Energy storage control method and device based on model predictive control

Abstract

The invention relates to the technical field of electrical engineering, and provides an energy storage control method and device based on model predictive control, and the method comprises the steps: building a regional power grid frequency response model which comprises a conventional unit and an energy storage element, and the regional power grid frequency response model also comprises a local load; each state variable in the regional power grid model is input into a model prediction controller, virtual inertia control and virtual damping control are carried out on the energy storage element, the output of the virtual inertia control is in direct proportion to the frequency change rate, and the output of the virtual damping control is in direct proportion to the frequency deviation. The virtual inertia coefficient and the virtual damping coefficient of the energy storage element can be adaptively adjusted, the frequency stability of the power grid is maintained, and the recovery of the frequency of the power grid is accelerated; meanwhile, on the premise that the frequency of the power grid is not out of limit, the energy consumption of the energy storage element can be reduced, and the service life of the energy storage element is prolonged.

Description

Technical Field

[0001] The present disclosure relates to the field of electrical engineering technology, and in particular to a method and device for energy storage control based on model predictive control. Background Art

[0002] At present, with the rapid development of new energy and the continuous expansion of the scale of power systems, the stability and reliability of power grids are facing increasing challenges. Traditional power systems mainly rely on generators to maintain the frequency stability of the power grid. However, the regulation speed of traditional units is limited, and the fuel cost is high, which also has certain limitations on the economy and environmental impact of the power system. In addition, due to the increasing number of new energy sources such as wind power and photovoltaic power generation in the power system, their volatility and intermittency have brought new challenges to the frequency stability of the power grid.

[0003] In order to solve the above problems, energy storage technology has been widely studied and applied in power systems in recent years to provide flexible energy regulation and frequency stability support. Among them, electrochemical energy storage, as an important branch of energy storage technology, has the advantages of high energy density, rapid response, and sustainability, and is considered to be a solution with great potential.

[0004] However, current electrochemical energy storage systems face some challenges in practical applications. Traditional control strategies often fail to fully utilize the advantages of energy storage systems, resulting in insufficiently fast and accurate grid frequency regulation, large power throughput of energy storage systems, and reduced service life of energy storage components. Summary of the invention

[0005] The purpose of the embodiments of the present disclosure is to provide an energy storage control method and device based on model predictive control, so as to at least solve the technical problems existing in the prior art that the power grid frequency regulation accuracy is insufficient, the power throughput of the energy storage system is large, and the performance and service life of the energy storage element are affected.

[0006] In order to solve the above technical problems, the embodiments of the present disclosure adopt the following technical solutions:

[0007] In a first aspect, an embodiment of the present disclosure provides an energy storage control method based on model predictive control, comprising:

[0008] Establishing a regional power grid frequency response model including traditional units and energy storage elements, wherein the regional power grid frequency response model also includes local loads;

[0009] Each state variable in the regional power grid model is input into a model predictive controller, and virtual inertia control and virtual damping control are performed on the energy storage element, wherein the output of the virtual inertia control is proportional to the frequency change rate, and the output of the virtual damping control is proportional to the frequency deviation.

[0010] In some embodiments, inputting each state variable in the regional power grid model into a model predictor to calculate the control variable of the energy storage element includes:

[0011] Establish the state space equations of model predictive control;

[0012] Performing state prediction according to each current state variable of the regional power grid model and the state space equation;

[0013] Establish the objective function of model predictive control;

[0014] Solving the objective function based on the predicted state variables;

[0015] According to the solution result of the objective function, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

[0016] In some embodiments, the objective function of the model predictive control is:

[0017] J=Q(Δf) 2 +P(ΔP ESS ) 2 ;

[0018] Where Δf is the frequency deviation, ΔP ESS is the power throughput of the energy storage element, Q is the weight coefficient of the frequency deviation, and P is the weight coefficient of the power throughput of the energy storage element.

[0019] In some embodiments, the method further comprises:

[0020] According to the control target of the model predictive controller, the weight coefficient of the objective function is adjusted.

[0021] In some embodiments, the method further comprises:

[0022] Determining whether the control parameters of the objective function meet preset requirements, wherein the control variables of the objective function include the frequency deviation of the power grid and the power throughput of the energy storage element;

[0023] If not, correct the control parameters;

[0024] According to the corrected control parameters, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

[0025] In some embodiments, the method further comprises:

[0026] Based on the restrictions imposed by the new energy generating units on the traditional generating units, the constraint conditions of the model predictive control are determined.

[0027] In a second aspect, an embodiment of the present disclosure provides an energy storage control device based on model predictive control, comprising:

[0028] A model building module, configured to build a regional power grid frequency response model including traditional units and energy storage elements, wherein the regional power grid frequency response model also includes local loads;

[0029] The control module is configured to input each state variable in the regional power grid model into a model predictive controller, and perform virtual inertia control and virtual damping control on the energy storage element, wherein the output of the virtual inertia control is proportional to the frequency change rate, and the output of the virtual damping control is proportional to the frequency deviation.

[0030] In some embodiments, the control module is configured to:

[0031] Establish the state space equations of model predictive control;

[0032] Performing state prediction according to each current state variable of the regional power grid model and the state space equation;

[0033] Establish the objective function of model predictive control;

[0034] Solving the objective function based on the predicted state variables;

[0035] According to the solution result of the objective function, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

[0036] In some embodiments, the objective function of the model predictive control is:

[0037] J=Q(Δf) 2 +P(ΔP ESS ) 2 ;

[0038] Where Δf is the frequency deviation, ΔP ESS is the power throughput of the energy storage element, Q is the weight coefficient of the frequency deviation, and P is the weight coefficient of the power throughput of the energy storage element.

[0039] In some embodiments, the energy storage control device based on model predictive control further includes an adjustment module configured to:

[0040] According to the control target of the model predictive controller, the weight coefficient of the objective function is adjusted.

[0041] In a third aspect, an embodiment of the present disclosure provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned energy storage control method based on model predictive control when executing the computer program on the memory.

[0042] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned energy storage control method based on model predictive control is implemented.

[0043] The disclosed embodiments provide a method and device for energy storage control based on model predictive control. A regional power grid frequency response model including traditional units and energy storage elements is established. Then, each state variable in the regional power grid model is input into a model predictive controller. Virtual inertia control and virtual damping control are performed on the energy storage elements. The advantages of fast response speed and flexible regulation of electrochemical energy storage can be fully utilized. By adaptively adjusting the virtual inertia coefficient and virtual damping coefficient of the energy storage element, the electrochemical energy storage power station can compensate for the power fluctuation of renewable energy power generation, maintain the frequency stability of the power grid, and accelerate the recovery of the power grid frequency. At the same time, under the premise of maintaining the power grid frequency within the limit, the energy consumption of the energy storage element can be reduced, and the service life of the energy storage element can be increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0045] Figure 1 A flow chart of an energy storage control method based on model predictive control according to an embodiment of the present disclosure;

[0046] Figure 2 A frequency response model of a microgrid according to an embodiment of the present disclosure;

[0047] Figure 3 is the power disturbance signal ΔP of the embodiment of the present disclosure dist Schematic diagram of

[0048] Figure 4 It is a schematic diagram of the frequency response (partial) when no analog predictive controller is added;

[0049] Figure 5 K Hv =10 when the frequency response diagram of the energy storage element;

[0050] Figure 6 K Dv =10 when the frequency response diagram of the energy storage element;

[0051] Figure 7 K Hv=10, K Dv =10 when the frequency response diagram of the energy storage element;

[0052] Figure 8 is the change of power throughput of energy storage element;

[0053] Fig. 9 The energy storage control method based on model predictive control of the embodiment of the present disclosure only focuses on the power throughput change of the energy storage element during energy storage;

[0054] Fig.10 The energy storage control method based on model predictive control in the embodiment of the present disclosure only focuses on the energy storage time K Hv , K Dv Schematic diagram of the changes;

[0055] Fig.11 It is a schematic diagram of the change of Δf when the energy storage control method based on model predictive control in an embodiment of the present disclosure only focuses on the frequency;

[0056] Fig.12 The energy storage control method based on model predictive control in the embodiment of the present disclosure only focuses on the frequency K Hv , K Dv Schematic diagram of the changes;

[0057] Fig.13 It is a structural schematic diagram of an energy storage control device based on model predictive control according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0058] Various aspects and features of the present disclosure are described herein with reference to the drawings.

[0059] It should be understood that various modifications may be made to the embodiments of the present application. Therefore, the above description should not be considered as limiting, but only as an example of an embodiment. Other modifications within the scope and spirit of the present disclosure will occur to those skilled in the art.

[0060] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the general description of the present disclosure given above and the detailed description of the embodiments given below, serve to explain the principles of the present disclosure.

[0061] These and other characteristics of the present disclosure will become apparent from the following description of a preferred form of embodiment given as a non-limiting example, with reference to the attached drawings.

[0062] It should also be understood that, although the present disclosure has been described with reference to some specific examples, those skilled in the art will be able to realize many other equivalent forms of the present disclosure that have the features described in the claims and are therefore within the scope of protection defined thereby.

[0063] The above and other aspects, features and advantages of the present disclosure will become more apparent from the following detailed description when taken in conjunction with the accompanying drawings.

[0064] Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it should be understood that the embodiments claimed are merely examples of the present disclosure, which may be implemented in a variety of ways. Well-known and / or repeated functions and structures are not described in detail to avoid obscuring the present disclosure with unnecessary or redundant details. Therefore, the specific structural and functional details claimed herein are not intended to be limiting, but merely serve as a basis and representative basis for the claims to teach those skilled in the art to use the present disclosure in a variety of ways with substantially any suitable detailed structure.

[0065] This specification may use the phrases "in one embodiment," "in another embodiment," "in a further embodiment," or "in other embodiments," all of which may refer to one or more of the same or different embodiments according to the present disclosure.

[0066] Embodiment 1

[0067] Figure 1 A flow chart of an energy storage control method based on model predictive control according to an embodiment of the present disclosure is shown. Figure 1 As shown, an energy storage control method based on model predictive control provided by an embodiment of the present disclosure includes:

[0068] S101: Establish a regional power grid frequency response model including traditional units and energy storage elements.

[0069] The regional power grid frequency response model also includes local loads. The energy storage element includes an energy storage power station, which uses its rapid response capability to smooth the power mutation caused by the local load after the new energy grid is connected to the traditional unit, so as to maintain the stability of the grid frequency.

[0070] In this embodiment, an isolated microgrid is taken as an example to specifically illustrate the energy storage control method. The isolated microgrid includes a 15MW local load, a 12MW thermal power plant, a 16MW new energy power plant, and an energy storage power station of undetermined size. Since the focus of this disclosure is on the control of energy storage, not on a certain type of energy storage, the limiting factors of the energy storage itself are not considered. The baseline power of the entire power grid system is 12MW.

[0071] The established regional power grid frequency response model is as follows: Figure 2 As shown, Figure 2 The parameters and corresponding parameters in are shown in Table 1.

[0072] Table 1 Parameters of isolated island microgrid

[0073] parameter value parameter value b 1 R 2.4 Ki 0.05 D 0.015 Tg 0.1 H 0.083 Tt 0.4 K 1 Vu 0.5 Tess 10 <![CDATA[V l ]]> -0.5 GRC 12

[0074] In order to make the regional power grid frequency response model closer to the actual situation of power grid operation, some practical limiting factors are considered in this embodiment. For example, thermal power plants are subject to generation rate constraints (GRC) and turbine units (V u and V l ) of the maximum / minimum valve opening or closing limit. The GRC of a thermal power plant is set to 12% puMW / min, where pu represents the standard power system value, i.e. the standard power generation rate, in MW / min, V u and V l Set to 0.5 and -0.5 respectively. Tess is the energy storage response time constant, which is related to the inverter.

[0075] S102: Inputting each state variable in the regional power grid model into a model predictive control (MPC controller) to perform virtual inertia control and virtual damping control on the energy storage element.

[0076] In this step, an energy storage system including virtual inertia damping control is established. The control of the energy storage element in the energy storage system includes two control branches, one is a virtual inertia control branch, and the other is a virtual damping control branch. The control signals of the two control branches are calculated and fused (for example, algebraically summed) and sent to the energy storage element to control the energy storage element.

[0077] That is, the control of energy storage includes inertia control link and damping control link. The output value of the damping control link is proportional to the frequency deviation (Δf). The function of the damping control link is that when the frequency deviates, the energy storage system provides or absorbs power to the grid to maintain the stability of the grid frequency. The output value of the inertia control link is proportional to the rate of change of frequency (RoCoF). Its function is similar to that of the rotating synchronous machine in the traditional power grid. When the power grid fluctuates on the generation side or the load side, the inertia control link can slow down the frequency fluctuation of the grid, but it cannot restore the frequency to the rated frequency. Therefore, it must be used in conjunction with the damping control link. The output value of the damping control link of the energy storage element is the virtual damping coefficient K Dv The output value of the inertia control link is the virtual inertia coefficient K Hv .

[0078] Frequency deviation refers to the difference between the actual value and the nominal value of the system frequency under normal operating conditions. The frequency change rate is one of the important characteristic parameters reflecting the frequency stability of the power system. After the power shortage occurs, the frequency change rate reaches an extreme value at the beginning of the system frequency change, and its magnitude is determined by the power shortage ratio and the system inertia (inertial response).

[0079] The energy storage control method based on model predictive control provided by the embodiment of the present disclosure establishes a regional power grid frequency response model including traditional units and energy storage elements; then, each state variable in the regional power grid model is input into the model predictive controller, and virtual inertia control and virtual damping control are performed on the energy storage elements. This can fully utilize the advantages of fast response speed and flexible adjustment of electrochemical energy storage, and realize compensation of power fluctuation of renewable energy power generation by electrochemical energy storage power station by adaptively adjusting the virtual inertia coefficient and virtual damping coefficient of the energy storage element, maintain the frequency stability of the power grid, and accelerate the recovery of the power grid frequency; at the same time, under the premise of maintaining the power grid frequency within the limit, the energy consumption of the energy storage element can be reduced, and the service life of the energy storage element can be increased.

[0080] In some embodiments, in step S102, each state variable in the regional power grid model is input into a model predictor, and virtual inertia control and virtual damping control are performed on the energy storage element, including:

[0081] S201: Establish state space equations for model predictive control.

[0082] In this embodiment, the model predictive controller includes a linear non-time-varying microgrid model (or linear time-invariant microgrid model), an optimization solver, an objective function, and a disturbance predictor. The goal of the MPC controller is to find the optimal control variable to minimize the objective function. Due to the limitation of the disturbance observer, the prediction time domain of the MPC controller is limited to 2 steps, and the step length of each step is 0.1s, that is, the unit time step is 0.1s.

[0083] The following state-space equations describe a linear time-invariant microgrid with an MPC controller. Figure 2 The regional power grid frequency response model shown can obtain the following state space equation:

[0084]

[0085] Among them, ΔP g is the actual power change of the traditional unit, ΔP ACE Area control error (ACE) of traditional units, ΔP ESS is the power throughput of the energy storage element, i.e., the change in energy storage power, ΔP distis the power disturbance signal, H is the inertia coefficient of the regional power grid, and D is the damping coefficient of the regional power grid.

[0086] Based on the above equations (1)-(5), the following state equation can be derived:

[0087]

[0088] in, is a non-homogeneous differential equation,

[0089] X=[ΔP g ΔP ACE Δf ΔP t ΔP ESS ] T (7)

[0090] U=[K Hv K Dv ] T (8)

[0091] W=ΔP dist (9)

[0092] Y=Δf (10)

[0093] From equation (5), we can see that there are items in the system that multiply the state quantity and the control quantity, so the system is not a typical linear system. However, within the time of each calculation step (0.1s), the change of the system state quantity can be ignored. For the sake of simplicity, the state quantity multiplied by the control quantity is regarded as a constant. The subsequent simulation results show that this simplification is feasible and effective.

[0094] The coefficient matrix is:

[0095]

[0096] C =

[00100] (14)

[0097] This state equation is continuous, and the forward Euler method is used to discretize the state equation to obtain the following equation. Where I is the unit matrix, and T is the control period, for example, T is 0.1s.

[0098]

[0099] Then we can get the discretized state space equation:

[0100]

[0101] Among them, y k 、x k 、u k 、w kare the output, state, control and disturbance at time k respectively, x k+1 is the state quantity at time k+1.

[0102] S202: Performing state prediction according to each current state variable of the regional power grid model and the state space equation.

[0103] After constructing the state space equation, equation (16) can be used to predict the state quantity at the next moment based on the state quantity at the current moment (for example, moment k).

[0104] Optionally, when the model predictive controller performs model predictive control, the method further includes:

[0105] Based on the restrictions imposed by the new energy generating units on the traditional generating units, the constraint conditions of the model predictive control are determined.

[0106] For example, according to the above V u and V l , ΔP g The following restrictions may not be violated:

[0107] -0.5≤ΔP g ≤0.5 (17)

[0108] S203: Establishing the objective function of model predictive control.

[0109] Model predictive control considers two optimization objectives: grid frequency and the power throughput of the energy storage plant.

[0110] The objective function of the model predictive controller is as follows:

[0111] J=Q(Δf) 2 +P(ΔP ESS ) 2 (18)

[0112] Where Q is the weight coefficient of the positive frequency deviation (Δf), and P is the power throughput (ΔP ESS )’s weight coefficient.

[0113] S204: Solving the objective function according to the predicted state variables.

[0114] The state variables predicted in step S202 are used to solve the objective function to obtain an optimal solution (optimal control variable), and the virtual inertia coefficient and the virtual damping coefficient are adaptively adjusted according to the optimal solution.

[0115] Through mathematical proof, it can be determined that the objective function is quadratically convex, so the solver can be conveniently used to calculate the optimal solution of the above objective function.

[0116] S205: According to the solution result of the objective function, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

[0117] The control objectives of the model predictive controller are: (1) reduce the frequency deviation (Δf) of the power grid and return the frequency deviation to the reference value (Δf = 0) as soon as possible; (2) reduce the power throughput (ΔP ESS ) to increase the service life of energy storage components and thus reduce their operating costs.

[0118] After using the current state variables to predict the future state variables, the predicted state variables can be used to solve the objective function to obtain the optimal solution of the objective function (Δf and ΔP ESS The optimal solution is the optimal control parameter that meets the above control objectives. According to the above control parameters, the virtual inertia coefficient and virtual damping coefficient of the energy storage element are adaptively adjusted. On the one hand, the recovery of the grid frequency can be accelerated, and on the other hand, the power throughput of the energy storage can be reduced and its service life can be increased. For example, when Δf deviates far from 0 during grid operation, K Hv It will be actively reduced so that Δf returns to near 0 as soon as possible.

[0119] It can be understood that, in this embodiment, the establishment of the state space equation and the objective function can be executed simultaneously, and there is no clear execution order between the two.

[0120] In the above embodiment, the control of the state quantity at time k+1 is predicted. It can be seen from the above formula (16) that the state quantity at time k+1 is completely determined by the state quantity, control quantity and disturbance at time k. Therefore, the state quantity at time k+2 can be obtained by recursion as follows:

[0121] x k+2 =A 2 x k +ABu k +AE k +Bu k+1 +Ew k+1 (19)

[0122] It can be seen from formula (19) that the state quantity at time k+2 is related to the state quantity, control quantity, disturbance at time k and the control quantity and disturbance at time k+1.

[0123] Since each step length of the MPC controller is 0.1s, and the time scale of the power disturbance is relatively large, in this embodiment, the power disturbance can be considered to be a constant, that is,

[0124] w k =w k+1 (20)

[0125] This approximation is acceptable in a short time scale, but as the prediction step increases, the error will also increase. Therefore, in this embodiment, the prediction step is selected as 2 (that is, the prediction time domain is 2 steps). However, the step cannot be selected as 1. From the above state equation, it can be seen that the control quantity directly controls ΔP ESS If the step size is set to 1, no matter how the control amount changes, it will not affect the value of Δf, so the minimum prediction step size is 2.

[0126] In formula (19), x k 、w k 、w k+1 is the current known quantity, by controlling u k 、u k+1 You can control x k+2 , x k 、x k+1 Written in matrix form:

[0127]

[0128] Among them, 0 is a 5×2 zero matrix and 1 is a 5×5 identity matrix.

[0129] Then we can get:

[0130]

[0131] in,

[0132] F=

[00001] (24)

[0133] The objective function is:

[0134]

[0135] From the above formulas (21)-(25), we can get:

[0136]

[0137] in,

[0138]

[0139] E=ψx k +ξw k (32)

[0140] in,

[0141]

[0142] E F =ψ F x k +ξ F w k(35)

[0143] in,

[0144]

[0145] Since we want to minimize the objective function J when solving the objective function, the independent variable of J is U k , in formula (26) Don't follow U k changes and can therefore be ignored.

[0146] make

[0147]

[0148] Therefore, the objective function can be rewritten as:

[0149]

[0150] That is, the standard form of the quadratic objective function, the independent variable U k is a 1×4 column vector. Using the quadratic programming solver, we can solve U k , the output U k is the current control variable. In this step, the control step is 1. k It is the control quantity of integrating the frequency deviation of the power grid and the power throughput of the energy storage element. The model predictive controller can be used according to U k Effective and reliable control of power grid operation.

[0151] In some embodiments, the method further comprises:

[0152] According to the control target of the model predictive controller, the weight coefficient of the objective function is adjusted.

[0153] According to the objective function, model predictive control can have different control objectives. Reasonable setting of the weight coefficients of different control objectives (setting P, Q) can ensure that the throughput of energy storage is as small as possible while ensuring that the frequency does not cross the limit, so as to extend the life of the energy storage battery and reduce the cost of the energy storage power station; based on the parameters of normal operation, increasing Q and reducing P can make the energy storage system focus on restoring the grid frequency and ignore its own energy storage throughput, which can be used in emergency situations with drastic frequency fluctuations; reducing Q and increasing P can reduce the energy throughput of energy storage, which can be used in situations where the energy storage system is about to be fully charged or fully released. That is, this embodiment can flexibly adjust the weight coefficients of the two control objectives according to different scenario requirements to achieve different functions.

[0154] In some embodiments, the method further comprises:

[0155] S301: Determine whether the control parameters of the objective function meet preset requirements, wherein the control variables of the objective function include the frequency deviation of the power grid and the power throughput of the energy storage element;

[0156] S302: If not satisfied, calibrate the control parameters;

[0157] S303: adjusting the virtual inertia coefficient and the virtual damping coefficient of the energy storage element according to the corrected control parameters.

[0158] The model predictive controller can perform predictive control in real time, for example, when the power disturbance signal ΔP dist When a sudden jump causes Δf to increase sharply and deviate far from 0, through steps S301 to S303, it can be determined that Δf needs to be reduced. At this time, the control parameters are corrected and K is controlled. Hv It adaptively decreases with Δf so that Δf can be restored to near 0 as soon as possible, thus speeding up the recovery of the grid frequency.

[0159] Next, use the Matlab / Simulink simulation model to simulate and build Figure 2 The frequency response model shown in Figure 1 uses the parameters shown in Table 1. Microgrid disturbance ΔP dist It consists of two parts: household or industrial power consumption and unstable power output of renewable energy. This part of the disturbance power is simulated by Gaussian noise signal, which is approximately a step to simulate the most serious fluctuation. At the same time, in order to increase the disturbance of the system, a square wave signal is superimposed. The disturbance signal is as follows: Figure 3 The frequency deviation of the system without the model predictive controller is shown in Figure 4 The controller proposed in the present disclosure is compared with the fixed coefficient, and the simulation results are as follows.

[0160] The virtual inertia coefficient simulates the rotational inertia of a traditional unit. The larger the virtual inertia coefficient, the stronger the system's ability to maintain its own frequency stability under disturbance. However, if the frequency has deviated from the rated power, a larger virtual inertia coefficient will also make it difficult for the frequency to return to the rated power. In this case, the virtual inertia coefficient must be reduced. The inertia control link can only keep the grid frequency relatively stable, but cannot eliminate steady-state errors. The virtual damping control link can eliminate steady-state errors, but a single damping control link may cause the frequency to oscillate rapidly, so the virtual damping control link needs to be used in conjunction with the virtual inertia control link.

[0161] In the embodiment of the present disclosure, the virtual inertia coefficient K is set Hv and the virtual damping coefficient K Dv The range of variation is [1, 10]. When adding the virtual inertia control link alone, let K Hv=10, the frequency response of the energy storage system is as follows Figure 5 When adding a virtual damping control link, let K Dv =10, the frequency response of the energy storage system is as follows Figure 6 As shown. Let K Hv =10, K Dv =10, the frequency response of the energy storage system is as follows Figure 7 As shown, the energy storage power change ΔP ESS (Power throughput changes) Figure 8 As shown, ΔP ESS A value less than 0 indicates output power, and a value greater than 0 indicates absorption power.

[0162] In order to quantitatively analyze the frequency deviation and the power throughput of energy storage, the absolute value of the area enclosed by the simulation result curve and the coordinate axis is calculated, as shown in the following formula:

[0163] f(x)=∫|x|dx (41)

[0164] Let F represent the frequency deviation value, E represent the energy storage power throughput, different K Hv , K Dv The corresponding frequency and energy storage performance (F value and E value) are shown in Table 2.

[0165] Table 2 Different K Hv , K Dv Frequency and energy storage performance

[0166]

[0167] Depend on

[0168] As can be seen from Table 2, the effect of the inertia control link alone on the frequency regulation is very limited. The damping control link alone can effectively reduce the frequency deviation, but there is a great deal of oscillation. The inertia control link and the damping control link can be used together to make up for each other's shortcomings, while reducing the frequency deviation and eliminating the high-frequency and high-amplitude oscillation of the frequency. At the same time, it can be seen that as the frequency deviation gradually decreases, the total amount of energy storage throughput also increases, which shows that the frequency deviation and energy storage throughput are contradictory indicators. As shown in Table 2, under the specified K Hv , K Dv When the upper limit of 10 is not more than 10, the optimal frequency control effect can be achieved. At this time, the frequency deviation F = 3.3 and the energy consumption E = 30.92.

[0169] Since the larger the fixed inertia coefficient and damping coefficient, the better the frequency control effect, but it is not the best control scheme to always keep the coefficient at the maximum. For example, when the frequency has deviated from the rated frequency, a larger inertia coefficient will hinder the frequency from returning to the rated frequency; similarly, if the frequency has stabilized near the rated frequency, an excessively large damping coefficient will cause the frequency to oscillate. Therefore, the energy storage control method of this embodiment can also set the virtual inertia coefficient K Hv and the virtual damping coefficient K Dv The variation range is set to [1, 10].

[0170] The correctness and feasibility of the energy storage control method of the embodiment of the present disclosure are verified below.

[0171] The scenario is set to the energy storage power station capacity is close to saturation, and the control target is to minimize the consumption of energy storage. Therefore, the frequency weight Q is set to 0 and the energy storage weight P is set to 1. Observe the energy storage consumption (power throughput of energy storage). The closer it is to 0, the more effective the energy storage control method is. The power throughput of energy storage is as follows: Fig. 9 As shown, K Hv , K Dv The adaptive changes of Fig.10 As shown. According to formula (41), the energy storage throughput E=0.38 can be calculated, indicating that the energy storage control method disclosed in the present invention achieves the expected goal.

[0172] In most cases, we hope that the energy storage power station can smooth out the grid frequency fluctuations caused by the instability of renewable energy generation and maintain the grid frequency at 50Hz. At this time, we focus on the frequency index of the grid and ignore the throughput of energy storage. Therefore, we set the frequency weight Q = 1 and the energy storage weight P = 0. This setting can achieve the best control of the frequency. The change of Δf is as follows Fig.11 As shown, K Hv , K Dv The adaptive changes of Fig.12 shown.

[0173] According to formula (41), the frequency deviation value F = 3.1, which shows that under the same K Hv , K Dv In the range of variation ([1,10]), the energy storage control method disclosed in the present invention is better than the case of optimal fixed coefficient (K Hv =10, K Dv =10, F = 3.3), which proves the performance advantage of the energy storage control method. Fig.11 and Fig.12 It can be found that K Dv Keep a large value all the time. When the frequency suddenly changes to 0 (a spike close to 0), K Dvwill be actively reduced, because the model predictive controller believes that this trend will continue. Dv If it is still large, then it may cross 0 and increase in the opposite direction to form oscillation at the next moment, so we should actively reduce K Dv The value of K Hv It always keeps a small value. Looking at the entire Δf change, the change trend of Δf is always close to 0. At this time, the smaller virtual inertia can reduce the resistance of Δf returning to 0. If Δf changes in the direction away from 0, K Hv It will grow to resist this change.

[0174] from Figure 7 and Fig.11 It can be clearly seen from the comparison that at 2.5min, due to ΔP dist When the frequency suddenly jumps from -0.1 to 0.1, Δf will increase sharply. When the coefficient is fixed, the frequency recovery is slow. When the energy storage control method provided by the embodiment of the present disclosure is used, when Δf deviates far from 0, K Hv It will be actively reduced so that Δf can be restored to near 0 as soon as possible, accelerating the recovery of the grid frequency. This also proves the effectiveness and superiority of the model predictive controller.

[0175] For scenarios that require attention to both energy storage and frequency, the desired effect can be achieved by adjusting the frequency weight Q, the energy storage weight P, or even dynamically adjusting Q and P. The value range of Q and P is [0, +∞). The absolute size of Q and P is not important. You only need to pay attention to the ratio of Q to P. The control effect of Q=10, P=1 is the same as that of Q=100, P=10.

[0176] Embodiment 2

[0177] Fig.13 The schematic diagram of the structure of the energy storage control device based on model predictive control according to the embodiment of the present disclosure is shown as follows: Fig.13 As shown, the embodiment of the present disclosure provides an energy storage control device based on model predictive control, comprising:

[0178] A model building module 10 is configured to build a regional power grid frequency response model including traditional units and energy storage elements, wherein the regional power grid frequency response model also includes local loads;

[0179] The control module 20 is configured to input each state variable in the regional power grid model into a model predictive controller, and perform virtual inertia control and virtual damping control on the energy storage element, wherein the output of the virtual inertia control is proportional to the frequency change rate, and the output of the virtual damping control is proportional to the frequency deviation.

[0180] In some embodiments, the control module 20 is further configured to:

[0181] Establish the state space equations of model predictive control;

[0182] Performing state prediction according to each current state variable of the regional power grid model and the state space equation;

[0183] Establish the objective function of model predictive control;

[0184] Solving the objective function based on the predicted state variables;

[0185] According to the solution result of the objective function, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

[0186] In some embodiments, the objective function of the model predictive control is:

[0187] J=Q(Δf) 2 +P(ΔP ESS ) 2 ;

[0188] Where Δf is the frequency deviation, ΔP ESS is the power throughput of the energy storage element, Q is the weight coefficient of the frequency deviation, and P is the weight coefficient of the power throughput of the energy storage element.

[0189] In some embodiments, the energy storage control device based on model predictive control further includes an adjustment module configured to:

[0190] According to the control target of the model predictive controller, the weight coefficient of the objective function is adjusted.

[0191] In some embodiments, the energy storage control device based on model predictive control further includes a correction module configured to:

[0192] Determining whether the control parameters of the objective function meet preset requirements, wherein the control variables of the objective function include the frequency deviation of the power grid and the power throughput of the energy storage element;

[0193] If not, correct the control parameters;

[0194] According to the corrected control parameters, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

[0195] In some embodiments, the control module 20 is further configured to:

[0196] Based on the restrictions imposed by the new energy generating units on the traditional generating units, the constraint conditions of the model predictive control are determined.

[0197] The energy storage control device based on model predictive control provided in the embodiment of the present disclosure corresponds to the energy storage control method based on model predictive control in the above-mentioned embodiment. Any optional items in the embodiment of the energy storage control method based on model predictive control are also applicable to the embodiment of the energy storage control device based on model predictive control, which will not be repeated here.

[0198] Embodiment 3

[0199] An embodiment of the present disclosure also provides an electronic device, comprising at least a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned energy storage control method based on model predictive control when executing the computer program on the memory.

[0200] The above-mentioned electronic equipment is mainly used to analyze and transmit the measurement data detected by the quantum detector to obtain accurate gas absorption rate.

[0201] In some embodiments, the processor executing the computer program may be a processing device including one or more general-purpose processing devices, such as a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), etc. More specifically, the processor may be a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a processor running other instruction sets, or a processor running a combination of instruction sets. The processor may also be one or more special-purpose processing devices, such as an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), a system on a chip (SoC), etc.

[0202] The memory may be a read-only memory (ROM), a random access memory (RAM), a phase-change random access memory (PRAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), an electrically erasable programmable read-only memory (EEPROM), other types of random access memory (RAM), a flash disk or other form of flash memory, a cache, a register, a static memory, a compact disk read-only memory (CD-ROM), a digital versatile disk (DVD) or other optical storage, a cassette or other magnetic storage device, or any other possible non-temporary medium used to store information or instructions that can be accessed by a computer device.

[0203] The electronic devices of the embodiments of the present disclosure may include but are not limited to fixed terminal devices such as servers, desktop computers, digital TVs, etc., and mobile terminal devices such as vehicle-mounted devices (such as head-up display devices), handheld devices (such as mobile phones, tablet computers, etc.), wearable devices (such as smart watches, smart bracelets, etc.), etc.

[0204] Embodiment 4

[0205] The embodiment of the present disclosure also provides a computer-readable storage medium, wherein the computer-readable medium stores a computer program, and when the computer program is executed by a processor, the above-mentioned energy storage control method based on model predictive control is implemented.

[0206] The computer-readable storage medium of the embodiments of the present disclosure may adopt any combination of one or more computer-readable media. The computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. In the embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, device or device, for example, the above-mentioned memory.

[0207] The computer programs of the embodiments of the present disclosure may be organized into one or more computer executable components or modules. Any number and combination of such components or modules may be used to implement the various aspects of the present disclosure. For example, the various aspects of the present disclosure are not limited to the specific computer executable instructions or specific components or modules shown in the drawings and described herein. Other embodiments may include different computer executable instructions or components with more or less functions than those shown and described herein.

[0208] The above description is only a preferred embodiment of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in the present disclosure (but not limited to) by each other to form a technical solution.

Claims

1. A method for energy storage control based on model predictive control, characterized in that: include: Establishing a regional power grid frequency response model including traditional units and energy storage elements, wherein the regional power grid frequency response model also includes local loads; Each state variable in the regional power grid model is input into a model predictive controller, and virtual inertia control and virtual damping control are performed on the energy storage element, wherein the output of the virtual inertia control is proportional to the frequency change rate, and the output of the virtual damping control is proportional to the frequency deviation.

2. The method according to claim 1, characterized in that Inputting each state variable in the regional power grid model into a model predictor to calculate the control variable of the energy storage element includes: Establish the state space equations of model predictive control; Performing state prediction according to each current state variable of the regional power grid model and the state space equation; Establish the objective function of model predictive control; Solving the objective function based on the predicted state variables; According to the solution result of the objective function, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

3. The method according to claim 2, characterized in that The objective function of the model predictive control is: J=Q(Δf) 2 +P(ΔP ESS ) 2 ; Where Δf is the frequency deviation, ΔP ESS is the power throughput of the energy storage element, Q is the weight coefficient of the frequency deviation, and P is the weight coefficient of the power throughput of the energy storage element.

4. The method according to claim 3, characterized in that The method further comprises: According to the control target of the model predictive controller, the weight coefficient of the objective function is adjusted.

5. The method according to claim 4, characterized in that The method further comprises: Determining whether the control parameters of the objective function meet preset requirements, wherein the control variables of the objective function include the frequency deviation of the power grid and the power throughput of the energy storage element; If not, correct the control parameters; According to the corrected control parameters, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

6. The method according to claim 3, characterized in that The method further comprises: Based on the restrictions imposed by the new energy generating units on the traditional generating units, the constraint conditions of the model predictive control are determined.

7. A energy storage control device based on model predictive control, characterized in that: include: A model building module, configured to build a regional power grid frequency response model including traditional units and energy storage elements, wherein the regional power grid frequency response model also includes local loads; The control module is configured to input each state variable in the regional power grid model into a model predictive controller, and perform virtual inertia control and virtual damping control on the energy storage element, wherein the output of the virtual inertia control is proportional to the frequency change rate, and the output of the virtual damping control is proportional to the frequency deviation.

8. The energy storage control device based on model predictive control according to claim 7, characterized in that: The control module is configured as follows: Establish the state space equations of model predictive control; Performing state prediction according to each current state variable of the regional power grid model and the state space equation; Establish the objective function of model predictive control; Solving the objective function based on the predicted state variables; According to the solution result of the objective function, the virtual inertia coefficient and the virtual damping coefficient of the energy storage element are adjusted.

9. The energy storage control device based on model predictive control according to claim 8, characterized in that: The objective function of the model predictive control is: J=Q(Δf) 2 +P(ΔP ESS ) 2 ; Where Δf is the frequency deviation, ΔP ESS is the power throughput of the energy storage element, Q is the weight coefficient of the frequency deviation, and P is the weight coefficient of the power throughput of the energy storage element.

10. The energy storage control device based on model predictive control according to claim 9, characterized in that: The energy storage control device further includes an adjustment module configured as follows: According to the control target of the model predictive controller, the weight coefficient of the objective function is adjusted.

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