Supplementary frequency control method and device for aggregating energy storage and load response

Abstract

The invention discloses a supplementary frequency control method and device for aggregating energy storage and load response. The method comprises the following steps: constructing a model framework of supplementary frequency control based on an equivalent frequency model of a high renewable energy permeability power system; defining a power disturbance observer and a double-layer model prediction control unit in the model framework to obtain a target equivalent frequency model; obtaining an observation value through a power disturbance observer; inputting the sampled discrete frequency deviation and observation value into a double-layer model prediction control unit to obtain a control track for the aggregation energy storage system and the load; and performing control error correction on the control track to obtain a final control track so as to regulate and control actual output and load response of aggregation energy storage. According to the method, the target equivalent frequency model comprising the power disturbance observer and the double-layer model prediction control unit is constructed, the load response and the energy storage output are reasonably distributed, and the frequency safety problem faced by a low-inertia power grid under the island event is effectively solved.

Description

Technical Field

[0001] The present application relates to the field of microgrid technology, and in particular to a method and device for supplementary frequency control that aggregates energy storage and load response. Background Art

[0002] In severe weather conditions, the output power of renewable energy will be severely limited, and physical damage may be caused to external transmission lines, which may lead to unexpected islanding of the power system and cause long-term severe internal power shortages. Under such power disturbances, low-inertia systems that rely only on some unreplaced synchronous generators for frequency regulation tend to experience more severe frequency deviations. Therefore, a series of SFC (Supplemental Frequency Control) methods are required for high-proportion renewable energy power systems under severe weather conditions.

[0003] Aggregate energy storage systems are one of the most common SFCs. Through the combination of multiple groups of distributed energy storage, aggregate energy storage can quickly provide a large amount of power to suppress frequency drops while meeting the plug-and-play requirements of internal energy storage. At present, the default combination of aggregate energy storage and 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 aggregating energy storage, SFC should also consider load response to prevent excessive frequency deviations. One of the most widely used methods is UFLS (under frequency load shedding), in which load shedding is divided into several stages and load shedding occurs when the frequency drops above a certain threshold. However, due to fixed parameters, UFLS can neither actively respond to power disturbances in the frequency range nor cooperate with other frequency regulation units.

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

[0006] In order to solve the above technical problems, the embodiments of the present application provide a method and device for supplementary frequency control by aggregating energy storage and load response.

[0007] In a first aspect, in order to solve the above technical problems, the present application provides a method for supplementing frequency control by integrating energy storage and load response, comprising:

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

[0009] A power disturbance observer and a double-layer model prediction control unit are defined within the model framework to obtain a target equivalent frequency model;

[0010] Using the target equivalent frequency model, obtaining an observation value through the power disturbance observer;

[0011] Sampling discrete frequency deviations, inputting the discrete frequency deviations and the observed values ​​into the double-layer model prediction control unit to obtain control trajectories for the aggregated energy storage system and the load;

[0012] The control trajectory is subjected to control error correction to obtain a 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.

[0013] The beneficial effects are:

[0014] In the technical solution provided in the embodiment of the present application, a model framework for supplementary frequency control is constructed based on an equivalent frequency model of a power system with high renewable energy penetration, and a power disturbance observer and a two-layer model prediction control unit are defined in the model framework to construct a target equivalent frequency model for frequency regulation of load and aggregated energy storage. During the supplementary frequency control, the target equivalent frequency model is used to obtain the observation value through the power disturbance observer; the discrete frequency deviation is sampled, and the discrete frequency deviation and the observation value are input into the two-layer model prediction control unit to obtain the control trajectory for the aggregated energy storage system and the load; the control error is corrected for 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. In this way, the present application can reasonably allocate the load response and energy storage output in the frequency regulation process under the conditions of the load response characteristics and the aggregated energy storage system, which can effectively solve the frequency security problem faced by the low inertia power grid under the islanding event, achieve a more active frequency regulation effect, and obtain a control strategy that can cope with various power disturbances, so as to achieve the safe operation of the power system 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] A building unit for constructing a model framework for supplementary frequency control based on an equivalent frequency model of a power system with high renewable energy penetration;

[0017] A model definition unit, used to define a power disturbance observer and a double-layer model prediction control unit within the model framework to obtain a target equivalent frequency model;

[0018] An observation unit, used for obtaining an observation value through the power disturbance observer by using the target equivalent frequency model;

[0019] A processing unit, used for sampling discrete frequency deviation, inputting the discrete frequency deviation and the observed value into the double-layer model prediction control unit, and obtaining a control trajectory for the aggregated energy storage system and the load;

[0020] A correction unit is used to perform control error correction on the control trajectory to obtain a 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 foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0023] Figure 1 is a flow chart of a complementary frequency control method for aggregated energy storage and load response shown in an exemplary embodiment of the present application;

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

[0025] Figure 3 It is a tracking effect diagram of a power disturbance observer under different power disturbance magnitudes in an exemplary embodiment provided by the present application;

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

[0027] Figure 5 is the control effect of the target equivalent frequency model under different power disturbances in an exemplary embodiment of the present application;

[0028] Figure 6 It is a comparative schematic diagram of the frequency evolution between the target equivalent frequency model provided by the present application and different SFCs;

[0029] Figure 7 It is a schematic diagram comparing the output power between the target equivalent frequency model provided by the present application and different SFCs;

[0030] Figure 8 is a block diagram of a supplementary frequency control device for aggregated energy storage and load response shown in an exemplary embodiment of the present application;

[0031] Fig. 9 It is a structural diagram of a computer system suitable for implementing an electronic device of an embodiment of the present application. DETAILED DESCRIPTION

[0032] Here, exemplary embodiments will be described in detail, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of devices and methods consistent with some aspects of the present application as detailed in the attached 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 may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0034] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0035] The term "multiple" as used in this application refers to two or more than two. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the related objects are in an "or" relationship.

[0036] By coordinating the load response and aggregated energy storage output in SFC, it is expected that the system will achieve a more active frequency regulation effect in severe weather. However, the cooperation between the two brings the following challenges. The non-global observability of the power system in severe weather will lead to the inability to implement the coordination strategy in a timely manner; in order to more effectively regulate the frequency, the load response and aggregated energy storage output should be adjusted quickly according to the power changes in the system, which means that accurate power disturbances need to be obtained to guide the operation of both; another challenge that needs to be solved is that coordination between multiple units with inconsistent control steps is difficult to apply.

[0037] In order to solve the above problems, the embodiments of the present application propose a method and device for supplementary frequency control of aggregated energy storage and load response, which mainly involve the supplementary frequency control technology of aggregated energy storage and load response based on double-layer model predictive control included in microgrid technology. These embodiments will be described in detail below.

[0038] First see Figure 1 , Figure 1 This is a flow chart of a method for supplementing frequency control of aggregated energy storage and load response shown in an exemplary embodiment of the present application. The method can be specifically executed by a server, which can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms, etc., and is not limited here.

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

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

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

[0042]

[0043] Where: H is the system inertia, D is the load damping coefficient; ΔP g , ΔP e , ΔP l are the power changes of the generator, aggregated energy storage and load respectively; ΔP d is the system power disturbance; Δf is the system frequency change.

[0044] From the above formula, we can know that the frequency evolution trend can be evaluated according to the different inputs of the aggregated energy storage system and the load, so as to guide the coordination between the two to improve the frequency regulation effect. It should be noted that in addition to the above two control inputs, it is also necessary to obtain ΔP d and ΔP g . Since communications are severely degraded in bad weather, it is not possible to directly measure ΔP d and ΔP gTherefore, this embodiment constructs a model framework for supplementary frequency control based on the equivalent frequency model of the power system with high renewable energy penetration, and constructs a target equivalent frequency model based on the model framework. The system power interference and frequency change are obtained through the target equivalent frequency model, so as to achieve a reasonable allocation of load response and energy storage output during the frequency regulation process.

[0045] Step S102, defining a power disturbance observer and a double-layer model prediction control unit within the model framework to obtain a target equivalent frequency model.

[0046] See also Figure 2 , Figure 2 is a schematic diagram of a target equivalent frequency model in an exemplary embodiment of the present application. Figure 2 As shown, based on the traditional SFC, the target equivalent frequency model of this embodiment is additionally provided with SMO (Sliding Mode Observer) and Dual-layers MPC (Model Predictive Control) for rapid convergence and evaluation of power disturbances, which can effectively coordinate the operation of the aggregated energy storage system and the load response in frequency regulation. Therefore, in this embodiment, when constructing the target equivalent frequency model, it is necessary to define the power disturbance observer and the dual-layer model prediction control unit within the model framework.

[0047] Step S103: using the target equivalent frequency model, obtaining an observation value through a power disturbance observer.

[0048] Step S104, sampling discrete frequency deviation, inputting the discrete frequency deviation and the observed value into the double-layer model prediction control unit, and obtaining the control trajectory for the aggregated energy storage system and the load.

[0049] After the target equivalent frequency model is constructed, 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 the observation value, and then the observation value and the discrete frequency deviation sampled from the traditional SFC structure part of the target equivalent frequency model are input into the double-layer model prediction control unit to obtain the control trajectory for the aggregated energy storage system and the load. In this way, the MPC can predict the evolution trend of the frequency at the next time under different control inputs, so as to form a control trajectory to guide the coordination of the aggregated energy storage system and the load, and considering the continuous discrete control problem caused by the different control steps of the two, the discrete frequency deviation is simultaneously input into the double-layer model prediction control unit to form a more reasonable control trajectory.

[0050] Step S105 , performing control error correction on the control trajectory to obtain a final control trajectory, so as to regulate 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 the aggregated energy storage and the control trajectory is corrected by controlling the error correction method, thereby obtaining the final control trajectory.

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

[0053] On the other hand, when supplementing frequency control, the target equivalent frequency model is used to obtain observation values ​​through the power disturbance observer; discrete frequency deviations are sampled, and the discrete frequency deviations and observation values ​​are input into the double-layer model prediction control unit to obtain the control trajectory for the aggregated energy storage system and the load; the control error is corrected for 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. In this way, the present application can reasonably allocate load response and energy storage output during the frequency regulation process under the conditions of load response characteristics and aggregated energy storage systems, which can effectively solve the frequency security problems faced by low-inertia power grids under islanding events, achieve more active frequency regulation effects, and obtain control strategies that can cope with various power disturbances, so as to achieve 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 may be as shown in Table 1 below.

[0055] Table 1:

[0056]

[0057]

[0058] In an exemplary embodiment provided by the present application, the target equivalent frequency model used is provided with a power disturbance observer and a two-layer model prediction control unit compared with the traditional SFC, and the two-layer model prediction control unit includes an upper model prediction control and a lower model prediction control. Therefore, the specific steps of constructing the target equivalent frequency model may include:

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

[0060] defining a first cost function of an upper-layer model predictive control of a two-layer model predictive control unit;

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

[0062] Define an upper-level model predictive control based on a first cost function and a first constraint condition to obtain a target upper-level model predictive control;

[0063] defining a second cost function of a lower-layer model predictive control of the two-layer model predictive control unit;

[0064] configuring the second constraint of the lower-level model predictive control;

[0065] defining a lower-level model predictive control based on a second cost function and a second constraint condition to obtain a target lower-level model predictive control;

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

[0067] In another exemplary embodiment provided by the present application, a power disturbance observer is defined within a model framework, and specific steps of obtaining a target power disturbance observer may include:

[0068] Based on the state equation of the power system with high renewable energy penetration, the state space model is derived:

[0069]

[0070] y=Cx

[0071] In the formula, the state variable x=[Δf ΔP e ] T , system disturbance d = ΔP d , the system output y = Δf, Δf is the system frequency change, and the coefficient matrix A, B, C, R is expressed as:

[0072]

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

[0074] In the formula, R e and τ e is the time constant and drop coefficient of energy storage in the aggregate energy storage, N e is the amount of energy stored in the aggregated energy storage;

[0075] The power disturbance observer is defined using the state space model to obtain the target power disturbance observer:

[0076]

[0077] Where, L = [l1 l2] T is the observation gain matrix, are the observed values ​​of x, d, and y respectively.

[0078] In this embodiment, after the state space model is derived based on the state equation of the power system with high renewable energy penetration, the corresponding power disturbance observer can be designed to quickly and accurately evaluate ΔP by combining the state space model and the common variable Δf. d , the power disturbance observer can be designed as

[0079]

[0080] The designed power disturbance observer is compared and x to provide an estimate of the system state.

[0081] in

[0082] like Figure 3 As shown, Figure 3 It is a tracking effect diagram of a power disturbance observer under different power disturbance magnitudes in an exemplary embodiment provided by the present application. In this embodiment, the power disturbance observer is a sliding mode observer SMO. Figure 3 The tracking effect of the power disturbance observer under different power disturbance sizes is shown, and it is compared with the Romberg observer with the same parameters. The two different power disturbance sizes are 1.5MW (indicated by the dotted line) and 3.5MW (indicated by the solid line). For the former, both observers can reduce Δf to Δf thr However, when a huge power disturbance occurs, causing Δf to drop to Δf within 0.1s. thr , the Romberg observer cannot converge to a steady state within the specified time, which causes the MPC to coordinate the aggregated energy storage and load under the wrong power disturbance. In contrast, the power disturbance estimated by the sliding mode observer can still track the actual value in time. Obviously, the faster convergence and better dynamic performance of the sliding mode observer applied in this application are particularly suitable for high power disturbance scenarios.

[0083] In this way, the present application can observe the system power disturbance through the constructed power disturbance observer through the above-mentioned embodiments, thereby making up for the defect that direct measurement cannot be performed due to severe deterioration of communication in bad weather.

[0084] MPC can be used to predict the characteristics of the evolution trend of the next time frequency under different control inputs, thereby forming a control trajectory to guide the coordination of the aggregated energy storage system and the load. Further taking into account the continuous discrete control problem caused by the different control steps of the two, the target equivalent frequency model provided in this application is provided with a double-layer model prediction control unit. By defining the first cost function of the upper-layer model prediction control and the second cost function of the lower-layer model prediction control, and configuring the first constraint condition of the upper-layer model prediction control and the second constraint condition of the lower-layer model prediction control, the construction of the double-layer model prediction control unit is completed.

[0085] In another exemplary embodiment provided by the present application, the specific steps of defining the first cost function of the upper-level model predictive control may include:

[0086] Obtaining a first correspondence between a predicted frequency deviation of an upper-layer model predictive control and a discharge power and load response of an aggregated energy storage;

[0087] A first cost function of the upper-level model predictive control is defined based on the first correspondence.

[0088] In this embodiment, by sampling at the time t up For a discrete continuous-time system, the equation of the power disturbance observer in the above embodiment can be reformulated into a state space form, and we can obtain

[0089]

[0090] Where: Matrix A up ,B up ,R up Specifically:

[0091]

[0092] MPC optimizes the control window (N c ) under the control trajectory, ensuring the prediction window (N p ) is close to the set value. p The value of must be greater than Nc.

[0093] Then, the aggregated energy storage output and k-(k+N c ) is determined by the load response under (k+1)-(k+N p ) can be expressed as:

[0094]

[0095] Where:

[0096]

[0097] The F matrix contains the set of predicted states of the frequency deviation from the next sampling moment 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,

[0098] The first correspondence between the predicted frequency deviation of the upper-level model predictive control and the discharge power and load response of the 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 based on this. up .

[0099]

[0100] Where R up is a symmetric positive definite matrix with dimension equal to N c , which is used to determine the weight of the control input.

[0101] In addition, the specific steps of configuring the first constraint condition of the upper-level model predictive control may include:

[0102] The power limit of the aggregated energy storage, the power limit of the load and the frequency deviation constraint of the upper-layer model predictive control are configured to form the first constraint condition;

[0103] Among them, the power limit of aggregate energy storage is:

[0104]

[0105] In the formula, p ei.min and p ei.max are the minimum and maximum output power of the i-th energy storage in the aggregated energy storage, respectively;

[0106] The power limit of the load response is 0≤ΔP l ≤ΔP lmax , ΔP lmax is the maximum load that can be removed;

[0107] The frequency deviation is constrained to Δf min ≤F≤Δf thr , Δf min is the minimum frequency allowed.

[0108] In this way, the present 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-mentioned embodiments, and obtains the target upper-level model predictive control to optimize the output and load response of the aggregated energy storage system when supplementing frequency control to achieve frequency stability.

[0109] In another exemplary embodiment provided by the present application, the specific steps of defining the second cost function of the lower-level model predictive control may include:

[0110] Obtaining a second corresponding relationship between the state of charge and output power of each energy storage in the aggregated energy storage;

[0111] A second cost function of the lower-level model predictive control is defined based on the first corresponding relationship.

[0112] In this embodiment, the aggregated energy storage system output under step k optimized by the upper-layer model predictive control is further input to the lower-layer model predictive control to specifically allocate the output power of each energy storage, thereby achieving SoC balance. Each step of the upper-layer model predictive control corresponds to N steps of the lower-layer model predictive control. s steps, which means that N s Each energy storage output power in each step should be guided by the same aggregated energy storage system output. Unlike the upper-level model predictive control, the step size of the lower-level model predictive control is represented by l.

[0113] In order to design the lower-level model predictive control that can balance the SoC between energy storages, we first analyze the relationship between the SoC and the output power of each energy storage, which can be expressed as:

[0114]

[0115] Under the control of the lower-level model predictive control determined by the output power of each energy storage, the predicted difference of SoC is:

[0116]

[0117] in

[0118]

[0119]

[0120] For the calculation formula that represents the relationship between SoC and the output power of each energy storage, α low and β low , calculation method and construction of upper-level model predictive control α up and β up same.

[0121] The second corresponding relationship between the state of charge and output power of each energy storage in the aggregated energy storage is obtained through the above calculation formulas, and the second cost function J of the lower layer model predictive control is defined low :

[0122]

[0123] In addition, the specific steps of configuring the second constraint condition of the lower-level model predictive control may include:

[0124] The output power relationship between the aggregated energy storage and the individual energy storages in the aggregated energy storage, the power limit of the energy storage and the ramp rate constraint of the energy storage are configured to form a first constraint condition;

[0125] Among them, the output power relationship between the aggregated energy storage and the individual energy storage in the aggregated energy storage is:

[0126] for have

[0127]

[0128] The power limit of energy storage is

[0129] The ramp rate constraint of energy storage is is the rate of change of the output power of the ith energy storage at step l, Δp ei.min and Δp ei.max is the ramp rate limit.

[0130] In this way, the present application considers the relationship between the lower-layer model predictive control and the upper-layer model predictive control and the characteristics of the lower layer itself through the above-mentioned embodiments to realize the definition of the target lower-layer model predictive control, which is used to construct a two-layer model predictive control unit to balance the SoC of different energy storages when supplementing frequency control.

[0131] Therefore, the upper-layer model predictive control and the lower-layer model predictive control after defining the cost function and the constraint conditions form a double-layer model predictive control unit, which is applied to the reasonable allocation of load response and energy storage output in the frequency regulation process through the target equivalent frequency model. Figure 4 , Figure 4 is a control block diagram of a double-layer model prediction control unit in an exemplary embodiment of the present application. Figure 4 As shown, the discrete frequency deviation Δf obtained from traditional SFC sampling k and the observed is input into the upper model predictive control unit of the double-layer model predictive control, which optimizes the output of the aggregated energy storage system and the k-order load response, respectively. and To achieve frequency stability. It is further input into the lower-level model predictive control and used as a reference value through the control step length t low In kt up to (k+1)t up The output power of energy storage is distributed within a certain time period, aiming to balance the SoC of different energy storages.

[0132] In an exemplary embodiment provided by the present application, the specific steps of performing control error correction on the control trajectory to obtain the final control trajectory may include:

[0133] The load shedding size of the load is corrected to obtain the objective function;

[0134] The output power relationship between the aggregated energy storage and the individual energy storages in the aggregated energy storage in the second constraint condition is converted into a form combining a penalty function and a constraint to obtain a converted second constraint condition;

[0135] The control trajectory is updated based on the objective function and the converted second constraint to obtain the final control trajectory.

[0136] For the load, since the load cannot be cut at any size, it must be the minimum cuttable load ΔP l min The load cannot run completely according to the control trajectory, and it is difficult to set the load up The relevant linear constraints are used to handle this constraint. Therefore, in this embodiment, the control error correction for the load is the load rejection size correction:

[0137]

[0138] For the aggregated energy storage system, the inconsistency of the control step lengths of the upper and lower model predictive control will lead to constraint conflicts. Therefore, in this embodiment, the constraint condition of the output power relationship between the aggregated energy storage and each energy storage in the aggregated energy storage in the second constraint condition is converted into a form combining a penalty function and a constraint to ensure that the sum of the output power of the energy storage can be close to the specified output power of the aggregated energy storage, which is expressed as:

[0139]

[0140] The output power of each energy storage obtained by solving the objective function obtained by correcting the load rejection size and the corrected constraints is regarded as Therefore, for Actual output of aggregated energy storage for The content represented, rather than

[0141] In this way, the present application uses the control error correction method in the above-mentioned embodiments to perform load shedding size correction and constraint condition conversion on the errors caused by the mismatch between the load and the aggregated energy storage and the control trajectory, 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 a high renewable energy penetration rate.

[0142] In an exemplary embodiment provided by the present application, a certain preset frequency change threshold is set to prevent the generator from repeatedly running due to very small power disturbances. The corresponding specific steps of starting to implement supplementary frequency control may include:

[0143] Detecting the magnitude relationship between the real-time frequency change and a preset frequency change threshold;

[0144] When the magnitude relationship characterizes that the real-time frequency change is reduced to a frequency change threshold, an observation value is obtained by using a target equivalent frequency model through a power disturbance observer.

[0145] In this embodiment, a certain frequency change threshold Δf is set. thr This prevents the generator from running repeatedly due to very small power disturbances. thr This means that the power disturbance cannot be ignored. The observed power disturbance and the frequency at the current moment are input into the double-layer model prediction control unit to evaluate the future frequency evolution trajectory, thereby guiding the coordination of aggregated energy storage and load response.

[0146] like Figure 5 As shown, Figure 5 It is the control effect of the target equivalent frequency model under different power disturbances in an exemplary embodiment of the present application. In this embodiment, in order to verify the superiority of the proposed strategy, the following two SFCs are simulated and compared: 1) SFC based on the continuous low-frequency load shedding (UFLS) scheme; 2) Traditional SFC including aggregated energy storage and droplet control of UFLS. Figure 5 It can be seen that when the power mutation of 8MW occurs in 10 seconds, the frequency of the system begins to decrease. In this case, ΔP lmax and Δf min The two-layer model prediction control unit, i.e., the two-layer MPC, predicts the subsequent frequency evolution trend based on the power disturbance observed by the power disturbance observer. Once Δf drops to Δf thr , ΔP lmax 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 control the frequency minimum point within the allowable range (-2Hz), which caused the aggregated energy storage system and generator to exit operation.

[0147] See also Figure 6 and Figure 7 , Figure 6 It is a comparative schematic diagram of the frequency evolution between the target equivalent frequency model provided by the present application and different SFCs; Figure 7 It is a schematic diagram comparing the output power between the target equivalent frequency model provided by the present application and different SFCs. Figure 6 and Figure 7 The comparison of the frequency evolution and output power of each unit of the target equivalent frequency model provided by the present application and the compared SFC is shown. Figure 7 (a) Figure 7 (b) Figure 7 (c) Figure 7 (d) shows the comparison diagrams between the target equivalent frequency model provided by the present application and the load response of different SFCs, generator output power, aggregated energy storage output power, and each energy storage output power. Since the target equivalent frequency model provided by the present application is provided with a double-layer model prediction control unit, Figure 6 and Figure 7 A double-layer MPC is used in FIG. 1 to represent the target equivalent frequency model provided by the present application.

[0148] Depend on Figure 6 and Figure 7 It can be seen that all equivalent frequency models can restore the frequency deviation to an acceptable value after the 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 aggregate energy storage output and load response are reasonably controlled. The 3MW load drops rapidly in the frequency reduction stage, and the aggregate 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), 1.5MW of load reduction is gradually re-invested to improve the self-recovery capability of the system, and the aggregate energy storage discharge power is reduced by 0.1MW to distribute the internal energy storage output to balance the SoC, such as Figure 7 (d) as shown.

[0149] In contrast, since the conventional SFC fails to coordinate load response with other frequency regulation units, the frequency regulation effect of the compared SFC is significantly inferior to that of the double-layer MPC. Specifically, the conventional SFC exceeds the load, resulting in a waste of the spinning reserve capacity of the generator, and the generator only outputs 1.7MW of power. For the SFC based on continuous UFLS, it can effectively avoid overshoot, but does not consider the generator power limitation due to the high inertia time constant, which leads to the most serious frequency deviation, with the lowest frequency point being -1.09Hz. In addition, Figure 6 and Figure 7 The two strategies shown in except for the target equivalent frequency model provided in this application fail to coordinate the load response with the aggregated energy storage output like the target equivalent frequency model, which may result in the aggregated energy storage being unable to fully and permanently participate in frequency regulation.

[0150] The following Table 2 shows the frequency regulation effects of three SFCs within 1 hour. After this time, there is usually additional power support such as mobile energy storage. The compared SFC does not consider SoC balance. Therefore, some distributed energy storage exits operation early during the long-term frequency regulation process, resulting in a secondary drop in frequency. In contrast, the target equivalent frequency model based on the double-layer model prediction control unit provided in this application avoids this situation and ensures the frequency regulation effect.

[0151] Table 2:

[0152] Control strategy Frequency secondary drop times The lowest frequency of the second drop (Hz) Traditional SFC 2 -0.69 / -0.81 Improved SFC 2 -0.74 / -0.86 SFC under two-layer MPC 0 1

[0153] Figure 8 FIG. 8 is a block diagram of a supplementary frequency control device 800 for aggregated energy storage and load response, as shown in an exemplary embodiment of the present application. Figure 8 As shown, the device comprises:

[0154] A construction unit 801 is used to construct a model framework for supplementary frequency control based on an equivalent frequency model of a power system with high renewable energy penetration;

[0155] A model definition unit 802 is used to define a power disturbance observer and a double-layer model prediction control unit within a model framework to obtain a target equivalent frequency model;

[0156] An observation unit 803 is used to obtain an observation value through a power disturbance observer using a target equivalent frequency model;

[0157] A processing unit 804 is used to sample discrete frequency deviations, input the discrete frequency deviations and observation values ​​into a double-layer model prediction control unit, and obtain a control trajectory for the aggregated energy storage system and the load;

[0158] The correction unit 805 is used to correct the control error of the control trajectory to obtain a final control trajectory, so as to regulate the actual output and load response of the aggregated energy storage based on the final control trajectory.

[0159] The device applies the supplementary frequency control method for aggregated energy storage and load response provided in the present application, constructs a model framework for supplementary frequency control based on the equivalent frequency model of the power system with high renewable energy penetration rate through the construction unit 801, and defines the power disturbance observer and the two-layer model prediction control unit in the model framework through the model definition unit 802, and constructs a target equivalent frequency model for frequency regulation of the load and aggregated energy storage. The target equivalent frequency model is used for supplementary frequency control, and the observation unit 803 obtains the observation value through the power disturbance observer; the discrete frequency deviation is sampled through the processing unit 804, and the discrete frequency deviation and the observation value are input into the two-layer model prediction control unit to obtain the control trajectory for the aggregated energy storage system and the load; the control error is corrected for the control trajectory through the correction unit 805 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. In this way, the present application can reasonably allocate load response and energy storage output during the frequency regulation process while taking into account the load response characteristics and the conditions of the aggregated energy storage system. It can effectively solve the frequency security problems faced by low-inertia power grids under islanding events, achieve more active frequency regulation effects, and obtain control strategies that can cope with various power disturbances, thereby achieving safe operation of power systems with high renewable energy penetration rates.

[0160] In another exemplary embodiment, the model definition unit 802 is also used to define a power disturbance observer based on the state equation of the power system with high renewable energy penetration within the model framework, and the power disturbance observer is suitable for large power disturbances; define a first cost function of the upper model predictive control of the two-layer model predictive control unit; configure a first constraint condition for the upper model predictive control; define the upper model predictive control based on the first cost function and the first constraint condition to obtain a target upper model predictive control; define a second cost function of the lower model predictive control of the two-layer model predictive control unit; configure a second constraint condition for the lower model predictive control; define the lower model predictive control based on the second cost function and the second constraint condition to obtain a target lower model predictive control; and construct a target equivalent frequency model based on the power disturbance observer, the target upper model predictive control and the target lower model predictive control.

[0161] In another exemplary embodiment, the model definition unit 802 is further configured to derive a state space model based on a state equation of a power system with high renewable energy penetration:

[0162]

[0163] y=Cx

[0164] In the formula, the state variable x=[Δf ΔP e ] T , system disturbance d = ΔP d, the system output y = Δf, Δf is the system frequency change, and the coefficient matrix A, B, C, R is expressed as:

[0165]

[0166] R=[-1 / 2H 0] T C=[1 0] T

[0167] In the formula, R e and τ e is the time constant and drop coefficient of energy storage in the aggregate energy storage, N e is the amount of energy stored in the aggregated energy storage;

[0168]

[0169] Where, L = [l1 l2] T is the observation gain matrix, are the observed values ​​of x, d, and y respectively.

[0170] In another exemplary embodiment, the model definition unit 802 is further used to obtain a first corresponding relationship between the predicted frequency deviation of the upper-layer model predictive control and the discharge power and load response of the aggregated energy storage;

[0171] A first cost function of the upper-level model predictive control is defined based on the first correspondence.

[0172] In another exemplary embodiment, the model definition unit 802 is further used to configure the power limit of the 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;

[0173] Among them, the power limit of aggregate energy storage is:

[0174]

[0175] In the formula, p ei.min and p ei.max are the minimum and maximum output power of the i-th energy storage in the aggregated energy storage, respectively;

[0176] The power limit of the load response is 0≤ΔP l ≤ΔP lmax , ΔP lmax is the maximum load that can be removed;

[0177] The frequency deviation is constrained to Δf min ≤F≤Δf thr , Δf min is the minimum frequency allowed.

[0178] In another exemplary embodiment, the model definition unit 802 is further used to obtain a second corresponding relationship between the state of charge and the output power of each energy storage in the aggregated energy storage; and define a second cost function of the lower-level model predictive control based on the first corresponding relationship.

[0179] In another exemplary embodiment, the model definition unit 802 is further used to configure the output power relationship between the aggregated energy storage and the individual energy storages in the aggregated energy storage of the lower layer model predictive control, the power limit of the energy storage and the ramp rate constraint of the energy storage to form a first constraint condition;

[0180] Among them, the output power relationship between the aggregated energy storage and the individual energy storage in the aggregated energy storage is:

[0181] for have

[0182]

[0183] The power limit of energy storage is

[0184] The ramp rate constraint of energy storage is is the rate of change of the output power of the ith energy storage at step l, Δp ei.min and Δp ei.max is the ramp rate limit.

[0185] In another exemplary embodiment, the correction unit 805 is also used to correct the load shedding size of the load to obtain the objective function; convert the output power relationship between the aggregated energy storage in the second constraint condition and the individual energy storages in the aggregated energy storage into a form combining a penalty function and a constraint to obtain a converted second constraint condition; update the control trajectory based on the objective function and the converted second constraint condition to obtain a final control trajectory.

[0186] In another exemplary embodiment, the observation unit 803 is also used to detect the size relationship between the real-time frequency change and a preset frequency change threshold; when the size relationship indicates that the real-time frequency change is reduced to the frequency change threshold, the target equivalent frequency model is used to obtain the observation value through the power disturbance observer.

[0187] It should be noted that the supplementary frequency control device for aggregated energy storage and load response provided in the above embodiment and the supplementary frequency control method for aggregated energy storage and load response provided in the above embodiment belong to the same concept, wherein the specific manner in which each module and unit performs the operation has been described in detail in the method embodiment and will not be repeated here. In actual applications, the supplementary frequency control device for aggregated energy storage and load response provided in the above embodiment can distribute 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, and this is not limited here.

[0188] An embodiment of the present application also provides an electronic device, comprising: one or more processors; a storage device for storing one or more programs, and when the one or more programs are executed by one or more processors, the electronic device implements the supplementary frequency control method of aggregated energy storage and load response provided in the above-mentioned embodiments.

[0189] Fig. 9 The structure diagram of the computer system suitable for implementing the electronic device of the embodiment of the present application is shown. It should be noted that: Fig. 9 The computer system 900 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0190] like Fig. 9 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage part 908 to the random access memory (RAM) 903, such as executing the method in the above embodiment. In the RAM 903, various programs and data required for system operation are also stored. The CPU 901, ROM 902 and RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0191] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, 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, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed so that a computer program read therefrom is installed into the storage section 908 as needed.

[0192] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 909, and / or installed from a removable medium 911. When the computer program is executed by a central processing unit (CPU) 901, various functions defined in the system of the present application are executed.

[0193] It should be noted that the computer-readable medium shown in the embodiment of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable computer program is carried. This propagated data signal can take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. A computer program contained on a computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0194] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0195] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0196] Another aspect of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for supplementary frequency control of aggregated energy storage and load response. The computer-readable storage medium may be included in the electronic device described in the above embodiment, or may exist independently without being assembled into the electronic device.

[0197] Another aspect of the present application also provides a computer program product or a computer program, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the complementary frequency control method of aggregated energy storage and load response provided in the above-mentioned various embodiments.

[0198] The above are only preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for supplementary frequency control combining energy storage and load response, characterized in that: The method comprises: A model framework for supplementary frequency control is constructed based on an equivalent frequency model of a power system with high renewable energy penetration; A power disturbance observer and a double-layer model prediction control unit are defined within the model framework to obtain a target equivalent frequency model; Using the target equivalent frequency model, obtaining an observation value through the power disturbance observer; Sampling discrete frequency deviations, inputting the discrete frequency deviations and the observed values ​​into the double-layer model prediction control unit to obtain control trajectories for the aggregated energy storage system and the load; The control trajectory is subjected to control error correction to obtain a 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.

2. The method according to claim 1, characterized in that: The power disturbance observer and the double-layer model prediction control unit are defined within the model framework to obtain a target equivalent frequency model, including: In the model framework, a power disturbance observer is defined based on the state equation of the high renewable energy penetration power system, and the power disturbance observer is applicable to large power disturbances; defining a first cost function of an upper-layer model predictive control of the two-layer model predictive control unit; configuring a first constraint condition of the upper-level model predictive control; Define the upper-layer model predictive control based on the first cost function and the first constraint condition to obtain a target upper-layer model predictive control; defining a second cost function of a lower-layer model predictive control of the two-layer model predictive control unit; configuring a second constraint condition of 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; A target equivalent frequency model is constructed based on the power disturbance observer, the target upper layer model predictive control and the target lower layer model predictive control.

3. The method according to claim 2, characterized in that The power disturbance observer is defined based on the state equation of the high renewable energy penetration power system to obtain a target power disturbance observer, including: Based on the state equation of the high renewable energy penetration power system, the state space model is derived: y=Cx In the formula, the state variable x=[Δf ΔP e ] T , system disturbance d = ΔP d , the system output y = Δf, Δf is the system frequency change, and the coefficient matrix A, B, C, R is expressed as: R=[-1 / 2H 0] T C=[10] T In the formula, R e and τ e is the time constant and drop coefficient of energy storage in the aggregate energy storage, N e is the amount of energy stored in the aggregated energy storage; The power disturbance observer is defined using the state space model to obtain the target power disturbance observer: Where, L = [l1 l2] T is the observation gain matrix, are the observed values ​​of x, d, and y respectively.

4. The method according to claim 2, characterized in that: The first cost function of defining the upper-level model predictive control comprises: Acquire a first corresponding relationship between the predicted frequency deviation of the upper-layer model predictive control and the discharge power of the aggregated energy storage and the load response; A first cost function of the upper-level model predictive control is defined based on the first corresponding relationship.

5. The method according to claim 4, characterized in that The first constraint condition of configuring the upper-level model predictive control includes: Configuring the power limit of the 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; Wherein, the power limit of the aggregated energy storage is: In the formula, p ei.min and p ei.max are respectively the minimum and maximum output power of the i-th energy storage in the aggregated energy storage; The power limit of the load response is 0≤ΔP l ≤ΔP lmax , ΔP lmax is the maximum load that can be removed; The frequency deviation is constrained to Δf min ≤F≤Δf thr , Δf min is the minimum frequency allowed.

6. The method according to claim 2, characterized in that The second cost function of defining the lower-level model predictive control comprises: Acquire a second corresponding relationship between the state of charge and output power of each energy storage in the aggregated energy storage; A second cost function of the lower-level model predictive control is defined based on the first corresponding relationship.

7. The method according to claim 6, characterized in that Configuring the second constraint condition of the lower-level model predictive control includes: Configuring the output power relationship between the aggregated energy storage and the individual energy storages in the aggregated energy storage, the power limit of the energy storage and the ramp rate constraint of the energy storage under the predictive control of the lower layer model to form a first constraint condition; Among them, the output power relationship between the aggregated energy storage and the individual energy storage in the aggregated energy storage is: for have The power limit of energy storage is The ramp rate constraint of energy storage is is the rate of change of the output power of the ith energy storage at step l, Δp ei.min and Δp ei.max is the ramp rate limit.

8. The method according to claim 7, characterized in that The step of performing control error correction on the control trajectory to obtain a final control trajectory includes: Correcting the load rejection size of the load to obtain an objective function; Converting the output power relationship between the aggregated energy storage and the individual energy storages in the aggregated energy storage in the second constraint condition into a form combining a penalty function and a constraint to obtain a converted second constraint condition; The control trajectory is updated based on the objective function and the converted second constraint condition to obtain a final control trajectory.

9. The method according to any one of claims 1 to 8, characterized in that: The using the target equivalent frequency model to obtain an observation value through the power disturbance observer includes: Detecting the magnitude relationship between the real-time frequency change and a preset frequency change threshold; When the magnitude relationship represents that the real-time frequency change is reduced to the frequency change threshold, the target equivalent frequency model is used to obtain an observation value through the power disturbance observer.

10. A supplementary frequency control device combining energy storage and load response, characterized in that: include: A building unit for constructing a model framework for supplementary frequency control based on an equivalent frequency model of a power system with high renewable energy penetration; A model definition unit, used to define a power disturbance observer and a double-layer model prediction control unit within the model framework to obtain a target equivalent frequency model; An observation unit, used for obtaining an observation value through the power disturbance observer by using the target equivalent frequency model; A processing unit, used for sampling discrete frequency deviation, inputting the discrete frequency deviation and the observed value into the double-layer model prediction control unit, and obtaining a control trajectory for the aggregated energy storage system and the load; A correction unit is used to perform control error correction on the control trajectory to obtain a 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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