable energy penetration.

[0144] In an exemplary embodiment provided in this application, a certain preset frequency change threshold is set to prevent the generator from repeatedly operating due to very small power disturbances. The specific steps for starting supplementary frequency control may include:

[0145] Detect the relationship between real-time frequency changes and preset frequency change thresholds;

[0146] When the magnitude relationship characterizes the real-time frequency change to the frequency change threshold, the observed value is obtained through the power perturbation observer using the target equivalent frequency model.

[0147] In this embodiment, a certain frequency change threshold Δf is set. thr To prevent the generator from repeatedly operating due to very small power disturbances. When Δf drops to Δf thr This means that power disturbances cannot be ignored. The observed power disturbances and the current frequency are input into the two-layer model prediction control unit to assess the future frequency evolution trajectory, thereby guiding the coordination of aggregated energy storage and load response.

[0148] like Figure 5 As shown, Figure 5 This embodiment describes the control effect of the target equivalent frequency model under different power disturbances in an exemplary embodiment of this application. In this embodiment, to verify the superiority of the proposed strategy, the following two SFCs will be simulated and compared simultaneously: 1) an SFC based on a continuous low-frequency load shedding (UFLS) scheme; 2) a traditional SFC incorporating aggregated energy storage and UFLS droplet control. Figure 5 It can be seen that when an 8MW power surge occurs within 10 seconds, the system frequency begins to decrease. In this case, ΔP lmax and Δf min The values ​​are calculated to be 2.4 MW and -2 Hz, respectively. The two-layer model predictive control unit (MPC) predicts the subsequent frequency evolution trend based on the power disturbance observed by the power disturbance observer. Once Δf decreases to Δf... thr ΔP lmax This will cause it to decrease. Therefore, the lowest frequency point is controlled at -1.73Hz, which is higher than Δf. min In contrast, due to the lag in load response, the compared continuous UFLS and conventional SFC failed to keep the lowest frequency point within the allowable range (-2Hz), which caused the aggregated energy storage system and generator to shut down.

[0149] Please see Figure 6 and Figure 7 , Figure 6 This is a schematic diagram comparing the frequency evolution between the target equivalent frequency model provided in this application and different SFCs; Figure 7 This is a schematic diagram comparing the output power of the target equivalent frequency model provided in this application with different SFCs. Figure 6 and Figure 7 This paper presents a comparison of the target equivalent frequency model provided in this application with the frequency evolution and output power of each cell of the compared SFC. Figure 7 (a) Figure 7 (b) Figure 7 (c) Figure 7 (d) This section presents schematic diagrams comparing the target equivalent frequency model provided in this application with the load response, generator output power, aggregated energy storage output power, and output power of each energy storage unit (SFC). Because the target equivalent frequency model provided in this application includes a two-layer model prediction control unit, therefore... Figure 6 and Figure 7 The target equivalent frequency model provided in this application is represented by a two-layer MPC.

[0150] Depend on Figure 6 and Figure 7 It is known that all equivalent frequency models can restore the frequency deviation to an acceptable value after an interference occurs. Since the target equivalent frequency model provided in this application can predict the impact of different control inputs on the frequency deviation, the aggregated energy storage output and load response are reasonably controlled. The 3MW load drops rapidly during the frequency reduction phase, while the aggregated energy storage outputs its maximum power. Therefore, the lowest frequency point is controlled at the optimal value of -0.86Hz. During the FRS (frequency recovery stage), the 1.5MW load reduction is gradually reintroduced to improve the system's self-recovery capability, while the aggregated energy storage discharge power is reduced by 0.1MW to allocate internal energy storage output to balance the SoC, such as... Figure 7 As shown in (d).

[0151] In contrast, the frequency regulation performance of the conventional SFC is significantly inferior to that of the dual-layer MPC because the conventional SFC fails to coordinate load response with other frequency regulation units. Specifically, the conventional SFC exceeds the load, resulting in wasted generator spinning reserve capacity and the generator outputting only 1.7MW of power. For the SFC based on continuous UFLS, it can effectively avoid overshoot, but it does not consider the generator power limitation due to the high inertia time constant, which leads to the most severe frequency deviation, with the lowest frequency point at -1.09Hz. Furthermore, Figure 6 and Figure 7The two strategies shown, other than the target equivalent frequency model provided in this application, fail to coordinate load response and aggregated energy storage output as well as the target equivalent frequency model, which may result in aggregated energy storage not being able to fully and persistently participate in frequency regulation.

[0152] Table 2 below shows the frequency regulation effect of three SFCs within one hour. After this time, additional power support is usually provided by mobile energy storage, etc. The SFCs compared do not consider SoC balancing. Therefore, some distributed energy storage may prematurely exit operation during the long-term frequency regulation process, resulting in a secondary frequency drop. In contrast, the target equivalent frequency model based on a two-layer model predictive control unit provided in this application avoids this situation and ensures the frequency regulation effect.

[0153] Table 2:

[0154] Control strategy Frequency second decrease number of times The lowest frequency point of the second decrease (Hz) Traditional SFC 2 -0.69 / -0.81 Improved SFC 2 -0.74 / -0.86 SFC under dual-layer MPC 0 1

[0155] Figure 8 This is a block diagram illustrating a supplementary frequency control device 800 for aggregated energy storage and load response, as shown in an exemplary embodiment of this application. Figure 8 As shown, the device includes:

[0156] Building unit 801 is used to construct a model framework for supplementary frequency control based on the equivalent frequency model of a power system with high renewable energy penetration.

[0157] Model definition unit 802 is used to define a power perturbation observer and a two-layer model prediction control unit within the model framework to obtain the target equivalent frequency model;

[0158] The observation unit 803 is used to obtain observation values ​​through a power perturbation observer using the target equivalent frequency model.

[0159] Processing unit 804 is used to sample discrete frequency deviations, input discrete frequency deviations and observations into the two-layer model prediction control unit, and obtain control trajectories for aggregated energy storage system and load;

[0160] The correction unit 805 is used to correct the control error of the control trajectory to obtain the final control trajectory, so as to regulate the actual output and load response of the aggregated energy storage based on the final control trajectory.

[0161] This device applies the supplementary frequency control method for aggregated energy storage and load response provided in this application. A model framework for supplementary frequency control is constructed by a construction unit 801 based on an equivalent frequency model of a power system with high renewable energy penetration. A model definition unit 802 defines a power disturbance observer and a two-layer model predictive control unit within this framework, thus constructing a target equivalent frequency model for frequency regulation of the load and aggregated energy storage. During supplementary frequency control, the target equivalent frequency model is used. The observation unit 803 obtains observations through the power disturbance observer. The processing unit 804 samples discrete frequency deviations and inputs the discrete frequency deviations and observations into the two-layer model predictive control unit to obtain the control trajectory for the aggregated energy storage system and the load. The correction unit 805 corrects the control trajectory for control errors, resulting in the final control trajectory, which is then used to regulate the actual output of the aggregated energy storage and the load response. Thus, this application can rationally allocate load response and energy storage output during frequency regulation, taking into account load response characteristics and aggregated energy storage systems. This can effectively solve the frequency security problem faced by low-inertia power grids under islanding events, achieve a more proactive frequency regulation effect, obtain control strategies that can cope with various power disturbances, and realize the safe operation of power systems with high renewable energy penetration.

[0162] In another exemplary embodiment, the model definition unit 802 is further configured to, within the model framework, define a power disturbance observer based on the state equation of a power system with high renewable energy penetration, wherein the power disturbance observer is suitable for high-power disturbances; define a first cost function for the upper-level model predictive control of the two-layer model predictive control unit; configure a first constraint condition for the upper-level model predictive control; define the upper-level model predictive control based on the first cost function and the first constraint condition to obtain a target upper-level model predictive control; define a second cost function for the lower-level model predictive control of the two-layer model predictive control unit; configure a second constraint condition for the lower-level model predictive control; define the lower-level model predictive control based on the second cost function and the second constraint condition to obtain a target lower-level model predictive control; and construct a target equivalent frequency model based on the power disturbance observer, the target upper-level model predictive control, and the target lower-level model predictive control.

[0163] In another exemplary embodiment, the model definition unit 802 is further configured to derive a state-space model based on the state equations of a power system with high renewable energy penetration:

[0164]

[0165] y = Cx

[0166] In the formula, the state variable x = [Δf ΔP] e ] T The system disturbance d = ΔP dThe system output y = Δf, where Δf is the system frequency change. The coefficient matrices A, B, C, and R are represented as follows:

[0167]

[0168] R = [-1 / 2H 0] T C =

[10] T

[0169] In the formula, R e and τ e N represents the time constant and descent coefficient of energy storage within aggregated energy storage. e It refers to the amount of energy stored in aggregated energy storage;

[0170] The power disturbance observer is defined using a state-space model, resulting in the target power disturbance observer:

[0171]

[0172] In the formula, L = [l1 l2] T The observation gain matrix, These are the observed values ​​of x, d, and y, respectively.

[0173] In another exemplary embodiment, the model definition unit 802 is further configured to obtain a first correspondence between the prediction frequency deviation of the upper-level model predictive control and the discharge power and load response of aggregated energy storage.

[0174] The first cost function for predictive control of the upper-level model is defined based on the first correspondence.

[0175] In another exemplary embodiment, the model definition unit 802 is further configured to configure the power limit of aggregated energy storage, the power limit of the load, and the frequency deviation constraint of the upper-layer model predictive control to form a first constraint condition.

[0176] The power limit for aggregated energy storage is as follows:

[0177]

[0178] In the formula, p ei.min and p ei.max These are the minimum and maximum output power values ​​of the i-th energy storage unit in the aggregated energy storage system, respectively.

[0179] The power limit for the load response is 0 ≤ ΔP l ≤ΔP lmax ΔP lmax It is the maximum load that can be removed;

[0180] Frequency deviation constraint is Δf min ≤F≤Δf thr , Δfmin This is the minimum allowed frequency.

[0181] In another exemplary embodiment, the model definition unit 802 is further configured to obtain a second correspondence between the state of charge and output power of each energy storage in the aggregated energy storage; and define a second cost function for the lower-level model predictive control based on the second correspondence.

[0182] In another exemplary embodiment, the model definition unit 802 is further configured to configure the aggregated energy storage of the lower-level model predictive control and the output power relationship between the individual energy storages in the aggregated energy storage, the power limit of the energy storage and the slope constraint of the energy storage, to form a first constraint condition.

[0183] Among them, the output power relationship between aggregated energy storage and the individual energy storage units within aggregated energy storage is as follows:

[0184] for have

[0185]

[0186] Energy storage power limit is

[0187] The slope constraint for energy storage is Δp is the rate of change of the output power of the i-th energy storage unit in the l-th step. ei.min and Δp ei.max It is a climbing speed limit.

[0188] In another exemplary embodiment, the correction unit 805 is further configured to correct the load shedding size of the load to obtain the objective function; convert the aggregated energy storage and the output power relationship between each energy storage in the aggregated energy storage into a form combining the penalty function and the constraint to obtain the converted second constraint; and update the control trajectory based on the objective function and the converted second constraint to obtain the final control trajectory.

[0189] In another exemplary embodiment, the observation unit 803 is further configured to detect the magnitude relationship between the real-time frequency change and the preset frequency change threshold; when the magnitude relationship indicates that the real-time frequency change has decreased to the frequency change threshold, the observation value is obtained by the power disturbance observer using the target equivalent frequency model.

[0190] It should be noted that the supplementary frequency control device for aggregated energy storage and load response provided in the above embodiments and the supplementary frequency control method for aggregated energy storage and load response provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the supplementary frequency control device for aggregated energy storage and load response provided in the above embodiments can allocate the above functions to different functional modules as needed, that is, divide the internal structure of the device into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0191] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the supplementary frequency control method for aggregated energy storage and load response provided in the various embodiments above.

[0192] Figure 9 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 9 The computer system 900 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0193] like Figure 9 As shown, the computer system 900 includes a Central Processing Unit (CPU) 901, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on programs stored in Read-Only Memory (ROM) 902 or programs loaded from storage portion 908 into Random Access Memory (RAM) 903. The RAM 903 also stores various programs and data required for system operation. The CPU 901, ROM 902, and RAM 903 are interconnected via a bus 904. An Input / Output (I / O) interface 905 is also connected to the bus 904.

[0194] The following components are connected to I / O interface 905: an input section 906 including a keyboard, mouse, etc.; an output section 907 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to I / O interface 905 as needed. Removable media 911, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 910 as needed so that computer programs read from them can be installed into storage section 908 as needed.

[0195] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 909, and / or installed from removable medium 911. When the computer program is executed by central processing unit (CPU) 901, it performs various functions defined in the system of this application.

[0196] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0197] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0198] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0199] Another aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned supplementary frequency control method for aggregated energy storage and load response. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently without being assembled into the electronic device.

[0200] Another aspect of this application provides a computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the supplementary frequency control method for aggregated energy storage and load response provided in the various embodiments described above.

[0201] The above are merely preferred embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A supplementary frequency control method for aggregated energy storage and load response, characterized in that, The method includes: A model framework for supplementary frequency control is constructed based on the equivalent frequency model of a power system with high renewable energy penetration. Within the model framework, a power perturbation observer and a two-layer model prediction and control unit are defined to obtain the target equivalent frequency model, specifically: Within the model framework, a power disturbance observer is defined based on the state equations of the power system with high renewable energy penetration. This power disturbance observer is suitable for high-power disturbances. Define a first cost function for the upper-level model predictive control of the two-level model predictive control unit. Specifically, obtain a first correspondence between the prediction frequency deviation of the upper-level model predictive control and the discharge power of the aggregated energy storage and the load response; define the first cost function for the upper-level model predictive control based on the first correspondence. Configure the first constraint condition for the upper-level model predictive control. Specifically, configure the power limit of the aggregated energy storage, the power limit of the load, and the frequency deviation constraint of the upper-level model predictive control to form the first constraint condition; wherein, the power limit of the aggregated energy storage is: In the formula, p ei.min and p ei.max These are the minimum and maximum output power values ​​of the i-th energy storage unit in the aggregated energy storage, respectively; the power limit for the load response is 0 ≤ ΔP. l ≤ΔP lmax ΔP lmax It is the maximum load that can be cut off; the frequency deviation constraint is Δf. min ≤F≤Δf thr , Δf min The minimum allowed frequency; Based on the first cost function and the first constraint, the upper-level model predictive control is defined to obtain the target upper-level model predictive control. Define the second cost function of the lower-level model predictive control of the two-level model predictive control unit; Configure the second constraint condition for the predictive control of the lower-level model; The lower-level model predictive control is defined based on the second cost function and the second constraint to obtain the target lower-level model predictive control. The target equivalent frequency model is constructed based on the power disturbance observer, the target upper-level model predictive control, and the target lower-level model predictive control. The target equivalent frequency model is used to obtain the observed values ​​through the power perturbation observer. The discrete frequency deviation is sampled, and the discrete frequency deviation and the observed value are input into the two-layer model prediction and control unit to obtain the control trajectory for the aggregated energy storage system and the load. The control trajectory is corrected for control errors to obtain the final control trajectory, which is then used to regulate the actual output of the aggregated energy storage and the load response.

2. The method according to claim 1, characterized in that, The state equation definition of the power disturbance observer based on the high renewable energy penetration power system yields the target power disturbance observer, including: Based on the state equations of the power system with high renewable energy penetration, a state-space model is derived: y = Cx In the formula, the state variable x = [ΔfΔP] e ] T The system disturbance d = ΔP d The system output y = Δf, where Δf is the system frequency change. The coefficient matrices A, B, C, and R are represented as follows: R=[-1 / 2H 0] T C=[10] T In the formula, R e and τ e N represents the time constant and descent coefficient of energy storage within aggregated energy storage. e It refers to the amount of energy stored in aggregated energy storage; The power perturbation observer is defined using the state-space model to obtain the target power perturbation observer: ˙ In the formula, L = [l1 l2] T The observation gain matrix, These are the observed values ​​of x, d, and y, respectively.

3. The method according to claim 1, characterized in that, The definition of the second cost function for the predictive control of the lower-level model includes: Obtain the second correspondence between the state of charge and output power of each energy storage unit in the aggregated energy storage; The second cost function for predictive control of the lower-level model is defined based on the second correspondence.

4. The method according to claim 3, characterized in that, Configure the second constraint condition for the lower-level model predictive control, including: Configure the lower-level model predictive control of aggregated energy storage and the output power relationship between each energy storage in aggregated energy storage, the power limit of energy storage and the slope rate constraint of energy storage to form the first constraint condition; Among them, the output power relationship between aggregated energy storage and the individual energy storage units within aggregated energy storage is as follows: for have Energy storage power limit is The slope constraint for energy storage is Δp is the rate of change of the output power of the i-th energy storage unit in the l-th step. ei.min and Δp ei.max It is a climbing speed limit.

5. The method according to claim 4, characterized in that, The step of correcting the control trajectory for control errors to obtain the final control trajectory includes: The load shedding dimensions of the load are corrected to obtain the objective function; The aggregated energy storage and the output power relationship between each energy storage in the aggregated energy storage are converted into a form that combines a penalty function with constraints, resulting in the converted second constraint. The control trajectory is updated based on the objective function and the transformed second constraint to obtain the final control trajectory.

6. The method according to any one of claims 1 to 5, characterized in that, The step of obtaining observations using the target equivalent frequency model and the power perturbation observer includes: Detect the relationship between real-time frequency changes and preset frequency change thresholds; When the real-time frequency change, characterized by the magnitude relationship, decreases to the frequency change threshold, the observed value is obtained by using the target equivalent frequency model through the power disturbance observer.

7. A supplementary frequency control device for aggregated energy storage and load response, characterized in that, include: The building block is used to construct a model framework for supplementary frequency control based on the equivalent frequency model of a power system with high renewable energy penetration. The model definition unit is used to define the power perturbation observer and the two-layer model prediction and control unit within the model framework to obtain the target equivalent frequency model. Specifically: Within the model framework, a power disturbance observer is defined based on the state equations of the power system with high renewable energy penetration. This power disturbance observer is suitable for high-power disturbances. Define a first cost function for the upper-level model predictive control of the two-level model predictive control unit. Specifically, obtain a first correspondence between the prediction frequency deviation of the upper-level model predictive control and the discharge power of the aggregated energy storage and the load response; define the first cost function for the upper-level model predictive control based on the first correspondence. Configure the first constraint condition for the upper-level model predictive control. Specifically, configure the power limit of the aggregated energy storage, the power limit of the load, and the frequency deviation constraint of the upper-level model predictive control to form the first constraint condition; wherein, the power limit of the aggregated energy storage is: In the formula, p ei.min and p ei.max These are the minimum and maximum output power values ​​of the i-th energy storage unit in the aggregated energy storage, respectively; the power limit for the load response is 0 ≤ ΔP. l ≤ΔP lmax ΔP lmax It is the maximum load that can be cut off; the frequency deviation constraint is Δf. min ≤F≤Δf thr , Δf min The minimum allowed frequency; Based on the first cost function and the first constraint, the upper-level model predictive control is defined to obtain the target upper-level model predictive control. Define the second cost function of the lower-level model predictive control of the two-level model predictive control unit; Configure the second constraint condition for the predictive control of the lower-level model; The lower-level model predictive control is defined based on the second cost function and the second constraint to obtain the target lower-level model predictive control. The target equivalent frequency model is constructed based on the power disturbance observer, the target upper-level model predictive control, and the target lower-level model predictive control. An observation unit is used to obtain observation values ​​through the power perturbation observer using the target equivalent frequency model. The processing unit is used to sample discrete frequency deviations and input the discrete frequency deviations and the observed values ​​into the two-layer model prediction and control unit to obtain the control trajectory for the aggregated energy storage system and the load. The correction unit is used to correct the control error of the control trajectory to obtain the final control trajectory, so as to regulate the actual output of the aggregated energy storage and the load response based on the final control trajectory.

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